Impact of implementing a computerised quality improvement intervention in primary healthcare
Bindu N Patel BS (High Distinction), MPH
This thesis is submitted in fulfilment of the requirements for the degree of Doctor of Philosophy at the University of Sydney.
School of Public Health, Faculty of Medicine and Health
October 2018 Declaration
This thesis is submitted to the University of Sydney in fulfilment of the requirement for the Doctor of Philosophy.
The work presented in this thesis is, to the best of my knowledge and belief, original except as acknowledged in the text. I hereby declare that I have not submitted this material, either in full or in part, for a degree at this or any other institution.
Name: Bindu Patel
Signature:
Date: June 8, 2018
ii Dedication
Three people in my life who travel with me through vast space:
To my loving husband, Nick, whose writing is prolific, exquisite and hilarious all together. I aspire to write like you.
To my eldest son, Benicio, you patiently endured the high demands on my time made by the PhD journey while providing me with limitless love, encouragement and joy.
To my son, Emilio, you made me believe that anything is possible. You gave me the stamina to finish.
iii Author’s Contribution
The work presented in this thesis has been carried out by the author under the supervision of Professor David Peiris, and co-supervisions of Professor Anushka Patel and Dr. Thomas Lung. This included: planning of research, design of component studies, ethics committee submissions, the collection, management, analysis and interpretation of data, writing of manuscripts for submission to peer- reviewed journals, and the writing of the thesis. Specific author and co-author contributions are specified at the beginning of the relevant chapters. This thesis has benefited from multidisciplinary collaboration.
In addition to the statements above, in cases where I am not the corresponding author of the published item, permission to include the published material has been granted by the corresponding author.
Signature: Date: June 7, 2018
Bindu Patel
As supervisor for the candidature upon which this thesis is based, I can confirm that the authorship attribution statements above are correct.
Signature: Date: 7th June 2018
David Peiris
iv Abstract
Health systems worldwide experience large evidence practice gaps with underuse of proven therapies, overuse of inappropriate treatments and misuse of treatments due to medical error. Quality improvement (QI) initiatives have been shown to overcome some of these gaps. Computerised interventions, in particular, are potential enablers to improving system performance. However, implementation of these interventions into routine practice has resulted in mixed outcomes and those that have been successfully integrated into routine practice are difficult to sustain.
The objective of this thesis is to understand how a multifaceted, computerised QI intervention for cardiovascular disease (CVD) prevention and management was implemented in Australian general practices and Aboriginal Community Controlled Health Services and assess the implications for scale-up of the intervention. The intervention was implemented as part of a large cluster-randomised controlled trial, the TORPEDO (Treatment of Cardiovascular Risk using Electronic Decision Support) study. The intervention was associated with improved guideline recommended cardiovascular risk factor screening rates but had mixed impact on improving medication prescribing rates.
In this thesis, I designed a multimethod process and economic evaluation of the TORPEDO trial. The aims were to:
i. Develop a theory-informed logic model to assist in the design of the overall evaluation to address study aims (Chapter 3).
ii. Conduct a post-trial audit to quantify changes in cardiovascular risk factor screening and prescribing to high risk patients over an 18-month post-trial period and understand the impact of the intervention outside of a research trial setting (Chapter 4).
v iii. Use normalisation process theory to identify the underlying mechanisms by which the intervention did and did not have an impact on trial outcomes (Chapter 5).
iv. Use video ethnography to explore how the intervention was used and cardiovascular risk communicated between patients and healthcare providers (Chapter 6).
v. Conduct an economic evaluation to inform policy makers for delivering the intervention at scale through Primary Health Networks in New South Wales (Chapter 7).
vi. Use a new theory to explain the factors that drove adoption and non-adoption of the intervention and assess what modifications may be needed to promote spread and scale-up (Chapter 8).
I found variable outcomes during the post-trial period with a plateauing of improvements in guideline recommended screening practices but an ongoing improvement in prescribing to high risk patients. The group that continued to have the most benefit was patients at high CVD risk who were not receiving recommended medications at baseline. The delay in prescribing recommended medication suggests healthcare providers adopt a cautious approach when introducing new treatments.
Six intervention primary healthcare services participated as case studies for the process evaluation. Qualitative and quantitative data sources were combined at each primary healthcare service to enable a detailed examination of intervention implementation from multiple perspectives. The process evaluation identified the complex interaction between several underlying mechanisms that influenced the implementation processes and explained the mixed trial outcomes: (1) organisational mission; (2) leadership; (3) the role of teams; (4) technical competence and dependability of the software tools. Further, there were different ‘active ingredients’
vi necessary during the initial implementation compared to those needed to sustain use of the intervention.
In the video ethnography and post-consultation patient interviews, important insights were gained into how the intervention was used, and its interpretation by the doctor and patient. Through ethnographic accounts, the doctor’s communication of cardiovascular risk was not sufficient in engaging patients and having them act upon their high-risk status; effective communication required interactions be assessed, discussed and negotiated.
The economic evaluation identified the cost implications of implementing the intervention as part of a Primary Health Network program in the state of New South Wales, Australia; and modelled data looked at the impact of small but statistically significant reductions in clinical risk factors based on the trial data. When scaled to a larger population the intervention has potential to prevent major CVD events at under AU$50,000 per CVD event averted largely due to the low costs of implementing the intervention. However, the clinical risk factor reductions were small and a stronger case for investment would be made if the effects sizes could be enhanced and sustained over time. The findings from chapters 4-6 provide insight into the intricacy of the barriers influencing implementation processes and adoption of the intervention.
Taken together, these studies provide a detailed explanation of the processes that may be required to implement such an intervention at scale and the factors that might influence its impact and sustainability. The findings are expected to assist policy makers, administrators and health professionals in developing multiple interdependent QI strategies at the organisational, provider and consumer levels to improve primary healthcare system performance for cardiovascular disease management and prevention.
vii Ethical clearance
The research included in this thesis has been approved by a Human Research Ethics Committee (HREC).
The studies were approved by The University of Sydney Human Research Ethics Committee (2012/2183) and the Aboriginal Health & Medical Research Council (AH&MRC) of New South Wales (778/11). Health professionals and patients gave written consent before participating in the interviews, surveys or videotaping of their consultations.
viii Project funding
Funding support for this project was provided by the Australian National Health and Medical Research Council project grant (# 1010547). Additionally, a postgraduate scholarship was awarded to support this research by the National Health and Medical Research Council (#APP1075308).
ix Acknowledgements
I consider myself extremely fortunate to have had the privilege of having three exceptional supervisors. Throughout my candidature they provided me with immense support and as a result I’ve learned more than I could have imagined.
I am especially grateful to my primary supervisor, David Peiris. David, thank you for your dedication and endless patience. You facilitated the opportunity to undertake this PhD and fostered my skills and knowledge in the areas of health systems, quality improvement and implementation science. The assistance and advice you provided in the preparation of manuscripts and chapters were invaluable. You are truly a source of inspiration for the way you seem to effortlessly balance your awesome enthusiasm to improve health systems worldwide while raising a family and running marathons. Thank you for your friendship over the years.
Second, I’d like to thank my associate supervisor Anushka Patel for her encouragement in commencing my doctoral studies. Anushka, your wisdom, varied expertise, exceptional achievements and tremendous kindness are inspiring. Thank you for taking a chance on me and nurturing my intellectual growth.
Third, I would like to thank my second associate supervisor, Tom Lung. Tom, you took on a supervisory role without hesitation knowing I would need substantial guidance in building my skills in economic evaluation. You tolerated my incessant queries about economic modelling and were generous with your time.
I would also like to acknowledge Professors Tim Usherwood and Stephan Jan for their expert advice and the time they devoted to assisting me with my research. Your continuous support was invaluable.
During my candidature, I had the opportunity to collaborate with Professors Chris Candlin, Sally Candlin and Catherine O’Grady, an exceptional team specialising in discourse analytics from the Department of Linguistics at Macquarie University. I am x honoured to have had the mentorship of Professor Chris Candlin who passionately offered his rich insights into doctor-patient communication. I was saddened by Chris’s passing during the time of our research but extremely grateful to have had the opportunity to work with him. Professors Sally Candlin and Catherine O’Grady were extremely helpful in expanding my knowledge and skills in discourse analysis. Thank you Sally and Catherine for sharing your expertise, your friendship and all the time you spent on our research, which greatly benefited from our multidisciplinary collaboration.
This research was made possible because of an amazing research team. Thank you, Marilyn Lyford, for your extensive time spent at ACCHSs collecting data and interviewing patients, Genevieve Coorey and Maria Agaliotis for the long hours spent at general practices collecting data and surveys, Shannon McKinn for your assistance in videotaping and patient interviews, and Madeline News for collating and managing survey data. Thank you, Qiang Li, for your expert statistical assistance, advice and patience. And thank you Tom Howell for your assistance in formatting and infographics.
Thank you to all the patients and health professionals that volunteered their time and participated in this research.
I have been fortunate to have met wonderful friends at The George Institute for Global Health that kept me sane, soothed my emotions and made me laugh during many challenging moments. Thank you, John Mulley, Rohina Joshi, Tracey Laba, Claire Johnson, Peta Steadman and Kellie Nallaiah for all the chats, text messages, emails and phone calls. Your support meant everything to me.
Professor Nicole Schupf and Dr. H. Jack Geiger stirred my passion for public health over 20 years ago with their remarkable and ground-breaking careers and social activism which has spanned many generations. Thank you for your endless encouragement, advice, and friendship no matter the circumstances.
xi Last but not least, my greatest gratitude is to my loving family. I thank my parents, Niranjan and Narmada Patel, for providing me the foundation of a life they were not afforded. Thank you for all your sacrifices you have made for me.
Navigating the demands of motherhood and pursuing a doctoral study is emotionally and physically challenging and strenuous. I don’t know how I would have done it without the generosity of my eldest son. Benicio, your unconditional love made my role as your mother effortless. Thank you for being forgiving of my flaws and teaching me about patience. The long-awaited arrival of Emilio, my second son, taught me about perseverance and hope. You provided me the incentive to finish my PhD. Benicio and Emilio, you are a source of constant joy.
The time and dedication required to complete a doctoral thesis is massive. This would not have been remotely possible without my husband, Nick Bhasin. My utmost gratitude is to you, Nick, for helping me to pursue my professional passion while you took on the extra burden of caring for our young sons and sacrificing your own writing projects. During this arduous time, you still managed to make me coffee every morning, let me sleep-in, prepare many delicious meals, proofread medical jargon and shower me with unwavering encouragement and love. I can’t thank you enough.
xii Table of Contents
Declaration ...... ii Dedication ...... iii Author’s Contribution ...... iv Abstract ...... v Ethical clearance ...... viii Project funding ...... ix Acknowledgements ...... x Table of Contents ...... xiii List of Tables ...... xvii List of Figures ...... xix List of Text Boxes ...... xxi List of Abbreviations ...... xxii Publications arising from this thesis ...... xxiv Other outcomes arising from this thesis ...... xxv Chapter 1: Introduction ...... 28 1.1 The Australian Healthcare System ...... 28 Primary healthcare structure ...... 31 General Practices ...... 31 Aboriginal Community Controlled Health Services ...... 31 Primary Health Networks ...... 32 1.2 Strategies to improve quality of primary healthcare ...... 33 1.3 The importance of process evaluations ...... 34 1.4 Structure of the thesis ...... 37 1.5 References ...... 38 Chapter 2: Literature review ...... 45 2.1 Chapter Overview ...... 45 2.2 Quality improvement strategies in healthcare ...... 45 Why the need for quality improvement strategies? ...... 45 Defining quality improvement? ...... 45 Measuring quality in healthcare ...... 46 Quality improvement benchmarks ...... 47 Models for quality improvement ...... 48 xiii Quality Improvement Collaboratives ...... 48 Chronic Care Model ...... 52 Patient-centred medical homes ...... 53 2.3 Health information technology to support QI ...... 54 2.4 Quality improvement strategies in Australia ...... 64 Australian Primary Care Collaboratives (APCC) ...... 64 Aboriginal and Torres Strait Islander health service quality improvement programs ...... 65 2.5 The evolution of HIT in Australia ...... 67 Glossary of eHealth programs stated in Figure 2.1: ...... 67 Electronic health record (EHR) ...... 69 EHR auditing tools ...... 71 Electronic care plans ...... 71 Personal health records ...... 72 2.6 Conclusion ...... 73 2.7 References ...... 74 Chapter 3: A theory-informed logic model to assist in the planning, conduct and implementation of the process evaluation ...... 91 3.1 Chapter Overview ...... 91 3.2 Publication details ...... 91 3.3 Author contributions ...... 91 3.4 Manuscript ...... 92 Chapter 4: A quantitative study evaluating the efficacy of the intervention in a post-trial setting to assess trends in outcomes over three years ...... 106 4.1 Chapter overview ...... 106 4.2 Publication details ...... 106 4.3 Author contributions ...... 107 4.4 Manuscript ...... 107 Chapter 5: Mixed methods evaluation exploring the underlying mechanisms of implementation of the intervention and interaction with contextual factors that resulted in variable outcomes ...... 121 5.1 Chapter overview ...... 121 5.2 Manuscript details ...... 121 5.3 Author Contributions ...... 122 5.4 Submitted manuscript ...... 122 Chapter 6: Ethnographic evaluation of how the intervention was used in practice, how cardiovascular risk communicated and perceived by patients and healthcare providers ...... 191 6.1 Chapter overview ...... 191 6.2 Book chapter details ...... 191 6.3 Author contributions ...... 192 xiv 6.4 Book chapter ...... 192 Chapter 7. Modelled cost-effectiveness of a multifaceted health information technology delivered within an Australian Primary Health Networks setting to improve cardiovascular disease prevention ...... 212 7.1 Chapter overview ...... 212 7.2 Introduction ...... 212 7.2 Methods ...... 217 Study design and setting ...... 217 Definition of high CVD risk ...... 217 Intervention ...... 217 Comparator ...... 218 Health outcomes ...... 218 Target population ...... 218 Economic model ...... 218 Efficacy and assumptions ...... 219 Baseline CVD risk assumptions ...... 219 Blood pressure lowering treatment effects ...... 219 Lipid lowering treatment effects ...... 220 Antiplatelet effects ...... 220 Adherence ...... 220 Costs ...... 220 Sensitivity analysis ...... 221 7.3 Results ...... 221 7.4 Discussion ...... 223 Conclusions ...... 225 7.5 References ...... 233 Chapter 8: Discussion and conclusions ...... 242 8.1 Findings ...... 243 The Condition/Illness ...... 244 The technology ...... 246 The Value Proposition ...... 248 Adopters (Staff, Patient, Caregivers) ...... 250 The Organisation(s) ...... 251 The wider context for the programme ...... 253 Adaption Over Time ...... 254 8.2. Strengths and limitations ...... 255 8.3 Conclusion ...... 256 8.4 References ...... 258
xv APPENDIX A: Policy, submission of PhD theses containing published work ...... 263 APPENDIX B: Ethics Approval Letters ...... 265 APPENDIX C: Participant Consent Forms ...... 277 APPENDIX D: Instruments and Tools ...... 300 APPENDIX E: Papers Arising From, But Not Contained Within This Thesis ...... 330
xvi List of Tables
xvii xviii List of Figures
xix xx List of Text Boxes
xxi List of Abbreviations
ACCHS Aboriginal Community Controlled Health Service
AHRQ Agency for Healthcare Research and Quality
APCC Australian primary care collaborative
BEACH Bettering and Evaluation and Care of Health
CABG Coronary artery bypass grafting
CCM Chronic Care Model
CDSS Computer decision support system
CHD Coronary heart disease
CI Confidence interval
CPOE Computer provider order entry
cRCT Cluster randomised controlled trial
CQI Continuous quality improvement
CVD Cardiovascular disease
DALY Disability adjusted life year
EHR Electronic health record
GP General practitioner
GPMP General practitioner management plan
HIT Health information technology
ICER Incremental cost-effectiveness ratio
IOM Institute of Medicine
LDL Low-density lipoprotein
xxii LHN Local hospital network
NASSS Nonadaptation, Abandonment, Scale-up, Spread, and Sustainability
NEHTA National Electronic Health Transition Authority
nKPIs National Key Performance Indictors
NPCC National primary care collaborative
NT Northern Territory
NSW New South Wales
OECD Organisation for Economic Cooperation and Development
OSR Online Services Report
PBS Pharmaceutical Benefits Scheme
PCEHR Personally Controlled Electronic Health Record
PCMH Patient-centred medical home
PDSA Plan-Do-Study-Act
PI Performance Indicators
PHN Primary Health Network
SBP Systolic blood pressure
TCA Team care arrangements
TORPEDO Treatment of Cardiovascular Risk in Primary Care Using Electronic Decision Support QAIHC Queensland Aboriginal and Islander Health Council
QALY Quality adjusted life years
QI Quality improvement
QIC Quality improvement collaborative
US United States xxiii Publications arising from this thesis
The thesis contains a mix of published and unpublished work. The University of Sydney’s Academic Board approved submission of published work as thesis on August 14, 2002. A copy of an updated policy can be found in Appendix A. Specific author contributions are specified at the beginning of each chapter.
Chapter Status Details Impact factor
3 Published Patel B, Patel A, Jan S, Usherwood T, Harris M, Panaretto K, Zwar N, 3.345 Redfern J, Jansen J, Doust J, Peiris D. A multifaceted quality improvement intervention for CVD risk management in Australian primary healthcare: a protocol for a process evaluation. Implementation Science 2014 9:187. DOI:10.1186/s13012-014-0187-8
4 Published Patel B, Peiris D, Usherwood T, Li Q, Harris M, Panaretto K, Zwar Z, 5.117 Patel A. Impact of Sustained Use of a Multifaceted Computerized Quality Improvement Intervention for Cardiovascular Disease Management in Australian Primary Health Care. Journal of the American Heart Association. 2017; 6: e007093. DOI: 10.1161/JAHA.117.007093
5 Re- Patel B, Usherwood T, Harris M, Patel A, Panaretto K, Zwar N and Peiris 3.345 submitted D. What drives adoption of computerised quality improvement tools by after 2nd primary healthcare providers? An application of Normalisation Process review Theory. Implementation Science (under review)
6 Published O’Grady C., Patel B., Candlin S., Candlin C.N., Peiris D., Usherwood T. Book (2016) ‘It’s just statistics … I’m kind of a glass half-full sort of guy’: The chapter Challenge of Differing Doctor-Patient Perspectives in the Context of Electronically Mediated Cardiovascular Risk Management. In: Crichton J., Candlin C.N., Firkins A.S. (eds) Communicating Risk. Communicating in Professions and Organizations. Palgrave Macmillan, London. https://doi.org/10.1057/9781137478788_17
xxiv Other outcomes arising from this thesis
National Conference presentations [oral and poster]
Patel B, Lyford M, Parker S, Usherwood T, Harris M, Patel A, Peiris D. The Treatment of Cardiovascular Risk in Primary Health Care Using Electronic Decision Support (TORPEDO) study. National Medicines Symposium. Sydney, May 2012. [poster]
Patel B, Lyford M, Panaretto K, Hunt J, Usherwood T, Harris M, Patel A, Peiris D. Understanding the impact of a multifaceted quality improvement intervention to improve cardiovascular disease risk management in Aboriginal Community Controlled Health Service: Mixed methods evaluation. The Lowitija Institute 2nd National Conference on continuous quality improvement in Aboriginal and Torres Strait Islander Primary Health Care. Melbourne, March 17-18, 2014. [oral]
International Conference oral presentations
Patel B, O’Grady C, Candlin S, Candlin C, Peiris D, Usherwood T. Doctor-patient engagement in the context of electronically mediated cardiovascular risk management. Communication, Medicine and Ethics COMET Conference. University of Hong Kong, June 25-27, 2015.
Patel B, Usherwood T, Harris M, Patel A, Panaretto K, Zwar N and Peiris D. What drives adoption of computerised quality improvement tools in primary healthcare settings? Consortium of Universities for Global Health. New York City, March 15-18, 2018.
Other invited oral presentations
Conference Presentation Summary: Treatment of Cardiovascular Risk in Primary Health Care Using Electronic Decision Support (TORPEDO) study. Cardiovascular xxv Health and Rehabilitation Association of NSW & ACT (CRA), Annual Meeting; Role: Invited Speaker; Place and year: Sydney, October 19, 2012
The Treatment of Cardiovascular Risk in Primary Health Care Using Electronic Decision Support (TORPEDO) study. Australian Institute for Health Innovations; Role: Invited Speaker; Place and year: Sydney, October 23, 2012
xxvi PART A BACKGROUND
27 Chapter 1: Introduction
In 1978, the principles of primary healthcare were brought to the forefront at an international conference convened by the World Health Organization (WHO) and the United Nations Children’s Fund held in Alma Ata. This meeting marked a shift in thinking around the role of primary healthcare services in society.1 The Declaration made at Alma Ata affirmed principles of universal access, affordability, multidisciplinary care and community participation.2,3
Over two decades later, Barbara Starfield’s seminal work demonstrated the importance of high quality primary care in improving health system performance.4 She found that countries with strong primary care infrastructure and a high proportion of primary care physicians, demonstrated improved health outcomes including reduced all-cause mortality, reduced health inequities and reduced health system costs.5 Starfield argued that ‘four Cs’ should be foundational to a strong primary care system - accessible first contact, continuous, comprehensive and coordinated care and that these elements should be present regardless of the socioeconomic, demographic or clinical characteristics of the population served.6,7 These elements align closely with the core principles of the Declaration of the Alma Ata.8,9
Despite this evidence being established over twenty years ago, the attainment of high performing primary healthcare systems remains a challenge not only in low income countries with gross underspends on healthcare, but also in high income countries with relatively large financial resources.10
In this chapter I provide a brief overview of the Australian primary healthcare system and outline the work I have undertaken to evaluate a complex strategy to improve primary healthcare quality for cardiovascular disease prevention and management.
1.1 The Australian Healthcare System
Australians have amongst the highest life expectancy in the world.11 Despite a steady downward trend in mortality, however, Australians experience the highest number of
28 years (10.9 years) with ill health amongst Organisation for Economic Cooperation and Development (OECD) countries.12 When looking at health system performance, Australia also ranks high in overall performance, but an area of concern is rising health inequities with Australia being amongst the lowest performing OECD countries (Table 1.1).13 These inequities (health outcomes and access to high quality health care) are most strongly experienced by those living in remote and rural regions and those in the lowest socioeconomic groups.14,15
The Commonwealth Fund’s report on the comparison of OECD countries’ healthcare system performance calculated the mean of the ‘measure performance scores’ in each domain and then ranked each country from 1 to 11. The overall performance scores and rankings were based on the mean of the five domain-specific performance scores and each domain is weighted equally for the overall performance scores.
Table 1.1 OECD countries healthcare performance
Source: Commonwealth Fund analysis13
In terms of health system financing, in 2016 Australia expended over 10% of gross domestic product on healthcare for the first time.16 These rising costs call into question the sustainability of the health system going forward. Australia’s publicly funded universal health care scheme, Medicare, has provided coverage for Australian citizens and permanent residents since 1984.17 Medicare rebated services are provided in a variety of ambulatory care settings by specialist and general medical practitioners,
29 nurses, allied health professionals, midwives, pharmacists, dentists and Aboriginal Health Workers. In addition, there is a publicly funded Pharmaceutical Benefits Scheme (PBS) that provides subsidised access to approved medications. These two schemes are funded by the federal government. The other major sourcing of funding of the health system include state and territory governments (primarily responsible for funding inpatient hospital services), the Department of Veteran’s Affairs, injury compensation funds, private health insurers and consumers themselves (Figure 1.1).18
Despite the obvious strengths of the current healthcare system, the Australian government faces major challenges of rising health expenditures, widening inequalities and increasing demands related to increasing burden of chronic diseases.19,20 Strategies that support Starfield’s four C’s are of critical importance to support Australia’s ability to successfully tackle these challenges.
Figure 1.1 Health Services – funding and responsibility, 2013-14
Source: AIHW, Australia’s Health 2016 12
30 Primary healthcare structure
Within Australia’s complex healthcare system, primary healthcare occurs in a diversity of organisations including but not limited to general practices, Aboriginal Community Controlled Healthcare Services (ACCHSs), state government funded services, allied healthcare facilities, and pharmacies.21 Services range from acute medical care, health promotion, prevention and screening, early intervention, treatment and management. My thesis focuses on three players in the primary healthcare systems – general practices, ACCHSs and Primary Health Networks (PHNs).
General Practices
General practices are the foundation of primary healthcare in Australia. Practices vary greatly in terms of size (ranging from solo doctor practices through to corporate practices with a large number of sites and doctors), location (urban, rural and remote), provision of multidisciplinary care, business models and co-payment charges, integration of care with hospital and speciality services and community outreach programs.22 General practices operate as gatekeepers to ‘secondary care’ provided by specialists. Without a general practitioner (GP) referral, specialists are generally unable to bill the government for reimbursement for services provided.
Aboriginal Community Controlled Health Services
Australia’s health system consistently performs worse for Aboriginal and Torres Strait Islander people compared with non-Indigenous Australians in terms of access and quality of care.23 In order to overcome barriers to accessing appropriate care, the first ACCHS was established in the early 1970s as a primary healthcare organisation governed and operated by the local Aboriginal community to “deliver holistic, comprehensive and culturally appropriate healthcare to the community which controls it”.24,25 ACCHSs are founded on the principle of self-determination supporting Aboriginal communities to control and tailor their multifaceted healthcare needs.26
Although it is difficult to quantify the exact number of ACCHSs in Australia, in 2014- 2015, 138 ACCHSs submitted data to the Australian Institute of Welfare and Health as a part of their funding agreements with the Australian government.27 Using these data,
31 Figure 1.2 gives an overview of the number of Aboriginal and Torres Strait Islander peoples accessing care in comparison to the total Aboriginal population.
Figure 1.2 Number of Indigenous population using ACCHSs reporting to the
Online Service Report (OSR)27
Primary Health Networks
On July 1, 2015, thirty-one Primary Health Networks (PHNs) were established as meso- tier, independent organisations with regional boundaries aligned to state and territory Local Hospital Networks.28 The PHNs’ main objectives are to improve efficiency and effectiveness of medical services for patients especially those with poor health outcomes; and to improve coordination of care between primary, secondary and hospital services. They are governed by a board of directors and often have community advisory committees.
32 PHNs play a role in identifying the health priorities of the regions they service; providing support and professional development for healthcare providers; integration of care with Local Hospital Networks; commissioning of services to address health care priorities; and supporting implementation of federal government programs. Flexible funding of up to $852 million was committed for PHNs over 3 years from 2015-2016.29 Although a relatively new player in the health care system, they have potential to play a major role in improving system performance, as has been the case in other countries such as New Zealand and the United Kingdom.30
1.2 Strategies to improve quality of primary healthcare
In this thesis I explore intervention strategies to improve primary healthcare quality in the Australian health system context. I focus on cardiovascular disease (CVD) which is the second leading cause of disease burden in Australia and draw particular attention throughout the thesis to Aboriginal and Torres Strait Islander communities who experience CVD at far greater rates than other Australians.31
Evidence-based clinical guidelines play an essential role in promoting quality of care and minimising unwanted variation and error. These guidelines are "systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances”.32 Despite widely available clinical guidelines, their implementation into routine practice remains a challenge. Studies in both the United States (US) and Australia have shown that adherence to evidence- based practices occurs only around 50% of the time.33,34 Specific to cardiovascular disease management and prevention, there are similar large evidence-practice gaps for screening and prescribing appropriately for cardiovascular disease risk prevention and management.35,36 The reasons underpinning these gaps are complex and operate at system, organisational, provider and consumer levels.37,38
To address evidence-practice gaps, quality improvement (QI) strategies have been implemented to improve efficiency and processes of healthcare with the goals of achieving sustained health outcomes. A potential facilitator of quality improvement strategies is the use of health information technologies (HIT). Various forms of HIT have
33 been shown to be effective in trial settings,39-43 but questions remain about generalisability, transferability to different settings and cost implications.44-46 In Chapter 2, I appraise the literature in more detail on quality improvement strategies with specific reference to the role of HIT.
1.3 The importance of process evaluations
Randomised controlled trials (RCT) are generally considered to be the gold standard for determining efficacy of interventions due to their ability to hold constant potential confounding variables, thus isolating the effects of the interventions being tested. However, such a design assumes interventions to be ‘fixed’ agents that can be isolated from the environment in which they are tested. When such interventions are implemented in complex social settings such as health, it is the interaction of the interventions and these contextual elements that need also be considered when assessing impact.47 Consequently, effectiveness data on its own does not sufficiently inform decision makers how and why interventions can be applied to different settings. Process evaluations embedded within trials are one potential solution to this issue. Process evaluations provide the ability to “assess fidelity and quality of implementation, clarify causal mechanisms and identify contextual factors associated with variation in outcomes”.48,49 Thus, the focus is on mechanisms and processes by which an intervention is implemented and understanding barriers to uptake at multiple levels within a complex health system.
In this thesis, I designed and implemented a multimethod process evaluation of a computerised quality improvement intervention for improving primary care management of CVD in Australian general practices and ACCHSs. A multimethod approach is defined as “the use of two or more research methods in a single study when one (or more) of the methods is not complete in itself”.50 This allows the integration of findings from different angles, illuminating strengths and weaknesses in a diversity of methods.51
The quality improvement intervention for CVD risk management was tested in a randomised controlled trial within 60 Australian General Practices and ACCHSs.52 The primary results paper (Appendix E) outlined mixed effectiveness in terms of clinical and process outcome measures without much clarity on which factors promoted or
34 hindered its impact. I designed a series of discrete but interrelated studies to provide a deeper understanding of the intervention’s impact and to draw broader conclusions on why complex health service interventions often produce equivocal effectiveness results.
Specific aims include the following (section 1.4):
a. Conduct a literature review of the trends and impact of quality improvement strategies and health information technologies in improving health systems (Chapter 2).
b. Design a theory-informed logic model to assist in the planning, conduct and implementation of the process evaluation (Chapter 3).
c. Quantify the efficacy of the intervention in a post-trial setting to assess trends in outcomes over a three-year period (Chapter 4).
d. Describe, using a case study approach, the underlying mechanisms of implementation of the intervention and interaction with contextual factors that resulted in variable outcomes at each case study health service (Chapter 5).
e. Explore through ethnographic methods, how the intervention was actually used in practice and how cardiovascular risk communicated and perceived by patients and healthcare providers (Chapter 6).
f. Conduct a modelled cost-effectiveness analysis of the intervention to inform policy makers on establishing a large-scale cardiovascular prevention and management program delivered by Primary Health Networks in Australia (Chapter 7).
The purpose of this research is to go beyond effectiveness studies and simple lists of barriers and facilitators to implementation of complex quality improvement interventions. It aims to provide a holistic understanding of what drives change in primary health care services, what role HIT plays in supporting that change and what
35 might be contextual factors that support or hinder the uptake of HIT into routine practice.
36 1.4 Structure of the thesis
Part A: Background
Chapter 1: Introduction
Chapter 2: Literature review of the trends and impact of quality improvement strategies and health information technology
Part B: Development of a Theory-informed Process Evaluation
Chapter 3: A theory-informed logic model to assist in the planning, conduct and implementation of the process evaluation
Part C: Sustainability, Adoption, Utilisation and Scalability
Chapter 4: A quantitative study evaluating the efficacy of the intervention in a post-trial setting to assess trends in outcomes over three years
Chapter 5: Mixed methods evaluation exploring the underlying mechanisms of implementation of the intervention and interaction with contextual factors that resulted in variable outcomes
Chapter 6: Ethnographic evaluation of how the intervention was used in practice, how cardiovascular risk communicated and perceived by patients and healthcare providers
Chapter 7: Modelled cost-effectiveness of the intervention delivered within Australian Primary Health Network settings
Part D: Implications
Chapter 8: Discussion and conclusions
37 1.5 References
1. Williams G. WHO: reaching out to all. World Health Forum. 1988;9(2):185-199.
2. Romanow RJ. Building on Values: The Future of Health Care in Canada – Final Report. Commission on the Future of Health Care in Canada2002.Available from: http://qspace.library.queensu.ca/handle/1974/6882. Accessed December 2017.
3. Anonymous. Declaration of ALMA-ATA. Am J Public Health. 2015;105(6):1094- 1095.
4. Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Q. 2005;83(3):457-502.
5. Starfield B, Shi L. Policy relevant determinants of health: an international perspective. Health Policy. 2002;60(3):201-218.
6. A manpower policy for primary health care : report of a study / Institute of Medicine, Division of Health Manpower and Resources Development. Washington D.C.: Institute of Medicine (US); 1978.Available from: https://catalogue.nla.gov.au/Record/2485895. Accessed December 2017.
7. Starfield B. Primary Care: Concept, Evaluation, and Policy. New York, NY: Oxford University Press; 1992.
8. Scheffler RM, Weisfeld N, Ruby G, Estes EH. A manpower policy for primary health care. N Engl J Med. 1978;298(19):1058-1062.
9. Starfield B. Is primary care essential? Lancet. 1994;344(8930):1129-1133.
10. Petrich M, Ramamurthy VL, Hendrie D, Robinson S. Challenges and opportunities for integration in health systems: an Australian perspective. Journal of Integrated Care. 2013;21(6):347-359.
38 11. Shifting the Dial: 5 year productivity review: Chapter 2: Healthier Australians. Canberra Productivity Commission 2017.Available from: http://www.pc.gov.au/inquiries/completed/productivity-review/report/2- healthier-australians. Accessed December 2017.
12. Australia’s health 2016. Canberra: Australian Institute of Health and Welfare; 2016.Available from: https://www.aihw.gov.au/reports/australias- health/australias-health-2016/contents/health-system. Accessed December 2017.
13. Schneider EC, Sarnak DO, Squires D, Shah A, Doty MM. Mirror, Mirror 2017: International Comparison Reflects Flaws and Opportunities for Better U.S. Health Care. New York: The Commonwealth Fund; 2017.
14. Health service usage and health related actions, Australia 2014-2015. Canberra: Australian Bureau of Statistics; 2017.Available from: http://www.abs.gov.au/ausstats/[email protected]/mf/4364.0.55.002. Accessed December 2017.
15. Rural & remote health. Canberra: Australian Instititute of Health and Welfare; 2017.Available from: https://www.aihw.gov.au/reports/rural-health/rural-remote- health/contents/rural-health. Accessed December 2017.
16. Health expenditure Australia 2015–16. Canberra: Australian Institute of Health and Welfare; 2017.Available from: https://www.aihw.gov.au/getmedia/3a34cf2c-c715-43a8-be44- 0cf53349fd9d/20592.pdf.aspx?inline=true. Accessed December 2017.
17. Biggs A. Medicare: a quick guide. 2016.Available from: https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parliam entary_Library/pubs/rp/rp1617/Quick_Guides/Medicare. Accessed December 2017.
18. How does Australia's health system work? Canberra: Australian Institute of Health and Welfare; 2016.Available from:
39 https://www.aihw.gov.au/getmedia/f2ae1191-bbf2-47b6-a9d4- 1b2ca65553a1/ah16-2-1-how-does-australias-health-system-work.pdf.aspx. Accessed December 2017.
19. Powell Davies G, Williams AM, Larsen K, Perkins D, Roland M, Harris MF. Coordinating primary health care: an analysis of the outcomes of a systematic review. Med J Aust. 2008;188(8 Suppl):S65-68.
20. Armstrong BK, Gillespie JA, Leeder SR, Rubin GL, Russell LM. Challenges in health and health care for Australia. Med J Aust. 2007;187(9):485-489.
21. Primary care. Canberra: Department of Health 2015.
22. Rodwell J, Gulyas A. The variety of primary healthcare organisations in Australia: a taxonomy. BMC Health Serv Res. 2013;13:130.
23. Indigenous reform 2010–11: Comparing performance across Australia. Sydney: COAG Reform Council; 2012.Available from: http://apo.org.au/node/30123. Accessed December 2017.
24. NACCHO: 2010-2011 Annual Report. Canberra, ACT: National Aboriginal Community Controlled Health Organisation 2011.Available from: http://www.naccho.org.au/wp-content/uploads/NACCH0-2010-2011-Annual- Report.pdf. Accessed December 2017.
25. Marles E, Frame C, Royce M. The Aboriginal Medical Service Redfern: Improving access to primary care for over 40 years. Aust Fam Physician. 2012;41(6):433- 436.
26. Pearson N. Our right to take responsiblity Cairns, Queensland: Noel Pearson and Associates; 2000.
27. Healthy Futures—Aboriginal Community Controlled Health Services: Report Card 2016. Canberra: Australian Institute of Health and Welfare 2016.Available from: https://www.aihw.gov.au/reports/indigenous-health-welfare-
40 services/healthy-futures-aboriginal-community-controlled-health-services-report- card-2016/contents/table-of-contents. Accessed December 2017.
28. Priamry Health Networks (PHNs). Canberra: Australian Government - The Department of Health 2015.Available from: http://www.health.gov.au/internet/main/publishing.nsf/Content/PHN-About. Accessed December 2017.
29. Primary Health Network Grant Programme Guidelines. Canberra: Australian Government - The Department of Health; 2016.Available from: http://www.health.gov.au/internet/main/publishing.nsf/Content/F4F85B97E22A 94CACA257F86007C7D1F/$File/Primary%20Health%20Network%20Grant%20 Programme%20Guidelines%20-%20V1.2%20February%202016.pdf. Accessed December 2017.
30. Booth M, Boxall A. Commissioning services and Primary Health Networks. Australian journal of primary health. 2016;22(1):3-4.
31. Australian Burden of Disease Study: Impact and causes of illness and death in Australia 2011. Canberra: Australian Institute of Health and Welfare; 2016.Available from: https://www.aihw.gov.au/reports/burden-of-disease/abds- impact-and-causes-of-illness-death-2011/contents/highlights. Accessed December 2017.
32. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Institute of Medicine; 2001.Available from: http://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/2001/Cr ossing-the-Quality- Chasm/Quality%20Chasm%202001%20%20report%20brief.pdf. Accessed November 2017.
33. McGlynn EA, Asch SM, Adams J, et al. The Quality of Health Care Delivered to Adults in the United States. The new england journal of medicine. 2003;348(26).
41 34. Runciman WB, Hunt TD, Hannaford NA, et al. CareTrack: assessing the appropriateness of health care delivery in Australia. Med J Aust. 2012;197(2):100-105.
35. Webster RJ, Heeley EL, Peiris DP, Bayram C, Cass A, Patel AA. Gaps in cardiovascular disease risk management in Australian general practice. Med J Aust. 2009;191(6):324-329.
36. Heeley EL, Peiris DP, Patel AA, et al. Cardiovascular risk perception and evidence--practice gaps in Australian general practice (the AusHEART study). Med J Aust. 2010;192(5):254-259.
37. Cabana MD, Rand CS, Powe NR, et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282(15):1458- 1465.
38. Runnacles J, Roueche A, Lachman P. The right care, every time: improving adherence to evidence-based guidelines. Arch Dis Child Educ Pract Ed. 2017.
39. Kruse CS, Beane A. Health Information Technology Continues to Show Positive Effect on Medical Outcomes: Systematic Review. J Med Internet Res. 2018;20(2):e41.
40. Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. Health Aff (Millwood). 2010;29(6):1214- 1219.
41. Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:f657.
42. Ivers NM, Grimshaw JM, Jamtvedt G, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29(11):1534- 1541.
42 43. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.
44. Payne TH, Bates DW, Berner ES, et al. Healthcare information technology and economics. J Am Med Inform Assoc. 2013;20(2):212-217.
45. Jacob V, Thota AB, Chattopadhyay SK, et al. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review. J Am Med Inform Assoc. 2017;24(3):669- 676.
46. Glasgow RE, Phillips SM, Sanchez MA. Implementation science approaches for integrating eHealth research into practice and policy. Int J Med Inform. 2014;83(7):e1-11.
47. Brook RH. Health systems, heuristics, and comparative effectiveness research. J Gen Intern Med. 2013;28(2):172-173.
48. Oakley A, Strange V, Bonell C, Allen E, Stephenson J. Process evaluation in randomised controlled trials of complex interventions. BMJ. 2006;332(7538):413-416.
49. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. 2008;337:a1655.
50. Morse JM. Approaches to qualitative-quantitative methodological triangulation. Nurs Res. 1991;40(2):120-123.
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52. Peiris D, Usherwood T, Panaretto K, et al. Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary
43 health care: the treatment of cardiovascular risk using electronic decision support cluster-randomized trial. Circ Cardiovasc Qual Outcomes. 2015;8(1):87- 95.
44 Chapter 2: Literature review
2.1 Chapter Overview
The translation of research evidence into routine clinical practice is a major challenge faced by health systems worldwide. This chapter provides a broad overview of the existing literature and evidence in which my research was conducted. The first section reviews the international literature on quality improvement (QI) initiatives both in general and those with a specific focus on health information technology. The second section focuses on strategies implemented in Australia to improve delivery of care.
2.2 Quality improvement strategies in healthcare
Why the need for quality improvement strategies?
Healthcare systems performing well below acceptable levels expose patients to wide variations in care despite advances in medicine and technology.1,2 In the landmark study on the state of the US healthcare system published in 2003, McGlynn et al found only 55% of patients were receiving recommended care (evidence-based clinical practice).3 Point-of-care evidence practice gaps accounted for more than $9 billion per year in lost productivity and approximately $2 billion per year in hospital costs in the US in 2004.4 This has sparked an urgency in improving the quality of healthcare worldwide along with system redesign proposals.5 The Institute of Medicine’s (IOM) seminal report, Crossing the Quality Chasm: A New Health System for the 21st Century, describes strategies to improve delivery of healthcare and address quality gaps focussing on underuse of evidence based practices, overuse of non-evidenced practices and misuse of therapies that compromise patient safety.1
Defining quality improvement?
QI is a well-established process to improve the efficiency and processes of health care with goals of achieving sustained improvements in system performance and health outcomes.2 The US Agency for Healthcare Research and Quality describes high quality
45 healthcare as “doing the right thing, at the right time, in the right way, for the right person – and having the best possible results”.3
The concept of modern day quality improvement came to the forefront by Walter Shewhart in 1931 when he determined that a reduction in excessive variation and waste minimised inspections in manufacturing processes resulting in reduced costs.4 His protégé Williams Edwards Deming recognised quality as a primary driver for industrial success when applied strategically. His framework, the System of Profound Knowledge, states that improvement of practice or organisation is based on four interdependent elements: appreciation of the system, understanding types of variation, psychology (importance of understanding the motivation of people), and the theory of knowledge (epistemology).5 At the core of these elements is his Plan-Do-Study-Act (PDSA) cycle of learning to continually improve and still today this remains one of the basic tools for implementing continuous quality improvement (CQI) programs in healthcare settings.
Measuring quality in healthcare
A systematic measurement of quality assesses whether improvement strategies: (1) lead to positive changes in the primary endpoints; (2) contribute to unintended consequences within the system; and (3) require additional resources to bring processes to an acceptable level of quality.6 Avedis Donabedian described key areas of assessment that should be considered: structure, processes, and outcomes.7 Structural measures include the accessibility, availability and quality of resources, management systems and policy guidelines. Process measures assess delivery of healthcare services by analysing activities of health professionals over time (e.g., rate of unscientific care, inappropriate care, geographic variations in practice, medical injury to patients). Outcome measures indicate the end results of healthcare received based on group data such as mortality, risk factor screening, medication prescribing and patient satisfaction.
46 Quality improvement benchmarks
In the 1990s, with the rise of evidence-based medicine and numerous studies demonstrating suboptimal quality of care, the need for systemic performance measurements became a priority. There was more pressure on government, decision makers and healthcare providers to engage in activities to measure and evaluate health system performance for improved accountability and understanding of the value of services. This led to the development of performance indicators (PIs) and measures that encompassed national targets for quality improvement.8
In the US for example, in 1999, legislation was put in place for the Agency for Healthcare Research and Quality (AHRQ) to undertake the planning effort to develop systematic collection and analysis of healthcare data for greater public reporting.9 This would allow identification of areas of greatest need, monitor trends over time, and identify and subsequently replicate strategies that are successful at addressing deficiencies. A Strategic Framework Board was established to develop performance indicators for the national quality measurement and reporting system that would be used to analyse variation in all areas of health system delivery and care as a national platform for quality improvement measurements. Two reports were to be published annually starting in 2003, (1) National Healthcare Quality Report and (2) National Healthcare Disparities Report which was to be the vehicle of communication for policy makers, healthcare providers and the public to enhance awareness and understanding of quality issues, track progress of improvement initiatives and policy changes.
Similar major initiatives to measure and report on their respective health system performance were taking place around the same time period in the UK (the Quality Outcomes Framework), Canada (Health Indicator Framework) and Australia (The National Health Performance Framework). In the meantime, conceptual frameworks for measuring performance indicators as a platform for international comparison have been developed for use by organisations such as the WHO and the OECD.10-12
47 Models for quality improvement
In the last three decades, there has been a multitude of models implemented for measurable improvements in provision of care, health systems and population health. Although by no means exhaustive, three models are relevant to my thesis: Quality Improvement Collaboratives (QICs), the Chronic Care Model (CCM), and Patient Centred Medical Homes (PCMHs).
Quality Improvement Collaboratives
One of the earliest QI models in healthcare was the QIC (also known as learning collaboratives, learning communities, learning networks, and communities of practice).13 First formed in the 1980s, such models are now used extensively to promote improvement in healthcare.14 A collaborative brings together multidisciplinary teams from different healthcare organisations to meet over several months to work towards identifying best practice and change strategies, applying improvement methods, reporting and sharing information on performance of a selected area of healthcare.15,16 The aim is to close the gap between potential and actual performance by testing and implementing changes quickly across many organisations allowing for broad dissemination of knowledge. Going beyond PI benchmarking, this methodology is intended to stimulate culture change within a network and increase the likelihood that improvements will be sustained.
One of the first successful QICs was the Northern New England Cardiovascular Disease Study Group formed in 1987.17,18 The collaboration took place among 23 cardiothoracic surgeons in the US States of Maine, New Hampshire, Vermont, and at five medical centres where surgeons worked. Mortality data as endpoint of coronary artery bypass grafting (CABG) procedures were collected from July 1987 through July 1993. During this time surgeons identified variability in mortality rates between centres and surgeons which they concluded as a “breakthrough in attitude, and notes that such breakthroughs are prerequisite for improvement”.14 A structured intervention was implemented between 1990-1991 with a 24% reduction of in-hospital morality rates associated with CABG. Another similar successful collaborative initiative that improved hospital care in the late 1980s was the Vermont Oxford Network.19 Box 2.1 outlines a generic set of key concepts derived from these two successful collaboratives.
48 Box 2.1 Key concepts behind multi-organisational collaborative improvement efforts
. Multiple organisations . Quantified variability in process or outcome . Open sharing . Internal process characterisation . Formal benchmarking visits . Identification of “best practices” . Replication efforts . Measured improvement Source: Plsek 199714
In 1995, the most well-known QIC model was developed by the Boston-based Institute for Healthcare Improvement known as the Breakthrough Series. Unlike earlier QICs, where participation was for several years, the Breakthrough Series was implemented for a shorter time-frame of 6-15 months for quicker impact.20,21 Although it has been continuously modified as more organisations adapt the model, the premises remain substantively unchanged (Box 2.2).
Box 2.2 Breakthrough Series foundation for collaborative improvement
Premise for Collaborative Improvement
. A substantial gap exists between knowledge and practice in healthcare. . Broad variation in practice is pervasive. . Examples of improved practices and outcomes exist, but they need to be described and disseminated to other organisations. . Collaboration between professionals working toward clear aims enables improvement. . Healthcare outcomes are the results of processes. . Understanding the science of rapid cycle improvement can accelerate demonstrable improvement. Source: Kilo, 199820
49 QICs are being used extensively in diverse health system settings, organisational contexts and other countries.22,23 Quality programs based on this strategy are being used increasingly across the UK, Canada, Australia and European countries. In the UK and Netherlands, health authorities support nationwide QICs. From 2000-2002, the UK National Primary Care Development Team implemented the National Primary Care Collaborative (NPCC) to over 2000 general practices serving 11.5 million patients, the largest primary health care improvement program in the world. This resulted in a 60% reduction in waiting time to see primary healthcare providers, reductions in delays in accessing secondary care, and improved care quality and reduction in mortality of patients with existing coronary heart disease.24,25 The successes of NPCC prompted the design and implementation of the Australian Primary Care Collaborative (APCC) program in 2003 (section 2.4).
Despite large-scale implementation in many settings, the evidence of effectiveness, cost effectiveness and change are inconclusive.15,26,27 Schoueten et al. in their systematic review found only nine out of seventy-two studies used controlled trial designs to measure effects of QIC on processes of care and/or outcomes.28 Two of the nine studies were RCTs with one based on the Breakthrough Series which did not show any effects on their key processes or intermediate outcomes of care; while the other RCT based on the Vermont Oxford method showed significant improvements in processes of care and no improvement in patient outcomes. Six controlled before and after studies and one interrupted time series based on a mix of the Breakthrough Series and Vermont Oxford Network largely demonstrated modest improvements in processes of care. Conversely, Wells et al recently conducted a systematic review of literature from January 1995 to December 2015.16 Sixty-four studies were identified (10 cluster RCTs, 24 controlled before-after studies and 30 interrupted time series studies). Around 85% found significant improvements in some of their intended aims, however there was considerable heterogeneity in outcomes, the quality of the studies reported was low and there is a substantial risk of reporting bias.
Despite this mixed evidence base, QICs clearly have demonstrated potential for diffusion of innovation and evidence.29 However, disentangling successful components of QICs and the settings in which they are most likely to work are complex and
50 challenging. QICs by nature are multifaceted with variation in teams, behaviour, cultural and social context between organisations, complexity of the area of change, along with potentially requiring substantial investment of time, effort, resources and funding. These challenges have prompted inquiry into understanding “how and why” QICs work – identifying the determinants of their effectiveness.30-32 AHRQ developed a taxonomy of key elements derived from fifteen studies over the last two decades. The taxonomy was mapped to four elements (innovation, communication, time and social systems) based on Roger’s diffusion of innovation framework, and then further layered to other theories to help guide and facilitate adoption, implementation, and spread of QIC strategies (Box 2.3).13 Such taxonomies may be useful in assisting policy makers and funders in identifying which factors and approaches can promote successful collaboratives in specific settings.
Box 2.3 The Agency for Healthcare Research and Quality’s learning collaborative taxonomy
Primary elements Secondary elements Innovation Type of change Degree of prescription Scope Supporting tools
Communication Mode or venue Directionality Frequency Degree of formality
Time Duration of learning collaborative Duration of member recruitment Rate of attainment or adoption Sustainability of learning collaborative
Social systems Degree of credibility of host or convener and leadership Membership characteristics Governance
51 Purpose and degree of shared vision Culture of the learning collaborative Members’ activity level Roles, process, and structure
Source: Nix et al. 13
Chronic Care Model
Since the late 1990s, the rapid rise in chronic illnesses has posed new challenges for health systems as it has exposed fragmentation in care delivery systems. This stimulated calls for integrated physical, psychological or social medical care with a greater emphasis on self-management.33 Edward Wagner with support from Robert Wood Johnson Foundation developed the CCM as a guide for healthcare systems to improve the care and management of chronic illnesses.34 The model can be applied in diverse healthcare organisations, however the majority of health services for chronic illnesses are performed in primary healthcare settings. Closely aligned with Starfield’s ‘four Cs’, the CCM has 6 essential domains to guide delivery of effective chronic care: (1) community resources (healthcare provider organisation needs to have linkages with community based resources such as exercise programs or senior centres); (2) health care organisation structure, goals and values need to be aligned with those of insurers, funders and other providers (i.e. insurance subsidies for chronic care), (3) self- management support (educating and helping patients and their families with the skills to use self-management tools, e.g., blood pressure cuffs, medication use, lifestyle support); (4) delivery system redesign (separating acute care from planned management of chronic conditions by creating practice teams with division of labour and planned visits); (5) decision support (evidence-based clinical guidelines integrated with patient records or reminder system); and (6) clinical information system (reminder system, physician performance feedback and clinical audit reports integrated with reminder system).35
QIC methods have been actively incorporated into the CCM to facilitate change in delivery of care. An evaluation of forty-two organisations implementing CCM alongside
52 QICs found that the model was associated with 41 organisations (98%) able to generate change and more closely align their system to at least five out of the six CCM elements; however the level of intensity (quantity and the depth of the intervention activities) was limited in year-long collaboratives.36 The majority of systematic reviews assessing effectiveness of CCM in improving health outcomes have found modest improvements.37-41 A limitation to the evidence base is that most studies include short- term randomised trials or before-after studies with a short follow-up, and outcomes have focussed mainly on processes of care and less on health outcomes. Davy et al in their systematic review evaluated in more detail the change in healthcare practices associated with implementation of CCM from studies worldwide.42 Consistent with the rationale for QICs, a key finding from this review was the importance of reflective practice by teams creating a collegial environment to reflect, learn and adopt change.43,44 However the findings went further, highlighting that structural factors including leader support, extensive time and resources are critical for implementation and sustainability of the intervention. This is resonant with Donabedian’s structure- process-outcome framework and highlights the importance of additional financial investment and support for practice transformation.
Patient-centred medical homes
The concept of “medical home” was conceived in 1967 by the American Academy of Paediatrics to describe holistic and dependable care for children with complex healthcare needs.45 Although there are variable definitions, medical homes are again closely aligned with Starfield’s ‘4 Cs’ and have five core attributes: (1) person-centred allowing patients to be maximally informed and involved in their care; (2) comprehensive care by a team of providers; (3) accessible- shorter wait times, after hours care and easy communication with providers; (4) coordinated with other elements of the system (e.g., hospitals and specialists); and (5) commitment to safety and quality processes and systems.
Despite being proposed several decades ago, PCMHs are now gaining prominence in health systems worldwide as the new model for primary healthcare delivery reform.46 Their renaissance in the 2000s was in response to highly fragmented primary
53 healthcare systems in the US, however now even countries with strong primary healthcare infrastructure are implementing such models.47
Although the evidence base for PCMHs is still immature, initial studies showed encouraging results with improvements in care coordination of chronic conditions and overall cost savings.48-50 However, there remain numerous unanswered questions about overall effectiveness. A recent systematic review was conducted assessing the effectiveness of PCMH models on patient and staff experiences, provision of evidence- based care processes, health outcomes and cost savings.51 The thirty-one studies included found small to modest effects on patient and staff experiences and preventive care and inconclusive effects on clinical and economic outcomes. Further, the requirements for transformation of clinical practices to become a PCMH are extensive. A systematic review evaluating the challenges of implementation of PCMH highlighted particular issues related to adopting electronic health records (EHRs), inadequate funding and reimbursement models to support transition to a PCMH model, and human resource incapacity.52
2.3 Health information technology to support QI
Optimal use of health information technology (HIT) is a fundamental component of QI strategies.53,54 HIT can be broadly defined as technologies (both hardware and software) that collect, store, share and analyse health information amongst consumers, providers, managers and payers.55 It ranges from electronic health records at the simplest level through to complex health information systems that incorporate multiple actors, system elements and information flows. Some of the earliest research evidence that HIT can improve care was in the area of medical error reduction.56 Three HIT QI interventions relevant to this thesis that have been most strongly associated with improvements in quality are: (1) computer decision support systems (CDSSs); (2) audit and feedback; and (3) computerised provider (or patient) order entry (CPOE) systems.55,57-59
CDSS is defined as “a process for enhancing health-related decisions and actions with pertinent, organised, clinical knowledge, and patient information to improve health and healthcare delivery…information delivered can include general clinical knowledge and
54 guidance, intelligently processed patient data, or a mixture of both; and information delivery formats can be drawn from a rich palette of options that includes data and order entry facilitators, filtered data displays, reference information, alerts and others”.60 It can be understood as a cognitive aide or knowledge dissemination platform that provides healthcare providers, patients and others with best scientific knowledge and patient specific information when needed continuously over time.61 Bates et al in 2003 summarised several guiding principles for successful CDSSs. These include: speed is everything, anticipate needs and deliver in real time, fit into the user’s workflow, little things can make a big difference, recognise that physicians will strongly resist stopping, simple interventions work best, monitor feedback and respond, and actively manage the knowledge-based systems.62 There have been many systematic reviews documenting the effectiveness of CDSSs. Table 2.1 lists several key systematic reviews.
55 Table 2.1: Summary of systematic reviews on effectiveness of CDSSs
Study Year Number Focus Findings of studies included
Effects of computer-based 1998 68 Controlled clinical trials 66% (43 of 65 studies) improved physician clinical decision support assessing the effects of performance; and 43% (six of 14 studies) systems on physician computer-based clinical found benefits in patient outcomes. performance and patient decision support systems outcomes: a systematic (CDSSs) on physician review 63 performance and patient outcomes
Effects of Computerized 2005 100 Effects of CDSSs in 64% of trials improved practitioner Clinical Decision Support controlled trials and to performance in diagnosis, preventive care, Systems on Practitioner identify study characteristics disease management, or drug prescribing. Performance and Patient predicting benefit Studies where users were automatically Outcomes: A Systematic prompted to use the system and those where Review 59 the developers of the CDSS were the authors of the trials performed better.
56 Improving clinical practice 2005 70 Identify features of CDSSs Decision support improved clinical practice in using clinical decision critical to improving clinical 68% of trials. Four features were independent support systems: a practice predictors of improved clinical practice: systematic review of trials to automatic provision of decision support as identify features critical to part of clinician workflow, provision of success 58 recommendations rather than just assessments, provision of decision support at the time and location of decision making, and computer based decision support. Of those that had all four features, 94% (30/32) significantly improved clinical practice. In addition, periodic performance feedback, sharing recommendations with patients and requesting documentation of reason was viewed positively.
Clinical decision support 2005 3 Evidence from controlled No significant effects on short-team outcomes systems for neonatal care 64 clinical trials on the effects of and longer-term outcomes. There was limited CDSS on neonatal care data to assess effect of CDSS.
Systematic Review: Impact 2006 257 Evidence on the effect of HIT Increased adherence to guideline based care
57 of Health Information on quality, efficiency, and in the area of preventive health, enhanced Technology on Quality, costs monitoring and surveillance activities, and Efficiency, and Costs of decreased rates of utilisation for potentially Medical Care 54 redundant or inappropriate care.
Effects of computerized 2007 8 Effects of CDSSs on nursing The results of this study were inconsistent. decision support systems on performance and patient nursing performance and outcomes patient outcomes: a systematic review 65
The use and effectiveness of 2008 17 Evaluation of the features of 67% used CDSS for chronic disease electronic clinical decision the CDSS, and effectiveness management in contrast to findings published support tools in the in ambulatory/primary care prior to 2000 which found CDSSs most often ambulatory/primary care settings used for prevention/screening and drug setting: a systematic review dosing. 82% embedded within EHR and used of the literature 66 with automated prompts. 83% of studies measured provider outcomes alone or in combination with patient outcomes. Patient outcomes were scarce due to the difficulty of measuring. 76% of studies had partial or
58 complete improvement in processes of care.
The impact of health 2009 23 Evidence of the impact of HIT 82% (14/17 studies) improved practitioners’ information technology on on the quality of healthcare, performance. There was insufficient evidence the quality of medical and focusing on clinician’s to statistically demonstrate change in patient health care: a systematic adherence to evidence- outcomes in the limited number of studies. review 67 based guidelines resulting in improved patient clinical outcomes
The Impact of eHealth on the 2011 108 Systematic review of Demonstrated benefits of eHealth Quality and Safety of Health systematic reviews assessing technologies to be modest and there was no Care: A Systematic Overview the effectiveness and evidence to support cost-effectiveness. 68 consequences of various eHealth technologies on the quality and safety of care
Computerized clinical 2011 55 To determine if CDSSs 52% of (25/48) studies measuring impact on decision support systems for improve processes of chronic processes of care showed significant chronic disease care (such as diagnosis, improvements. 31% (11/36) that measured management: A decision- treatment, and monitoring of patient outcomes demonstrated benefit. maker-research partnership disease) and associated
59 systematic review 69 patient outcomes (such as effects on biomarkers and clinical exacerbations)
Computerized clinical 2011 41 Review of randomised CDSSs improved processes of care in 63% decision support systems for controlled trials assessing the (25/40) of studies. Four of 14 improved primary preventive care: A effects of CDSSs for primary patient outcomes. Many trials were not decision-maker-researcher preventive care on process of powered to evaluate patient outcomes. Costs partnership systematic care, patient outcomes, and adverse events were poorly supported. review of effects on process harms, and costs of care and patient outcomes 70
Can computerized clinical 2012 15 Effects of CDSSs in Results were inconclusive with no evidence decision support systems ambulatory diabetes that CDSSs enhances patient outcomes or improve diabetes management compared with practitioner performance. Study was unable to management? A systematic a non-computerised CDSSs pool HbA1c, triglycerides and practitioner review and meta-analysis 71 performance outcomes.
Features of effective 2013 162 Identify factors that CDSS improved processes of care in over computerised clinical differentiate between 50% of studies with marginal improvements in decision support systems: effective and ineffective patient outcomes.
60 meta-regression of 162 CDSSs in processes of care randomised trials 72 and patient outcomes in randomised controlled trials
Effectiveness of 2014 28 Evaluation of the impact of Across clinical settings, CDSSs integrated Computerized Decision CDSSs linked to EHRs on with EHRs did not affect mortality and Support Systems Linked to mortality, morbidity and costs moderately improved morbidity outcomes. Electronic Health Records: A Systematic Review and Meta-Analysis 73
Clinical Decision Support 2015 45 The effectiveness of CDSSs CDSSs were effective in improving processes Systems and Prevention: A in improving screening for of care related to screening, preventive Community Guide CVD risk factors, preventive services, clinical tests, and treatments. Cardiovascular Disease care services and prescribing However results were inconsistent in Systematic Review 74 of treatments improving CVD risk factor outcomes.
Computer-Based Clinical 2015 15 Effect of CDSS on patient Three studies showed significant positive Decision Support Systems reported outcomes (PRO) impact of CDSS on PRO, and no negative and Patient-Reported and the features of CDSS effects of any of the studies. The three Outcomes: A Systematic that lead to improvement positive studies provided decision support at Review 75 point of care, and one study provided
61 justification to a recommendation.
Health Information 2018 37 An update on previous 81% of the studies improved at least one Technology Continues to systematic reviews of the medical outcome as a result of adoption of Show Positive Effect on effectiveness of HIT, and HIT. Medical Outcomes: review of current literature of Systematic Review 76 the association between adoption of HIT and medical outcomes
62 Table 2.1 highlights generally positive effects from CDSSs, particularly relating to processes of care. Effects on patient outcomes are more modest and there is less information on economic impacts. Further, there is substantial heterogeneity in the types of CDSS interventions implemented. These studies generally had short term follow-up making it difficult to interpret what should be implemented in non-trial settings. Several lessons have been learnt which have continued the evolution and increased use of CDSSs working towards addressing the current health system challenges around the world. However, enhancements are needed for standardising methods for knowledge and data representation, integration of CDSS within workflow at point of care, and patient data and knowledge exchange.77
Audit and feedback is a “summary of the clinical performance of healthcare provider(s) over a specified period of time”.78 This approach is theorised to support clinical behaviour change and potentially have effects on processes of care and patient outcomes. Four systematic reviews conducted over the course of 10 years (2003 to 2013) had consistent findings that “audit and feedback generally leads to small but potentially important improvements in professional practice”.79-82 An update from the initial Cochrane systematic review on effects of audit and feedback that included 64 comparisons of dichotomous outcomes from 49 trials (until 2004) found baseline adherence to recommended practice was the main determinant in explaining the wide variation in effectiveness across studies. The efficacy of audit and feedback was larger when baseline adherence was low and intensity of the audit and feedback high.80 Ivers et al. found that feedback was most effective when delivered from a supervisor or respected colleague, presented more than once, featuring both specific goals and action-plans, aiming to decrease the targeted behaviour, and recipients were non-physicians.82
CPOE is the process of a health professional entering instructions electronically for medication orders or other management instructions. It provides structured templates for healthcare providers to prescribe medications and order tests electronically which are then transferred electronically to the receiving service.83 CPOE systems automate the ordering process with standardisation, audit trail,
63 legibility, key specifications of data, route of administration, and storage and recall of records.84 The potential benefits include increased safety, efficiency and quality of patient care. A systematic review on the effects of medication error reduction in US hospitals found CPOEs decrease order errors by 48%.85 Several studies have found that CDSSs which are integrated with CPOE systems reduce duplicate orders, dosage errors, drug interactions, and missed or delayed orders.86-88 However, CPOE systems are known to have adoption barriers that have led to abandonment of the system. These include making users perform new tasks, workflow problems, excessive system demands, increased cognitive load, generation of new kinds of errors, unexpected changes in provider power relations and over-dependence on the technology.89 Additional adoption barriers include high implementation costs, the need for robust integration of the CPOE into the organisational team especially in hospital environments, alert fatigue, lack of leadership, and provider’s resistance to change.83,90 The CPOE systems mentioned above are studies in hospital settings. There are limited studies examining impact of CPOE systems in ambulatory/primary care settings and although they found less prescription writing, there was no difference between adverse drug event rates at CPOE health services compared to hand writing sites.91,92 Additional research into the implementation processes and effectiveness of CPOE is needed especially in ambulatory care settings.
2.4 Quality improvement strategies in Australia
Australian Primary Care Collaboratives (APCC)
In 2004, the Department of Health and Ageing funded $15.6 million for the APCC adopting the Breakthrough Series collaborative methodology. The program is run by a not-for-profit non-government organisation, the Improvement Foundation. There were seven waves of collaboratives each wave lasting 18 months.93 Initial focus areas included to improve chronic disease care, particularly diabetes and coronary heart disease (CHD) and improve timely access for patients. The first wave commenced in March 2005 and reported positive changes in indicators related to diabetes and CHD. Later waves have also focussed on addressing chronic obstructive pulmonary
64 disease, preventive health activities, patient self-management, and Aboriginal and Torres Strait Islander health. Approximately 4,216 health professionals from over 2000 health services in all states and territories within Australia have participated in this program. In before-after studies improvements have been reported in data quality, process measures and some clinical outcomes.94 Other benefits from participation in the program include improvements in administrative processes, team functioning, and accurate collection of data.
Aboriginal and Torres Strait Islander health service quality improvement programs
Several initiatives originating in the Northern Territory (NT) were antecedents to quality improvement programs in Aboriginal and Torres Strait Islander health. To address the high demands on Indigenous community health centres, the department of health and community services in NT in 1997 developed an integrated life course strategy (the Preventable Chronic Disease Strategy) to address chronic illnesses with a focus on renal disease and other associated chronic conditions.95 This was an evidence-based framework for early detection and management to be used in clinical care. A follow-on from this was the implementation of the Coordinated Care Trials in 1998 which had three objectives: 1) significantly increase funding and management of health services controlled by the community, 2) implementation of best practice clinical guidelines and 3) improvement of computerised information systems.96
Following this, the Audit and Best Practice for Chronic Disease research partnership was formed to develop auditing tools and quality improvement strategies for primary healthcare services that service Aboriginal and Torres Strait Islander people. The CQI intervention used random case record audits entered into a web-based database, and PDSA cycles. It took a flexible approach, allowing health centres to adapt the intervention to suit their circumstances.97 The initial project ran from 2002-2006 in 12 Aboriginal Community Controlled Health Services (ACCHSs) in the NT. The Cooperative Research Centre for Aboriginal Health (government funded) supported the project that brought together federal, state and territory government health agencies, ACCHSs, and research organisations. The program achieved high levels of acceptance and willingness to engage by primary healthcare organisations and
65 significant improvements in systems and processes of care and some intermediate outcomes.98 An Audit and Best Practice for Chronic Disease Extension project was implemented between 2005 and 2009 to inform operational and policy requirements for a larger scale implementation of the program and by the end of 2009 over 140 health centres around Australia were using Audit and Best Practice for Chronic Disease tools.99
There are several performance monitoring programs involving ACCHSs. The Queensland Aboriginal and Islander Health Council (QAIHC) represents 26 ACCHSs and is led by a community-elected board. In August 2008, it developed a set of 25 indicators to provide an overview of the quality of care delivered, health status of the patient population, and indicators of workload, patient access and workforce-related issues.100 The majority of ACCHSs submit aggregated data to the QAIHC data repository on a monthly basis via an automated clinical audit tool. Feedback to the health service is provided through a web-portal and a copy is stored on the health services’ servers.101 The systematic collection and monitoring of performance has resulted in improvements in several clinical care activities.
In the NT, a similar key performance indicator program was developed by the NT Aboriginal Health Forum which comprises the Commonwealth Department of Health, the NT Department of Health and the peak community controlled organisation in the NT - the Aboriginal Medical Services Alliance. A data platform provides information on a range of indicators comprising both government and non-government health services. It takes a collaborative approach to improving primary healthcare quality across the NT by understanding population wide trends in health outcomes, identifying factors that influence those outcomes and informing appropriate action, planning and policy development at the service and system levels.102
A Commonwealth government key performance indicator program has also been implemented since 2012. Data are collected from over 200 Indigenous health services who received funding to provide primary health care under the Indigenous Australians’ Health Program. This occurs through extraction of data from clinical information systems and is transferred to a secure data portal. Regular reports are 66 provided by the Australian Institute of Health and Welfare for 22 process and health outcome indicators and has generally shown improvements in quality across most of these indicators.103 The New South Wales government has recently adopted a similar model and has started reporting on a range of preventive health, drug and alcohol and mental health indicators.104
2.5 The evolution of HIT in Australia
Like many countries worldwide and especially those in the OECD, HIT is evolving rapidly in Australia. In 2005, the National Electronic Health Transition Authority (NEHTA) was established to progress Australia’s national eHealth infrastructure and standards.105 Figure 2.1 provides an overview of key activities.
Figure 2.1 Timeline of eHealth initiatives and milestones 106
Glossary of eHealth programs stated in Figure 2.1:
• ePIP – An eHealth Practice Incentives Program (PIP) to encourage general practices to keep up-to-date and adopt the latest digital health technology to improve administration processes and patient care.
• HealthConnect Trials – Australia’s change management strategy to transition from paper-based towards electronic health records.
• Wave Sites – eHealth Lead Sites to provide a community of services for individuals using a range of community and health service providers with
67 appropriate linkages to clinical support. Wave sites granted $14.5 million to support up to 243 participating general practices and 90,000 individuals. Pharmacies, after hours services, and out-patient services were also engaged.
• M2N – The MyEHR-to-National (‘M2N’) Transition Project is transitioning the existing MyEHR Service over to the National eHealth Record System.
• 5th Community Pharmacy Agreement – A five-year agreement between the Australian Government and the Pharmacy Guild of Australia to deliver to PBS medicines and related services.
• Telehealth MBS Items – For patients located in remote, regional and other metropolitan areas to conduct consultations via video conference with medical specialists, known as Telehealth.
• Healthcare identifiers - A national system that uses a unique number to match healthcare providers to individuals.
• Opt out trials – rule to specify the classes of healthcare recipients who will participate in opt-out trials of the My Health Record system, unless they choose to opt-out.
In mid-2016, NEHTA transitioned to become the Australian Digital Health Agency and in 2017 Australia’s first National Digital Health Strategy was released. The strategy outlines seven priority outcomes to be achieved by 2022:
1) health information that is available whenever and wherever it is needed
2) health information that can be exchanged securely
3) high quality data with a common understood meaning that can be used with confidence
4) better availability and access to prescriptions and medicines information
68 5) digitally-enabled models of care that drive improved accessibility, quality, safety and efficiency
6) a workforce confidently using digital health technologies to deliver health and care,
7) a thriving digital health industry delivering world-class innovation.107
Although there are many additional building blocks to the digital health system (secure messaging, electronic prescription exchange services, telehealth services to name a few), here I review four aspects that provide important context for this thesis: electronic health records, auditing tools, electronic care plans and personal health records.
Electronic health record (EHR)
EHR functions are broad, ranging from documenting the delivery of care, assessing health outcomes, setting targets, development of patient care plans, means of communication with healthcare teams, financial and epidemiological.108,109 In Australia, 98% of general practices use EHRs according to the Bettering the Evaluation and Care of Health (BEACH) survey for 2013-2014.110 A 2015 Commonwealth Fund survey comparing use of EHRs internationally from 2006- 2015 reported 92% use in Australia which has remained relatively consistent since 2009 (Figure 2.2).111
69 Figure 2.2: Doctors’ Use of Electronic Medical Records in OECD countries
A key driver for early adoption of EHRs in Australia was related to government financial incentives similar to countries reporting use greater than Australia. These incentives were provided from 1998-2002 to support general practices to install computers and clinical software packages for prescribing and transmission of clinical data.112 Although a voluntary scheme, the initiative stimulated an increase in use of EHRs from 15% in 1997 to 70% in 2000 in general practices.113 Currently, there are over ten primary healthcare EHR software packages in use across Australia with Medical DirectorTM and Best PracticeTM being the two dominant systems, estimated to supply around 80% of the market.110
70 EHR auditing tools
Most EHR software packages provide basic query and data extraction tools for audit and feedback purposes. However, for chronic disease indicators on a population and individual level, primary care providers tend to rely on third party software providers who have built data extraction software that integrate with the EHR. These tools provide auditing functions for chronic diseases such as diabetes, coronary heart disease and chronic obstructive pulmonary disease, enable identification of particular patient population segments that are receiving or not receiving particular care processes and allow for data export to custodians such as Primary Health Networks (PHNs) for population health monitoring. These tools have been extensively used in the APCCs quality improvement program described above and the majority of PHNs in Australia purchase a license for these tools on behalf of their member practices. The MedicineInsight program, run by the National Prescribing Service, also extracts data from over 650 general practices nationally with unique records for around 3.6 million patients. It uses these data for a range of purposes including post-marketing surveillance of medications, quality improvement activities, and a range of research projects.114
As mentioned above, Aboriginal and Torres Strait Islander health services have extensive experience in use of data extraction tools particular for reporting requirements to the federal government. The National Key Performance Indicator program (nKPIs) program commenced in 2010 to measure performance for health services provided to Aboriginal and Torres Strait Islander people across Australia. Data are uploaded from around 230 primary healthcare organisations including ACCHSs (58.9%), state and territory-managed organisations (34.4%), PHNs (4.1%) and other organisations providing services (2.5%) to Aboriginal people.103 The majority of the data is extracted from EHR systems to a secure portal.115,116
Electronic care plans
An evolving area in extending the use of the EHR relates to shared records and care plans. Government subsidies through Medicare claims are provided to generate
71 ‘General Practitioner Management Plans’ (GPMPs). Additional subsidies are provided for collaboration with specialists and allied health providers as part of a ‘Team Care Arrangement’ (TCAs). Despite the subsidies, uptake of these services is relatively modest with, for example, less than 14% of patients with diabetes having a current GPMP and/or TCA.117 Consequently, several software vendors are developing electronic software tools that are integrated with EHRs to assist with generating management plans and allowing external care providers to contribute to these plans. These tools are intended to overcome the barriers of time constraints, fragmented communications with healthcare teams and dissemination of evidence-based care.118,119 Although care planning software tools have not been extensively evaluated, in one study there were significant improvements in processes and clinical outcomes for patients receiving a GPMP.120,121 However, the Diabetes Care Project, which was a large, federal government funded randomised controlled study that tested a care delivery model which combined electronic QI tools (including electronic shared care planning) and financial incentives found that the electronic QI tools on their own were not associated with improved process or clinical outcomes. Further, only modest improvements were noted when these tools were combined with a flexible funding model, and these were not considered cost-effective.122
Personal health records
A core component of NEHTA’s initial activities was the establishment of a personal health record for all Australians – now called ‘My Health Record’ (previously Personally Controlled Electronic Health Record (PCEHR)). Launched in 2012, and reliant on voluntary consent, initial adoption was slow and by March 2016 there was only a total of 2,642,278 active digital records (11% of Australia’s population) and a total of 8,139 organisations registered with majority in general practices and pharmacy. In June 2018, My Health Record system changed to an opt-out model. This model automatically provides every Australian with a My Health Record unless they prefer not to and opt-out.123 This is aimed at allowing for faster health and economic benefits such as avoided hospital admissions, fewer adverse drug events, reduced duplication of tests, better coordination of care, and provision of
72 recommended treatment by the end of 2018.124 As of May 20, 2018, there are 5,804,614 consumers (54% female; 46% male), and 11,818 healthcare provider organisations registered.125
My Health Record is viewed as a foundational pillar for implementation of the digital health strategy. In the area of new digitally enabled models of care, the Health Care Homes trial is currently being implemented in 200 general practices and ACCHSs across Australia in collaboration with ten PHNs.107 Targeting chronic and complex care patients, the model includes a risk stratified, bundled payment for chronic disease care that replaces traditional fee-for-service payments, shared care plans, and mandatory use of the My Health Record.
2.6 Conclusion
There are several promising QI strategies being implemented worldwide that can improve care delivery systems and address Starfield’s ‘four Cs’ (accessible first contact, continuous, comprehensive and coordinated care). An important enabler of these QI approaches is HIT. Although there is great potential for digitally enabled health care models to improve the effectiveness and efficiency of health services, there remain substantial challenges in implementation, adoption, sustainability and scalability.
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89 PART B DEVELOPMENT OF A THEORY- INFORMED PROCESS EVALUATION
90 Chapter 3: A theory-informed logic model to assist in the planning, conduct and implementation of the process evaluation
3.1 Chapter Overview
This chapter describes the methods of a theory-informed process evaluation of four discrete but inter-related studies. A multimethod approach is taken drawing on a logic model to assist with the planning, conduct and evaluation of the research components. The chapter consists of a single manuscript titled: A multifaceted quality improvement intervention for CVD risk management in Australian primary healthcare: a protocol for a process evaluation.
This protocol allows for a multilevel analysis of implementation of the intervention providing both micro- and meso-system perspectives.
3.2 Publication details
Patel B, Patel A, Jan S, Usherwood T, Harris M, Panaretto K, Zwar N, Redfern J, Jansen J, Doust J and Peiris D. A multifaceted quality improvement intervention for CVD risk management in Australian primary healthcare: a protocol for a process evaluation. Implementation Science. 2014; 9:187.
DOI: 10.1186/s13012-014-0187-8
3.3 Author contributions
BP, DP, and TU made substantial contribution to the conception of the process evaluation. BP developed the theory informed logic model. BP, DP, TU, MH, KP, NZ, JR, JJ, JD, and AP provided input and contributed to the overall design of the process evaluation. BP, AP and MH contributed to the quantitative evaluation design.
91 SJ contributed to the economic evaluation design. BP wrote the initial draft of the manuscript. DP provided critical review and editing of the initial draft of the manuscript. All authors provided advice and input on the final manuscript for submission. BP prepared the final draft of the manuscript for publication.
3.4 Manuscript
92 Patel et al. Implementation Science 2014, 9:187 http://www.implementationscience.com/content/9/1/187 Implementation Science
STUDY PROTOCOL Open Access A multifaceted quality improvement intervention for CVD risk management in Australian primary healthcare: a protocol for a process evaluation Bindu Patel1*, Anushka Patel1, Stephen Jan1, Tim Usherwood2, Mark Harris3, Katie Panaretto4, Nicholas Zwar3, Julie Redfern1, Jesse Jansen2, Jenny Doust5 and David Peiris1
Abstract Background: Cardiovascular disease (CVD) is the leading cause of death and disability worldwide. Despite the widespread availability of evidence-based clinical guidelines and validated risk predication equations for prevention and management of CVD, their translation into routine practice is limited. We developed a multifaceted quality improvement intervention for CVD risk management which incorporates electronic decision support, patient risk communication tools, computerised audit and feedback tools, and monthly, peer-ranked performance feedback via a web portal. The intervention was implemented in a cluster randomised controlled trial in 60 primary healthcare services in Australia. Overall, there were improvements in risk factor recording and in prescribing of recommended treatments among under-treated individuals, but it is unclear how this intervention was used in practice and what factors promoted or hindered its use. This information is necessary to optimise intervention impact and maximally implement it in a post-trial context. In this study protocol, we outline our methods to conduct a theory-based, process evaluation of the intervention. Our aims are to understand how, why, and for whom the intervention produced the observed outcomes and to develop effective strategies for translation and dissemination. Methods/Design: We will conduct four discrete but inter-related studies taking a mixed methods approach. Our quantitative studies will examine (1) the longer term effectiveness of the intervention post-trial, (2) patient and health service level correlates with trial outcomes, and (3) the health economic impact of implementing the intervention at scale. The qualitative studies will (1) identify healthcare provider perspectives on implementation barriers and enablers and (2) use video ethnography and patient semi-structured interviews to understand how cardiovascular risk is communicated in the doctor/patient interaction both with and without the use of intervention. We will also assess the costs of implementing the intervention in Australian primary healthcare settings which will inform scale-up considerations. Discussion: This mixed methods evaluation will provide a detailed understanding of the process of implementing a quality improvement intervention and identify the factors that might influence scalability and sustainability. Trials registration: 12611000478910. Keywords: Knowledge translation, Process evaluation, Quality improvement intervention, Clinical decision support systems, Cardiovascular prevention, Cardiovascular risk, Primary healthcare, Mixed methods, Theory-based
* Correspondence: [email protected] 1The George Institute for Global Health, University of Sydney, Sydney, NSW 2006, Australia Full list of author information is available at the end of the article
? 2014 Patel et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Patel et al. Implementation Science 2014, 9:187 Page 2 of 12 http://www.implementationscience.com/content/9/1/187
Background majority of CVD events can potentially be averted with Cardiovascular disease burden adequate implementation of established treatments and Non-communicable diseases (NCDs)?including cardio- interventions that are effective, efficient, and universally vascular diseases (CVD), cancers, respiratory diseases, and accessible in Australian primary healthcare settings. diabetes mellitus?are the leading cause of death world- Primary healthcare is the ?front-line?for effective pre- wide [1,2]. It is predicted that by 2030, NCDs will account vention and management of the increasing burden of for 75% of all deaths with the largest proportion of deaths chronic diseases. With 88% of Australians visiting a gen- attributed to CVD [3]. Globally, approximately 17 million eral practitioner each year, the opportunities to improve (30%) deaths per year and 151 million disability-adjusted primary and secondary prevention of CVD at this level are life years (DALYs) are caused by CVD [4]. great [20]. In Australia, CVD is responsible for 34% of deaths and 18% of the burden of disease and injury, making it Knowledge translation strategies the largest contributor to health system expenditure Given the magnitude of these evidence practice gaps in [5]. Aboriginal and Torres Strait Islander people experi- CVD prevention, effective quality improvement (QI) in- ence a greater CVD burden than other Australians, and novations that support health services to improve their this is a major contributing factor to the 10-year gap in outcomes are urgently needed. QI is a multidimensional life expectancy [6,7]. The majority of CVD is caused by concept that focuses on improving the efficiency and modifiable risk factors, which include blood pressure, process of a program, service, or organisation, resulting lipids, diabetes, body mass index (BMI), tobacco smok- in improved health outcomes [21,22]. The Agency for ing, alcohol use, unhealthy diet, physical inactivity, and Healthcare Research and Quality (AHRQ) has identified psychosocial stress [8]. Recent modelling suggests that nine broad QI strategies to improve quality of care: pro- a 25% reduction in the prevalence of six NCD risk fac- vider reminder systems, facilitated relay of clinical data tors alone (tobacco, alcohol, salt, blood pressure, obes- to providers, audit and feedback, provider education, pa- ity, and glucose) could reduce global disease burden by tient education, promotion of self-management, patient 25% in the next 10 years [9]. reminder systems, organisational change, and financial incentives, regulation, and policy [23]. An increasingly Evidence practice gaps important component of QI strategies is an adoption of International clinical guidelines recommend that assess- health information technologies (HITs). Meaningful use ment for CVD prevention and management should be of electronic health records (EHRs), computerised pro- based on a combination of risk factors (the ?absolute risk? vider order entry systems, and electronic decision sup- approach) rather than treating risk factors such as ele- port (EDS) are increasingly recognised as key enablers to vated blood pressure and cholesterol in isolation [10-13]. improvements in quality and delivery of healthcare [24]. Absolute risk calculation estimates an individual?srisk However, the overall impact of computerised QI inter- of a CVD event over time based on modifiable and ventions to improve CVD burden has been limited, and non-modifiable risk factors such as age and gender. This effects on patient outcomes remain unclear [25-27]. enables early identification, management, and primary Whilst QI strategies have been well characterised, there prevention of CVD for individuals at high risk. In combin- is relatively little knowledge translation research to help ation with well-established secondary prevention recom- guide how these strategies can be optimally implemented mendations for people who have experienced a previous into routine practice [28,29]. The Canadian Institute of CVD event, the absolute risk approach offers considerable Health Research defines knowledge translation as ?the ex- potential for reducing CVD burden [14]. change, synthesis and ethically sound application of know- Despite the widespread availability and consistency of ledge?within a complex system of interactions among these guidelines and the availability of validated risk pre- researchers and users?to accelerate the capture of the diction equations, there are large evidence practice gaps. benefits of research for patients through improved health, Health professionals tend to use these guidelines and more effective services and products and a strengthened equations sporadically and inconsistently [15,16]. In the health care system?[30]. Whilst it is critical that QI inter- Australian context, studies have found that only 50% of ventions are robustly assessed for effectiveness, equally adults attending primary healthcare have been screened important is a detailed understanding of how those QI for CVD risk in accordance with guideline recommenda- interventions are implemented, using process and eco- tions, and only 40% of those identified as high risk have nomic evaluations. This will allow a deeper understand- been prescribed recommended medications [17,18]. In- ing of how the intervention worked/did not work, in ternational studies have similarly demonstrated that a which contexts was it most effective/ineffective, and why. minority of people are being provided with appropriate Given QI interventions are inevitably complex in nature, screening measures and preventive treatments [19]. The robust evaluation requires the use of multiple theories and Patel et al. Implementation Science 2014, 9:187 Page 3 of 12 http://www.implementationscience.com/content/9/1/187
frameworks to answer these questions. By using know- trial (cRCT) involving 60 health services. Details of the ledge translation frameworks, we are able to better iden- trial are published elsewhere [38]. In brief, the system was tify active ingredients of the intervention that change integrated with the healthcare provider?sEHRs,andin- behaviour, causal mechanisms of change, effective modes cluded (1) a real-time decision support interface using an of delivery, and the intended population or target [31]. algorithm derived from several evidence based national This will enable promotion and integration of the inter- guidelines (Figure 1); (2) a patient risk communication ventions into clinical practice, health systems, and policy. interface which included ?what if scenarios?to show the benefits from particular health risk factor improvement The treatment of cardiovascular risk in primary care using during a consultation (Figure 2); (3) an automated clin- electronic decision support (TORPEDO) study ical audit tool for extraction of data and review of There have been few randomised evaluations of QI in- health service performance (Figure 3); and (4) a web terventions in the Australian primary healthcare setting. portal where services can view peer-ranked perform- We designed a multifaceted QI intervention for CVD ance over time (Figure 4). Healthcare providers could risk management in Australian primary healthcare. The use the point-of-care tool as part of a routine clinical intervention drew on two established QI mechanisms: consultation. For the audit and feedback component, 1) electronic decision support and 2) audit and feedback quality indicators were developed for patients who had (summary of the clinical performance over a specified visited the health service at least three times in the pre- period of time) [32-37]. The intervention was evaluated ceding 2 years and once in the preceding 6 months. in the TORPEDO study, a cluster-randomised controlled The population studied was based on national guideline
Figure 1 Real-time decision support interface. Patel et al. Implementation Science 2014, 9:187 Page 4 of 12 http://www.implementationscience.com/content/9/1/187
Figure 2 Patient-oriented risk communication interface. recommendations for CVD risk screening and included prescriptions to high risk patients were not accompan- Aboriginal and Torres Strait Islander people over 35 years ied by increased prescribing rates for patients at low and all others over 45 years [38]. risk [39]. Data were collected for 38,725 people from the 60 Although the intervention exhibited significant im- health services. Overall, when compared with control, provements in some outcomes, it remains unclear how the intervention was associated with a 25% relative (10% this intervention was actually used in practice and what absolute) improvement in CVD risk factor screening. factors promoted and hindered its use. Answers to these Overall, there was no significant difference in the pre- questions are critical in order to inform future directions scribing rates of recommended medicines to people at for its implementation and for implementation of similar high CVD risk. The intervention was, however, strongly interventions in other settings. associated with improvements in the sub-group at high In this paper, we outline our protocol for a theory- CVD risk that was not prescribed with recommended based process evaluation of the TORPEDO intervention. medicines at baseline. There were also improvements The broad objectives are to understand how, why, and in intensification of existing, recommended medication for whom the intervention produced the observed out- regimens. There were modest improvements in attain- comes and to develop effective strategies for translation ing blood pressure targets but no differences in other and dissemination. The evaluation will identify which in- clinical outcomes. The improvements in recommended tervention components promoted or had minimal impact Patel et al. Implementation Science 2014, 9:187 Page 5 of 12 http://www.implementationscience.com/content/9/1/187
Figure 3 Automated data extraction tool? sample health service performance on CVD risk factor screening.
at the provider, patient, and system levels and the me- sequential design whereby the qualitative data analysis chanism of change. It will also identify the contextual will be used to gain a better understanding of the quan- influences on the delivery of the intervention and its titative findings [40]. outcomes. The specific objectives are the following: Logic model 1. To understand whether intervention effects are Drawing on the RE-AIM framework, a logic model was sustained in a post-trial setting; developed to assist in the planning, conduct, and evalu- 2. To identify implementation barriers and enablers; ation of the research components (Figure 5) [41-44]. 3. To understand how CVD risk is communicated in The model assesses five dimensions of the intervention the doctor/patient interaction both with and without at different levels (individual, health service/clinic or the use of the intervention; and organisation, and community/population): (1) partici- 4. To identify cost considerations for delivering the pant Reach; (2) Effectiveness of the intervention; (3) intervention at scale in the Australian primary Adoption by the target health service; (4) Implementa- healthcare system. tion fidelity, costs, and adaptations made during delivery; and (5) Maintenance of intervention effects over time. Design and methods The model identifies and describes inputs, activities, out- Taking a mixed methods approach, we will conduct puts, and outcomes of the intervention [45]. The four ob- four discrete but inter-related studies to address our study jectives have been mapped onto the relevant components objectives. Specifically, we will adopt an explanatory of the RE-AIM framework. Patel et al. Implementation Science 2014, 9:187 Page 6 of 12 http://www.implementationscience.com/content/9/1/187
Figure 4 Quality improvement portal.
The logic model will be underpinned by three theore- Theoretical domain framework tical perspectives?realist evaluation [46], the theoretical Successful implementation of evidence-based guidelines domain framework (TDF) [47,48], and normalisation and QI interventions depends largely on changing the be- process theory (NPT) [49,50]. haviour of healthcare professionals and patients, who are influenced by external (i.e. organisational, environmental, Realist evaluation resources) and internal (i.e. motivation, capability) factors. Realist evaluation seeks to answer the question what TDF is a consensus of numerous behaviour change mo- works, for whom, and in what circumstances? [46]. This dels and comprises 14 domains derived from psycho- is accomplished by identifying and understanding the logical and organisational theory (knowledge, skills, social/ underlying mechanisms by which the intervention suc- professional role and identity, beliefs about capabilities, ceeds or fails in varying contexts to produce the patterns optimism, beliefs about consequences, reinforcement, in- of outcomes. Therefore, unpacking of the underlying tentions, goals, memory attention and decision process, generative mechanisms of the intervention and its effects environmental context and resources, social influences, is contingent on understanding the features of the con- emotion, and behavioural regulation) [47,48,53]. text (i.e. roles and relationships of personnel at health services, IT infrastructure, economic conditions, demo- Normalisation process theory graphic, motivation and skills of health professionals, In order for healthcare innovation and technology to be- etc.) [46,51,52]. come routinely embedded in every day work, we need to Patel et al. Implementation Science 2014, 9:187 Page 7 of 12 http://www.implementationscience.com/content/9/1/187
Figure 5 Logic model for TORPEDO process evaluation. understand how the innovation is integrated within the for an additional 12 months. A post-trial clinical audit existing practices of a healthcare organisation. NPT assists data extraction at minimum of 24 months from baseline in understanding how health professionals implement an will be conducted for the health services expressing inter- intervention. NPT identified four main components: (1) est to use the intervention. The objective of this study is coherence (sense making by participants), (2) cognitive to assess the impact of the use of the intervention over participation (commitment and engagement by partici- time on the two primary outcomes: (1) proportion of pants), (3) collective action (the work participants do to CVD risk factor screening and (2) proportion of appropri- make the intervention function), and (4) reflexive moni- ate medication prescription in the high risk individuals for toring (the degree to which participants reflect on or ap- the intervention arm and usual care arm at post-end of praise the intervention [49,54,55]. study following baseline.
Study 1: Post-trial effectiveness of the intervention (objective 1) Analysis Aim Log-binomial regression will allow direct estimation of To assess the effects of the intervention at one year fol- risks and risk ratios (i.e. relative risks) on each outcome. lowing completion of the cRCT. The model will be adjusted with intervention (yes vs. no) and time (baseline, 12 and 24 month) as categorical Methods data where baseline is set as reference and interaction At the end of study follow-up visit for the TORPEDO between intervention and time. The model will be ad- study, all 60 health services have the option to either justed with the intervention (yes or no) and with a ran- continue the use of the intervention or have the interven- dom centre effect. Chi-square test and 95% confidence tion implemented for use in the usual care health services intervals (CI) will be computed. Patel et al. Implementation Science 2014, 9:187 Page 8 of 12 http://www.implementationscience.com/content/9/1/187
Study 2: Multilevel modelling study (objective 2) identify contextual influences, and by drawing on mul- Aim tiple empirical data sources will increase the robustness To determine what patient and health service level vari- of the findings [58,59]. ables correlate with the trial outcomes. We will purposively sample health services to achieve maximum variation in trial primary outcomes, numbers Methods of staff at each site, and type of service (GP versus ACCHS, Patient variables (age, gender, ethnicity, history of CVD, urban versus rural). We will select six cases from the inter- and diabetes) will be obtained from the automated clin- vention arm (four GPs and two ACCHSs) and three cases ical audit tool, and health service level variables will be from the usual care arm (two GPs + one ACCHSs). There collected through Team Climate Inventory (TCI) and will be two methods of data collection. Warr-Cook-Wall job satisfaction surveys, customised for use with general practices (GPs) and Aboriginal Com- munity Controlled Health Services (ACCHSs), adminis- Health professional interviews tered to all general practitioners and other practice and Semi-structured interviews with site staff will provide us health service staff at the participating 60 sites [56,57]. with their knowledge, views, and experience of the im- In addition, health service characteristics such as service plementation of the intervention at their health service. size, participation in quality improvement programs, and Interview questions have been developed to explore the type of primary healthcare (GP verses ACCHS) will be realist evaluation domains of context, mechanism, and collected at randomisation. outcome. Some questions include the following: (1) why health staff did/did not use the intervention; (2) Analysis how was the intervention used in routine practice and TCI is a 44-item questionnaire, and items are rated on a by whom; (3) what were the contextual factors that in- 5-point scale [56]. Job satisfaction is a 15-item question- fluenced its uptake; and (4) what impact did it have on naire, and items are rated on a 7-point scale [57]. Multi- the way personnel did their work. We will conduct ap- level regression model analysis will be conducted to proximately 20 health professional interviews with ge- evaluate the influence of team climate and job satisfaction neral practitioners, nurses, managers, Aboriginal health on the primary study outcomes, controlling for patient?s workers (AHWs), and administrative assistants from age, gender, practice size, type of PHC, and participation within our cases. The final number of interviews will be in quality improvement programs. The results will be dependent on thematic saturation [60]. The interviews interpreted within the context of the three conceptual per- will take place at the health service, and all interviews will spectives to better understand what health service factors be audio-recorded and transcribed. The interviews will (if any) are important drivers of use of the intervention in take place face to face with a thematic and topic-centred routine practice. interview guide. The interview guide will be flexible to allow exploration of emergent themes. Study 3: Interview study and video ethnography (objectives 2 and 3) Aim Video ethnography To identify and understand which intervention compo- Qualitative data obtained from ethnographic studies nents promoted or had minimal impact on behaviour can enhance our understanding of how to introduce a change at the provider and patient levels, the mechanism technological innovation into healthcare [61,62]. In of change, and contextual influences. order to augment our interview data, video ethnogra- phy will be used to give us insight into (1) the possible Methods ways general practitioners used the intervention tools; Case study methods will be used to explore system chan- (2) how cardiovascular risk is talked about between ges over time, through in-depth qualitative data collection general practitioner and patient; (3) how patients receive involving multiple sources of information. The cases will and interpret this information; and (4) what impact the be individual GPs and ACCHSs participating in the intervention tools have on the decision-making process, TORPEDO study. Quantitativedataobtainedfromthe particularly related to recommending and taking medica- primary study and the studies 1 and 2 above will be tion. Approximately 20% of patients video-recorded (ap- drawn on as part of the analysis of the cases, and new proximately two per general practitioner) will be selected qualitative data will be obtained using semi-structured to be interviewed after videotaping of their consultation. interviews, video ethnography, and surveys. This will Patients agreeing to participate will be interviewed at their ensure that both intervention effects and implemen- home or the health service at a time suitable for the tation processes are comprehensively assessed. It will patient. Patel et al. Implementation Science 2014, 9:187 Page 9 of 12 http://www.implementationscience.com/content/9/1/187
Analysis GP, etc.), capacity constraints within individual practices, Framework analysis will be used to organise the inter- the investment needed to adopt the intervention, and the view data [63]. Key issues and emergent themes will be potential returns to the practice in terms of patient care. identified and then a coding framework will be devel- These will be assessed across a diverse range of practices. oped to index and chart interview transcripts and videos. This evidence will be obtained through clinical audit Videos will be subject to fine-grain discourse analysis data, surveys, and health professional semi-structured [64]. NVivo 10 software (QSR International) will be used interviews. The findings will be used to determine the for data management and coding of interview tran- economic viability of the widespread adoption and im- scripts, field notes, and videos. The analysis will occur plementation of this intervention and inform policy by simultaneously with data collection. Themes will be devel- ascertaining the support that individual practices will oped both deductively (pre-defined themes) and induct- need to accomplish these tasks and ultimately the costs ively (emerging themes), and then coded in an iterative to government of scaling up. process to identify patterns, and interpret the meaning of the themes within and across cases. Throughout this Ethical considerations process, we will meet regularly with a project working ThestudyisapprovedbyTheUniversityofSydney group of expert researchers and collaborators to discuss Human Research Ethics Committee (2012/2183) and the theoretical framework within which the data will be the Aboriginal Health & Medical Research Council collected, coded, and interpreted. Reflexivity will be in- (AH&MRC) of New South Wales (778/11). Participa- corporated into the qualitative analysis process to take tion agreements were signed between the participating into account personal assumptions and biases, so these health services and the coordinating research institute. do not influence the way and the type of data that are De-identified patient level data is being extracted from collected or the data analysed. health service software systems to analyse post-trial Our three chosen theoretical perspectives will be re- outcomes (study 1) and assess health service utilisation gularly drawn on to assist with interpretation of the costs (study 4). Participants in the TCI and job satisfaction qualitative findings. Using realist evaluation, we expect surveys (study 2) and the qualitative study components the analysis to yield insights into particular context- will be provided with an information sheet and asked to mechanism-outcome configurations that explain patterns provide written informed consent to participate. Partici- associated with use and non-use of the intervention. The pants will be reassured of the confidential nature of any TDF will complement these analyses and explore to data collected, and they will be identified by a unique what extent the intervention influenced behaviour change identification number only. Participants will be reminded by various actors (health professionals, managers, and that they can opt not to answer any questions or can stop patients). Data will be used to make an assessment of interviews or videotaping at any time, and they will have a the underlying capacity, motivation, and opportunities right to withdraw consent and cease involvement in the of these actors and the extent to which the intervention study without penalty. influenced these areas. NPT will be used to provide a better understanding of the ways in which health ser- Trial status vices as organisational structures respond to the inter- Data collection is underway. Preliminary qualitative data vention. Interview codes will be aligned with the four analysis is being conducted contemporaneously with data NPT domains of coherence, cognitive participation, col- collection. Quantitative data analysis has not commenced. lective action, and reflexive monitoring, and it is expected this will facilitate our analyses and derivation of the key Discussion messages. Addressing the challenges of CVD burden requires im- plementation strategies for increasing the uptake of well- Study 4: Cost consideration of scale-up (objective 4) established evidence into practice. Our attempt to address Aim this with a multifaceted QI intervention was moderately The cost implications for health services to adopt the but not uniformly successful, suggesting the need for a intervention, and deliver at scale in Australia. rigorous process evaluation to understand how and in what ways it was taken up in practice. Such evaluations Summary are crucial to understanding how implementation strat- A business model will be developed for health services egies should be applied in non-trial settings. to adopt and maintain the intervention. We will both Multifaceted interventions, by their nature, invariably quantitatively and qualitatively explore the factors that lead to complex usage patterns which can make inter- will influence costs for the various types of health services pretation of study outcomes difficult. In order to maxi- (i.e. large, medium and small health services, patient load/ mise understanding that is relevant to other settings, Patel et al. Implementation Science 2014, 9:187 Page 10 of 12 http://www.implementationscience.com/content/9/1/187
process evaluation is therefore critical. The strength of Authors?contributions our process evaluation is its multicomponent, multi- BP, DP, and TU contributed to the conception of the process evaluation. BP, DP, TU, MH, KP, NZ, JR, JJ, JD, and AP contributed to the overall design of theory approach combining diverse study designs to the process evaluation. BP implemented the study and conducted the data make sense of how this particular knowledge translation collection. AP and MH contributed to the quantitative evaluation design. SJ strategy was adopted into practice. Further, by examining contributed to the economic evaluation design. BP wrote the initial draft of the manuscript. DP provided critical review and editing of the initial and implementation from multiple perspectives (provider, subsequent drafts of the manuscript. All authors read and approved the final patient, health services, and system) the findings are ex- manuscript. pected to provide both micro- and macro-system per- spectives which will be of interest to policy makers and Acknowledgements This study would not have been possible without the dedicated health implementers. There are two key limitations to our ap- professionals and patients that volunteered their time to improve the quality proach (1) the majority of the data collection will occur of care. We also thank our research staff Shannon McKinn, Marilyn Lyford, toward the end of the trial and in the post-trial phase Genevieve Coorey, and Maria Agaliotis who aided in the data collection. Funding support for this project is provided by an Australian National Health and may miss critical insights gained from early phase and Medical Research Council (NHMRC) Project Grant (ID 1010547) and NSW adoption processes; and (2) the study setting is limited Health. BP is supported by NHMRC post-graduate scholarship (ID APP1075308). to Australian primary healthcare settings and therefore DP was supported by an NHMRC Translating Research into Practice fellowship and now a NHMRC post-doctoral fellowship (ID 1054754). AP (APP1079301) and may be only of relevance to health systems with similar SJ (ID 1020430) are supported by NHMRC senior research fellowships. JJ was contexts, financing, workforce structures, and adoption supported by NHMRC post-doctoral fellowship (ID 1037028). JR is funded by a of electronic medical records. Career Development and Future Leader Fellowship co-funded by the NHMRC and National Heart Foundation (APP1061793). Despite these caveats, the adoption and successful implementation of computerised QI interventions and Author details 1 strategies are the key challenges for healthcare systems The George Institute for Global Health, University of Sydney, Sydney, NSW 2006, Australia. 2University of Sydney, Sydney, NSW 2006, Australia. worldwide. In 2009, the US government passed the 3University of New South Wales, Sydney, NSW 2052, Australia. 4Queensland Health Information Technology for Economic and Cli- Aboriginal and Islander Health Council, 21 Buchanan St., West End, QLD 5 nical Health Act as a stimulus to promote and adopt 4101, Australia. Bond University, 14 University Dr, Robina, QLD 4226, Australia. ?meaningful use?of information technologies. The Act provided incentive payments to hospitals and individual Received: 19 November 2014 Accepted: 1 December 2014 practices totalling $14?27 billion to adopt EHRs within 3 years to avoid financial penalties. 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doi:10.1186/s13012-014-0187-8 Cite this article as: Patel et al.: A multifaceted quality improvement intervention for CVD risk management in Australian primary healthcare: a protocol for a process evaluation. Implementation Science 2014 9:187.
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105 Chapter 4: A quantitative study evaluating the efficacy of the intervention in a post-trial setting to assess trends in outcomes over three years
4.1 Chapter overview
This chapter reports the results of an observational study assessing the changes in cardiovascular disease (CVD) risk factor screening, prescribing to high-risk patients and patients outcomes in the post-trial period of implementation of the intervention. The chapter consists of a single manuscript titled: Impact of Sustained Use of a Multifaceted Computerized Quality Improvement Intervention for Cardiovascular Disease Management in Australian Primary Health Care.
The study demonstrated that there was no change in CVD risk factor screening in the post-trial period when compared with the end of the trial period. Conversely, for patients at high CVD risk, there were statistically significant improvements in recommended prescriptions at the end of the post-trial period.
The post-trial study complements the TORPEDO trial looking at impact when trial support is scaled back. The findings of the observational study suggest that implementation of the quality improvement intervention may take some time for healthcare providers to initiate medications, and the potential impact of the intervention beyond trial settings.
4.2 Publication details
Patel B, Peiris D, Usherwood T, Li Q, Harris M, Panaretto K, Zwar Z, Patel A. Impact of Sustained Use of a Multifaceted Computerized Quality Improvement Intervention for Cardiovascular Disease Management in Australian Primary Health Care. Journal of the American Heart Association. 2017; 6: e007093.
DOI: 10.1161/JAHA.117.007093
106 4.3 Author contributions
All authors contributed to the overall study design. BP implemented the study, conducted the data collection, interpretation of the results and writing of manuscript. BP, AP and QL contributed to the statistical analysis plan. QL conducted the statistical analysis with advice from AP and BP. BP interpreted the data with input from AP, QL and DP. BP wrote the initial draft of the manuscript. AP and DP provided critical review and editing of the initial and subsequent drafts of the manuscript. All authors provided advice and input in preparation of the final manuscript. BP prepared the final draft of the manuscript for publication.
4.4 Manuscript
107 ORIGINAL RESEARCH
Impact of Sustained Use of a Multifaceted Computerized Quality Improvement Intervention for Cardiovascular Disease Management in Australian Primary Health Care Bindu Patel, MPH; David Peiris, MBBS, MIPH, PhD; Tim Usherwood, MBBS, MD; Qiang Li, MBiostat; Mark Harris, MBBS MD; Kathryn Panaretto, MBBS, MPH; Nicholas Zwar, MBBS, PhD; Anushka Patel, MBBS, SM, PhD
Background-—We evaluated a multifaceted, computerized quality improvement intervention for management of cardiovascular disease (CVD) risk in Australian primary health care. After completion of a cluster randomized controlled trial, the intervention was made available to both trial arms. Our objective was to assess intervention outcomes in the post-trial period and any heterogeneity based on original intervention allocation. Methods and Results-—Data from 41 health services were analyzed. Outcomes were (1) proportion of eligible population with guideline-recommended CVD risk factor measurements; and (2) the proportion at high CVD risk with current prescriptions for Downloaded from guideline-recommended medications. Patient-level analyses were conducted using generalized estimating equations to account for clustering and time effects and tests for heterogeneity were conducted to assess impact of original treatment allocation. Median follow-up for 22 809 patients (mean age, 64.2 years; 42.5% men, 26.5% high CVD risk) was 17.9 months post-trial and 35 months since trial inception. At the end of the post-trial period there was no change in CVD risk factor screening overall when compared with the end of the trial period (64.7% versus 63.5%, P=0.17). For patients at high CVD risk, there were significant improvements in http://jaha.ahajournals.org/ recommended prescriptions at end of the post-trial period when compared with the end of the trial period (65.2% versus 56.0%, P<0.001). There was no heterogeneity of treatment effects on the outcomes based on original randomization allocation. Conclusions-—CVD risk screening improvements were not observed in the post-trial period. Conversely, improvements in prescribing continued, suggesting that changes in provider and patient actions may take time when initiating medications. Clinical Trial Registration-—URL: http://www.anzctr.org.au. Unique identifier: 12611000478910. ( J Am Heart Assoc. 2017;6: e007093. DOI: 10.1161/JAHA.117.007093.) by guest on January 27, 2018 Key Words: cardiovascular disease prevention • computer decision support systems • health information technology • intervention • long-term use • primary health care • quality improvement
uality issues affecting healthcare organizations world- providers are screened for CVD risk in accordance with Q wide include inadequate access to healthcare services, guideline recommendations, and only about 40% of those suboptimal provision of evidence-based preventive services identified at high CVD risk are prescribed recommended and treatments, and poor coordination of care across health- medications.3,4 Meaningful use of health information technol- care systems.1,2 Our previous work found that only around one ogy (HIT) has the potential to be an important enabler in half of adults routinely attending Australian primary healthcare increasing the quality of healthcare delivery.5,6 Two such HIT tools are computer decision support systems and computer- ized audit and feedback tools (the provision of summarized From the George Institute for Global Health, University of Sydney, Camper- clinical performance indicators to healthcare provider(s) over a down, Australia (B.P., D.P., T.U., Q.L., A.P.); University of New South Wales, specified period of time). There is a well-established evidence Sydney, Australia (M.H., D.P., Q.L., A.P.); University of Queensland, Teneriffe, base demonstrating that these tools improve processes of care Australia (K.P.); University of Wollongong, Australia (N.Z.). and modestly improve clinical outcomes.7–10 Despite this Correspondence to: Bindu Patel, MPH, PO Box M201 Missenden Rd, Sydney, New South Wales 2050, Australia. E-mail: [email protected] evidence, there remain substantial challenges in implementing 11,12 Received July 4, 2017; accepted September 11, 2017. these tools in routine clinical care. ª 2017 The Authors. Published on behalf of the American Heart Association, We designed a computerized multifaceted quality improve- Inc., by Wiley. This is an open access article under the terms of the Creative ment (QI) intervention for CVD risk management in Australian Commons Attribution-NonCommercial License, which permits use, distribu- tion and reproduction in any medium, provided the original work is properly primary health care. The intervention, termed HealthTracker, cited and is not used for commercial purposes. comprised 4 elements: (1) real-time decision support
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 1 Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL
understanding of the impact of integration of the intervention Clinical Perspective into routine clinical care. What Is New? • This observational study examines the impact of a multi- Methods faceted computerized quality improvement intervention to reduce cardiovascular disease risk beyond the randomized Intervention trial period. Forty-five of the original 60 primary health services (29 • The intervention in a post-trial period was associated with general practices [GPs] and 16 Aboriginal community maintenance of trial-related improvements in screening of controlled health services) agreed to participate in the cardiovascular disease risk and ongoing improvements in prescribing recommended medicines to patients at high post-trial study (Figure 1). Fifteen health services chose not cardiovascular disease risk. participate because of the health service closing or moving (n=4); limited resources (n=3); concerns about HIT tool slowing down their computer system (n=3); changing to an What Are the Clinical Implications? incompatible electronic health record software system • The study demonstrates evidence of potential longer-term (n=3); using another online CVD risk tool (n=1); and lack impact of a computerized quality improvement intervention of interest (n=1). Four services later withdrew because of Downloaded from on primary prevention of cardiovascular diseases. concerns about resources required to support intervention use and the effects of the software on the speed of their integrated with the patient electronic record utilizing a systems, leaving 41 services (21 intervention and 20 guidelines-based algorithm; (2) a patient risk communication control) included for evaluation purposes. The study popu- interface that included “what if scenarios” to show the lation comprised Aboriginal and Torres Strait Islander http://jaha.ahajournals.org/ potential benefits from particular health risk factor modifica- people aged ≥35 years and all others aged ≥45 years, tions during a consultation; (3) an automated clinical audit who were regular attendees of the health service. Regular tool for extraction of data and review of health service attendance was defined as having visited the health service performance; and (4) a web portal where services could view at least 3 times in the preceding 2 years and once in the peer-ranked performance over time. Details of the interven- preceding 6 months. tion have been published.13 Health services that had been previously randomized to not
by guest on January 27, 2018 HealthTracker was evaluated between 2011 and 2013 in receive HealthTracker were trained in use of the intervention the TORPEDO (Treatment of Cardiovascular Risk in Primary at the end of the cRCT. The health services previously Care Sing Electronic Decision Support) study, a cluster- randomized to receive HealthTracker were given a refresher at randomized controlled trial (cRCT) involving 38 725 people the end of the cRCT period, if requested. Ongoing training and from 60 primary healthcare services. Detailed results of the technical support was provided to the health services on study have been previously published.14 In brief, when request, but this was minimal during the post-trial period. An compared with services that did not have HealthTracker average of 13-minute support per month comprising on-site installed, and after a median of 17.5 months follow-up, training, remote clinical Webinars, and IT helpdesk services services with HealthTracker demonstrated a 25% relative (10% was provided during the post-trial period, 35 minutes less absolute) improvement in CVD risk factor screening. There than during the cRCT. All software was provided free of was no significant difference overall in prescribing rates of charge. recommended medicines for people at high CVD risk. However, for the subgroup of individuals at high CVD risk who were not prescribed optimal recommended medicines at Data Collection baseline, the intervention was associated with a 59% relative Nonidentifiable data extracts were obtained from each service (17% absolute) improvement in prescribing rates. using a validated extraction tool17 at 3 time points—(1) at At the completion of the trial, health services in both trial baseline for the cRCT; (2) at the end of the cRCT; and (3) at arms were offered access to HealthTracker and invited to the end of the post-trial period. Data extractions were either participate in a post-trial evaluation. Randomized controlled conducted by the research team through a virtual remote log- trials generally have short-term follow-up periods, and in system or by health service staff themselves. Data were evidence of sustained impact in nontrial settings is sent via a secure file transfer protocol or encrypted email to sparse.15,16 In this observational study, we analyze changes the coordinating research institute. An encrypted identifier in CVD risk factor screening, prescribing to high-risk patients code was added to each patient’s data, and this was used to and patient outcomes in this post-trial period to improve our enable longitudinal follow-up of individual patients.
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 2 Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL
61 health services randomized
31 randomized to intervention 30 randomized to control
(21GPs, 10 ACCHSs) (20 GPs, 10 ACCHSs) • 1 GP withdrew from the study shortly after randomization
30 health services analyzed at end of cRCT 30 health services analyzed at end of cRCT
• Median follow-up - 17.3 months (IQR • Median follow-up - 17.7 months (IQR 15.3-18.0) 14.3-18.3)
7 – did not wish 8 – did not wish to participate to participate
Downloaded from (2 closed; 1 (2 closed/moved; limited resources, 2 limited 1 concerns about resources, 2 system slowness; concerns about 2 changed to system slowness; incompatible 1 changed to incompatible http://jaha.ahajournals.org/ software system; 1 not interested) software system; 1 using another online CVD risk tool)
23 health services agreed to participate in 22 health services agreed to participate in post-trial period post-trial period by guest on January 27, 2018 2 withdrew (1 2 withdrew (1 limited resources; limited 1 concerns about resources; 1 system slowness organizational change)
21 health services analyzed at end of post-trial 20 health services analyzed at end of post-trial
• N = 12, 534 • N = 10, 275 • Median follow-up in post-trial phase • Median follow-up in post-trial phase 17.7 months (IQR 15.5-20.8) 18.5 months (IQR 15.6-23.1)
Figure 1. Study flow diagram. ACCHS indicates Aboriginal Community Controlled Health Service; cRCT, cluster randomized controlled trial; CVD, cardiovascular disease; GP, general practice; IQR, interquartile range.
We invited all general practitioners (GPs) from the inter- Wales (778/11). Participation agreements were signed vention health services to complete an end-of-study survey. between the participating health services and the coordinat- The survey obtained information on GP use of the intervention ing research institute. along with their professional and health service characteris- tics and prior use of information technology. The study was approved by The University of Sydney Study Outcomes Human Research Ethics Committee (2012/2183) and the The follow-up study outcomes were prespecified and corre- Aboriginal Health & Medical Research Council of New South spond to those reported in the TORPEDO study. Co-primary
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 3 Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL
outcomes are 1 and 2, and secondary outcomes are 3 and 4 for continuous variables. The random allocation was the below: independent variable in the model. Study outcome analyses were conducted using generalized estimating equations 1. The proportion of eligible patients who received appropri- regression model for continuous outcome, assuming Gaussian ate screening of CVD risk factors. Appropriate screening distribution and binary outcomes assuming binomial distribu- was defined as smoking status recorded at least once; tion with a log link function. Effect estimates between systolic blood pressure recorded in the previous intervention and usual care health services for the primary 12 months; and total cholesterol and high-density lipopro- and secondary outcomes were obtained from the generalized tein recorded in the previous 24 months. estimating equations model with adjustments made for 2. The proportion of eligible patients defined at baseline as baseline measurements, size of service, type of service, and being at high CVD risk, receiving recommended medica- current participation in QI initiatives. Given that the event rate tions. This was defined as (1) current medication of at is higher in our prospective study, the log link function was least 1 blood pressure (BP)–lowering drug and statin for used to produce a rate ratio between intervention and usual high-risk patients without established CVD, and (2) current care group rather than deriving an odds ratio using a logit link medication of at least 1 BP–lowering drug, statin, and function. Although odds ratios produced by logit link tend to antiplatelet agent (unless contraindicated) for patients be equivalent to rate ratios produced by log link when the with established CVD. This outcome was evaluated in the event rate is small, they may overestimate the relative risk
Downloaded from overall high-risk cohort, as well as the subgroup of high- when event rate is high. risk individuals who were undertreated (defined as those Trend analysis was conducted using data from 2 time not prescribed guideline-recommended medications at points: end of cRCT and end of post-trial period. Additionally, baseline). paired comparisons were performed to evaluate the effects 3. Escalation of prescription medication (either newly pre- of receiving the intervention during the post-trial period. For http://jaha.ahajournals.org/ scribed or additional numbers of antiplatelet, BP–lowering outcomes in the post-trial period, P values were calculated or lipid-lowering medications) among patients at high CVD based on change from end of the cRCT to the end of the risk. post-trial period. For the post-trial period, heterogeneity in 4. Change in BP and serum lipid levels among people at high effects between the previously randomized intervention and CVD risk. control groups was assessed based on the significance of Based on Australian guidelines, high CVD risk was defined interaction term in the model. Statistical analyses were
by guest on January 27, 2018 as (1) history of CVD (diagnosis of coronary heart disease, carried out using SAS enterprise guide 7.1 (SAS Institute Inc, cerebrovascular disease, or peripheral vascular disease); (2) Cary, NC). the presence of any guideline-stipulated clinically high-risk conditions such as diabetes mellitus and age >60 years, diabetes mellitus and albuminuria, stage 3B chronic kidney Results disease, or extreme individual risk factor elevations: systolic BP ≥180 mm Hg, diastolic BP ≥110 mm Hg, total cholesterol Cohort >7.5 mmol (290 mg/dL); or (3) a calculated 5-year CVD risk The original TORPEDO cRCT included 38 725 individuals from of >15% based on the Framingham equation.18 60 health services with a median follow-up of 17.3 months for the intervention group, and 17.7 months for the control group. Of these, 22 809 patients from 41 health services Statistical Analysis were followed up in the post-trial period. Twenty-one services Analyses were performed on the cohort of patients for whom (n=12 534) were originally randomized to the intervention data were recorded at baseline, end of the cRCT, and end of and 20 services (n=10 275) to control (Figure 1). the post-trial period. Descriptive data are presented as mean For the screening outcome, data from the 3 extraction (SEs) or proportions. Patient-level analysis was conducted periods for the overall post-trial cohort (22 809 patients) were using generalized estimating equations to account for clus- analyzed. For the prescribing outcome, data from the 3 tering of patients within services with exchangeable correla- extraction periods were analyzed for those patients identified tion structure. A v2 test for categorical variables was used to as high CVD risk patients at baseline in the cRCT (6106 test for comparison between intervention and control health patients in total and 3039 patients who were undertreated). services since they are at the cluster level. The P value for The median post-trial follow-up was 17.7 months for the patient characteristics was obtained from the generalized intervention group and 18.5 months for the control group. estimating equations model, taking into account the cluster- Recruitment into cRCT commenced in September 2011 and ing effect using log link for binary variables and identity link the final health service data collection for the post-trial period
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was completed in February 2015 (total median follow-up of Table 1. Baseline Health Service Characteristics 34.8 and 35.0 months for the intervention and control groups, respectively). Intervention Control Health Service (N=21, (N=20, Characteristics (n=41) n=12 534) n=10 275) P Value Demographics Eligible population <500 9/21 (42.9%) 9/20 (45.0%) 0.89 For those health services participating in the post-trial ≥ period, there were no significant differences in baseline 500 12/21 (57.1%) 11/20 (55.0%) health service characteristics between the intervention and Type of services control groups (Table 1). There was no difference in risk Aboriginal 6/21 (28.6%) 6/20 (30.0%) 0.92 factor screening and appropriate prescriptions for high CVD community controlled health risk patients based on our 3 prespecified health service service characteristics of health service type (Aboriginal Community General practice 15/21 (71.4%) 14/20 (70.0%) Controlled Health Service versus general practice), service size (<500 and ≥500), and if health service participated in a Medical software used QI program prior to randomization. Table 2 compares patient Best practice 5/21 (23.8%) 7/20 (35.0%) 0.43 characteristics at baseline and at the end of the cRCT. With Medical director 16/21 (76.2%) 13/20 (65.0%) Downloaded from the exception of albuminuria rates, the variables with Information technology support fi signi cant between-group differences at the end of the Both local and 2/21 (9.5%) 8/20 (40.0%) 0.07 cRCT were similarly observed at baseline. Further, there external were no differences in the health services and patient External 12/21 (57.2%) 8/20 (40.0%) characteristics of those who participated and those who did http://jaha.ahajournals.org/ Local 7/21 (33.3%) 4/20 (20.0%) not participate in the post-trial period. Staff currently using data extraction tools Most 1/21 (4.8%) 0/20 (0.0%) 0.38 Survey on Attitudes to the Software Tools None 9/21 (42.9%) 6/20 (30.0%) Thirty-two GPs within 21 intervention health services (70%) Some 11/21 (52.4%) 14/20 (70.0%) completed the end of study survey. Of these, 6% reported Current participation in a quality improvement initiative
by guest on January 27, 2018 always using the intervention, 25% used it more than half of No 14/21 (66.7%) 12/20 (60.0%) 0.66 the time, 53% used it less than half of the time, and 15% never Yes 7/21 (33.3%) 8/20 (40.0%) used it for our study patient population. The majority of GPs had positive attitudes to all intervention components (Fig- ure 2). compared with rates at the end of the cRCT ([65.2% versus 56.0% overall; P-trend<0.001] Figure 4). There was no Outcomes heterogeneity of effects by original intervention allocation There were no differences in appropriate CVD risk factor group (P=0.57). screening rates during the post-trial period when compared There were significant absolute increases in medication with rates at the end of the cRCT ([64.7% versus 63.5% escalation (new prescriptions or increased numbers of overall; P-trend=0.17] Figure 3). There was also no hetero- medications) for patients at high CVD risk in the post-trial geneity of effects by original intervention allocation group period. An absolute improvement of 15.8% was observed for (P=0.18). Thirty-five percent of patients were not screened antiplatelet drugs (P<0.01); 14.8% for lipid-lowering medica- appropriately according to guideline recommendations at the tions (P<0.01); and 19.0% for BP-lowering medications end of the post-trial period. Of these, 31.1% had insufficient (P<0.01). There was no heterogeneity in these outcomes by data to calculate absolute CVD risk, 39.8% were low-risk original intervention allocation group. patients with missing or out-of-date information, and 29.1% For the high CVD risk patients who were undertreated (not high-risk patients with missing or out-of-date information prescribed guideline-recommended medications) at baseline, among high-risk patients. The main driver for this was an out- the prescribing rates of recommended medications prescrip- of-date or missing lipid value, which accounted for 71.3% of tions in the post-trial period was significantly higher overall the screening gap. compared with the rates at the end of the cRCT (42.1% versus There was a significant improvement in prescription rates 33.4%, P-trend<0.001). This improvement was apparent in for patients at high CVD risk in the post-trial period when both the intervention and control groups (44.9% intervention
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Table 2. Patient Characteristics at Baseline and End of the Randomized Trial Phase
Baseline of cRCT (Sites n=41) End of cRCT (Sites n=41)
(Intervention=21, n=12 534) (Control=20, n=10 275) (Intervention=21, n=12 534) (Control=20, n=10 275)
Variable Name N n (%) or Mean (SE) N n (%) or Mean (SE) P Value N n (%) or Mean (SE) N n (%) or Mean (SE) P Value Age, mean (SD) 12 533 62.5 (12.3) 10 275 60.0 (11.4) 0.72 12 534 63.9 (12.2) 10 275 61.3 (11.4) 0.71 Male 12 531 5649 (45.1%) 10 268 4056 (39.5%) 0.01 12 534 5647 (45.1%) 10 273 4054 (39.5%) 0.01* Aboriginal/Torres Strait Islander 12 534 1636 (13.1%) 10 275 1726 (16.8%) 0.68 12 534 1647 (13.1%) 10 275 1747 (17.0%) 0.68 Current smoker or ex-smoker in the past 10 983 1967 (17.9%) 9013 1838 (20.4%) 0.96 11 650 1915 (16.4%) 9512 1747 (18.4%) 0.92 12 mo ae tal et Patel Systolic BP (mm Hg), mean (SD) 11 265 130.3 (16.1) 9743 129.1 (17.3) 0.38 12 036 129.7 (15.8) 9929 129.0 (17.0) 0.72 Total cholesterol (mmol), mean (SD) 11 103 5.0 (1.1) 8629 5.0 (1.1) 0.57 11 887 4.9 (1.3) 9347 5.0 (1.1) 0.21 High-density lipoprotein (mmol), mean (SD) 10 688 1.4 (0.4) 7702 1.4 (0.4) 0.94 11 582 1.4 (0.4) 8582 1.4 (0.4) 0.91 HbA1c for those with recorded diagnosis of 2062 8.0 (4.9) 1677 7.5 (1.8) 0.33 2280 12.7 (16.0) 1857 8.4 (7.3) 0.42 diabetes mellitus, mean (SD) Body mass index >30 kg/m2 8778 3304 (37.6%) 7511 2681 (35.7%) 0.11 9736 3648 (37.5%) 8269 2879 (34.8%) 0.10 † Albuminuria 2405 657 (27.3%) 2352 569 (24.2%) 0.97 4087 892 (21.8%) 3127 763 (24.4%) 0.04* Estimated glomerular filtration rate 11 062 1285 (11.6%) 8768 739 (8.4%) 0.01* 11 805 1545 (13.1%) 9450 925 (9.8%) 0.02* ‡ <60 mL/min per 1.73 m2 Recorded diagnosis Coronary heart disease diagnosis 12 533 1421 (11.3%) 10 275 989 (9.6%) 0.12 12 534 1598 (12.7%) 10 275 1119 (10.9%) 0.17 recorded Cerebrovascular disease diagnosis 12 533 374 (3.0%) 10 275 226 (2.2%) 0.11 12 534 432 (3.5%) 10 275 270 (2.6%) 0.10 recorded
ora fteAeia er Association Heart American the of Journal Peripheral vascular diagnosis recorded 12 533 97 (0.8%) 10 275 93 (0.9%) 0.90 12 534 126 (1.0%) 10 275 113 (1.1%) 0.83 Diabetes mellitus diagnosis recorded 12 533 2196 (17.5%) 10 275 1768 (17.2%) 0.87 12 534 2367 (18.9%) 10 275 1919 (18.7%) 0.86 Left ventricular hypertrophy diagnosis 12 533 24 (0.2%) 10 275 66 (0.6%) 0.01* 12 534 31 (0.3%) 10 275 79 (0.8%) 0.01* recorded Atrial fibrillation diagnosis recorded 12 533 482 (3.9%) 10 275 318 (3.1%) 0.18 12 534 566 (4.5%) 10 275 405 (3.9%) 0.64 Heart failure diagnosis recorded 12 533 229 (1.8%) 10 275 116 (1.1%) 0.31 12 534 286 (2.3%) 10 275 175 (1.7%) 0.66
Continued
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Table 2. Continued
Baseline of cRCT (Sites n=41) End of cRCT (Sites n=41)
(Intervention=21, n=12 534) (Control=20, n=10 275) (Intervention=21, n=12 534) (Control=20, n=10 275)
Variable Name N n (%) or Mean (SE) N n (%) or Mean (SE) P Value N n (%) or Mean (SE) N n (%) or Mean (SE) P Value § CVD risk assessment Missing information 12 534 3171 (25.3%) 10 275 3041 (29.6%) 0.28 12 534 1737 (13.9%) 10 275 2022 (19.7%) 0.02* <10% 12 534 4966 (39.6%) 10 275 4138 (40.3%) 0.79 12 534 5915 (47.2%) 10 275 4769 (46.4%) 0.42 ae tal et Patel 10%–15% 12 534 850 (6.8%) 10 275 537 (5.2%) 0.19 12 534 922 (7.4%) 10 275 639 (6.2%) 0.43 >15% 12 534 353 (2.2%) 10 275 254 (2.5%) 0.53 12 534 419 (3.3%) 10 275 280 (2.7%) 0.19 k Clinically high-risk condition 12 534 1461 (11.7%) 10 275 1096 (10.7%) 0.30 12 534 1578 (12.6%) 10 275 1191 (11.6%) 0.27 ¶ CVD diagnosis 12 534 1733 (13.8%) 10 275 1209 (11.8%) 0.19 12 534 1963 (15.7%) 10 275 1374 (13.4%) 0.21 Primary outcomes at baseline Eligible patients assessed at high CVD 3547 1734 (48.9%) 2559 1333 (52.1%) 0.67 3516 2035 (57.9%) 2514 1341 (53.3%) 0.17 risk receiving appropriate prescriptions for their CVD risk factors at baseline Current prescription for at least 1 BP- 1814 773 (42.6%) 1350 618 (45.8%) 0.54 1783 1057 (59.3%) 1305 738 (56.6%) 0.27 lowering medication and a statin for people at high CVD risk Current prescription for at least 1 BP- 1733 961 (55.5%) 1209 715 (59.1%) 0.95 1733 978 (56.4%) 1209 603 (49.9%) 0.08 lowering medication, a statin and an antiplatelet agent for people with CVD
BP indicates blood pressure; cRCT, cluster randomized controlled trial; CVD, cardiovascular disease; HbA1c, glycated hemoglobin. ora fteAeia er Association Heart American the of Journal *Statistical significance (P≤0.05). † Urinary albumin:creatinine ratio >2.5 men and >3.5 women. ‡ Calculated using the Chronic Kidney Disease Epidemiology Collaboration formula. § Calculated using the 1991 Anderson Framingham risk equation. k Any of the following based on Australian guidelines: diabetes mellitus and age >60 y, diabetes mellitus and albuminuria, estimated glomerular filtration rate <45 mL/min per 1.73 m2, systolic BP ≥180 mm Hg, diastolic BP ≥110 mm Hg, total cholesterol >7.5 mmol/L. ¶ Any of the following: coronary heart disease, cerebrovascular disease, and peripheral vascular disease.
7 RGNLRESEARCH ORIGINAL Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL
HealthTracker tool 25
20
15 # of GPs 10
5
0 Overall Calcula on of the Recommenda ons Recommenda ons The CVD risk Overall, Health I would like to use HealthTracker was CVD risk score for screening helped for treatment helped projec on graph Tracker-CVD HealthTracker a er easy to use helped me to me to improve me to improve increased pa ents increased the quality the study finishes improve quality of quality of care quality of care awareness of their of my interac on Downloaded from care heart health
Strongly Disagree Disagree Neutral Agree Strongly Agree
Figure 2. General practitioner’s opinion on HealthTracker intervention use. CVD indicates cardiovascular disease. http://jaha.ahajournals.org/
versus 37.8% control at post-trial period; Figure 5); however, in the post-trial period compared with mean levels at the end there was significant heterogeneity by original intervention of cRCT for either high-risk patients or undertreated high-risk allocation group (P=0.03), with greater effects observed in the patients. There was no heterogeneity between patient control arm. There were no changes in BP or cholesterol levels outcomes based on original randomization. by guest on January 27, 2018
Figure 3. Patients receiving appropriate screening of their CVD risk factors. CVD indicates cardiovascular disease; cRCT, cluster-randomized controlled trial.
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 8 Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL Downloaded from
Figure 4. High CVD risk (>15% and with CVD) patients receiving recommended medications. CVD indicates cardiovascular disease; cRCT, cluster-randomized controlled trial. http://jaha.ahajournals.org/
Discussion measurements and ongoing improvements in prescription of appropriate medications to patients at high CVD risk. Implementation of HealthTracker, a multifaceted QI interven- Sustainability of health innovations has been defined as “the tion, in a post-trial setting was associated with maintenance extent to which desired health benefits are maintained or of trial-related improvements in CVD risk factor improved upon over time after initial funding or support have by guest on January 27, 2018
Figure 5. Undertreated high CVD risk (>15% and with CVD) patients receiving recommended medications. CVD indicates cardiovascular disease.
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 9 Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL
been withdrawn.”19,20 From this perspective, our findings the possibility that the intervention was able to address provide some indication of sustainability of the HealthTracker doctor therapeutic inertia (defined as failure to initiate or intervention in the Australian primary healthcare environment. increase medication when treatment goals are not being A post-trial plateauing in CVD risk factor screening rates met).26 Given the multifaceted nature of the intervention, it was observed at health services in both trials arms. Despite can be difficult to assess what were the “active ingredients” our evidence-based QI intervention being available, there that may have promoted improvements in prescribing remained an overall CVD screening gap of 35%. The plateau- recommended treatments. ing of CVD risk factor screening is suggestive of a “ceiling” An important study limitation is that for technical reasons we effect.18 There are some factors that partly explain this were unable to obtain usage data for the various intervention phenomenon. Of the 35% screening gap, the vast majority had tools. Based on GPs’ opinions on use of the intervention in the an out-of-date or missing lipid value. The majority of out-of- end-of-study survey it would, however, appear that the inter- date values were from those identified at low CVD risk, and vention was partially used and when used the GPs viewed the providers may have not considered it necessary to re-screen intervention favorably. To better understand implementation these patients within a 2-year timeframe. Although absolute issues, a detailed mixed-methods process evaluation of both CVD risk assessment is recommended 2-yearly for low-risk the trial and post-trial phases is under way to better understand individuals in Australia,18 some guidelines recommend lipid which intervention components promoted or had minimal screening every 5 years.21 For those at moderate to high risk impact on behavior change at the provider, patient, and system Downloaded from who are lacking lipid measurements, there are possibly levels. We are capturing insights on usage patterns through patient factors (eg, difficult-to-reach populations, refusal to be staff surveys, in-depth practitioner and patient interviews, and tested) and provider factors (active disregard or passive ethnographic analyses of videotaped consultations.27,28 A omission) at play. realistic evaluation approach is being taken to analyze the Conversely, there was a significant improvement in mechanisms of change, contextual influences, and the resultant http://jaha.ahajournals.org/ recommended prescribing of appropriate treatments among outcomes.28,29 Preliminary findings indicate a complex inter- those at high CVD risk in the post-trial period both for the action between the intervention, organizational mission and intervention and control groups. The delayed onset of values, the role of leaders and teams, and the technical improvement in the intervention group might be related to competence of the software tools. Understanding these doctors not prescribing recommended medications immedi- interactions is expected to shed further light on why mixed ately following institution of lifestyle change recommenda- outcomes are often observed with implementation strategies to
by guest on January 27, 2018 tions or initiatives, or a generally more cautious approach to improve healthcare quality. introducing or accepting new treatments.22 The improvement Despite an abundance of research on HIT interventions, in prescriptions in the control group during the post-trial most studies are limited by examining short-term impact. period suggests the intervention had an impact outside the Consequently, knowledge on their implementation into routine trial setting, although there are obvious limitations in making practice and sustainability of their use is sparse.30 Studies have this assertion given the observational and uncontrolled nature found that implementation of interventions outside of a trial of the study design. The secular effects of wider distribution setting is influenced by several inter-related factors including of a new guideline advocating for an absolute risk approach to hardware and software computing infrastructure, clinical con- management of cardiovascular risk may have also had tent, human–computer interface, people, workflow and com- relevance to the observed change.18,23 munication, internal organizational policies and procedures and The group that continued to have the most benefit was the culture, external rules and regulations and pressures, and undertreated high CVD risk patients. The intervention group system measurement and monitoring.31 Some of these factors had significant improvements in prescribing recommended will be addressed in the process evaluation. For future HIT treatments both in the trial and post-trial period and the studies, these factors need to be evaluated on an ongoing basis control group significantly improved in prescribing after for successful implementation of HIT. introduction of the intervention. Study strengths include pragmatic implementation of the HealthTracker contains components known to improve intervention within routine clinical practice, evaluation in a processes of care and outcomes in trial settings.9,24,25 Key large cohort, and longer follow-up after initial implementation features include real-time, computer-guided decision support during the post-trial period. The representativeness of the that is integrated into routine clinical workflows, patient- general practices and Aboriginal community controlled health specific recommendations and management rather than services in Australia32,33 helps strengthen the generalizability assessments alone, screening and therapy alerts, and regular of the findings to similar healthcare environments in Australia. audit and feedback advice to practitioners.14 The continued The main study limitation is related to the lack of a escalation of medication in those at high CVD risk suggests comparable control group during the post-trial period.34 This
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 10 Long-Term Use of a Complex eHealth Intervention Patel et al RGNLRESEARCH ORIGINAL
limits our ability to make causal inferences from the data Genevieve Coorey, and Maria Agaliotis, who aided in implementation extracted in the post-trial period. In addition, the 2 groups of study and data collection. (intervention and control) experienced different exposure periods to the intervention and levels of support. Another limitation relates to the use of electronic medical records and Sources of Funding data extraction tools to evaluate the effects of the interven- Funding support for this project is provided by an Australian tion. This meant that (1) lifestyle advice, which is often National Health and Medical Research Council (NHMRC) entered as free text in the record, was not able to be captured Project Grant (ID 1010547) and NSW Health. B. Patel is and we were unable to assess effects on diet and physical supported by a NHMRC postgraduate scholarship (ID activity (although there were no effects observed on body APP1075308). A. Patel is supported by a NHMRC senior mass index and smoking status); (2) the data extraction tool research fellowship (APP1079301). Peiris is supported by a only extracts data for active patients who meet the criteria for NHMRC postdoctoral fellowship (ID 1054754). being a regular attendee and if a patient were to die during the trial period from our cohort, they would not be included in the follow-up period; and (3) information on medication adherence Disclosures was not able to be captured, and this may be an important fi driver in achieving BP- and lipid-lowering reductions. Given The George Institute has a wholly owned for-pro t subsidiary,
Downloaded from George Care, which has been assigned the IP for Health- this intervention targeted practitioner rather than patient Tracker to explore bringing this to market. behavior and adherence barriers are complex, it is quite likely that such interventions need to be companioned with additional patient-focused interventions in order to achieve References reductions in CVD biomarkers. http://jaha.ahajournals.org/ 1. Grol R, Grimshaw J. From best evidence to best practice: effective This observational study, despite being weak in study implementation of change in patients’ care. Lancet. 2003;362:1225–1230. design compared with rigorous randomized control trials, has 2. McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr EA. The quality of health care delivered to adults in the United States. N Engl J Med. the benefits of demonstrating the impact of the intervention 2003;348:2635–2645. beyond trial settings and assessing integration into clinical 3. Heeley EL, Peiris DP, Patel AA, Cass A, Weekes A, Morgan C, Anderson CS, 35 Chalmers JP. Cardiovascular risk perception and evidence—practice gaps in care. It demonstrates some evidence of potential longer- Australian general practice (the AusHEART study). Med J Aust. 2010;192:254– term impact of a multifaceted QI intervention on management 259. 4. Webster RJ, Heeley EL, Peiris DP, Bayram C, Cass A, Patel AA. Gaps in by guest on January 27, 2018 of CVD risk. Further research on understanding how best to cardiovascular disease risk management in Australian general practice. Med J implement the intervention in various complex health systems Aust. 2009;191:324–329. and social/economic settings is needed. This would provide a 5. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362:382–385. 6. Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health broad range of stakeholders and funders key information information technology: a review of the recent literature shows predominantly needed to allocate resources and to understand the best positive results. Health Aff (Millwood). 2011;30:464–471. strategies for implementation and modification of interven- 7. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems tions to maximize the use of technology-driven, QI strategies. on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293:1223–1238. 8. Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM, Nieuwlaat R, Souza NM, Beyene J, Van Spall HG, Garg AX, Haynes RB. Features of effective computerised clinical decision support systems: meta-regression Author Contributions of 162 randomised trials. BMJ. 2013;346:f657. Peiris and A. Patel conceptualized the intervention and study. 9. Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, All authors contributed to the overall study design. B. Patel Lobach D. Effect of clinical decision-support systems: a systematic review. implemented the study and conducted the data collection. Li Ann Intern Med. 2012;157:29–43. 10. Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, conducted the data analysis. A. Patel and B. Patel contributed O’Brien MA, Johansen M, Grimshaw J, Oxman AD. Audit and feedback: effects to the analysis plan. B. Patel wrote the initial draft of the on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;6:Cd000259. manuscript. A. Patel and Peiris provided critical review and 11. Ivers NM, Grimshaw JM, Jamtvedt G, Flottorp S, O’Brien MA, French SD, Young editing of the initial and subsequent drafts of the manuscript. J, Odgaard-Jensen J. Growing literature, stagnant science? Systematic review, fi meta-regression and cumulative analysis of audit and feedback interventions All authors reviewed and edited the nal manuscript. in health care. J Gen Intern Med. 2014;29:1534–1541. 12. Lobach DF. The road to effective clinical decision support: are we there yet? BMJ. 2013;346:f1616. Acknowledgments 13. Peiris DUT, Panaretto K, Harris M, Hunt J, Patel B, Zwar N, Redfern J, Macmahon S, Colagiuri S, Hayman N, Patel A. The Treatment of cardiovascular This study would not have been possible without the dedicated Risk in Primary care using Electronic Decision supOrt (TORPEDO) study- intervention development and protocol for a cluster randomised, controlled health professionals and patients who volunteered their time to trial of an electronic decision support and quality improvement intervention in improve quality of care. We thank our research staff, Marilyn Lyford, Australian primary healthcare. BMJ Open. 2012;2:e002177.
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14. Peiris D, Usherwood T, Panaretto K, Harris M, Hunt J, Redfern J, Zwar N, Task F. Clinical decision support systems and prevention: a community guide Colagiuri S, Hayman N, Lo S, Patel B, Lyford M, MacMahon S, Neal B, Sullivan cardiovascular disease systematic review. Am J Prev Med. 2015;49:784–795. D, Cass A, Jackson R, Patel A. Effect of a computer-guided, quality 25. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice improvement program for cardiovascular disease risk management in primary using clinical decision support systems: a systematic review of trials to health care: the treatment of cardiovascular risk using electronic decision identify features critical to success. BMJ. 2005;330:765. support cluster-randomized trial. Circ Cardiovasc Qual Outcomes. 2015;8:87– 95. 26. Okonofua EC, Simpson KN, Jesri A, Rehman SU, Durkalski VL, Egan BM. Therapeutic inertia is an impediment to achieving the Healthy People 2010 15. Murphy EV. Clinical decision support: effectiveness in improving quality blood pressure control goals. Hypertension. 2006;47:345–351. processes and clinical outcomes and factors that may influence success. Yale J Biol Med. 2014;87:187–197. 27. O’Grady C, Patel B, Candlin S, Candlin CN, Peiris D, Usherwood T. ‘It’s just statistics... I’m kind of a glass half-full sort of guy ’—The Challenge of Differing ’ 16. Glasgow RE, Lichtenstein E, Marcus AC. Why don t we see more translation of Doctor-Patient Perspectives in the Context of Electronically-Mediated Cardio- fi health promotion research to practice? Rethinking the ef cacy-to-effective- vascular Risk Management Communicating Risk (Communicating in Professions – ness transition. Am J Public Health. 2003;93:1261 1267. and Organizations). Houndmills, Basingstoke: Palgrave Macmillan; 2016. 17. Peiris D, Agaliotis M, Patel B, Patel A. Validation of a general practice audit and 28. Patel B, Patel A, Jan S, Usherwood T, Harris M, Panaretto K, Zwar N, Redfern J, data extraction tool. Aust Fam Physician. 2013;42:816–819. Jansen J, Doust J, Peiris D. A multifaceted quality improvement intervention for 18. National Vascular Disease Prevention Alliance. Guidelines for the management CVD risk management in Australian primary healthcare: a protocol for a of absolute cardiovascular disease risk. 2012; http://strokefoundation.com.a process evaluation. Implement Sci. 2014;9:187. u/site/media/AbsoluteCVD_GL_Webready.pdf 29. Pawson RT, Tilley N. Realistic Evaluation. London: Sage; 1997. 19. Wiltsey Stirman S, Kimberly J, Cook N, Calloway A, Castro F, Charns M. The 30. Glasgow RE, Phillips SM, Sanchez MA. Implementation science approaches for sustainability of new programs and innovations: a review of the empirical integrating eHealth research into practice and policy. Int J Med Inform. literature and recommendations for future research. Implement Sci. 2014;83:e1–e11. 2012;7:17. 31. Sittig DF, Singh H. A new sociotechnical model for studying health information 20. Scheirer MA. Is sustainability possible? A review and commentary on empirical technology in complex adaptive healthcare systems. Qual Saf Health Care. studies of program sustainability. Am J Eval. 2005;26:320–347. 2010;19(suppl 3):i68–i74.
Downloaded from 21. The Royal Australian College of General Practitioners. Guidelines for 32. Britt H, Miller MG, Henderson J, Charles J, Valenti L, Harrison C, Byram C, preventive activities in general practice. 9th edn. East Melbourne, Vic: Zhang C, Pollack AJ, O’Halloran J, Pan Y. General Practice Activity in Australia RACGP, 2016. 2011–2012. General Practice Series No 31. Sydney. Sydney University Press 22. Lebeau JP, Cadwallader JS, Vaillant-Roussel H, Pouchain D, Yaouanc V, Aubin- 2012. Auger I, Mercier A, Rusch E, Remmen R, Vermeire E, Hendrickx K. General 33. Australian Institute of Health and Welfare. Aboriginal and Torres Strait Islander practitioners’ justifications for therapeutic inertia in cardiovascular prevention: health services report 2011–12: Online Services Report – key results. Cat. no. an empirically grounded typology. BMJ Open. 2016;6:e010639. IHW 104. Canberra: AIHW. 2013. http://jaha.ahajournals.org/ 23. Jackson R. Guidelines on preventing cardiovascular disease in clinical practice. 34. Carlson MD, Morrison RS. Study design, precision, and validity in observational BMJ. 2000;320:659–661. studies. J Palliat Med. 2009;12:77–82. 24. Njie GJ, Proia KK, Thota AB, Finnie RK, Hopkins DP, Banks SM, Callahan DB, 35. Rohrer JE. Ten important elements for observational studies in primary care Pronk NP, Rask KJ, Lackland DT, Kottke TE; Community Preventive Services and community health. J Prim Care Community Health. 2014;5:158–159. by guest on January 27, 2018
DOI: 10.1161/JAHA.117.007093 Journal of the American Heart Association 12 Impact of Sustained Use of a Multifaceted Computerized Quality Improvement Intervention for Cardiovascular Disease Management in Australian Primary Health Care Bindu Patel, David Peiris, Tim Usherwood, Qiang Li, Mark Harris, Kathryn Panaretto, Nicholas Zwar and Anushka Patel Downloaded from J Am Heart Assoc. 2017;6:e007093; originally published October 24, 2017; doi: 10.1161/JAHA.117.007093 The Journal of the American Heart Association is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Online ISSN: 2047-9980 http://jaha.ahajournals.org/
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Chapter 5: Mixed methods evaluation exploring the underlying mechanisms of implementation of the intervention and interaction with contextual factors that resulted in variable outcomes
5.1 Chapter overview
This chapter reports the results of a mixed methods evaluation to identify and explain the underlying mechanisms by which the intervention did and did not have an impact amongst the participating case study health services. The chapter consists of a single manuscript titled: What drives adoption of a computerised multifaceted quality improvement intervention for cardiovascular disease management in primary healthcare settings? A case study analysis using Normalisation Process Theory. This manuscript has been resubmitted after initial comments to Implementation Science and is currently under review.
The use of Normalisation Process Theory within the UK Medical Research Council framework assisted in explaining how contextual factors influence the work that is done by individuals and teams when implementing a multifaceted computerised intervention. The study provided insight into the complexity and multiple levels of interactions between implementation processes and several contextual factors affecting uptake of the intervention.
Overall the study found that there were four spheres of influence that appeared to enhance or detract from adoption of the intervention: organisational mission and history, leadership, team environment and technical integrity of the intervention.
5.2 Manuscript details
Patel B, Usherwood T, Harris M, Patel A, Panaretto K, Zwar N and Peiris D. What drives adoption of computerised quality improvement tools by primary healthcare
121 providers? An application of Normalisation Process Theory. Implementation Science (Under review).
5.3 Author Contributions
BP, DP, TU, and MH made substantial contribution to the conception of the mixed methods evaluation. All authors contributed to the overall study design of the process evaluation. BP developed the interview guides and consent forms for ethics submission, implemented the study, conducted the data collection and analysis. All authors contributed to the interpretation of the results as part of the project working group. MH advised on the to data analysis of the surveys. BP wrote the initial draft of the manuscript. DP provided critical review and editing of the manuscript. All authors provided advice and input to prepare the manuscript for journal submission. BP prepared the final draft of the manuscript for publication.
5.4 Submitted manuscript
122 Title What drives adoption of a computerised, multifaceted quality improvement intervention for cardiovascular disease management in primary healthcare settings? A case study analysis using Normalisation Process Theory
Authors *Bindu Patel MPH, The George Institute for Global Health, University of Sydney [email protected] Tim Usherwood MBBS, MD, University of Sydney [email protected] Mark Harris MBBS, MD, University of New South Wales [email protected] Anushka Patel MBBS, SM, PhD, The George Institute for Global Health, University of New South Wales [email protected] Kathryn Panaretto, MBBS, MPH, University of Queensland [email protected] Nicholas Zwar MBBS, PhD, University of Wollongong, University of New South Wales [email protected] David Peiris MBBS, MIPH, PhD, The George Institute for Global Health, University of New South Wales [email protected]
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ABSTRACT Background A computerised, multifaceted quality improvement (QI) intervention for cardiovascular disease (CVD) management in Australian primary healthcare was implemented and evaluated in a cluster randomised controlled trial (cRCT). The trial improved CVD risk factor screening but did not increase prescribing of guideline-recommended medicine for patients at high CVD risk. The aim of this study was to conduct a process evaluation of the cRCT (TORPEDO trial) to identify and explain the underlying mechanisms by which the intervention did and did not have an impact amongst the participating health services. Methods/Design A conceptual framework based on Normalisation Process Theory (NPT) was used to understand factors that supported or constrained normalisation of the intervention into routine practice. We applied a case study methodology in which health services were our cases. Six of the 30 participating intervention health services were purposively sampled to obtain a mix of size, governance, structure and performance. Multiple quantitative and qualitative data sources from both cases and other sites were drawn on including trial outcome data, surveys of job satisfaction and team climate (246 health professional participants within 39 (65%) health services), a survey of attitudes to the intervention (32 doctors within 21 (70%) of intervention health services), and health professional interviews (n=19). These data were primarily analysed within cases but also compared with findings in other trial sites. Results We found a complex interaction between implementation processes and several contextual factors affecting uptake of the intervention. There was no clear association between team climate, job satisfaction and intervention outcomes. There were four spheres of influence that appeared to enhance or detract from normalisation of the intervention: organisational mission and history (e.g. strategic investment to promote a QI culture enhanced cognitive participation), leadership (e.g. ability to energise or demotivate others influenced coherence), team environment (e.g. synergistic activities of team members with different skill sets influenced collective action) and technical integrity of the intervention (e.g. tools that slowed computer systems limited reflective action). Discussion
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Use of NPT helped explain how certain contextual factors influence the work that is done by individuals and teams when implementing a novel intervention. Although these factors do not necessarily distil into a recipe for successful uptake, they may assist system planners, intervention developers, and health professionals to better understand the trajectory that a health service may take when developing and engaging with complex interventions.
Australian Clinical Trials Registry 12611000478910.
Keywords Quality improvement, health information technology, primary healthcare, health service, Normalisation Process Theory, process evaluation, mixed methods, adoption
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Background In the area of cardiovascular disease (CVD) risk management, around 50% of adults attending primary healthcare are adequately screened for CVD risk and only around 40% of those identified at high risk are prescribed recommended medications [1-3]. To address these entrenched gaps the US National Academy of Medicine recommended changing the healthcare environment in four ways: increasing the uptake of evidence in healthcare delivery, leveraging information technology, aligning payment reform with quality improvement, and enhancing workforce support [4, 5].
Quality improvement (QI) is a well-established process to improve the efficiency and processes of healthcare with goals of achieving sustained improvements in health outcomes and system performance [6, 7]. Three inter-related QI strategies are pertinent to this paper. The first is the Chronic Care Model, particularly the sub-domains of decision support and optimising clinical information systems. Several evaluations of varying quality have indicated contributions to improved processes of care and patient health outcomes [8, 9]. The second is the Breakthrough Series Collaborative model organised around principles of closing evidence-practice gaps, minimising unwanted variation in care, disseminating and diffusing best practice activities, fostering collaborative work ethics, and implementing rapid evaluation and action (plan-do-study-act) cycles [10][11]. There have been relatively few randomised evaluations of collaborative models and outcomes have been mixed [12]. Third, health information technology (HIT) is a key enabler of the Chronic Care Model and the Collaborative model to improving QI. HIT strategies with the strongest evidence base include computerised clinical decision support systems and audit and feedback of performance to providers. These have been shown to improve processes of care with a modest impact on healthcare outcomes [13-17].
Development of the intervention Drawing on the above literature, we developed a multifaceted QI intervention for CVD management in Australian primary healthcare. A sociotechnical approach was taken in the design of the intervention, recognising that integrating technology into practice requires human work to re-contextualise knowledge for different uses within complex social settings [18]. Influential theories and previous literature that helped guide the development of the intervention included: (1) Lipsky’s concept of street-level bureaucrats which in this context
4 relates to the ways in which GPs expeditiously process large workloads and technological tools can influence this process [19]; (2) Kawamoto’s systematic review of decision support systems that found the timing of advice when decisions are being made was important [20]; and (3) Gabbay and Le May’s concept of ‘mindlines’ which are professionally sanctioned codes of practice that may or may not relate to evidence based guidelines [21].
The intervention, named ‘Health Tracker’, has been described in detail elsewhere [22]. It incorporated two “in-consultation” tools which included real-time decision support integrated with electronic medical records to support workflow when clinical decisions are being made; and an interactive risk communication tool to support conversations between provider and patient about CVD risk during the consultation. It also included two “out of consultation” tools to support reflective practice. These included an automated clinical audit tool which provided feedback to providers on their performance and allowed them to identify patients who were potentially not being treated according to guidelines; and a web portal which provided peer-ranked performance trends using a technical platform that is used in the Australian Primary Care Collaboratives (APCC) programme [23].
During the cRCT, as part of the intervention, health services received introductory training visits from staff in use of the software and a clinical lead researcher conducted both face to face and training webinars on the management of CVD risk. The technical help desk support was also provided for any software related problems. During the post-trial period this support was scaled back and mainly restricted to technical software support. All software licences and technical support associated with the intervention were provided free to intervention sites during the trial period, and to all sites participating in the post-trial phase. Patient and practice costs associated with patient care occurred as per usual practice.
Clinical effectiveness evaluation The intervention was evaluated between September 2011 and June 2013 in the TORPEDO (Treatment of Cardiovascular Risk in Primary Care Using Electronic Decision Support) study - a cluster-randomized controlled trial involving 38,725 people (40 general practices and 20 Aboriginal Community Controlled Health Services (ACCHSs) [22]. At completion of the cRCT, the intervention was provided for a further 18 months to end 2015 to both intervention and control sites. The primary outcomes of the cRCT and the post-trial period have been
5 previously published and related to guideline-recommended CVD risk factor screening and prescribing of recommended medicine to those identified at high CVD risk [24, 25]. In the cRCT, when compared with services that did not receive the intervention, and after a median of 17.5 months’ follow-up, intervention services had a 25% relative improvement in CVD risk factor screening. There was no significant difference in prescribing rates of recommended medicines for people at high CVD risk. However, for the sub-group of individuals at high CVD risk who were not prescribed optimal recommended medicines at baseline, the intervention was associated with a 33% relative improvement in prescribing rates and significant improvements in intensification of existing blood pressure and blood- thinning medications. In the post-trial evaluation period there was a plateauing of improvement in measurement of recommended risk factors but an ongoing improvement in prescribing of recommended medicines in both intervention and control arms of the trial [25].
The objective of this study was to conduct a process evaluation of the TORPEDO trial to identify the underlying mechanisms by which the intervention did and did not have an impact on trial outcomes amongst health services participating in the study. It forms part of a broader multimethod process evaluation in which several studies are being conducted to examine the implementation and impact of the intervention.
Methods and design The process evaluation was designed prior to the commencement of the cRCT by a project working group which comprised researchers who were involved in the trial development and external researchers not involved in the intervention. The logic model, which articulates all the component studies of the process evaluation, has been previously published in this journal and has been included here as an additional file [26]. The mixed methods analysis in this paper refers to study 2 in the logic model which included semi-structured health professional interviews and quantitative survey data.
For this study we applied a case study methodology. This approach facilitated exploration of implementation processes within the health service by answering “how” and “why” questions from multiple sources and taking into account contextual influences [27, 28]. ‘Cases’ refer to the intervention arm health services who agreed to participate in this study. ‘Non-case intervention sites’ are intervention health services that were not selected as cases. ‘Control
6 sites’ are health services that were assigned to the usual care arm of the cRCT, whose quantitative data were used as a comparison for the trial outcomes. In determining case selection, we purposively invited intervention health services that exhibited a broad variation in trial primary outcomes, numbers of staff at each site and type of service (general practice versus ACCHS, urban versus rural and size). We took a “small N” approach where six intervention health services were followed to understand how various influential factors interacted with one another and why the intervention played out differently in some places compared to others.
Theoretical framework Nilsen identified a wide range of theories, models and frameworks that have been applied to understand implementation of complex interventions including process models, determinant frameworks, classic theories, implementation theories and evaluation frameworks [29]. We used a framework (the UK Medical Research Council (MRC) guidance on process evaluations for complex interventions) [30] and a theory (Normalisation Process Theory (NPT)) to understand the factors and mechanisms involved in implementation of the intervention.
The MRC guidance provides practical guidance on designing and conducting evaluations to assess implementation (fidelity, dose, and reach) of complex interventions, explain causal mechanisms (how change is produced) and identify contextual factors (anything external to the intervention) associated with variation in outcomes [31, 32]. NPT seeks to understand the implementation processes and the extent to which an intervention became “normalised” in the service environment [33, 34]. NPT is focused on the work people do individually and collectively to implement, embed and integrate new interventions into their physical and social context. This is characterised by four generative mechanisms of coherence (‘what is the work?’), cognitive participation (‘who does the work?’), collective action (‘how does the work get done?’) and reflexive monitoring (‘how is the work understood?’). By embedding NPT within the MRC guidance, we sought to understand the interaction between health service context, the generative mechanisms related to the implementation of the intervention and the outcomes observed in the cRCT.
Data collection Multiple quantitative data sources were used:
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1. To assess effectiveness of the intervention on the trial outcomes within cases, data from electronic medical records were collected using a validated extraction tool at baseline, end of trial and end of post-trial phase. 2. To assess the support requirements provided by the project staff, support time was calculated based on contact time logged by both the technical helpdesk and the research team. 3. To assess acceptability and fidelity of the intervention staff were invited to complete three surveys at the end of cRCT - (1) An end of study mail survey was conducted asking about satisfaction with the intervention and frequency of use. Although we had intended to look at usage analytics to look at intervention fidelity, due to technical problems with the software database we were unable to generate accurate usage logs and therefore had to rely on staff self-report. Drawing on the NPT sub-domain of “collective action” in which team members work together to incorporate and innovation into practice, a Team Climate Inventory (TCI) [35] survey was administered. This is a 44-item questionnaire which assesses team vision (11 items); participative safety (12 items); task orientation (7 items); support for innovation (8 items) and social desirability (6 items) with each item rated on a five-point Likert scale. 4. In order to assess if job satisfaction may be an influential factor in driving outcomes, the Warr-Cook Wall job satisfaction survey was administered [36]. Based on previous work [37], this 10-item questionnaire assesses physical work conditions, income, amount of responsibility given, freedom in the job, variety, work colleagues, opportunity to use abilities, recognition, and hours of work. The internal reliability of the scale is well established with rank order correlation between item-whole values for each item in the scale averaging 0.95. It was validated and adapted for use with general practices and ACCHSs using a 7-point Likert scale [37].
The TCI and job satisfaction surveys were either distributed by mail or in person during the end of trial data collection period. Health services were followed up one week later by telephone on expected completion timeframe. For surveys not received within the month, a second attempt to follow-up was made.
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In addition to these quantitative data sources, semi-structured interviews were conducted by two researchers with staff between May 2013 and February 2014 at each of the cases. A purposive sample of staff were asked about their knowledge, views and experience of the intervention. We sought a diverse mix of general practitioners, nurses, managers, Aboriginal health workers (AHWs) and administrative assistants to participate. Interviews took place at the health services towards the end of the trial and during the post-trial phase to not overly influence the implementation phase of the intervention. Interviews were audio-recorded and professionally transcribed. The interview questions were aligned with NPT domains. Broad domains of inquiry included the following: (1) why health staff did/did not use the intervention; (2) how was the intervention used in routine practice (3) how did the intervention help use of guidelines (4) how was the intervention integrated at the health service (5) what impact did the intervention have on the way personnel do their work (see additional file for the interview guide). During the process of conducting the interviews, questions were modified in light of earlier interviews to allow exploration of emergent themes in consultation with the project working group.
Data analysis
We conducted a mixed methods analysis adopting an explanatory sequential design whereby both survey and qualitative data were analysed to gain better understanding of the processes of implementation and the resultant quantitative outcomes [38]. The systematic integration of qualitative and quantitative data within a single case allowed for examination of rich empirical data through varied perspectives.
Effectiveness data were analysed using SAS Enterprise Guide V4.5. Contact time with services was tabulated in a spreadsheet and simple frequency analyses conducted. The satisfaction survey results were reported as frequencies or proportions. For the TCI, mean scores were calculated for each sub-domain and a total mean score was calculated across all domains (maximum score 44) [39]. For the job satisfaction survey, each of the seven domains were equally weighted and a total mean score (maximum score 7) was calculated [40, 41]. Scores were reported for each case and overall mean scores were calculated to compare three groups: case study sites, non-case study intervention sites and control sites. Associations between the TCI and job satisfaction survey scores and trial outcomes overall and for these three groups were analysed using univariate analyses of variance. Although we intended to do a multilevel regression model analysis (as per our published study protocol); the univariate
9 analyses of these surveys did not reveal any substantive associations between Team Climate Inventory and Job Satisfaction survey scores and the trial outcomes.
Interview data were analysed in three stages and assisted by Nvivo 11 (QSR International Melb. Vic). An initial familiarisation stage was conducted in which five interviews from three cases (cases 1, 2 and 6) were analysed and discussed with the project working group. Following this an initial thematic coding framework was developed that consisted of both descriptive codes derived from the initial thematic framework and new codes that were inductively developed as we became more familiar with the data. Thematic saturation was achieved after interviewing nineteen health professionals with no new codes being created. Several meetings with the project working group were held to discuss the findings and their significance. The NPT constructs were continuously drawn onto assist with the interpterion of the findings. In particular May and Finch’s outline of the mechanisms, components and investments to understand and identify “the trajectory and outcomes of implementation process” [34, 42]; and Mair and colleagues’ meta-review of implementation of e-health interventions using an NPT-based explanatory framework was used to evaluate barriers and facilitators [43]. Key elements of the framework are summarised in Table 1.
[insert Table 1]
RESULTS Eight intervention health services were approached towards the end of the trial phase with six agreeing to participate (4 general practices and 2 ACCHSs). Figure 1 summarises key characteristics of the cases and figures 2 -7 provide detailed summaries of the health service context, attitudes to and use of the intervention and the trial and post-trial outcomes.
Table 2 shows the mean TCI and Warr-Cook Wall job satisfaction scores for the cases, the other intervention health services, and the control arm health services. There were no statistically significant differences overall in mean TCI and job satisfaction scores between the cases and other participating intervention and usual care health services. There were also no statistically significant associations found between trial outcome variables and either the TCI or job satisfaction scores, and no heterogeneity between groups (cases, non- case intervention and control arm sites). There were also no significant associations in terms of
10 size of health service, type of health service (general practice vs ACCHS), location and previous participation in a continuous quality improvement (CQI) program.
[insert Table 2]
A total of 19 interviews were conducted (9 general practitioners (GPs), 4 practice managers (PMs), 3 Aboriginal health workers (AHWs), 1 practice nurse (PN), 1 health information officer (HIO), and 1 administrative assistant/practice manager (AA/PM)) from the six health services (‘cases’) (Table 3). Key findings for each of the cases organised by NPT domains are summarised below and in figures 2 -7. More detailed findings with supporting quotes are shown in Additional file 2.
[insert Table 3]
Case 1 (Figure 2) Context and outcomes Case 1 was a small, urban general practice in a socioeconomically disadvantaged region of Western Sydney. Staff members were the full-time GP owner and a practice manager (PM) who had both worked there for over 30 years with part time clinical and administrative staff. Screening and prescribing outcomes significantly improved over the trial period but then plateaued with a slight decrease in prescribing in the post-trial period. The practice was strongly dependent on the research team to stay engaged with the intervention. There was approximately 10 hours of training and technical support provided during the trial phase and negligible support provided in the post-trial phase. There were multiple visits to the practice by the project officer to assist both the PM and GP but little dedicated training for any of the other staff.
Mechanisms of implementation The high level of understanding of the objectives (coherence) and engagement (cognitive participation) for the two main staff members (GP and PM) in combination with support from the research team strongly influenced implementation of the intervention. The GP and PM valued different aspects of the intervention – the former using the tools to enhance communication with patients, while the latter assessed aggregated data for monitoring
11 practice performance. Despite the clear appeal of different intervention components for each of these staff, overall there was a lack of collective action at this practice. There was little evidence of modifications to prevailing policies, procedures and resourcing. The PM was influential in encouraging the GP to use the decision support tool, providing feedback on his performance, however other practice team members were almost completely non-engaged. Consequently, there was little evidence of enhanced interactions or relations between team members. The PM had tried to engage receptionist staff in use of the tools, however, there appeared to be little interest, possibly due to a lack of coherence for these staff. Further, there was little evidence of ongoing appraisal and evaluation of the use of the interventions, setting goals and/or strategies to overcome any barriers. These factors may provide some explanation as to why the outcomes may have declined in the post-trial setting.
Case 2 (Figure 3) Context and outcomes Case 2 was a small urban, residential Sydney general practice with a full-time GP owner, several part-time GPs, administrative staff and no practice nurse. Much of the practice management was handled by the owner GP and there was a high degree of turnover of administrative staff. The owner GP had a strong interest in use of software interventions to improve health care quality, however, there was little formal participation in QI programs prior to participation in this research project. Total support time was 12 hours (mostly IT support) and this was mainly during the trial period with minimal support provided in the post-trial period. Trial outcomes were significantly improved over the trial period for both screening and prescribing and then plateaued and slightly declined in the post-trial period for screening.
Mechanisms of implementation
The intervention had a high degree of coherence for the owner GP who was strongly interested in appraising performance at his practice compared to other general practices involved. Improvement in peer-ranked performance was the major motivation to participating in the study for this GP. However, sustained engagement was a major barrier, where major software technical issues were encountered resulting in prolonged periods where the tools were inaccessible despite multiple calls to helpdesk support. This led to sporadic engagement
12 and diminished any possibility of systematically incorporating usage into every day work. There was virtually no evidence of collective action at this practice with staff members working in isolation of one another. Although the owner GP became highly competent in using the intervention, these skills were not transferred to other staff members. One other part-time GP expressed interest in using the tools following a training visit, but in the face of the technical barriers rapidly lost interest. Consequently, the substantial trial period improvements appear to be almost entirely attributed to the activities of the owner GP. In the post-trial period, his motivation to remain engaged was diminished and this may explain the plateau in outcomes.
Case 3 (Figure 4) Context and outcomes Case 3 was a solo GP practice with a full-time manager/receptionist in a diverse Sydney community near a major business district. The GP had been in practice for over twenty years but had little experience with QI programs. Initial implementation of intervention and training to this GP was provided by the GP principal investigator and only one additional onsite training visit was provided by the research team. Total support time was approximately 3 hours. Trial outcomes were above average at baseline. Screening rates decreased but overall remained broadly unchanged by the end of the post-trial period. There were improvements in prescribing rates over both the trial and post-trial periods.
Mechanisms of implementation
The intervention lacked coherence for both the GP and PM. The GP found the initial training session to be overwhelming with trying to fit in patient consultations during the training. Although he appreciated the relevance of CVD risk screening and management, he did not readily see what value this intervention provided in addition to his usual practice. He also felt that his patients had a low level of understanding of absolute CVD risk scores and that it was not appropriate to engage them in the risk communication tools (interactional workability). Consequently, over time he saw the intervention primarily as a laborious data collection exercise with little utility and no financial compensation. This lack of coherence and cognitive participation was further compounded by technical issues midway through the trial where the decision support software appeared to be slowing down performance of his computer systems and at times cause disruptions to clinical practice (contextual integration). 13
The PM was not encouraged to be involved in use of the intervention and consequently there was limited collective action evident at this practice (relational integration and skill set workability). A key driver for increased engagement was related to financial incentives. The lack of a sustainable business case meant that the work of engaging in the intervention was primarily to benefit the research team and not the practice or his patients.
Case 4 (Figure 5) Context and outcomes
Case 4 is a mid-sized teaching general practice in a rural outer Western Sydney suburb. The owner GP established the practice over 30 years ago and has been active in supervising GP registrar training for many years. There is a full-time practice manager, part-time nurse and up to three full-time training GP registrars who generally did 6-month placements at the practice. Despite extensive involvement in teaching and supervision, the practice has not been involved in any formal QI programs. There was extensive onsite training provided by project officers throughout the trial period with frequent refresher courses when new GP registrars commenced at the practice. Total support time was approximately 11 hours. Trial outcomes were below average at baseline. Screening rates improved substantially over the trial period but deteriorated in the post-trial period. There were steady improvements in prescribing rates over both the trial and post-trial periods.
Mechanisms of implementation
The owner GP found value in the intervention as a teaching tool to train GP registrars. The GP had moderate coherence because of his specific interest in the use of tools to assess CVD risk and was interested in gauging his practice performance. Although he himself had low confidence in using computer tools in his practice, he saw it as an inevitable aspect of future clinical practice. Consequently, cognitive participation was high as a clear expectation was set by the owner GP that staff learn how to use the tools. Weekly staff meetings were used as a teaching platform and team-building forum. At these meetings, the intervention and performance outcomes were discussed intermittently throughout the trial period. The major barrier to integration across team members was the high turnover of GP registrars. The owner GP lacked capacity and confidence to train new staff and consequently this was left to the research team staff. The decline in screening performance in the post-trial period may in part 14 relate to the high turnover as newer registrars commenced, and research team support was less intensive. Although relational integration between the doctors appeared high, there was little evidence of engagement with the practice nurse or practice manager in the intervention. There was some evidence of appraisal with both the practice manager and owner GP pleased with the performance improvements relative to peers.
Case 5 (Figure 6) Context and outcomes
Case 5 was a large ACCHS in a remote region with strong Aboriginal community representation on its all Aboriginal governing board. The service was over 20 years old and received funding from both state and federal governments. It had a chronic disease strategy in place for 12 years which included a strong focus on CQI programs. Initial training was conducted by the GP principal investigator over three separate sessions for a total of 23 staff including GPs, nurses, Aboriginal Health Workers (AHWs) and a health information officer (HIO). Additional support was provided via webinar and phone when required over the course of the trial and minimal support in the post-trial period. Total support time was approximately 12.5 hours. TCI and job satisfaction scores were lower than average compared to other cases, especially in the TCI subgroup regarding ‘Vision’ and ‘Task Orientation’. The health service had a high baseline performance in the trial outcomes reflective of its extensive involvement in CQI programs. Trial outcomes in prescribing increased during the trial period and subsequently plateaued, and screening stayed relatively unchanged.
Mechanisms of implementation
There was strong overall coherence by all levels of staff in understanding the objectives of the intervention and its alignment with existing activities. This in turn fostered immediate action to engage with the intervention (cognitive participation). These actions and processes were variably implemented because management decided to not provide non-GP clinical staff with the tools due to concerns of it impacting on existing workloads. This impacted relational and contextual integration of the intervention into the organisation. Some GPs would have preferred AHWs to use the tools to engage patients as part of their existing role in performing frontline screening assessments prior to seeing the GPs. Despite this limitation, the HIO played a central role in cognitive participation, collective action and reflexive monitoring.
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Use of the service’s existing monitoring and evaluation platform assisted in communal appraisal. Regular team meetings were held where data and performance were reviewed with a specific focus on improving GP prescribing to the high CVD risk patient population. This likely played a key role in the increase in prescribing during the trial period.
Case 6 (Figure 7) Context and outcomes Case 6 was a large urban ACCHS in the state of Queensland. The service has two clinic locations with the main branch in a mixed residential and commercial area; and a smaller clinic located in a shopping centre. The service was over 20 years old and is governed by an all Aboriginal and Torres Strait Islander board of directors. There were around 26 staff in total with 5 GPs (3 full-time and 2 part-time) with the following staff participating in the semi-structured interview: 3 full time GPs, 2 AHWs, practice manager and nurse/practice manager. The service had been regularly involved in a CQI collaborative run by an external state-based organisation representing Aboriginal and Torres Strait Islander health services (peak body), but at the time of the study, this program was not actively being implemented. Initial implementation of intervention and training was conducted by two project officers in a group forum and then individually to each staff member. Additional training support was provided via webinar and phone when required over the course of the trial. As with Case 5, TCI and job satisfaction scores were lower than average compared to other cases, especially in the TCI subgroup regarding ‘Vision’ and ‘Task Orientation’. Total support time was 17 hours. Slightly above average performance in screening and prescribing was observed at baseline and this largely remained unchanged throughout the trial and post-trial periods.
Mechanisms of implementation
All levels of staff appeared to have moderate understanding (coherence) of the intervention and its objectives. AHWs were enthusiastic about using the tool for screening and patient risk communication with support from lead GPs. Most staff used the patient risk communication graph component only and were less confident in using the other components. Many described time constraints in using the tools. In addition, IT infrastructure and technical issues were major barriers at this service. This prevented long-term use of the intervention by all staff and greatly diminished any prospects of collective action. Only the lead GP remained enthusiastic in use of the tool over time, however this required extensive 16 time with the IT helpdesk to resolve software problems. Further, although this GP was the senior clinical lead, he lacked the authority to make managerial decision as this is the role of the chief executive officer. Another factor that inhibited collective action was a perception the data extracted from the audit tool was unreliable and this discouraged use by the PM and a newly employed GP. This also inhibited opportunities for reflexive monitoring despite the research team’s efforts to provide performance feedback reports and explain the reasons for data quality issues. This lack of collective action and reflexive monitoring are likely drivers in no change in the screening and prescribing outcomes.
Discussion This process evaluation of a multifaceted computer-guided QI trial in Australian primary healthcare settings sought to better understand why there was increased CVD risk factor screening and no effect on the prescribing rates in the intervention health services compared to the usual care during the trial. In depth examination of six case studies revealed a complex interaction between implementation processes and several contextual factors. The findings complement previous work highlighting the multiple barriers to uptake of health technologies into routine practice. These include knowledge-related barriers, sufficient training, specific features of the technologies themselves, the external environment, coherence for both providers and patients, and organisational context [44-47].
Despite knowledge of these barriers the challenge remains in identifying strategies to overcome them. Given the diversity of the cases and the contextual circumstances in which they operated, there is clearly no one recipe for success or failure. The findings illustrate that there may be different factors at play during initial implementation compared to those that are needed to influence sustained use of the intervention. There appear to be spheres of influence that when aligned enhance normalisation of the intervention into routine practice. The first broadly relates to the mission of the health service, its organisational culture and the antecedents to participating in this project. The second related to the leadership structures and the role of influential leaders in changing the activities of others. The third relates to the team environment and the extent to which certain actors within the team influence the activity of others. The fourth relates to the tools themselves and the degree to which they are fit-for- purpose from content, workflow and technical perspectives. 17
Organisation mission and history – One of the ACCHSs (case 5) had prioritised use of CQI processes over 10 years and this was evident in strategy documents, staffing allocations and prior use of various CQI tools. Its high baseline performance is reflective of this commitment. However, the intervention was strategically determined by CEO to prioritise use of the tools by GPs only to the exclusion of other clinical staff. This supported improved performance in prescribing outcomes (the domain of GPs) and less movement in screening outcomes (the domain of nurses and AHWs). Such strategic choices were made explicit in this large organisation and could be linked to its policies and procedures around QI processes. In the smaller general practices, such strategic processes were less explicit but still played an important role in driving engagement with the intervention. For example, the teaching practice (case 4) had made as part of its mission a long-standing commitment to teaching excellence. Consequently, the intervention tools were avidly promoted to GP registrars as a part of this overall organisational commitment. NPT describes the alignment of the innovation and organisational mission as contextual integration and in our cases this was a driving factor [48].
Leadership – A recent systematic review of the impact of clinical leadership on adoption of health information technologies found that the leader’s attributes and behaviours strongly influenced engagement [49] which supports Bodenheimer’s notion of ‘engaged leadership’ being the foundational building block of a high performing primary care [50]. We found that the influence of leaders varied greatly. Although in case 1 there was strong motivation from the GP to improve CVD risk management practices, this alone appeared insufficient. Importantly when the motivated leader’s interests were aligned with those of his trusted practice manager then engagement in the new practice was enhanced. However, this also appeared to be insufficient and when the support provided by the research team was removed the intervention rapidly ‘de-normalised’ and was used infrequently emphasising the importance of ongoing provider training. Cases 2 and 3 represented ‘one-person shows’ where utilisation of the intervention was entirely dependent on the GP owner. In the former case, utilisation was high and strongly driven by a desire to outperform peers. In the latter case, utilisation was low from the outset and over time came to be seen as a nuisance. In both cases the intervention had little prospect of normalising across the practice once the study was completed. Curiously, however, in case 3 where the GP was least enthusiastic about the
18 intervention there were large sustained improvements in prescribing outcomes suggesting some behaviour change had occurred despite antipathy for the intervention. In case 6, although the GP leader was strongly engaged in some elements of the intervention and encouraged staff to use the intervention, he did not appear to take on a role of mentoring/training other staff. Further, organisational goals were more focused on individual patient care rather than CQI. This limited the impact of the audit and feedback and peer- ranked performance tools which are intended to make organisational performance more transparent.
While in case 5, the long history of leadership in CQI, supported by the governing board and CEO, influenced improvements in trial outcomes but it did not translate to normalisation of the intervention. This highlights the importance of ‘special people’ as key to successful implementation of HIT [51]. We found that these ‘special people’ include both clinical champions and non-clinical staff who have the ability to both broker and stifle engagement with an innovative practice. The findings suggest that when implementing QI interventions, it is important to identify and support ‘engaged leaders’ early to maximise potential for embedding new practices.
Team work – Although teamwork is a key ingredient to enhance uptake of innovative practices [52, 53], the influence of teams manifested in complex ways in the case studies. There was no evidence of association between the team climate or job satisfaction scores and uptake of the intervention or trial outcomes. Indeed, in some cases where these scores were lower than average, performance was higher than average (case 5) and vice versa (case 3). This contrasts with previous studies which have shown that team climate scores are positively associated with staff satisfaction, and improved quality of care [41, 54]. NPT conceives healthcare as a collective activity requiring a multitude of interactions between professionals, patients, managers and others. Rather than affecting only one individual or group a ‘successful chain of interactions’ is required [55] such as leadership, strong managerial relations, readiness for change, a culture of staff training and resource availability. Where teams are small and aligned (e.g. practice manager and solo GP in Case 1) the chain of successful interactions may be less complex, making the work of integration less onerous. Conversely, in large teams (e.g. multidisciplinary care teams and several administrative staff in ACCHSs) with multiple roles the intervention appeared less likely to influence staff
19 interactions. Cases 5 and 6 (ACCHSs) had low ‘team vision’ and ‘task orientation scores’. These measures relate to a shared sense of purpose, belief in the team objectives and reflective action on the outcomes that the innovation is generating. Even in case 4 (a general practice) where there was strong alignment of mission, leadership and shared purpose by the team, the high turnover of training GPs was an important barrier. Further, there was little engagement with practice nurses despite chronic disease screenings often being a core role for these staff. The intervention components were viewed mainly as GP and management tools rather than whole-of-practice tools.
It was clear from this study that support provided by the research team played a central role in driving engagement and it is not surprising that there was a plateauing of trial outcomes in many cases once support was reduced in the post-trial period. A recent systematic review of decision support systems for prescribing highlighted that lack of training and limited computer skills were significant barriers to uptake [56]. In addition, several studies have found that the most effective training is tailored to specific provider’s needs [57], offers a variety of training formats, and is provided on an ongoing basis [47, 58]. This suggests that there is an important role for external practice facilitators to reduce the work that insiders may have to do to support uptake [59]. In Australia, primary health networks [60] employ QI support officers to provide such a role, however, the degree to which they are accepted into practice processes is currently unknown.
Tools that are fit-for-purpose – One appealing feature of the tools was their multifaceted nature targeting gaps at the system, provider and patient level [61]. In case 1 the tool components were synergistically incorporated into the practice with the manager taking ownership of the audit tool and the GP focussing on the in-consultation decision support tool. and this facilitated normalisation. Certain staff gravitated to features of the intervention (clinical managers using the audit tools and GPs using the decision support and risk communication tools) with lack of cohesiveness within the health service staff to integrate these features collectively. This prevented reinforcement of the value of the intervention to others in the health service. Although in majority of cases, the tools had high appeal in terms of content and usefulness, there were two cases (case 3 and case 6) where technical problems grossly impacted its use and led to early abandonment. In case 3 the low level of initial interest combined with frustrations that the tool was slowing software systems virtually
20 eliminated any prospect of it being used (enrolment). In cases 2 and 6 it was only the high level of motivation by the lead GPs to solve the software installation problems that enabled sustained use over the trial period. In addition, time constraints and lack of financial incentives was a major issue in using the intervention during the trial and beyond. Both cases 2 and 3, stated that financial incentives would have helped to sustain the use of the intervention.
Strengths and limitations Applying NPT both in the design of the process evaluation and coding framework provided a practical way to understand key activities involved individually and collectively in investing and enacting on the meaning, commitment, effort and appraisal of the intervention over time and across diverse primary healthcare settings. Our findings provide important insights into the interaction of context and mechanism (socio-technical change) to produce the resultant outcomes. It enabled us to systematically analyse complex social and behavioural processes through several different ‘lenses’ moving beyond psychological theories of behaviour [62]. There are multitude of theories, models and frameworks to gain insight into how implementation of complex and multifaceted interventions can succeed beyond trial settings [29] and identify how these processes influenced the overall trial outcomes. NPT provided an explanatory focus through its emphasis on human agency [63]. By elucidating differences in implementation processes over time and between settings and various actors, we have been able to develop a nuanced understanding of intervention fidelity moving beyond whether it ‘worked’ or not.
A number of limitations need to be mentioned. The process evaluation was implemented toward the end of the trial and whilst this was intentionally planned so as to not unduly influence conduct of the trial providers may have limited recall of the intervention in its early stages. The cases studied clearly represent a limited snapshot of Australian primary care, particularly given most general practices were located in an urban setting, two cases did not agree to participate due to time constraints and only selected providers were interviewed. Consequently, there may be other important phenomena that influence intervention normalisation in different settings that we did not observe. Further, by focussing mainly on staff, we were not able to fully appraise how the tools influenced the interactions between patients and health professionals (interactional workability). Another important issue was that
21 the lack of usage analytics (for reasons described in the methods) limited our ability to look more closely at adoption and fidelity measures. As part of the overall process evaluation, we conducted a video ethnography study and post-consultation patient interviews to provide insights into how the intervention tools were drawn upon in the clinical encounter. Initial discourse analysis has been published and further analyses are currently underway [64]. We also did not explore technical support staff perspectives which may have shed more light on the technical challenges encountered at some sites. Finally, resource constraints are likely to be major barriers to the “work” done by staff members and we did not conduct detailed analyses of existing IT infrastructure, budget allocations to support use of IT tools and staffing allocations for quality improvements.
Conclusion This study evaluated the processes by which primary healthcare services engaged in a multifaceted computerised intervention. In doing so we identified key mechanisms of why particular outcomes were observed highlighting the complex interaction of the tool and the environments in which they are implemented. These processes do not necessarily distil into a formula for successful uptake and improved outcomes. Rather, they may help to determine what trajectory a service is likely to take when engaging with such interventions. The findings suggest that there needs to be sufficient lead time at the health service to identify and act on any organisational changes that are needed prior to the intervention being implemented (e.g., management processes, resource allocation, and staff roles and duties routines). An organisational mission that embraces quality improvement, engaged leadership and activation of all team members, dedicated quality improvement personnel, financial support, strong IT infrastructure and regular appraisal of outcomes are all key contextual enablers. Greater appreciation of these factors can yield important information for intervention designers, academics, providers and policy makers to assist in adoption of computerised, quality improvement initiatives.
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List of abbreviations CVD: Cardiovascular disease QI: Quality improvement HIT: Health information technology TORPEDO: The Treatment of Cardiovascular Risk in Primary Care using Electronic Decision Support ACCHS: Aboriginal Community Controlled Health Service cRCT: cluster Randomised controlled trial MRC: Medical Research Council NPT: Normalisation process theory TCI: Team Climate Inventory GP: General practitioner PM: Practice manager AHW: Aboriginal health worker PN: Practice nurse HIO: Health information officer AA: Administrative assistant CEO: Chief executive officer IT: Information technology CQI: Continuous quality improvement NHMRC: National health and medical research council
Declarations Ethics approval and consent to participate The study was approved by The University of Sydney Human Research Ethics Committee (2012/2183) and the Aboriginal Health & Medical Research Council (AH&MRC) of New South Wales (778/11). Health professionals gave written consent before participating in the interviews and surveys. Participation agreements were signed between the participating health services and the co-ordinating research institute.
Consent for publication Not applicable
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Availability of data and materials All data generated or analysed during this study are included in this published article [and its supplementary information files].
Competing interests The authors declare that they have no competing interests.
Funding Funding support for this project was provided by an Australian National Health and Medical Research Council (NHMRC) project grant (ID 1010547). BP was supported by an NHMRC post graduate scholarship (1075308). DP is supported by an NHMRC career development fellowship (1143904) and AP is supported by an NHMRC research fellowship (1079301).
Authors’ contributions BP, DP, TU, and MH contributed to the conception of the mixed methods evaluation. All authors contributed to the overall design of the process evaluation. BP implemented the study, conducted the data collection and initial analysis. All authors contributed to the analysis as part of the project working group. MH contributed to data analysis for the TCI and Job satisfaction surveys. BP wrote the initial draft of the manuscript. DP provided critical review and editing of the initial and subsequent drafts of the manuscript. All authors reviewed and edited the final manuscript.
Acknowledgements This study would not have been possible without the dedicated health professionals who volunteered their time to improve quality of care. We thank our research staff Marilyn Lyford, Genevieve Coorey and Maria Agaliotis who aided in data collection, and Madeline News who assisted in data management.
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Table 1: Coding framework: context and mechanism influencing outcomes of implementation of computerised QI intervention
Case description COMPONENTS CONTEXT MECHANISM OUTCOMES Coherence Cognitive participation Collective action Reflexive monitoring (sense-making work) (relationship work) (enacting work) (Appraisal work)
Process and work of sense- Process and work that The work that individuals and The work inherent in
making and understanding that individuals and organisations organisations have to do to enact informal and formal Physical individuals and organisations have to go through in order the new practice. appraisal of a new practice environment/setting have to go through in order to to enrol individuals to once it is in use, in order to of health service: promote or inhibit the routine engage with the new Interactional Workability (IW) assess its advantages and
embedding of practice. practice Does the e-health* service make disadvantages, and which General description of people’s work easier? (The impact develops users’ Components (1) the health service Differentiation Initiation that a new technology or practice comprehension or the Immediate work Is there a clear understanding Are key individuals willing to has on interactions, particularly the effects of a practice. Size of health service of how a new e-health* service drive the implementation? interactions between health
differs from existing practice? professionals and patients Systematisation Description of the type Legitimation (consultations).) How are benefits or of health service Individual Specification Do individuals believe it is problems identified or
Do individuals have a clear right for them to be Relational Integration (RI) measured?
understanding of the aims, involved? Do individuals have confidence in
objectives and expected the new system? (The impact of the Individual Appraisal
benefits of the e-health* new technology or practice on How do individuals
service? relations between different groups appraise the effects on
of professionals. A positive impact them and their work Components (2) on RI is more likely if the environment? Organising work technology does not disrupt
Communal Specification Enrolment current lines of responsibility and Roles, interactions Do individuals have a shared Do individuals “buy into” the accountability.) Communal Appraisal and relationships: understanding of the aims, idea of e-health* service? How do groups judge the
objectives and expected Contextual Integration (CI) value of the e-health # of Staff benefits of the e-health Activation Is there organisational support? service?
service? Can individuals sustain (The fit between new technology Training and support involvement? and the overall organisational Reconfiguration received Internalisation context. This includes the goals of Do individuals try to alter
Do individuals understand the the organisation, morale, practice to incorporate
Use of HT value, benefits and importance leadership and resources.) new e-health service?
of the e-health* service?
Skill Set Workability (SSW)
How does the innovation affect
roles and responsibilities or training
1
needs? (The degree to which the e- health service fits with existing working practices, skill sets, and perceived job role.)
Meaning Commitment Effort Comprehension (insert quote) (insert quote) (insert quote) (insert quote) Investments *e-health = electronic health Sources: May, C. and T. Finch (2009) Mair, F. S., C. May et al. (2012) Finch et al (2102)
2
Table 2: Team Climate Inventory (TCI) and Warr-Cook Wall Job satisfaction scores
TCI sub-domains Mean total Mean job Sum of Participant Support for Vision Task Social TCI score satisfaction health Safety Innovation (max = 8) (max = 11) Orientation Desirability (max = 44) scores professionals (max = 12) (max = 7) (max = 6) (max = 7) completing the surveys Case 1 4 9.2 6 9.8 6.5 4.5 36.0 5.7 Case 2 3 10 6.4 10 6.5 4.8 37.7 5.9 Case 3 2 9.8 6.8 9.5 6.8 5.2 38.1 6.8 Case 4 8 9.3 6.6 9.2 6.4 4.8 36.3 6 Case 5 34 9.1 6 8.4 5.8 4.6 33.9 5.7 Case 6 17 8.5 5.9 8.3 5.4 4.5 32.6 5.5 Mean score of all 68 9.3 6.3 9.2 6.2 4.7 35.8 6.0 cases (n*=6) Mean score of other 113 8.6 5.9 8.3 5.7 4.4 33.2 5.4 intervention sites (n=18) Mean score of 65 9.5 6.4 8.8 5.7 4.7 35.0 5.7 control sites (n=15) n* = health services max = maximum score TCI and Job Satisfaction surveys were completed in year 2013 during end of cRCT data collection through the end of the post-trial phase.
3
Table 3. Interview participants’ characteristics
Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Participants GP PM GP GP PM GP PM/ GP PM GP AHW HIO GP GP GP PM PM/ AHW AHW receptionist N
Employment status Full- time x x x x x x x x x x x x x x x x x Part-time x x Age group 20-29 x 30-39 x x x x x x 40-49 x x x x x x x 50-59 x x 60-69 x x x 70+ Gender Male x x x x x x x x x x Female x x x x x x x x x Years worked in primary healthcare 9 30 17 8 5 40 10 34 7 1.5 1.5 5.5 26 5.5 1 6.5 46 5 5 in Australia (years) Length of time at current health 9 30 5 1 5 8 8 7 7 1.5 1.5 18 5.5 1 6.5 16 5 5 service (years) Given access to HealthTracker-CVD yes no yes yes no yes no yes no yes no yes yes yes yes no yes yes yes (yes or no) GP = general practitioners, PM = practice managers, AHW = Aboriginal health workers, HIO = health information officer, N = nurse
4
Figure 1. Primary healthcare service characteristics
Case 1 Case 2 Case 3 Case 4 Case 5 Case 6
cas General Practices Aboriginal Community Controlled Health Services
Location – Urban Rural Rural/remote Urban Size – Small Medium Large Large
CQI involvement No CQIⱡ involvement prior to study prior to study
On site IT-Off site Off site Off site On site Off site (Main doctor)
Training Training Training Training Training Training /Support: /Support: /Support: /Support /Support /Support 10 hours 12.4 hours 2.6 hours 11.4 hours 12.5 hours 6.1 hours
*Training/Support is dependent on size of primary healthcare service, staff availability and technical issues ⱡ CQI = continuous quality improvement
General Practice
Coherence Cognitive Collective action Reflexive monitoring • Small general practice participation Trial outcomes - Case 1
• 305 eligible patients (4.3% Aboriginal and Differentiation *** Initiation *** Interactional Workability ** Systematization ** Torres Strait Islander) Individual specification *** Legitimation *** Relational Integration ** Individual appraisal ** Communal specification *** Enrolment *** Contextual Integration * Communal Appraisal * • 1 full time GP, 2 part-time GPs, 1 part-time Internalization *** Activation * Skill Set Workability ** Reconfiguration * 100% nurse, 1 full time PM, and 3 part-time 90% receptionists 80% 70% • Previous use of data extraction tools Meaning Commitment Effort Comprehension 60% • Communication/rapport good between staff GP: useful tool in the GP: the routine PM: I’m going to be PM: having the sense of knowing how of checking for running these lists and I patient coming in, 50% • PM – 30 years at general practice to, sort of how to reduce the know who’s going to be that[HealthTracker] 40% • 4 hrs - training; 6 hrs - tech support the patients’ risks with microalbumin is looking at these people coming up and the 30% • Figure 1 The principal GP reported using the cardiovascular disease something I and cleaning that data doctor being able to 20% intervention: less than 50% of the time, found 10% and kidney disease and didn’t do and it is a really, I mean better treat the easy to use and recommendations provided to 0% so on. before, and now it’s actually quite patient would be, I be valuable. baseline end of RCT end of post- I realise its exciting when you do it. would say, the main benefits. And I actually quite like impact ...referred to trial period doing that and I think if the nurse and at the end of the day obviously, she’s then you’re helping that got the data that the patient you know, that’s doctor’s put in and the best thing about it. makes her life a bit easier as well... Overall caring of the patient I think, it really has improved Screening Prescribing
Figure 2. Case 1
1
General Practice
Coherence Cognitive Collective action Reflexive participation monitoring Trial outcomes - Case 2 • Medium sized general practice Differentiation *** Initiation *** Interactional Workability ** Systematization ** • 503 eligible patients (0.4% Aboriginal and Individual specification ** Legitimation ** Relational Integration * Individual appraisal * Communal specification ** Enrolment ** Contextual Integration * Communal Appraisal * Torres Strait Islander) 100% • 1 full time GP, 1 part-time GP, 2 consultant Internalization ** Activation * Skill Set Workability * Reconfiguration * 90% GPs (paediatrics), 1 part-time PM, multiple Meaning Commitment Effort Comprehension 80% part time receptionists (>5) Main GP: there are Main GP: it strikes Main GP: It cannot be Main GP: No, I don’t 70% • Main GP responsible for overall management certainly increased me as a good trial to based on just me to do [think] it’s 60% and operations vigilance to screen the get involved, to look the analysis all the time, integrated very well 50% • 2.7 hrs – training; 9.7 hrs - tech support patient who is supposed at how robust my which is impractical at at the moment, 40% • The principal GP reported using the to have the screening practice is, or was the end of it...fully because one is I 30% intervention less than 50% of the time, found done. To look at their during the trial. And integrated in terms of haven’t allocated a 20% easy to use and recommendations provided risk factors in a more is not a big area not just the doctors are task to my staff to to be very valuable. comprehensive way analysis, it’s a small using it, the staff are use it, we haven’t 10% 0% • because HealthTracker area of clinical using it at the same trained the staff to itself is look at it in a practice. That’s why time. So you need a bit train once or twice. baseline end of RCT end of post- comprehensive, give you I think I could get of education for the trial period an assessment risk of a involved. It’s only staff, show them how to five-year period. the cardiovascular install it, how to log in, side of it and it’s got how to logout, where to an existing tool that get the information, you can use and you which area they should just see how we use be looking at, maybe it, how good it is even our administrative and so on. side of it, to help the doctors maybe. Screening Prescribing Figure 3. Case 2
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General Practice
Coherence Cognitive Collective action Reflexive monitoring participation Trial outcomes - Case 3 • Solo general practice Differentiation ** Initiation ** Interactional Workability ** Systematization * Individual specification * Legitimation * Relational Integration * Individual appraisal * • 333 active patients (0.3 % Aboriginal and Communal specification * Enrolment * Contextual Integration * Communal Appraisal * Torres Strait Islander) Internalization * Activation * Skill Set Workability * Reconfiguration * 100% 90% • 1 full-time GP and 1 full-time 80% PM/receptionist Meaning Commitment Effort Comprehension 70% • Appointments available 7 days/week GP: I could see that it GP: this one GP: “Oh, I’m all right. GP: it’s a bit of a 60% • 1.1 hrs – training; 1.5 hrs – tech support was specific to most of here, bugger, I’m doing okay so all I nuisance and apart from 50% my patients. Now, most it’s just a have to do is just carry the fact that it is • The principal GP reported it always, found easy 40% of my patients had to do matter of on doing what I’m beneficial in terms of to use and recommendations provided to be with heart disease and, collecting doing”. putting figures down 30% very valuable. of course, you could data. That’s and calculating it, it 20% incorporate kidney how it feels, appears as if I’m 10% disease as well in that. you see, so I working for somebody 0% haven’t got else with no baseline end of RCT end of post- any, how compensation and no trial period shall I say it, recompense and it’s very enthusiasm annoying sometimes, about it. especially when the thing clogs my computer now.
Screening Prescribing Figure 4. Case 3
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General Practice
Coherence Cognitive Collective action Reflexive monitoring participation Trial outcomes - Case 4 Differentiation *** Initiation ** Interactional Workability ** Systematization ** Individual specification ** Legitimation ** Relational Integration *** Individual appraisal * • Medium sized teaching general practice Communal specification ** Enrolment ** Contextual Integration ** Communal Appraisal ** • 765 eligible patients (0.3 % Aboriginal and Internalization ** Activation * Skill Set Workability * Reconfiguration * 100% Torres Strait Islander) 90% • 1 full time GP, 3 full time registrars, 1 part- Meaning Commitment Effort Comprehension 80% time nurse, 1 full time PM, and 2 receptionists Main GP: I’ve been Main GP: you Main GP: I remember Main GP: It’s 70% • Regular team meetings looking for non-paper had a tool you and xxx [chief integrated well, and 60% • High GP turnover due to GP registrars based prompts for where you investigator] would there needs to be still 50%
• 6 hrs – training; 5.4 hrs – tech support cardiovascular or chronic could instantly come around and you’d more work. Look it’s a 40% illness prevention. And see what an explain things and there tool, and it’s a tool • The principal GP reported using the 30% that’s one reason. And evidence base would be things that I which has very, very intervention less than 50% of the time, found 20% secondly because I risk calculator would see the benefit of, many uses, and quite easy to use and recommendations provided to 10% thought it’s something shows and you and then it was like a lot of potential. 0% be valuable. which was good to be were able to “Okay, I’ve learned 25 baseline end of RCT end of post- measured. And thirdly I modify it. new things. I remember trial period just wanted to see Right, okay. three very well, I know maybe overall how we And that was how to use five, and I were performing. some of the know I can sort of do this other features but I don’t quite like bringing up remember how to do it”, the feedback and then after three online and so weeks you’d think “Was on, “Let’s do there something like this, remember that there or not?” And the password, you thought “Okay, I can do this, it’s the go for a walk or I can sit Screening Prescribing
internet’s slow. here for half an hour and Bugger it. I’m work out how to do it.
going to go I’ll go for a walk”. Figure 5. Case 4 have lunch”.
1
ACCHS
Trial outcomes - Case 5 • Large Aboriginal Community Controlled Health Coherence Cognitive Collective action Reflexive monitoring participation Service (ACCHS) • 454 active patients (76.4% Aboriginal and Differentiation *** Initiation *** Interactional Workability ** Systematization *** 100% Torres Strait Islander) Individual specification *** Legitimation *** Relational Integration ** Individual appraisal ** 90% • > 4 full-time GPs, multiple AHW, multiple Communal specification *** Enrolment ** Contextual Integration ** Communal Appraisal *** Internalization *** Activation ** Skill Set Workability ** Reconfiguration * 80% nurses, CQI staff, multiple administrative 70% assistants 60% • Team based care/diverse skill set Meaning Commitment Effort Comprehension 50% • Use of data extraction and other eHealth tools GP: It's helped my practice GP: if I was GP: It's got a big HIO: …for example 40% • Strength in Indigenous capacity building individually because I can running my own potential to help the only 30% of the high 30% • 10.4 hrs – training; 2.1 hrs – tech support get an accurate complex practice I'd pay practice but you've got risk patients are being 20% • Two GPs reported using the intervention less summary that takes into for this to have people willing to prescribed with triple 10% than 50% of the time or never and were account all these software even if use the computers and therapy and they go, 0% indifferent to the tool. additional factors and I was charged have the time in the whoa, you know. So baseline end of RCT end of post-trial other relevant history, all to use it and I'd consultation to go they really like that, period the numbers, it gives me use it often. through it with the and we can then the reassurance that I'm patient and actually provide them getting an accurate unfortunately that with the names of cardiovascular risk, the hasn't been the situation people, you know, and most accurate really that in the last six to nine then sort of next time I've seen by a long way. So months at xxx [ACCHS they present to the it gives me confidence to name]. service they can have prescribe the medicines a quick look at their, that I prescribe knowing you know, medication that my cardiovascular risk management. is accurate. Screening Prescribing
Figure 6. Case 5
1
ACCHS
Coherence Cognitive Collective action Reflexive monitoring Trial outcomes - Case 6 participation • Large ACCHS (2 locations) Differentiation *** Initiation ** Interactional Workability *** Systematization * • 507 active patients (52.1% Aboriginal and Individual specification ** Legitimation ** Relational Integration ** Individual appraisal ** Torres Strait Islander) Communal specification ** Enrolment *** Contextual Integration * Communal Appraisal * • 3 full time GPs, 3 part-time GPs, multiple Internalization ** Activation * Skill Set Workability * Reconfiguration * 100% AHWs, 1 PM, 1 PM/practice nurse (2nd 90% location), multiple nurses, multiple Meaning Commitment Effort Comprehension 80% Lead GP: it sets the bar Lead GP: I’d Main GP: focused on case AHW2: there was 70% administrative staff, transport care teams 60% • Previous CQI high and it does, it’s a still go looking conferences and individual no follow up with it great programme and I for it [when cases, and we don’t so [intervention] within 50% • Off-site IT support 40% • 13 hrs – training; 4.8 hrs – tech support guess the constraints are HealthTracker much look at public health. xxx [ACCHS] itself to just the time constraints. disappeared]. … not a lot of 30% • The principal GP reported using the say, ‘How’s 20% intervention less than 50% of the time, found But it’s something that you You know, the remuneration for it everyone going with 10% easy to use and recommendations provided to need to look at, you know, benefits immediately, so maybe this?’ ‘Are you using 0% be valuable. if you’re going to do a outweigh the we’re chasing the cash it?’ like, ‘Was it baseline end of RCT end of post- good general practice and problems…I’m cow a little bit that way useful?’ and stuff you’re going to do primary very much an rather than stepping back like that. There was trial period prevention you’ve got to advocate of it. and getting a bit nothing use something like academic. this…using something like this it’s got to be part of your practice otherwise you’re just doing reactive medication.
Screening Prescribing
Figure 7. Case 6
1
Additional file 1: Adapted - Center TRT Evaluation Framework Continuous Engagement of Stakeholders, Intended Users
Inputs Activities Outputs Data sources Outcomes/Effectiveness
Problems 1. Formulation • Establish partners and • High prevalence of • Review evidence vendors for Short Term (1-3 years) • Maintenance of Long Term Goal CVD • Pilot study (Peiris et al. implementation & funding, (Public Health • Lack of screening for evaluation 2009 & 2011) Primary outcome evaluation 10% absolute increase in partnerships, Impact) CVD risk & provision • Proposed plans • Modify QI intervention recommended CVD risk screening. implementation, of recommended from RCT (4-9 years) No improvement in prescription enforcement & treatments reach 2. Enactment • Enacted plans and of recommended treatments • Unaware of latest • Increase in CVD • Engage stakeholders policies overall guidelines and • Modifications for screening and • Enact plan • Type and number of recommendations intervention and prescribing resources leveraged Solutions implementation Study 1 – Quantitative Study 1: Plateauing of CVD risk factor • Increase control • Multifaceted QI Plateauing of CVD risk factor based on Studies 1-4 intervention screening performance in the 18 of CVD risk Post cRCT effectiveness monthsscreening post performance completion in of the the 18 factors • Evidenced based 3. Implementation • Reach to health RCT.months Ongoing post completion improvement of thein approach on use of professionals and RCT. Ongoing improvement in prescribing performance in both • Decrease in CVD absolute risk patients (intended prescribing performance in both control and intervention arm sites incidence • Use of point of care population) control and intervention arm
electronic decision practices Study 2: • Improved support system • Adoption by PHC Subject of this paper quality of life • Use of audit and TORPEDO Study (cRCT) (settings/sectors) Study 2: Study 2 – feedback tool for [Peiris et al. 2012; Subject of this paper Quantitative/Qualitative Subject of this paper • Increase practice level Peiris et al. 2015] • Implementation as Multi-level modeling/health efficiency in the improvements intended & acceptable/ professional interviews primary health • Engaging patients feasible/ care system • Training and support affordable Study 3 by experts Study 3 – Qualitative Doctor’s communication of CVD Doctor’s communication of CVD Equitable Stakeholders/ Studyrisk alone 3 is not sufficient to risk alone is not sufficient to distribution of Politics Video-ethnography of actual Doctor’sengage patients. communication Rather, effectiveof CVD engage patients. Rather, effective improvements • PHC -GPs and ACCHSs clinical encounters and post- riskcommunication alone is not requiredsufficient skilled to communication required skilled across population • Healthcare providers engageinteractions patients. on the Rather, part of effective both consultation patient interactions on the part of both subgroups, (HP) communicatdoctor and patiention required to enable skilled interviews doctor and patient to enable particularly those • Administrative staff 4. Maintenance/ interactionsmeaningful use on theof risk part of both meaningful use of risk at greatest risk • Patients and doctorcommunicat and patiention tools to enable Modification communication tools communities meaningful use of risk • Maintain relationship Minimal/ no Other communication tools with PHC Study 4 unintended • Existing policies/ QI • Monitor performance Modelling intervention in PHN Study 4 – Incremental costs of consequences programs • Ongoing training and populationStudy 4 demonstrated ICER implementation <$15AUD per • Administrative support by experts Quantitative/Qualitative <$50,000Modelling thres interventionhold in PHN person. structures population demo nstrated ICER If optimal effect sizes could be • IT infrastructure Cost consideration of scale-up <$50,000 threshold sustained over 5 years, the • Incentives for HP and If implemented by an incremental cost per CVD event patients Australian Primary Health averted would be <$20,000AUD • Material resources Network • Monitoring health services Gather credible evidence
Disseminate & utilize findings Justify conclusions Grey shaded portions represent the CDC evaluation framework Additional file 2: Health professional interview findings and quotes
Case 1 – Small urban general practice Interview date: August 22, 2013; Time of interview: 11 am (PM) and 12:30 pm (GP)
MECHANISM Coherence Cognitive participation Collective action Reflexive monitoring (sense-making work) (relationship work) (enacting work) (Appraisal work) Differentiation Initiation Interactional workability Systematisation The main two staff (the GP and PM) both had The PM is the main driver in helping HT aides in communicating CVD risk. Patients find graphs CAT tool helped health a clear understanding of HealthTracker (HT) coordinate implementation of intervention. to be helpful and enlightening. The GP uses the heart- service assess how they and Clinical Audit Tool (CAT) and how it PM is familiar with e-health and its benefits of age projectile graph to communicate CVD risk to were performing, and if differs from existing practice. GP focused on improving patient care through data being patients. there were any issues HT and PM on CAT. PM encouraged GP to linked with various e-health tools. with their data quality. use HT and to populate data in correct fields GP was not familiar and comfortable with all the The tool assisted the PM within EHR for CAT to be accurate. PM is familiar with the intricacies of all the GPs features of HT and only used the traffic light assessment to cleanse data and and staff work/habits and use of technology. prompts and heart-age projection graph. He does not identify any issues. Individual specification completely understand absolute risk % and therefore After participation in webinar, GP had good Legitimation does not use to explain to patients. Further, he was not Individual appraisal overall understanding of the HT intervention Both GP and PM found the study to be familiar with CAT or IF portal in relation to intervention. PM gives GP reassurance and reason for use. valuable to the practice in different aspects; GP He did view the peer ranked graphs distributed to him that he is performing well got involved initially to improve CVD by study team member. in screening his patients For PM, CAT could assist in data quality and management/knowledge, and PM was using HT. helped other staff (i.e. nurse) with chronic sceptical. PM later was a believer in the GP: Visual presentation of what they can do with it. It’s disease management. She believes that intervention due to the substantial much easier to show them, you know, you can tell them, GP: seeing their audit tools help improves data quality and improvement in data quality at the health but to actually for them to see where they should be cholesterol going down assess outstanding patient indicators. She is service. ideally, and then where they are at the moment, that and their kidney functions a promoter of audit tools. does make them realise what a big difference that is improving, you know, GP: I could see it was going to be beneficial for there. that’s the real, yeah, PM: ….he’s taking blood pressures all the me and the practice. So, it was really good. when those results come time but what was happening he actually Relational integration through that makes me wasn’t putting in the right spot in Medical Enrolment PM assesses GPs’ data quality and outstanding CVD risk happy. Director….by using that audit tool you can go GP used HT routinely for screening which screening using CAT. She gives main GP feedback on his “hold on”. became routine. Other components of HT use of HT. were not used to their full potential due to Communal Appraisal Communal Specification unawareness and lack of confidence in tool. The intervention has made the GP and PM’s jobs more GP received reports of Both PM and GP collectively understood that efficient. peer ranked graphs to see value and benefits of the tool was to Both GP and PM use their respective tools (HT how they were improve patients’ health and their and CAT). PM uses it to inform others of data performing compared to
November 7, 2017 knowledge of care processes. quality and can see how it can affect data PM: It helps our nurse, so then we ah look, this person other health services. He being uploaded to the hospital and benefit the hasn’t had such and such for a while wo when you’re found it beneficial to Internalisation patient. PM sees the bigger picture of use of e- doing a chronic disease management say for know how they were GP and PM are motivated by health interventions. cardiovascular or diabetes or anything like that, that all performing relative to improving quality of care and shows up as well. And so then we can then say to Dr. other practices; however, patient outcomes. PM is training one receptionist on why and XXX (main GP) well look this hasn’t been done either doesn’t put too much how to use CAT. Other GPs are not familiar because he might not have seen that patient in that last emphasis on comparing GP convinced more than before of treating with HT. One part-time GP after several years six months….so it’s like feedback from the nurse and also themselves to other early for high risk without CVD. recently started to use EHR and other GP is the doctor as well. health services. Further, only involved in women’s health. PM the report was not PM believes that CAT helps entire health suggested main GP to train part-time GPs. Contextual integration discussed with health system inevitably helping patients attain GP would prefer HT to be incorporated within MD. He service team members. better quality of care. PN could benefit from HT however not using it. finds it frustrating that it is separate. Also, SideBar is too She was not approached to participate in the large and must minimise the tool which causes GP to Reconfiguration GP: …focus on improving the study despite her main role taking care of forget to open to use with patients. There was no patients’ sort of health, and chronic disease patients. reconfiguration of the improving my knowledge of it GP had problems signing in and needed regular extra health service. as well. ….Greater service, a better GP: …highlighted the issues that’s important in support. He would prefer someone to call to check in on service to patients, especially treating these patients, and so it’s becomes how things are progressing with the intervention. Comprehension those with chronic health second nature now to know what to do and PM: having the patient problems and with CVD risk. I how to do it. CAT would stop working occasionally. PM would have to coming in, that [HT] think it’s really good. call PEN support. Needed a reminder on using it and coming up and the doctor Activation how to use it on a regular basis. being able to better treat Meaning With regular support and training for GP and the patient would be, I GP: useful tool in the sense of knowing how PM, the intervention can be sustained. Need When tools were working properly, GP and PM found it would say, the main to have integrated within their routine work. to, sort of how to reduce the patients’ risks all staff to be involved to be able to be sustain impact … referred if so to
with cardiovascular disease and kidney completely and integrate into routine work. the nurse and obviously, disease and so on. GP: It’s not, it [HT] doesn’t really take any more time or effort; you get to the stage where you can use it without she’s then got the data Commitment: that the doctor’s put in GP: the routine of checking for the even thinking anymore and makes her life a bit microalbumin is something I didn’t do before, PM: they’ve [patients] come to the doctor and said look easier as well... Overall and now I realise its benefits. can you, you know, send a report up into the clouds, so if caring of the patient I
they end up in the hospital or the hospital can send think, it really has things to us. But now you have that you need to have improved the right data in there, you need to make sure that the medications are up to date, their last blood pressure and you know, the pathology that you know, whatever their chronic condition is, it needs to be equalling whatever is in the system. So to be able to get rid of the old
November 7, 2017 prescriptions out of there or the people who are no longer patients I wouldn’t have been able to find that any other way.
Skill set workability GP given the capability of the knowledge and reasoning behind the tool and its benefit by a ‘champion’ via webinar. This motivated GP and gave him confidence to use two components of HT: prompt (traffic light) and patient risk communication.
PM was proficient in using another audit tool (Canning) therefore she had the skills and knowledge to learn CAT quickly.
GP and PM operationalised part of the intervention that was appropriate for their skills and role. There is partial collective participation. Not all staff are involved and trained.
GP: …eventually I’ll get to the stage where I’ll want to use them also for my resources and guidelines as well. It’s just a matter of time, just get used to it because, you know, just once I get the hang of it and it gets more useful I guess I just need to expand what I’m using, because I guess it can offer me so much more than what I’m using it for.
Effort PM: I’m going to be running these lists and I know who’s going to be looking at these people and cleaning that data and it is a really, I mean it’s actually quite exciting when you do it. And I actually quite like doing that and I think if at the end of the day you’re helping that patient you know, that’s the best thing about it.
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Case 2: Small urban general practice Date of interview: January 22nd and 23rd 2014 (main GP); January 30, 2014 (PM); January 31, 2014 (part-time GP)
MECHANISM Coherence (sense-making work) Cognitive participation (relational work) Collective action (enacting work) Reflexive monitoring (appraisal work)
Differentiation Initiation Interactional workability Systematisation Main GP and part-time GP see the Main GP was the main driver of ‘What If” CVD risk graphs were helpful to Main GP found peer ranked reports to benefits of using absolute risk and implementation of the intervention at communicate to the patients their heart risk. Patient be beneficial in assessing how he is believe this is a better way for CVD the practice. GP has the knowledge and resources could have been better doing compared to other GP practices. prevention and management. skills to be a ‘champion’ of the if more concise and on one page. Patients were He used a few times during the year. Both GPs saw the benefits of HT and it’s intervention for his staff and patients. impressed with graphs and found resources useful use with patients. However time and resource constraints when GP found time to use. He prefers someone to come in and tell prevented him from using it regularly. him how he is doing and where he is Main GP: Prior to the Torpedo trial I used HT tool wasn’t used frequently by main GP because of regarding the study indicators. Time a fair bit of that [online absolute risk Legitimation time constraints. Further it was dependent on if constraint prevents him from analysing calculation]. Very similar in terms of GP liked HT and the risk communication patient were high risk or not. data on a regular basis, and lack of a what to do. But they are a bit more component however only used for dedicated staff member. tedious. You got to punch in the patients he thought of as high risk. Due to technical issues, part-time GP was not able to information and so on. Whereas the Participation in the trial gave GP an use HT. She uses similar patient risk communication Individual appraisal HealthTracker can just extract the incentive to use during trial period. HT tool online at her other practice. Data illumination from CAT and IF information much quicker. So those allowed GP to become more vigilant in portal reports for main GP. He was guidelines I look at more often. I haven’t screening and prescribing according to Main GP: Explaining to the patient, the graph is quite motivated by data quality. looked at a lot of other cardiovascular guidelines. good. But I have to admit that I haven’t used it very guidelines...normal practice you might often. Part-time GP has reviewed reports that not consciously go in and do it. Being Part-time GP uses other CVD risk tools on main GP has given her however does having a HealthTracker sitting there, a regular basis and if this had worked for Main GP: I’ve moved away from using single factors a not motivate her because not aware of giving you a prompt and say “You need her, she would have incorporated into few years back now. Is to use a combined risk factors. the bigger picture of the study. The to go look at it”. her daily work. She found the And it’s always championed to me that is the better reports informed the GP they are an intervention to be novel and easy and way to analyse to the patient. But I find that they’re average practice. Individual specification beneficial for the care of the patients. harder to explain to the patient with a number. Data quality is important to the main GP. Main GP think HT helped improve their He saw the benefits and value of CAT for PM is not interested in any additional Relational integration data. data cleansing. However, other staff work besides what has been delegated to There was lack of relational integration. GP is only were not involved and did not know her due to time constraints. staff using HT and CAT. GP has mentioned it to the PM Main GP: I did tell someone halfway what value it would add to the practice. however she is not keen on taking on any extra tasks through that I was a bit embarrassed Enrolment outside her current role due to time and resource about our data of how many patients Main GP wanted to see how his practice Part-time GP was excited about the constraints. are supposed to have blood pressure was doing regarding CVD management prospect of using HT. Using absolute risk pills and they’re not on blood pressure compared to others as main reason for and risk projectile graphs is common pill. And that I think there was another
November 7, 2017 participation in the trial. Thought tool practice for the GP at her other surgery. There are no formal meetings on CQI programs or group, there was discussion and they would be good platform to view how he She was persistent in trying to use HT at studies that practice is involved with. They have say, I say “But after I look at all the was performing. This would assist in different periods of the trial. However meetings only for big policy or system changes, other practices and I wasn’t feeling too picking up additional patients that have never could overcome technical issues. otherwise meeting once a year on a weekend. A lot of bad, even though it was bad”. Even incomplete screening and prescribing. corridor chat. though the number was quite low, and I Motivated by competition. Other staff not involved in the study due said that I’m not far difference. to time constraints. They did not seem Main GP: the administrative side of it needs Both GPs recognized the benefits of to think it was something they should be improvement. If you ask the doctor to just go and look Main GP: …others [GP practices] who using HT to improve patient care and concerned about with their roles and at it [CAT] himself every three months or so I think improved. Improvement is a much data quality. responsibilities. GP didn’t want to you’ll be hitting the wall. Okay. But if you get an better of course, so I know that yes, you overburden them with other work. allocated person to say “Well I’ve been allocated this can do better, by using all this Health PM does not see the benefits or value of task, and so every three months I do it [review CAT]. Trackers and looking at the CAT tools the intervention for the health service Accreditation for all GPs or financial and so on. due to lack of interest and capacity to incentive for compulsory use of HT and Main GP: A good single person allocated, keep participate. CAT can help motivate the use of the monitoring, keep going, these tools will be very, very Communal Appraisal intervention. good. Yeah. For communal appraisal at the practice, Part time GP: it is something that there needs to be regular formal [absolute risk calculations] we do as Main GP is more vigilant in prescribing Contextual integration meetings. Also, there needs to be an common practice, I do it in the other after awareness of guidelines. Ongoing technical issues with HT and CAT prevented allocated person driving the monitoring practice but it’s not the same, just that integration of the tool at the health service. Part-time and evaluation of the goals of the trial online one and we discuss it with a Main GP: belief that if not using HT, GP had the intentions to use the intervention; and intervention. patient and do implicate whatever it is. numbers [screening] would have dropped however ongoing issues discouraged use. So I do use, like to use it [HT], and I was or stayed steady. Prompts are helpful. Main GP: get the person allocated to really very excited. Main GP sees the value in HT however does not see it analyse and then feedback and say Activation as a “robust” intervention. The intervention needs “Well we’ve extracted our data. This is Communal Specification Main GP is the sole user of all improvement. He would like it to be used quickly and the pictures. This is what it is” and you Both GPs used absolute risk as method of components of intervention. Due to time efficiently. Time constraint and lack of resources is a can just read that summary quickly and managing CVD risk. They both saw value constraints, he is unable to sustain long- factor. say “I need to concentrate on my, say, and benefits in HT tool for the term use of HT beyond the study. He blood pressure documentation for this assessment/care of their patients. needs more external incentives to get Part-time GP: Every time I do the data on that [HT], it groups of patients”, or “My others at practice involved. is just, it’s frozen up so I don’t think I have any input in microalbumin has not been done for Part-time GP did not get involved in their things. That’s what I you know, was a bit of, groups of patients and there’s too many other parts of the intervention and was Main GP became more vigilant on using because I didn’t, you know I used it in the beginning that are not being done” and the data not aware of their objectives for the HT and CAT during the trial period; but every time then it was a struggle and then we tried will come out with the summary. And study. however, he believes there is more work to fix it and it didn’t work, then I just stopped doing that each three months or six months that is needed to improve the it….They [Developers] come but we tried it many times you would get a little bit of summary “I Other staff were not made aware of the intervention. Further, he needs allocated but then that’s it, we give up like after a while, I better do better in this area. Or this purpose of the intervention and study. person to assist in implementation and couldn’t, it’s always, it didn’t work area’s missed out, or this area missed maintenance of the intervention. out”. Internalisation
November 7, 2017 Main GP initially participated to improve Screening and prescribing that have PM: I simply don’t have enough time because I have to Reconfiguration health service patient data quality become somewhat routine for the main do the recall while attending to the phone calls and Main GP worked with developers to fix however realised the importance of HT in GP as a result in participation in the receiving the patients. I don’t think it’s feasible. It problems so both GPs can use the screening and prescribing according to study. wouldn’t be effective. intervention, however ongoing guidelines. technical barriers deterred GPs from Main GP: I’d like to use it more. Skill set workability using or changing their practice to Meaning: Unfortunately timing issues. But at the GPs have the skills and knowledge to promote the use improve their data any more than it Main GP: there are certainly increased end of the day if there are some criteria of the intervention. Regular training/support over the had. vigilance to screen the patient who is that are set, that it becomes compulsory phone for part-time GP and face supposed to have the screening done. To for accreditations, that could be a way of to face for main GP would encourage its use and Main GP would like intervention to be look at their risk factors in a more forcing all the doctors and the staff and provide confidence. Both GPs learn best by hands on quicker, integrated within EHR and comprehensive way because the nurses, if you have one, to look at it training. Our training was sufficient initially. have an allocated staff to help drive the HealthTracker itself is look at it in a at the regular fashion. And if you come intervention at the practice to use long comprehensive, give you an assessment out and say “Yes, the CAT tools are good” Other staff were not trained on use of TORPEDO or term. risk of a five-year period. you’re supposed to be doing what is any of the components of the intervention. supposed to be doing, is good enough to No delegated person/point person to be applied to 80% of the surgery for GP performed work from administrative, IT and regular present ongoing reports of the study example, and we want at least 80% of patient care. indicators (CAT and IF reports), trouble the surgery using it to do it regularly, shooting of intervention with maybe at the three-monthly fashions, Main GP: I haven’t allocated a task to my staff to use it developers or study personnel, and and extract the data, [intervention], we haven’t trained the staff. organising training.
Commitment: Effort Comprehension Main GP: it strikes me as a good trial to Main GP: It cannot be based on just me to do the Main GP: No, I don’t [think] it’s get involved, to look at how robust my analysis all the time, which is impractical at the end of integrated very well at the moment, practice is, or was during the trial. And is it…fully integrated in terms of not just the doctors are because one is I haven’t allocated a task not a big area analysis, it’s a small area using it, the staff are using it at the same time. So you to my staff to use it, we haven’t trained of clinical practice. That’s why I think I need a bit of education for the staff, show them how to the staff to train once or twice. could get involved. It’s only the install it, how to log in, how to logout, where to get the cardiovascular side of it and it’s got an information, which area they should be looking at, existing tool that you can use and you maybe even our administrative side of it, to help the just see how we use it, how good it is and doctors maybe. so on.
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Case 3: Small urban general practice Interview date: February 16, 2014; ~ 10:30-11:45 am (GP) and 12:15 pm (Practice manager/receptionist)
MECHANISM Coherence (sense-making work) Cognitive participation (relational Collective action (enacting work) Reflexive monitoring (appraisal work) work) Differentiation Initiation Interactional workability Systematisation GP has a personal interest in kidney disease. GP is the solo driver of all programs At his practice, he wants to give his patients “total GP did not know how to use and read HT was a tool that was going help GP to be and studies including patient care. GP patient care”. the peer ranked graph provided by the more aware of risk factors for diagnosis and works autonomously. He used HT tool study team or use the web-based treating kidney disease. for kidney disease and cardiovascular He initially used HT tool however went back to portal. He needed additional training. patients and those he suspected of practicing the way he was prior to the Therefore, there was no measure of Prior to using HT tool GP did not use absolute being high CVD risk. implementation of the tool. health service performance of the risk calculations since it was too complicated study indicators. and time consuming. Tried to assess CVD risk GP: I began looking at everybody with Doesn’t find the graphs helpful in explaining to from individual risk factors. The tool assisted in kidney problems. I do lots and lots of patients their risk. The mathematics of is too There are no systematic method or assessing overall risk and kidney disease risk. blood tests. complicated for the patients, unless patient shows practice of identifying benefits or interest. issues with the intervention. GP did not know the purpose for other Legitimation components (audit tool and web-based portal) GP has a difficult time convincing high GP: occasionally when a patient is a bit difficult to Individual appraisal of the intervention; therefore, did not use. He risk patients in taking medication. This understand and difficult about realising that he has There was lack of interest in assessing used portal initially but did not see much point. is a regular occurrence and assumes got a risk with cardiovascular disease, then I how the intervention was impacting his patients wouldn’t be interested so produce the file in the hope of convincing them overall performance in CVD doesn’t bother trying. management. He was happy with his PM was not involved in the study therefore did they have to do something about it. initial performance of being above not know the purpose. Further, he believes he a high average compared to other general … “I’m quite happy with that. That’s a good sign. performing site and didn’t need to practices. GP: we couldn’t complete that [absolute risk Thanks, Doctor. Thanks very much, you know, change much after initial training from calculation prior to HT]. We could simply say you’re the first one who showed me”. So, you implementation team. GP: I was more keen on this fact that I to him, “You are in the bad department” … know, things like that do happen. Out of the 10 was doing almost as well, then I wasn’t patients that I would use that with, about seven of …It [absolute risk calculation] was tedious, it Enrolment paying too much attention to the later them would say that. was hard work. It still is like that but that gives GP believes the concept of the tool is graphs that came.
us a bit more about absolute risk calculations. good; however, does not know how to Relational integration use it properly. Needs incentive to use GP: Well, looking at this [performance PM finds the study interesting. She has seen the Individual Specification intervention to have impact, and needs on web portal], for example, where the audit tool, and peer ranked reports when study GP lacks knowledge of the specifics and further training. red one is, it applies to me. I’d rather research team visited the practice. She would like purpose of all components of the intervention. be on the other end like I did in the first to learn and be involved but her assumption from He is familiar with HT assessment and “what if’ GP: difficult to convince patients to what GP has said in past is that he doesn’t want graphs. take the medication, although
November 7, 2017 admittedly I understand the benefits of her involved. GP excludes PM from anything time, like this one. …I suppose I didn’t GP: HealthTracker made it more attractive it [prescribing to high risk patients]. related to software system. know how to change that. because that showed that I was doing something and I was going somewhere, …It’s a very good tool. It’s a very good GP thinks she is not “keen” on the study and stated Communal appraisal whereas all the others, just collect this, collect system and I know it is developing he would ask her. Participants including patients did not this, collect this. because it’s looks newer, different each appraise the intervention and its value. year and the use is just a matter of PM: I think he doesn’t like me doing it, he wants to They gave some positive feedback Communal Specification having to use it. do it himself, that’s why. So I have no idea what initially when GP was using the ‘What The PM did not know purpose of the he’s doing. If” graphs however impact was intervention or study. GP did not believe she Activation unknown. was interested; and PM believes GP did not GP’s method of patient care is face to GP: Time factor is primarily that. You are right, if I want PM involved. face without focus on the computer. could train xxxx(PM) to do it, I suppose she’d do it, Reconfiguration He uses computer to see if patients but essentially, I didn’t know what it is for. There was nothing done to Patients found it useful however GP not able need further assessments and what accommodate the intervention. to convince patients of changing their was discussed previously. Contextual integration Financial incentive would have helped behaviour or medications for high CVD risk The tool slowed down his whole system. This use of the intervention. patients without CVD. GP set in his ways in patient care. He caused him to dislike the intervention. needs a lot of hand holding to become There was lack of reflexive monitoring Internalisation comfortable with new method of GP lacks time and resources to fully implement and at the practice. Saw overall benefits of using HT tool for assessing CVD risk. embed the intervention. cardiovascular patients and incorporating Lack of understanding of the study and kidney screening. His personal influence of GP: It’s a good tool. It’s a good tool. If GP: Slows the damn thing down. It’s very intervention. Initial purpose to be using a tool to screen for kidney disease was a it is put to proper use it’s, the use will annoying. involved was to assess kidney disease major factor in its use. eventually serve the purpose for which more efficiently. it was built… Skill set workability GP: I have prescribed for people to whom I can GP needs additional training and support to Comprehension GP: it’s a bit of a nuisance and apart demonstrate that something is wrong and they …now that I’ve used the thing for a understand the rationale and use of the from the fact that it is beneficial in need to fix it. little while, I’m aware, I’m conscious of intervention. He found the initial training to be terms of putting figures down and its, of the management, of the use of it. overwhelming. Incremental training and delegated calculating it, it appears as if I’m GP: it’s worthwhile knowing, however when it time flagged explicitly with administrative working for somebody else with no comes to treating people, they want to be Commitment assistant. GP: this one here [HT], bugger, it’s just compensation and no recompense and looked upon as individuals and they want to be it’s very annoying sometimes, a matter of collecting data. That’s how shown where they are going, not relative to Doesn’t feel comfortable using absolute risk to especially when the thing clogs my it feels, you see, so I haven’t got any, somebody else prescribe. Needs more training and skill computer now. how shall I say it, enthusiasm about it. development. Age is a barrier in using health Meaning information technology GP: I could see that it was specific to most of my patients. Now, most of my patients had to GP: I wasn’t paying 100% attention to that [study/intervention training] because these were
November 7, 2017 do with heart disease and, of course, you could just not appointment type attendances. Somebody incorporate kidney disease as well in that. just blew in and began talking about this [intervention] and I, so my patients are used to waiting but I don’t like them waiting.
…you’re really given a utensil and you don’t know too much about how to use it.
Effort GP: “Oh, I’m all right. I’m doing okay so all I have to do is just carry on doing what I’m doing”.
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Case 4: Medium rural general practice Date of interview: July 17, 2014; time of interview: ~11 am (PM) and ~12 pm (GP)
Mechanism Coherence (sense making) Cognitive participation (relationship work) Collective action (enacting work) Reflexive monitoring (appraisal work) Differentiation Initiation Interactional workability Systematisation Benefits of using HT as CVD risk management and In order for all GPs to use HT, main GP needs For the main GP, the - ‘What-if” graph – he only Used IF peer ranked teaching tool. Provided evidence for teaching to be a ‘clinical champion’ for using showed the graph and didn’t manipulate since didn’t reports to view the registrars. intervention. feel comfortable with explaining rationale. The graph practice’s performance. was engaging for the patients. Values feedback about Main GP sees the benefits of having the best Main GP dislikes computers therefore didn’t how service is doing quality improvement tools at his health service use as often as he may have wanted to and did Main GP: People are interested. It’s a mode of regarding screening and however using the e-health tools has its obstacles. not know the full capabilities of HT. engaging people. management of patients.
Main GP: was using it [HT] as a risk measurement Main GP used in meetings as teaching tool and Relational integration tool and as a patient teaching tool that people had research team attend meetings to train all Practice has good staff rapport. Have regular weekly Individual appraisal would, we would, a registrar would say “Gee I’ve the GPs. Registrars were keen to use. meetings as a teaching and team-building forum. HT Peer ranked reports that just seen somebody who’s come in, who’s new, and was used to teach about difficult cases and its use identify progress needs to have got a cholesterol of this and a blood pressure Main GP: ..my age range is against me. It was, with patients. be summarised concisely of this, and their sugar’s a bit high but they claim I’m not someone who’s intuitively familiar. So I via email in order for attempt to use [HT], as I say I really hate main GP to view the they’re not diabetic. What do I do?” and instantly Main GP...often will use it in case discussions in computers…I tend not to sort of sit and say report. we’d use it to say “Okay, let’s have a look at the teaching that use it to actually say “Look, it’s there. “Ooh I wonder what this will do” or “I wonder if risk. Let’s see what things we can modify. What You can use it and you can calculate and you can use I can find out this? I wonder if I can find out it as a teaching tool for the patient, and you can use Main GP: it was most can we do straightaway? What can we do down that?”….therefore if that’s my approach then likely once again, having it as a prompt for yourself, what you can modify”. the track? What’s what? What can be, which it’s difficult for me to pass that on to other a very, very short simple
order do you do it in” and so on. And at times people [GPs]. I can’t sort of say go and do this summary [IF report]. This Contextual integration what’s the evidence for it? Use it to teach wider [use HT] when I don’t usually do it [use HT]. kind of reporting is good Uses regular weekly team meetings to standardise range. where you sit down and practice and ‘continuing medical education’. Both Legitimation have the introspection. administrative and clinical staff are invited to the Individual Specification Main GP finds the tool to be beneficial but meeting where both areas will be discussed. Due to the practice having mainly registrars with does not use regularly due to his confidence in Communal Appraisal
the owner being the main full time GP, it was a computer tools. He needs additional training PM can add reviewing The need of permanent GP staff could enhance the good way to train and teach doctors on best for him to promote it within his health service. peer ranked reports at use of the intervention. Main GP was an advocate practice based on evidence-based practice, and the Thursday meetings on for the use of PCHR however doctor’s ineptness for use of new technology to enhance patient care. PM: I think the younger doctors found it [HT] a regular basis. This was computer was affecting its use. Young registrar soon useful, so that made things, the pop up done a few times to be permanent doctor had tips for using it more Main GP values prompts to help screen and reminders, “Okay, this person needs this however not regularly.
November 7, 2017 manage CVD and chronic diseases. checked, this person needs that checked”, that efficiently. Main GP was impressed and learned from PM has shown/given the was useful for them. young GP. reports to the Main GP. PM understood what the study was about however was not engaged in it due to time Enrolment Three permanent GPs to join health service which Reconfiguration constraints. Theoretical concept was great however lack of could increase engagement in the intervention. There has been minimal confidence and knowledge with computer alteration in the health Communal Specification affected use of tool. Young keen and proactive Non-GP staff were not involved and not interested. service to ensure regular Main GP works on being the ‘clinical champion’ doctors would be more apt to use. This would have assisted in overall use of the appraisal and use of the during team meetings. GP is an expert on diabetes intervention and integration. intervention. This management, and works at a diabetic clinic. PM not involved in the study and did not use occurred a few times CAT component for the study. PN could have Main GP needs incremental training for him to use during team meetings. Other GP registrars saw the benefits of HT; been better able to assist with CAT. the tool regularly. however unaware of the frequency of use. Due to Main GP: …use of the staff turnover, interest and awareness of the Activation Skill set workability tools was increased intervention was unknown. Young doctors would likely be proactive and Further training for main GP, PM and PN needed. obviously. Right, okay. use HT; however due to staff turnover Incremental training and once a month phone call But my use, or my way of Internalisation (registrars), use of HT was variable at the for support would enhance use of the intervention. using the guidelines really Main GP valued it as a good platform to teach practice. Possibly an online training demonstration could help. didn’t change. registrars and use to communicate risk to patients. Main GP was less likely to use HT unless he felt High staff turnover is a cause of lack of collective Comprehension Meaning confident in the rationale and knowledge of action. Main GP: It’s integrated Main GP: I’ve been looking for non-paper based using it properly. well, and there needs to prompts for cardiovascular or chronic illness be still more work. Look prevention. And that’s one reason. And secondly Commitment GP: the sole barrier was familiarity. it’s a tool, and it’s a tool Main GP: you had a tool [HT] where you could which has very, very because I thought it’s something which was good Effort instantly see what an evidence base risk many uses, and quite a to be measured. And thirdly I just wanted to see Main GP: I remember you and xxx [chief investigator] calculator shows and you were able to modify lot of potential. maybe overall how we were performing. would come around and you’d explain things and it. Right, okay. And that was some of the there would be things that I would see the benefit of, other features like bringing up the feedback and then it was like “Okay, I’ve learned 25 new online and so on, “Let’s do this, remember the things. I remember three very well, I know how to password, do this, it’s the internet’s slow. use five, and I know I can sort of do this but I don’t Bugger it. I’m going to go have lunch”. quite remember how to do it”, and then after three
weeks you’d think “Was there something like that there or not?” And you thought “Okay, I can go for a walk or I can sit here for half an hour and work out how to do it. I’ll go for a walk”.
November 7, 2017
Case 5: Large remote Aboriginal Community Controlled Health Service Date of interview: May 28-29, 2013; Time: ~10 am (GP); after lunch (AHW); May 29th at 10 am (HIO)
Mechanism Coherence (sense making) Cognitive participation (relationship work) Collective action (enacting work) Reflexive monitoring (appraisal work) Differentiation Initiation Interactional workability Systematisation Staff that were introduced to the HIO worked exclusively on CQI programs and health Patients appreciate the ‘what-if’ graph and being HIO would report to GPs their intervention saw the benefits of using the system reports. She was the delegated staff working able to see their risk visually. GP used performance in TOPREOD point of care component of the intervention. on TORPEDO and driving the implementation of the infrequently due to time constraints. He thought especially prescribing to high They saw the benefits of having easy access tool. it was valuable component. Time constraint is a risk patients quarterly at the to the CVD risk screening profile and big barrier at the health service for GPs. GP meetings. She would absolute risk displayed with ‘one click’. GP found the tool to be valuable and when possible, have lists of patients that
GP registrar would demonstrate the HT tool to other were not properly managed. GP: if you're going to go through all this you The screening team and GPs had already GPs. GP promoted and educated about the tool to GP were illuminated by the actually, you've got to be prepared to have a been using the Doctor’s Control Panel (DCP) colleagues. He would like AHWs who are at the reports from HIO. They were good 10 minute chat with the patient because however the tool lacked calculation of frontline screening patients to use the HT tool. keen to review patients that you actually want to engage them and help them absolute risk and patient risk were suboptimally treated. understand where they're at and make a communication. It was more of a screening Legitimation difference and that's the time. So it's not the tool. Therefore, they were keen on using HT. HIO reviewed TORPEDO data on a regular basis (1-2 Individual appraisal program time it's actually alright we're going to times per month). However, after trial completion, GP suggested providing all have a proper chat today…..that's what takes the AHW: it is just the way it tabulates so much she did not review data as frequently. Driven by trial GPs with reports of time. So to just have that chat without this tool more information than what our other participation. performance of study would, you know you'd be drawing all sorts of options have been. So again, it gives us a indicators by point person, pictures over the paper and the patient might get real accurate point of where they are with HIO used audit tool more often than peer-ranked web and then reviewing the data the point but not really. But with this tool if their risk assessments. portal. Web portal was used to give an overview of the with GPs to motivate use of you've got the time you can really get your health service. HT. This will demonstrate the message across. Yeah but it's that, sort of that Individual Specification benefits of using absolute 10 minutes to have a proper chat with the GP was an advocate in calculating absolute AHW was keen on using HT with his patients. He risk. patient that I haven't, yeah. CVD risk, and prescribing according to their screens chronic disease patients before they see the
risk. HT made GP aware of prescribing GP, so it would have been valuable for him to show the At quarterly meetings, HIO guidelines and gave him confidence to visual presentation to the patient. Relational integration presents data from CAT and prescribe “early”. Further GP had HIO worked with GPs to install HT, set up training IF portal on performance of professional development motives for using AHW: I think as I work in that clinical sort of direct and report on performance at meetings. all study indicators. GPs are the audit tool and absolute risk calculations. patients and really working close with doctors and all very receptive. that, it could be a great tool for me to use. Someone in There was lack of use of HT across different roles AHW: I liked it where we could show the the community might be, it’d still be good for them but despite interests. If non-GP staff used tool, it Communal appraisal predictions if they’d changed this or done they wouldn’t have that, I think the visual tool, and would be beneficial to GPs and overall patient Health service’s focus is on that. Even though I know that doctors would outcome. seeing how they are doing
November 7, 2017 go through that, I think also having an that’s probably what I’m looking is that it’s just a great amongst themselves. Less indigenous person as a health worker who visual tool for me to use with my patients There are multiple care teams that work with concerned with comparing only help maybe on that cultural side to say, different patient cohorts. There was lack of themselves with others. well this is what this means, and it will be Enrolment implementation of the intervention within these Focus in improving patient really handy in screening ‘cause not all our GP used tool to help reassure himself of his diagnosis groups. outcomes and quality. patients want to go through to a doctor. and risk calculation, and management of high CVD risk patients. GP would buy the tool if he owned his own HIO: will extract the There was staff turnover that affected use of the Communal Specification practice. information out and then put intervention with new GPs. GP was an advocate of using guidelines. He it into our own, you know, took on the responsibility of sharing AHW wanted to use the tool; however never given xxxx [ACCHS name] looking guidelines with other GPs by making multiple access. He was not aware of reason for not being able Contextual integration report, and then report that photocopies. to have access. He was disappointed. GP would like to see HT move information from back to the staff. Because HT into the EHR. only the GPs here use that we AHW thought the ‘what-if’ graphs would be GP: it did change my prescribing criteria and having usually tie it into the GP valuable in giving patients an Indigenous the Health Tracker tool available to help me calculate There was an issue with the resource and time meeting, so they’re held perspective. that reassured me that I was doing what is perceived constraints. quarterly and we might not to be the right thing. put TORPEDO on every HIO: we do conduct business using absolute It would be valuable, and increase capacity for quarter, but maybe, you cardiovascular risk as our means to AHW: it was interesting though that we were still change if non-GP staff used HT tool. know, it’s six monthly to determine, you know, who’s looked after by shown it [intervention], and, so, you know. So you sort allow for, you know, a bit what team of, look we’ve got this great tool here, but guess what, GP: it's like a one way street and doesn't come more, so certainly I think, you can’t use it. It was sort, that’s what it felt like to back. And so if I wanted to get any of these nice yeah, they’ve had at least two Internalisation me. documents into my notes I don't really know how to three reports it must be, HT provided evidence based medicine for to do it apart from print it out then type it all in. handed back to the GPs. CVD management which was a key reason Activation And I'm not going to do that. for participation in the study. Everyone The intervention can become embedded and Reconfiguration introduced to the intervention at the start integrated if non-GP staff are given access to HT. And AHW: I don’t know whether from management HIO incorporated reporting really believed it would be beneficial to the regular support and training is offered to GPs and non- and whether they thought it was for the safety on CVD risk factor indicators health service. GP staff. sake, you know, patient’s safety and the safety of at GP meeting. HIO worked to us workers, you know, maybe too much stuff I check regularly that training HIO: I think it’s a very good tool to have Commitment think. But as we’re involved clinically I think it’s was provided to new staff. added to our kit. The fact that it’s electronic, GP: if I was running my own practice I'd pay for this still, it’d be great to have access [HT]. the fact that it integrates with the medical software even if I was charged to use it and I'd use it GP: I've seen these graphs record, I certainly see its value and purpose. I often. The ACCHS has a strong foundation of CQI [peer ranked web portal think the doctors certainly see its value and programs. Our intervention fit in with other reports] before in it’s, you know, sort of streamlined their already integrated programs. presentations, when consultations from the what do I need to do presentations have been point of view, and then extended their Skill set workability given, but I've never accessed consults on the how to engage with a patient Skill set depends on age of the doctor. Young it myself. doctors are more ‘IT savvy’.
November 7, 2017 Meaning It would be beneficial to have follow up calls and Comprehension GP: It's helped my practice individually training session within 2 weeks of initial training. HIO: …for example only 30% because I can get an accurate complex Follow up training and support is crucial in of the high risk patients are summary that takes into account all these helping GPs use HT. being prescribed with triple additional factors and other relevant history, therapy and they go, whoa, all the numbers, it gives me the reassurance HIO, delegated CQI staff had experience using you know. So they really like that I'm getting an accurate cardiovascular the audit tool and found web-portal as an that, and we can then risk, the most accurate really that I've seen important avenue of relaying information to the actually provide them with by a long way. So it gives me confidence to GPs. the names of people, you prescribe the medicines that I prescribe know, and then sort of next knowing that my cardiovascular risk is HIO: I think it’s just about that training. So, and time they present to the accurate. then following up, you know, so for example, the service they can have a quick doctor has training, you know, following up a look at their, you know, month later just do we need not a re-training medication management. but, you know, any questions. What we find is most of the doctors when they go to the initial training session they’ve, you know, they may not even have a login so they’ve never actually seen Health Tracker at all…. they really benefit from having maybe a following up session. But, yeah, sometimes I feel that, you know, the time delays are really too far gone and they actually need complete re-training again.
Effort GP: It's got a big potential to help the practice but you've got to have people willing to use the computers and have the time in the consultation to go through it with the patient and unfortunately that hasn't been the situation in the last six to nine months at xxxx [ACCHS name]
November 7, 2017
Case 6: Large urban Aboriginal Community Controlled Health Service Date of interview: November 25th-27th 2013 and February 6, 2014 # of staff: Large health service with multiple staff at two locations. (3 full time GPs and 2 part-time GPs) Mechanism Coherence (sense making) Cognitive participation (relationship work) Collective action (enacting work) Reflexive monitoring (appraisal work)
Differentiation Initiation Interactional workability Systematisation GPs, PN, AHWs, and PMs, saw the PM worked with research team to install The ‘what-if’ graphs have impacted PN/PM working on data quality and clinical audits. benefits of the intervention and impact HT and scheduling training; however, no positively in helping patients understand PN works at new smaller location. This affects it could have on patients with one click. delegated person for ongoing training or their CVD risk profile. communication of data quality results. trouble shooting. Lead GP: you’ve just got an Engaging and easy for clinical and non- Main GP sees value in CQI programs but no Lead GP and GP2 were an advocate of incentives to participate. Patient care is focus. independent source and it’s new data clinical staff to explain graphs. AHWs and nurses using HT as part of their so it’s just refreshing your minds because the guidelines are always screening practice. However, GP3 does not Individual appraisal think others should have access to HT only Lead GP: you can look at the specific risk There was partial self-monitoring on performance changing, the targets are always factors and what you can do about it, changing, blood pressure targets, lipid because this will cause more time after using intervention. Staff would see benefits if constraints. and see how you can change your risk. there was an impact on patient lifestyle after viewing targets, so I, yeah, just brings And so I like using, in that instance I love ‘what if’ graphs. everything, gives you a fresh review of AHW: I've been there a couple of times using the graph, I love using the change, everything, and it’s on your computer, with GP x [lead GP] doing this [‘what if’ the smoking status, to look at how you PM: Find these [peer-ranked reports from web it’s on your desktop. graphs]. And he's entered in the data. And can change your heart age, and I’ll say, portal] very valuable in looking at how we’re going then it showed on the projection. What “If we put you on a statin it will drop your compared to other services, and whether or not AHW: Well, usually when a patient would they do if they changed this, you cholesterol from 6.9 to 4, and here’s we’re actually getting to the potential risk factors in goes in for a consult, they don't usually know, change their smoking and then what happens to your cardiovascular risk; the patients that we’re seeing. see anything like this. They might get a change that and then change this and your heart age drops to this, or it has bit of paper with their care plan on it watch it come down and see the patient's relatively no change.” So yeah, just Communal appraisal that's written in medical jargon and reaction to that? And even that for me, looking at the what-ifs. Main GP/CEO would like a delegated point person,
they don't understand it. When you've 'cause I'm a smoker [laughs], yeah, and preferably GP, to report on study progress at team GP2: pictures make more for impact than got something as basic and there was a considerable jump in the risk meetings. the words, kind of thing. So they were straightforward as that, it's an easy to when he just took away the smoking. So, read tool for anyone. more affected by that. And some of them yeah, it was a bit of an eye-opener. And it's New GP working on CKD audit, and used this as made the decision to, you know, to stop pretty easy to read graph educational session on UACR screening and kidney Individual specification smoking, for example. And yeah, so that disease. Lead GP was sceptical at first due to was a good effect. Legitimation technical nature; however, he is an All staff believe that this intervention can PM: I’m just really, really pleased that you’re advocate for managing patients based …Advantages, definitely showing the improve quality of care. However many are continuing for another 12 months, because I think for on absolute risk. HT provided a simple patient on a graph [What if graph] is not aware of the full capabilities of the the first six months of the project no one got it right, explanation for patients through the intervention. effective. Because it’s a picture, they can’t, it’s clear, it’s not complicated, the and I will be even more interested to see how much
November 7, 2017 ‘what-if’ graphs. graph. So you can actually see with a improvement we’ve made at the end of the next 12 Lead GP: routinely I would use it [HT] when simple explanation and it’s effective. It’s months. All GPs gained knowledge and skills on I’ve done some, I’ve done a health check impressive. screening and prescribing based on and I’ve done their bloods and they’re Reconfiguration absolute risk coming back and they’re looking at their Relational integration CEO requested one of the GPs to focus on CQI once a lipid results, and people get a bit fixated on Non-GP staff were provided assess and week which can enhance intervention use. Need PM, PN and two AHWs believed that why is that number up, why is that number training to HT. Those screening patients regular reporting of performance of TOPREDO study HT would be beneficial for their patient down or whatever, “Do I need to fix that?” used HT till it disappeared. Main GP and indicators at meetings. population at the HS. They were And so you give them a holistic view of GP2 an advocate for AHWs to use HT for excited about the prospect of using the cardiovascular risk. CVD assessment. Lead GP: [GP3] got a whole day a week that he’s risk communication component. supposed to do this [data review extracted from
audit tool], then that’s the place because I don’t GP3: At the moment there’s no one doing PM: Initially, when we first got the side Enrolment really know what he does on most days, and I bar [HT], I was really impressed, and I All staff trained found the ‘what-if’ graphs that role [CQI officer], so everyone’s just occasionally ask him and then I drift off when he tells did like the fact that it reminded you to be powerful and engage patients. It was busy seeing patients and no one’s really me. So I think that’s, you’ve got him being paid one that things needed to be done, or not easier to explain to patients their CVD risk looking into quality improvement. a day a week, you know, that’s where it should that needed to be done, but things that in pictures. They did not use often due to happen. hadn’t been done that perhaps could time constraints. Contextual integration be done, and I found that really helpful. Problems with audit tool not extracting Comprehension Activation data from EHR accurately. This AHW2: there was no follow up with it [intervention] GP3: I think it’s [intervention] definitely One GP and AHWs stopped using HT after discouraged GP3 and PM/PN from using within xxxx [ACCHS] itself to say, ‘How’s everyone made an impact. It’s helped me with it disappeared one day. AHWs were too for data quality purposes. PN used CAT going with this?’ ‘Are you using it?’ like, ‘Was it my clinical practice. It saves me having busy to follow up. They were using for frequently for audit purposes and study useful?’ and stuff like that. There was nothing. to manually input all the things into the screening purposes. reporting. Due to working across two calculator, the Framingham risk locations, there were issues with data calculator, and it’s a good reminder Lead GP was more computer savvy and accuracy. tool. proactive in trying to fix issues on his own They were unable to resolve the issue or call/email research team. However Communal Specification worked mostly autonomously due to his Most staff did not use the intervention to All staff using intervention found risk personality. its full capabilities. There was lack of communication ‘what if’ graphs to knowledge of what comprised TORPEDO have the most impact on patient Commitment components. outcomes. Lead GP: I’d still go looking for it [when HT Team meeting on a regular basis with disappeared]. You know, the benefits focus on case conferences. Internalisation outweigh the problems…I’m very much an
GP and non-GP staff see the overall advocate of it. Lead GP: advantages are it’s on the side value in intervention being a of the screen and it’s interacting with preventive care tool. your software…
November 7, 2017 AHW: It saves you heaps of time … disadvantages ..it did take a little while because in a blink of an eye you can see to load if you didn’t have it running what they're due and what they're not, already, and just a few software glitches, whether or not it could be save a load not with your programme but mainly of time and provide a better care for with Medical Director, because most of the patient, making sure we're not the time the questions about this come missing anything. up during a health assessment, Aboriginal and Islander health GP2: Torpedo, as I said, when I see assessment which is a horrible those graphs that I’m sharing with the programme in Medical Director that runs patients, yes, for example I change, I alone and you can’t move from it to any change the medication like blood other programme. So you’ve almost got pressure medication from one level to to hand write your little notes and then two more levels, like three combination come back to at the end, and you always medications instead of just giving one. run out of time. So that’s just an So it’s still one tablet, but three unfortunate part of Medical Director, but medications in it to improve the blood no, that’s about all. pressure. AHW: Just because of some discrepancies Meaning of where it's pulling information from, it's Lead GP: it sets the bar high and it really let itself down. I mean, it saves you does, it’s a great programme and I using the assessment tool. It saves you guess the constraints are just the time heaps of time because in a blink of an eye constraints. But it’s something that you can see what they're due and what you need to look at, you know, if you’re they're not, whether or not it could be going to do a good general practice save a load of time and provide a better and you’re going to do primary care for the patient, making sure we're prevention you’ve got to use something not missing anything, you know. like this…using something like this it’s got to be part of your practice Lead GP: then when the print status otherwise you’re just doing reactive came up that was good because I could medication. give people a hard copy to take with them.
GP3: It didn’t pick up all the patients, so for example say in April of last year I tried to see how many Aboriginal patients we saw for that entire month. And it came up with a number of about 20 or 30 something, which is not right because I
November 7, 2017 had to, I went through manually all the patients for that month, and it turned out to be about 200. So it was not picking up all the patients. I don’t know why that’s happening. But I couldn’t use the clinical audit tool to verify my numbers, because it’s underestimated by a lot.
Skill set workability Main GP trained GP2; didn’t provide in- depth training. Newer GP did not receive formal training. He was given training over the phone by webinar.
Need for additional follow up training and support on regular basis. Computer literacy is variable.
100% staff turnover for AHWs during middle of the trial. Lack of transfer of training and knowledge about HT and study.
Main GP: I mean I think you guys came around and did a few live tutorials, and that was helpful, and especially when new doctors came on board they could hear it from someone else; they don’t want to hear it everything from me. I’m not very good at explaining things, just ask my kids.
Effort Main GP: focused on case conferences and individual cases, and we don’t so much look at public health. … not a lot of remuneration for it immediately, so maybe we’re chasing the cash cow a little
November 7, 2017 bit that way rather than stepping back and getting a bit academic.
November 7, 2017 Additional file 3: Job Satisfaction survey data
Other Intervention Other HS (n=18); Control HS Warr-Cook-Wall Scale of Job Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 All cases (n=6); 113 (n=15); 65 Satisfaction (4) (3) (2) (8) (34) (17) 68 participants participants participants Amount of Responsibility 5.75 6.3 6.5 6.5 5.5 5.5 6 5.7 6 Freedom 6.25 6.3 7 7 5.8 5.7 6.3 5.6 5.9 Amount of Variety 5 6.7 7 5.75 5.7 5.5 5.9 5.8 6.1 Colleagues & Fellow Workers 6 6 6.5 5.6 6.2 5.7 6 5.5 5.8 Physical Work Conditions 5.75 5.7 7 6.6 5.5 5.7 6 5.4 5.9 Opportunities to use abilities 5.5 6 7 6.25 5.7 5.4 6 5.7 5.9 Your Income 5.5 4.3 6 4.75 5.2 4.7 5.1 4.8 5.3 Recognition 5 4.7 7 5.9 5.5 5.1 5.5 5 5.3 Hours of work 6 6.3 7 5.6 5.7 5.8 6.1 5.4 5.8 Everything Consideration 6 6.3 7 6 5.8 5.8 6.2 5.5 5.9 Overall score (mean) 5.7 5.9 6.8 6.0 5.7 5.5 6.0 5.4 5.7
TORPEDO Health Professionals Interview Guide
DATE Health Service Name Health Service # Participant Code Interviewer Name
/ / 13
Job Title……………………………………………………………………......
Locations currently working at:…………………………………………......
Do you have access to HealthTracker-CVD tool on your computer? Yes/No (if yes, answer question below)
Sampling Matrix:
Type of Health Service General Practice (GPs) Aboriginal Community Controlled Urban Health Service (ACCHSs) Rural EHR Medical Director Best Practice Change in CVD Large (>10% ) Medium (5- Small to None Large (>10%) Medium (5-10%) Small to none (<5%) screening Outcome 10%) (<5%) Complexity of HS # of # Full PM (Y/N) # of # Full # of # Full PM/(Manager # of # # of # Full staff GPs time PNs time GPs time type) (Y/N) Nurses Full Healthworkers time time
# Part- # Part # Part # # Part time time time Part time time
MASTER_TORPEDO_Health Professionals Interview Guidelines_V2.4_December 13, 2013_clean
Social-demographic
Age 20-29 30-39 40-49 50-59 60-69 70+ Gender First language Other languages Country of medical graduation. Postgraduate medical qualifications Years worked in general practice Years worked in Aboriginal Health Length of time at current ACCHS/General practice Social-demographic AHW, Nurse, and other Health Profesionals
Age 20-29 30-39 40-49 50-59 60-69 70+ Gender First language Other languages Time in practicing as Nurse, AHW, other HP Time at site Highest level of education Year 10 or below Year 11-12 College diploma or similar Undergraduate university degree Postgraduate university degree Formal Cross cultural training CALD Indigenous
Notes
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TORPEDO - Health Professionals Interview Guide
Area of Interest Initial Broad Descriptive Possible Probing Questions (These are a guide only. It is not expected that you ask all these Questions questions) [1] Reviewing your results from - show them their randomization results and their monthly progress from the IF portal data Views of the reason your health service, what do (print out EOS feedback report) for outcomes you think might be the reason - Did you use the TORPEDO portal for viewing your data? Why/why not? for these outcomes? - How often did you use the IF portal?
- If you did use it, how useful did you find it? - What benefits did the IF portal have at your practice or health service? - Do you see a value in using IF portal? What would influence your use of the portal more often? - How did you use the TORPEDO portal feedback reports given to you by the project officers? How was this discussed with the team or other GPs involved?
Now, let’s take a look at your - Did you use the CAT? Why/why not? data in CAT. We will then open - If you did use it, how useful was it? How often did you use it? up your last data extraction, and - Did you use it only for HT or did you use it for other health outcomes and data quality review your data? information? - For what purpose would you use CAT at your health service? Who would be the main person using CAT? How would information be relayed to the team at the Health Service?
[2a] Can you remember the last - What purpose do you use HT for? Use of HealthTracker- patient you used HealthTracker - Roughly how often do you use HT in a day for your patients? CVD tool for? And how did you use - What kinds of patients do you use HT with? HealthTracker for this patient? - How was using the HealthTracker tool at point of care different from when you would not use
the tool? Did you at a later time assess if use of HealthTracker improved care?How long did it take you to use the tool confidently? How confident are you in your knowledge and skills in using HealthTracker-CVD? - What do you see as major advantages of using the HT tool? - What do you see as the major disadvantages or barriers of using the HT tool?
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- Did you use the risk projection graphs? Why/why not? [2b] Use of Risk How did you use the risk - If you did use it, what did the patient think of the “What if” graphs? Projection graph communication graph for your - How did the patients find the print outs of their risk summary and recommendations? last patient? - Did you give your patients print outs of resources? What type of impact do you think the risk
summary and resources print out has on patients (if applicable)?
- At a later visit from the patient, did you review again with the patients their risk score?
[2c] Implementation of To what extent were you using - What recommendations in the guidelines were you implementing and using? Guidelines the National Vascular Disease Prevention Alliance Guidelines - Are you aware of the new updated NVPDA guidelines on absolute risk management? If yes, in patient care before participating in our Study? what are your thoughts on the guidelines?
- How did you generally access the NVDPA guideline? Hardcopy, electronically, other doctors, meeting, etc?
- How has HealthTracker helped you to use NVDPA guideline? Or any other guidelines used in HealthTracker?
- What knowledge in relation to the NVPDA guideline or any other guidelines have you gained after using HealthTracker?
[3]- For GPs Only What are your views about - Have you been using absolute risk calculations to calculate patients estimated risk of heart Knowledge and use of absolute risk calculation in the attack or stroke? If yes, how do you find this useful in managing/treating patients CVD? absolute risk management of your patients? - How did absolute risk score in HealthTracker-CVD facilitate treatment or care of the patient? calculation and How would you normally treat patients if you did not have access to HealthTracker? management
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How have they changed in your - What are your thoughts on prescribing to high risk patients who do not have established prescribing methods as a disease? consequence of taking part in TORPEDO? - What are your thoughts on prescribing to low risk patients who may have high BP or
cholesterol who do not have established disease?
- How do you prescribe to those patients you have co-morbidities?
- How do you handle patients who need blood pressure and/or cholesterol medication and do not want to take any medication? What steps do you take to help them understand their condition when they don’t have an visible systoms?
[5a] What barriers did you/your Software: Support/training practice face in taking part in - Were there any issues installing SideBar on your practice staffs’ computers the study? In using HT? o Once SideBar was installed, did you have any issues using HealthTracker-CVD? o How did SideBar perform after any updates from PEN Computer systems? How did the
update affect you?
o What types of issues, if any, did you have overall with performance of SideBar and/or HealthTracker tool?
What were the facilitators? Support Training: - Were you provided with enough information and support from study staff to confidently use the tool? If not, what do you think you would have wanted to happen? - How did the initial training impact your use of the tool? - How did you find the support/training from the study team? Who at the health service was dedicated to work with the study team, resolving issues, and communicating to software provider if needed?
Prior use of e-health tools: [5b] Had you used any e-health - If yes, which tools and how long have you been using them? Prior knowledge and tools, such as SideBar and - How long have you been using electronic health records? skills of electronic Clinical Audit, Doctor’s Portal, - Do you have knowledge of electronic decision support tools? If yes, what have you heard health technology before taking part in TORPEDO? about EDS tools? - How confident are you in general with new computer programs?
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Have you started using any e- health tools since then?
[6] Workability and What helped you/your practice Staff at Health Service: integration of take part in the study? How was HealthTracker-CVD at HealthTracker-CVD integrated - How do you work with other GPs and staff at your Health Service? (if applicable) practice/HS within your practice/HS? - What incentives do GPs receive to participate in studies?
- What type of support is given to non-GP staff? Are there incentives at your health service for the staff? If yes, what types of incentives?
- What was the % of staff turnover in the last year?
- At your health service, do you have regular meetings with team? If yes, what is discussed at these meetings?
Resources: - Did you have competing work demands and time constraints while participating in the study? If yes, how did you handle competing work demands and time constraints? - What type of financial support do you receive at your health service, if any?
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- Do you have any external management groups involved at your health service? - How would you describe your financial stability at your health service?
Involvement of programs to improve health services:
- What types of continuous quality improvement programs is your health service involved in? - What are your thoughts on continuous quality improvement programs? - If you have not been involved in continuous quality improvement programs, would you be interested in participating? How do you think this will impact your health service? What types of incentives would encourage you to participate in CQI programs?
[7] In what ways do you think the - What was the impact on you and your health service in choosing to be a part of this study? General Impact of the ACCHS/general practice (in - Were there any benefits to you and your health service of participating in the study? If so Study on the health which you work) has changed please explain? service? as a result of participating in the - Were there any problems with you and your health service in participating in the study? If so study? To understand how the please explain? study integrated into - What impact did HealthTracker have on the consultation process? everyday practice - How did it impact on the length of the consultation?
[8]-For GPs Only What were your reasons for - Were there any benefits to you and your practice in participating in the study? If so, explain? taking part in the TORPEDO - Do you think you would take part in a similar study in future? Motivation to study? - What factors would make you more likely to take part? participate - What factors would make you less likely to take part?
[9]-For GPs Only E-health is becoming a major - What are your thoughts on electronic decision support tools as best practice use in primary government priority. health care? Attitudes to e-health - What types of resources, support, and training do you think would be needed to change
primary health care to use more e-health tools? - What advice would you give to government on implementation of HealthTracker-CVD in primary health care?
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[9] - Is there anything else you would like to add that we have not discussed in this interview? Final comments
Note: Some questions in the guidelines will be modified in response to participant’s answers and themes emerging from the data.
Thank you for your participation. Notes:
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Chapter 6: Ethnographic evaluation of how the intervention was used in practice, how cardiovascular risk communicated and perceived by patients and healthcare providers
6.1 Chapter overview
This chapter contributes to real-world context of how and when the intervention was used by the doctor and how patients perceived it. This chapter consists of a single published book chapter titled: ‘It’s just statistics … I’m kind of a glass half-full sort of guy’: The Challenge of Differing Doctor-Patient Perspectives in the Context of Electronically Mediated Cardiovascular Risk.
Successful dissemination of the intervention in a real-world context requires understanding of how it is used in practice. How is the intervention deployed strategically in doctor-patient interaction to communicate cardiovascular risk? What are the factors that support or hinder its impact? Taking a case study approach that combines fine-grained discourse analysis of video-recorded consultations in which cardiovascular disease risk is discussed with insights derived from interviews with participants, this research examines consultations use and non-use of the intervention.
The findings suggest a step-wise, strategic and cumulative use of the intervention as valuable entry point for engaging patients in discussion of their CVD risk. The discourse analysis reveals doctor’s commitment to addressing risk relies upon interpersonal engagement between doctor and patient as well as cognitive engagement with the topic of risk.
6.2 Book chapter details
O’Grady C., Patel B., Candlin S., Candlin C.N., Peiris D., Usherwood T. (2016) ‘It’s just statistics … I’m kind of a glass half-full sort of guy’: The Challenge of Differing
191 Doctor-Patient Perspectives in the Context of Electronically Mediated Cardiovascular Risk Management. In: Crichton J., Candlin C.N., Firkins A.S. (eds) Communicating Risk. Communicating in Professions and Organizations. Palgrave Macmillan, London. https://doi.org/10.1057/9781137478788_17
6.3 Author contributions
BP developed the methodology for the video ethnography, conducted the video recording of all consultations, and conducted the patient and doctor interviews. CO, CNC and SC designed the discourse analysis and BP, DP and TU provided detailed insights into the overall study and the health service context for each video. All authors contributed to the design of the discourse analysis of the video recordings and interpretation of the findings. CO wrote the initial draft of the manuscript in close consultation with BP. All authors contributed to the final draft.
6.4 Book chapter
192 17 ‘It’s just statistics ... I’m kind of a glass half-full sort of guy’: The Challenge of Differing Doctor-Patient Perspectives in the Context of Electronically Mediated Cardiovascular Risk Management Catherine O’Grady, Bindu Patel, Sally Candlin, Christopher N. Candlin, David Peiris and Tim Usherwood
Introduction
Best practice guidelines for the prevention and management of cardio- vascular disease recommend that management decisions be informed by estimation of a patient’s ‘absolute risk’ of a cardiovascular event over time. Such a risk calculation is based on a combination of non- modifiable factors such as age and gender and modifiable factors that include blood pressure, lipids, diabetes, body mass index (BMI), tobacco smoking, alcohol use, unhealthy diet, physical inactivity, and psycho- social stress. In Australia, a quality improvement intervention has been devel- oped to assist general practitioners to calculate an individual’s ‘abso- lute risk’ at the point of care and to engage the patient in considering factors that might modify this risk (Peiris et al., 2009). Integrated with the healthcare provider’s electronic health record, the interven- tion, known as ‘HealthTracker’, includes a real-time decision support interface using an algorithm derived from evidence-based guidelines, a patient risk communication tool including ‘what if’ scenarios to show benefits from risk factor improvement, as well as mechanisms for provider audit and feedback. Robust, trial-based evaluation of this electronic intervention indicates that its use is associated with a 25% relative improvement in cardiovascular risk factor screening (Peiris et al., 2015).
285 J. Crichton et al. (eds.), Communicating Risk © The Editor(s) 2016 286 Catherine O’Grady, et al.
Successful dissemination of the intervention in a post-trial context requires understanding of how it is used in practice. How is the inter- vention deployed strategically in doctor-patient interaction to com- municate cardiovascular risk? What are the factors that support or hinder its impact? In this chapter we focus on a single case study drawn from a com- prehensive post-trial evaluation of the intervention (Patel et al., 2014) to offer a response to these questions. Taking an interactional socio-lin- guistic approach (Gumperz, 1999; Roberts & Sarangi, 2005), we combine fine-grained analysis of the discourse of a transcribed, video-recorded primary care consultation in which the intervention is used with ethno- graphic interviews that bring the perspective of the participating patient and doctor to bear on our analysis. Findings indicate the potential of the intervention as a valuable entry point for engaging a patient in discussion of their CVD risk. Discourse analysis brings to light the doctor’s role as mediator between risk knowledge made available by HealthTracker and the patient. It uncov- ers the doctor’s strategic, step wise deployment of HealthTracker as a means to focus the patient’s attention on the cumulating factors that contribute to his overall risk. In particular, analysis reveals the doctor’s skilled use of tool outputs that display risk calculations and estimations of heart age for the patient in graphic numerical and visual formula- tions. Yet, as Alaszewski (2010, p. 104) points out ‘Individuals are not passive recipients of [risk] information …’. Ethnographic findings from a post-consultation interview with the patient reveal how he reframes and reformulates the HealthTracker risk calculations in light of his own experience and perspective to dilute their significance. It appears that the value of risk calculations is not fixed and immuta- ble but subject to differing interpretations informed by different values and perspectives (Sarangi & Candlin, 2003). Risk communication that emphasises the one-way flow of knowledge from doctor to patient albeit enhanced by HealthTracker cannot take such different perspec- tives into account. Effective risk talk and the effective deployment of HealthTracker may rely upon the negotiation of disparate participant values and experiences by way of collaborative, co-constructed doctor- patient interaction.
The computer in the consultation
By examining an instance of electronically mediated risk communica- tion our case study is also illustrative of the worldwide phenomenon It’s just statistics ... 287 of the computer in the primary care consultation. In Australia computerisation in general practice is all but complete with 93% of doctors using a computer on their desks for a range of functions includ- ing decision support (Pearce, Dwan, Arnold, Phillips, & Trumble, 2009). In the UK computerisation of the clinical encounter has long been in progress and the electronic patient record is now used pervasively in general practice to support patient care (Swingelhurst, Roberts, & Greenhalgh, 2011). The presence of the computer in the general practice consultation has changed the nature of doctor-patient interaction placing new demands on the communicative expertise of doctors. The computer’s presence introduces a potential third party into the previous dyadic relationship of doctor and patient opening the way for a variety of complex rela- tional configurations and reconfigurations that would not otherwise have been possible. For example, within the newly configured ‘par- ticipation framework’ (Goffman, 1981) of doctor, patient and computer the doctor may act to position the computer as a ‘bystander’ (Goffman, 1981) to the interaction as he or she engages with the patient. Alternatively, prompted by the computer the doctor may side-line the patient to interact with the screen. The presence of the computer also provides the patient with the opportunity to disengage from what is going on as doctor and computer interact. Yet the triadic participation structure that the computer affords is of itself neither advantageous nor disadvantageous to the interpersonal relationship between doctor and patient nor to the purposeful trajectory of a consultation. The computer is an inanimate actor in the consultation. Whilst not devoid of influ- ence it is a neutral tool that is brought into the interaction and enabled to play its part largely by the strategic work of the doctor but potentially by the actions of the patient as well. ‘Integrating technology … always requires human work to re-contextualise knowledge for different uses within complex social settings’ (Greenhalgh et al., 2009 in Swingelhurst et al., 2011, p. 4). In our case study we bring to light the discursive work that the doctor does as he strives to integrate HelathTracker purposefully and strategi- cally into the consultation so as to persuade the patient of his CVD risk. We then go on to look beyond the immediate context of the con- sultation to the patient’s social setting so as to access those life-world perceptions, values and attitudes that shape the patient’s response to risk. Finally, in light of insights derived from analysis of the discourse of the consultation as well as from our ethnographic interview with the patient, we consider the nature of communicative expertise that is 288 Catherine O’Grady, et al. required for the effective integration of tools such as HealthTracker into the clinical encounter.
The case study
Clinical context The patient is a 66 year-old-man who has been taking medication for high blood pressure for 40 years. He has been seeing the participating GP for two years. He is visiting the doctor today for a blood pressure check following change of medication and for comprehensive blood tests including a fasting blood sugar test following a previous high blood sugar reading.
The computer as participant
Throughout the consultation the doctor is seated in a swivel chair that allows for easy shifts in body orientation and gaze between the patient in person and ‘the patient inscribed’ (Robinson, 1998) in records and data on the computer screen. The patient is seated at the end of the desk and to one side of the computer. His line of vision towards the screen is unimpeded. This configuration allows for potential triadic engagement between doctor, patient and HealthTracker. Tracker is a mutually avail- able information resource. Space however is more complex than physical layout or physical envi- ronment. Space is socially constructed and participants in interaction ‘do’ or ‘enact’ space through their relationships with each other, with objects and with what is going on at a particular moment (Jones, 2010). Through the meanings they assign to various tools introduced into the consultation space such as blood pressure monitor, computer screen or HealthTracker, doctor and patient create and circumscribe a ‘sphere of attention’ (Jones, 2010). This sphere of attention may expand or con- tract or otherwise diversify as they shift their orientation to objects to each other and to what it is that is going on. Thus, for a time in this consultation the doctor enacts a ‘participa- tion framework’ (Goffman, 1981) or ‘relational space’ (Jones, 2010) that sidelines the computer screen as he engages exclusively with the patient. At other times his sphere of attention narrows to exclude the patient or expands to encompass patient and HealthTracker so that all are potential participants in the interaction. The patient too is engaged in these shifting reconfigurations of space that display his engagement or disengagement with HealthTracker and with the talk of risk that is going on. It’s just statistics ... 289
Doctor as mediator
As the consultation gets under way, the patient raises concern about symptoms associated with withdrawal from a longstanding migraine medication. Once the doctor has reassured the patient about this mat- ter, he turns to the tasks of taking the patient’s blood pressure and extracting blood. We pick up the interaction from this point, focusing on three ‘critical moments’ (Candlin, 1987) to trace a risk talk trajec- tory that culminates in the deployment of HealthTracker to provide the patient with visual formulations of his absolute CVD risk and heart age. In this way we bring to light the doctor’s discursive responses to the challenge of mediating between HealthTracker and the patient.
Consultation extract 1: Introducing risk factors
Talk Action Screen information
23 D Let’s see D swivels chair to face desk P’s electronic drawers at patient’s side health record open on screen 23 I guess because the sugar Takes tools for extracting was a bit high there’s a blood from drawer as speaks very high chance that you might have diabetes so that’s why the three sugar tests today yeah : 24 P Yep 25 D (inaudible) Puts tools on desk; places hands on knees. Directs gaze towards Blood Pressure monitor but legs and torso are oriented to P D One fifty five seventy six so that’s a bit high 26 P ((nods)) P looks into middle distance; Barely perceptible nod 27 D What have we got you D turns head to glance at on at the moment (.) screen; legs, torso remain we’ve got the ten one oriented towards P as fits sixty yeah tourniquet 28 P I don’t know what the dosage is
(continued) 290 Catherine O’Grady, et al.
29 D I only give you two packs last time yeah : 30 P Yeah two packs 31 D ( might have to go) to the higher strength one yeah What I’m going to do D swabs arm of P in today (.) is using one of preparation for extracting the software called health blood Tracker (.) have a look at your cardiovascular risk factors yeah : 32 P Yeah 33 D Because looks like you’re D prepares syringe; P with probably at a high risk head raised directs gaze anyway towards middle distance; arm extended awaiting insertion of needle D … blood pressure’s a D continues to prepare bit high you’re being syringe. P shifts body very overweight and let’s see slightly away from D and sugar being a bit high raises eyes towards ceiling yeah
Throughout this sequence the computer is open on the desk display- ing the patient’s electronic health record. Whilst the doctor glances momentarily towards the screen (27) to check the dosage of the patient’s blood pressure medication his legs and torso remain directed towards the patient, communicating that his primary orientation is to the patient and the tasks at hand. The computer that houses HealthTracker remains largely outside his sphere of attention. It is positioned as a ‘bystander’ (Goffman, 1981) to the interaction that is going on. Yet CVD risk and the potential for usefully deploying HealthTracker in this consultation are clearly on the doctor’s mind as the following extract from his post-consultation interview confirms.
… I was thinking I’d done the blood pressure, I was about to take the blood and I may be thinking about ‘Hey you know this fellow you know you can see he’s obese, he’s got blood pressure and his sugars so there you go he’s the cardiovascular risk guy (.) maybe we can use the Tracker.
From turn 23 the doctor works to bring the topic of risk factors into the discourse of the consultation. As he goes about the task of preparing to It’s just statistics ... 291 extract blood, he simultaneously uses the strategy of ‘thinking aloud’ (Dowell, Stubbe, Scott-Dowell, Macdonald, & Dew, 2013) to share his reasoning about the likelihood that the patient has diabetes (23), and to refer to the elevated blood pressure reading (25) that might neces- sitate an increase in medication. Against the backdrop of these cumu- lating risk factors, he refers to HealthTracker as a tool for examining the patient’s level of risk (31), bringing the sequence to a close with a summarising statement of known factors that already place the patient in the high risk category (33). However this risk talk has no observable effect on the patient. Whilst he acknowledges the likelihood of a diagnosis of diabetes with an immediate and unqualified ‘yep’ (24), his responses to the doctor’s utterances are otherwise minimal. At turn 26 he receives news of his elevated blood pressure reading with a barely perceptible nod. Further, across this sequence he directs his gaze into the middle distance as if to create a private interactional space that enables him to disengage from the interaction. This apparent disengagement from talk of risk might be accounted for by the patient’s discomfort with the process of having blood taken. As Heath (2006) has shown, patients almost invariably maintain a ‘middle distance orientation’ whilst subject to clinical procedures as a means to detach themselves from what is going on. Talk at such moments of detachment is unlikely to have impact as the following extract from the patient’s interview suggests:
Interviewer: … and how did you feel when he said that you’re prob- ably high risk Patient: Oh Interviewer: Have you been have you heard that before Patient: I don’t remember him even saying that. I think when you’re facing a needle in the eye, you know (.) you tend not to tend (laughs) I’m more worried about getting jabbed in the arm than (.) you know
But it also appears that the patient is somewhat immured against risk talk. He goes on to explain that he constantly hears about his risk. The likelihood that he is at high risk is not news to him.
Patient: I mean I hear things like high risk all the time you know. Waking up in the morning’s always a positive thing for me. 292 Catherine O’Grady, et al.
For the discourse analyst the patient’s apparent disengagement from interaction as the doctor talks of risk represents a moment of uncer- tainty where the patient is difficult to read. Can his disengagement be accounted for by his need to detach himself from the process of hav- ing blood extracted as Heath (2006) would suggest? Or is he indeed hardened against risk talk and taking a fatalistic stance as ethnography implies? Similarly, the patient’s action at this particular moment may pre- sent a problem of interpretation for the participating doctor. What is the patient thinking? How can the doctor raise the stakes so that the patient is more likely to engage with the matter of risk? Will bringing HealthTracker into the interaction have some impact?
Deploying HealthTracker
As the consultation proceeds, the doctor turns his attention to HealthTracker as a means to involve the patient with the matter of risk. Following a number of turns in which the doctor completes the task of extracting the patient’s blood, revisits the need for a urine test and provides the patient with a sugar drink in preparation for his upcom- ing blood sugar test, he initiates an interactional sequence in which, prompted by HealthTracker, he gathers data that the Tracker will use to update and modify the patient’s risk profile.
Consultation extract 2: Gathering risk data
Up until this point in the consultation the computer that houses HealthTracker has been sidelined from a ‘participation framework’ (Goffman, 1981) encompassing doctor and patient only. But now the doctor moves to reconfigure this framework so as to bring HealthTracker into the interaction as an active and ‘ratified participant’ (Goffman, 1981).
Talk Action Screen information
57 D And while you’re D swivels chair towards Tracker is doing that (inaudible) computer; directs gaze to sitting on right might use this software computer screen P turns head hand side of to have a look at your slightly towards screen; sugar screen awaiting cardiovascular risk drink in hand activation It’s just statistics ... 293
We use the Health D turns head briefly towards Tracker which is on P then returns gaze to screen trial with the university under the torpedo study (…) when you (.)finish off that drink first 58 P ((nods)) P nods; continues drinking from bottle; D attends to screen tracing screen prompts with left index finger P directs gaze to middle distance 59 D Does anyone in your D’s index finger on screen. D Tracker family have a very turns head to address patient prompts high cholesterol level? then back to screen 60 P Cholesterol not that P continues to drink; gaze I’m aware[ of directed at middle distance 61 D [not that you’re aware of ok 62 P I mean I’ve got members P sustain gaze at middle of my family that have distance got heaps of other problems but not that I’m aware of cholesterol 63 D Right ok and none of D gazes to screen Tracker prompts your family is probably (inaudible) forty five or fifty five with heart attack (.) coronary heart disease 64 P My father died at fifty D directs gaze towards P then seven back to screen 65 D Fifty seven 66 P With heart attack P places empty bottle on desk 67 D First degree; did he D sustains gaze on screen as P have a lot of chest pain responds to questions before : 68 P Yeah always had chest P redirects gaze to middle pain distance as he answers questions 69 D For a few years before that= 70 P =yep P sustains gaze at middle distance 71 D So that’s probably early D looks at screen coronary [heart disease
(continued) 294 Catherine O’Grady, et al.
72 P [Yep coronary that’s P raises head slightly as gazes what he had to middle distance 73 D Younger than sixty yeah 74 P Yep= 75 D = so that’s your father= 76 P =yep 77 D So that’s a tick for that Swivels chair to face patient. Tracker prompts Takes tape measure from D to check BMI drawer and waist
At turn 57 the doctor swivels his chair towards the computer, commu- nicating a shift in his dominant physical orientation from the patient to the screen. In this way he signals the inclusion of HealthTracker as an active participant in the interaction. In the same turn the doctor works strategically to ratify HealthTracker, that is to invest it with the authority to act as a bona fide participant in risk talk. By deploying the inclusive institutional pronoun form ‘we’ he invokes an ‘institutional identity’ (Sarangi & Roberts, 1999) to speak as a member of the insti- tution of general practice rather than as an individual. In this way he invests use of HealthTracker with institutional authority. In the same utterance, HealthTracker is further endorsed with reference to the uni- versity study of which it is a part. At turn 58, the patient acknowledges this endorsement of HealthTracker with a nod. Then, as the doctor attends to the screen, tracing the Tracker checklist with his index finger, the patient redirects his gaze into the middle distance, a position that is sustained across the entire sequence. However, this shift in gaze does not appear to signify the patient’s disengagement from the interaction or from the activity of risk information gathering that is going on. As Greatbatch (2006) points out the diverting of gaze by a patient whilst the doctor is engaged with computer related tasks can be seen as a means to reduce the interactional demands on the doctor. Here the patient’s sustained middle distance gaze functions to free the doctor to attend to the prompts and checklist questions as they appear on the HealthTracker screen. Yet, despite their physical misalignment doctor and patient continue to occupy the same ‘relational space’ (Jones, 2010). As the doctor orients to the screen and the patient looks into the distance they engage with each other and with HealthTracker as they go about the task of co-constructing the risk information that Tracker requires. From turn 59, prompted by HealthTracker the doctor initiates a question answer sequence to explore risk factors associated with the patient’s family history including high cholesterol level (59) and early It’s just statistics ... 295 age heart attack (63). At turn 64 the patient states that his father died at 57. This prompts the doctor to redirect his gaze momentarily from screen to patient in an action that displays that this news is of sig- nificance to him. Then, across ensuing turns he pursues confirmation of this newly disclosed risk information with reiterated confirmation checks (65, 67, 69, 71, 73, 75) to ascertain that a family history of early coronary disease involving a first degree relative is indeed a risk fac- tor for his patient. The patient’s latched and overlapping responses to the doctor’s confirmation checks (70, 72, 75) indicate his involvement in the interaction and with the risk factor topic. At this point in the consultation he is engaged in the collaborative task of building his risk profile. HealthTracker’s diagnostic prompting, mediated by the doctor has enabled a collaborative conversation in which doctor and patient are mutually involved with factors that may place the latter at high risk. In the moments that follow, and prompted by HealthTracker the doctor weighs the patient so as to calculate his body mass index (BMI) and meas- ures his waist circumference. Doctor and patient then resume their seats as the doctor enters new and updated risk factor data into the computer so that HealthTracker might calculate the patient’s absolute CVD risk.
Mobilising HealthTracker to communicate risk
The stage is now set to mobilise HealthTracker to the task of engaging the patient in recognising his current level of absolute risk as well as his risk projections. As the consultation proceeds the doctor draws upon the combined multi- modal resources of wording, gesture, gaze, pausing and silence as he works to deploy HealthTracker calculations and semiotic tools in a way that might impact on the patient. His communicative expertise in mediating between HealthTracker risk information and the patient is now on display.
Consultation extract 3: Mobilising HealthTracker
Talk Action Screen information
85 D So let’s go back and D indicates data on screen with Tracker open have a look at this left hand. P shifts torso slightly on screen and turns head to direct gaze to screen. Arms unfold and then fold again as he leans a little closer towards screen
(continued) 296 Catherine O’Grady, et al.
D Ahh cardiovascular risk D inputs data Tracker profile I need to add calculates on your family history because that hasn’t (.) before sixty yeah (…) and we haven’t got your urine yet so when we check that yeah D So looking at that D presses key to refresh screen you’re (…) just refresh that (inaudible) 86 D Your risk is sitting at Indicates data on screen with Tracker about nineteen to twenty left hand; turns head towards calculation on percent at the moment patient screen yeah 87 P Ok P sustains gaze on screen 88 D In terms of getting a P nods heart attack over the next five years (pause) 89 D Or a stroke (pause) D keeps index finger on data; sustains focused gaze on patient’s face P slow repeated nods 90 D Twenty percent is about Sustains gaze towards patient’s one in five (pause) face. Left hand closes but remains at screen. P sustains gaze on screen. Nods; slight eye brow rise 91 D So that’s (.) if you look D’s hand moves across screen Tracker risk at the colour you’re in to indicate relationship projection the red zone between data and visual on visual on screen. D looks to P and then screen to screen P gazes at screen 92 P (nods) 93 D Now this is (.) not adding Indicates screen with finger on the diabetes here. Directs gaze towards patient’s If you are having face diabetes jump to twenty five percent easily I would think And this is a little Indicates patient’s projection Heart age picture we can show on graph with left hand graph on you (.) your heart which P sustains gaze l towards screen screen is here
(continued) It’s just statistics ... 297
compared with the Slides finger across screen general population(..) at to indicate line for general your age population 94 P mm 95 D So your heart is about Directs gaze towards patient’s (…) seventy five years old face then back to screen then (.) even though you’re back to patient. Finger remains only sixty five on screen 96 P Yeah ↑ P looks at screen 97 D =Yeah this is taking all these [risk factors that we collected into[ account 98 P [Yeah yeah 99 D Your smoking history P gazes at screen your blood pressure and of course we haven’t got the urine we haven’t got the (.) I’m just going to save this (.) haven’t got the urine results yet (inaudible) I’ll see if I can print one out for you to look at yeah (.) I’ll just print the picture instead (inaudible) cardiovascular projection D So this is give you an Runs finger over screen idea about where you P looks to screen are at yeah : D What we need to Runs finger over screen monitor you here yeah : P continues to gaze at screen
At turn 85, using his left hand to draw attention to the screen, the doctor employs the inclusive form ‘Let’s’ to invite the patient to join him in examining the HealthTracker outputs. In response the patient reorients to the screen, shifting his torso slightly towards the computer, redirecting his gaze and leaning forward to display his attention. At the same time he unfolds and refolds his arms in a move that suggests he is settling in to take in the information that HealthTracker is about to offer. Doctor and patient are mutually engaged with Tracker. Then, as the HealthTracker calculation appears on the screen, the doctor turns his head towards the patient, representing the calculation 298 Catherine O’Grady, et al. verbally to state that the patient has a 19–20% chance of having a heart attack or stroke over the next five years. By way of strategic pausing he fragments this statement into a series of discrete utterances that draw attention to each aspect of the message (86, 88, 89). Whilst the patient’s responses are largely minimal, including the minimal acknowledge- ment token ‘OK’ and a single slight nod, the slow repeated nods (89) with which he receipts news of possible stroke suggest that he is not simply attending but absorbing and considering this information. In the next turn (90) the doctor moves to intensify the patient’s engagement by reformulating the HealthTracker calculation of a 20% risk as a one in five chance. This reframing of the patient’s risk projec- tion in more accessible terms is accompanied by the doctor’s sustained gaze towards the patient’s face. Gaze direction together with a further strategically placed pause invests the doctor’s risk reformulation with added importance. The patient’s raised eyebrow response (90) signifies that it has had some effect. In light of these signs of the patient’s receptiveness, the doctor acts to mobilise the semiotic formulations of the patient’s risk that HealthTracker affords. At turn 91 he directs the patient’s attention to the visual representation of his risk projection that places him in the high risk red zone. Then, as the patient nods, gaze fixed on the screen the doctor redirects his own gaze from patient to screen and back to the patient. As his gaze settles on the patient’s face in a way that intensifies his message, he states that if the patient has diabetes his risk will jump to 25% (93). Whilst the patient continues to gaze at the screen, he offers no verbal or visual response. In a final move the doctor mobilises HealthTracker’s visual represen- tation of the patient’s heart age in relation to the general population. Once again he strategically deploys gesture, gaze direction, and verbal reformulation to strengthen the effect of HealthTracker’s message. As turn 93 continues he uses his index finger to trace the heart age projec- tions on the screen so as to highlight the contrast between the patient’s projection and that for the general population. The patient attends but responds with a minimal ‘mm’. Finally (95) as the doctor redirects his gaze from patient to screen and then back to the patient in order to emphasise his message, he brings his deployment of HealthTracker towards its completion with an upshot that summarises what this risk information means:
So your heart is about (…) seventy five years old (.) even though you’re only sixty five. It’s just statistics ... 299
This simply formulated upshot appears to have considerable impact on the patient. The patient’s response ‘yeah’ (96), marked by a sharp rise in tone constitutes a relatively strong ‘news receipt’ (Heritage, 1984) that displays recognition of the significance of this news. As the consultation draws to its close it seems that doctor and patient are in alignment with each other in their mutual understanding of the significance of the patient’s high CVD risk status. Such apparent mutu- ality is the foundation upon which doctor and patient might go on to make shared decisions about how the patient’s risk is to be managed. But as Candlin points out (2002, p. 25) ‘… mutual understanding is always a shifting and temporary matter’ and mutual agreement is ‘… an unstable state of becoming’. Alignment with the medical perspective during a consultation cannot be taken as evidence of on-going concordance. Behind the risk calculations and projections communicated to this patient in the context of the consultation lies his life-world accessed through ethnography. As finding from the post-consultation interview with the patient suggest, the meaning and value of risk calculations are not fixed and immutable but subject to reinterpretation by the patient in light of his values, attitudes, and life-world perceptions.
The patient’s perspective
To what extent has HealthTracker generated knowledge of the patient’s absolute cardiovascular risk been made tractable for the patient by the actions of the doctor? What value does the patient bring to this knowl- edge? What is its importance to him? During the interview conducted with the patient in the days follow- ing his consultation he consistently reframes HealthTracker calculations and projections in ways that dilute their significance. For example, when asked to comment on the projection of a one in four chance of having a heart attack or stroke in the next five years he takes a positive stance to reformulate this calculation as a three in four chance that he will not experience such an event.
… I’m not a gambling person, but I know statistics reasonably well, and I know that, you know, okay, you’ve got one chance in four, but that means you’ve also got three chances in four that you’re not going to get it, so the odds are, you know, statistically, you’re alright (laughs) you know, so you’ve got more chances of not having a heart attack or a stroke than you have. 300 Catherine O’Grady, et al.
For the patient the upshot of this statistical information is that his chance of not having a cardiovascular event far outweighs the chance that he will. In a similar way, he takes the HealthTracker calculation of his heart age that seemed to have considerable impact during the con- sultation and reframes it in a positive light.
… you know I’ve only got a heart of a 75yearold and not a 95-year- old. I’m kind of a glass half-full sort of a guy, you know.
Clearly, this patient is not a passive recipient of risk information. Whilst he does not refute the risk calculations and projections that have been presented to him, the meaning that he invests in them is at odds with the medical perspective. Patient and doctor frame risk knowledge in different ways to give risk calculations different valence and such disparate perspectives are likely to affect the patient’s commitment to management advice as the following interview extract suggests:
… it’s no good saying we’ll change your lifestyle … I’m 66 years old … I have a lifestyle, you know. I’m not an alcoholic (.) I don’t over-drink (.) I don’t you know I don’t overeat. I’m just, just a big bloke. … Look, I didn’t walk out of there thinking, oh, I’m only going to eat salad and, you know, drink water for the rest of my life.
HealthTracker has afforded the patient the opportunity to consider those statistical calculations and projections that are indicative of his CVD risk. But it appears that contemplation of health risk as repre- sented in statistical terms may not lead easily in a linear fashion to mutual management decisions or to determination to take action to reduce that risk. Whilst the patient appeared to defer to the authority of HealthTracker during the consultation, in the post-consultation period his life-world perceptions intervene and the impact of risk knowledge begins to decay.
Concluding comment
In this case study, discourse analysis has brought to light the doc- tor’s skilled use of language and other semiotic means to mobilise HealthTracker to the task of informing the patient about his absolute CVD risk. Yet, as ethnographic accounts have shown, HealthTracker risk information mediated by the doctor has not been sufficient to engage the patient in acknowledging and acting upon his high risk status. Risk It’s just statistics ... 301 is not absolute but subject to different interpretations and the impact of risk knowledge on the patient is tempered by his own perceptions. What then is the nature of communicative expertise required for the effective deployment of tools such as HealthTracker? The participating doctor in this case study adheres largely to a ‘rational model of risk com- munication’ (Alaszewski, 2010, p. 103) that characterises risk commu- nication as the flow of risk knowledge from the knowledgeable expert, in this case HealthTracker mediated by the doctor, to the less informed patient. But such a one-way flow of information does not allow for the patient’s perspective to enter the discourse of the consultation. The effective communication of risk may require that tools such as HealthTracker be integrated into collaborative co-constructed interac- tion whereby the patient’s perceptions and accounts can be accessed, discussed, and negotiated. As Candlin and Candlin state (2002, p. 103) ‘Expertise in the manage- ment of risk is not solely – or even primarily – a matter of knowledge but of discursive negotiation among participants’ values and experiences’.
References
Alaszewski, A. (2010). Risk communication: Identifying the importance of social context. Health, Risk & Society, 7(2), 101–105. Candlin, C. (1987). Explaining moments of conflict in discourse. In R. Steele & T. Threadgold (Eds.), Language Topics: Essays in Honour of Michael Halliday. Amsterdam: John Benjamin. Candlin, C. (2002). Alterity, perspective and mutuality in LSP research & prac- tice. In M. Gotti, D. Heller & M. Dossena (Eds.), Conflict and Negotiation in Specialized Texts. Bern: Peter Lang Verlag. Candlin, C., & Candlin, S. (2002). Discourse, expertise, and the management of risk in health care settings. Research on Language & Social Interaction, 35(2), 115–137. Dowell, A., Stubbe, M., Scott-Dowell, K., Macdonald, L., & Dew, K. (2013). Talking with the alien: Interaction with computers in the GP consultation. Australian Journal of Primary Health, 19, 275–282. Goffman, E. (1981). Forms of talk. Oxford: Blackwell. Greatbatch, D. (2006). Prescriptions and prescribing: Co-ordinating talk-and text-based activities. In J. Heritage & D. Maynard (Eds.), Communication in Medical care. Interaction Between Primary care Physicians and Patients (pp. 313– 339). Cambridge: Cambridge University Press. Greenhalgh, T., Potts, H., Wong, G., Bark, P., & Swingelhurst, D. (2009). Tensions and paradoxes in electronic patient record research: A systematic literature review using the meta-narrative method. The Millbank Quarterly, 87(4), 729–788. Gumperz, J. (1999). On interactional sociolinguistic method. In S. Sarangi & C. Roberts (Eds.), Talk, Work, and Institutional Order. Berlin; New York: Mouton de Gruyter. 302 Catherine O’Grady, et al.
Heath, C. (2006). Body work: The collaborative production of the clinical object. In J. Heritage & D. Maynard (Eds.), Communication in Medical care. Interactions Between Primary care Physicians and Patients (pp. 185–213). Cambridge: Cambridge University Press. Heritage, J. (1984). A change-of-state token and aspects of its sequential place- ment. In J. Atkinson & J. Heritage (Eds.), Structures of Social Action: Studies in Conversational Analysis (pp. 299–345). Cambridge: Cambridge University Press. Jones, R. (2010). Cyberspace and physical space: Attention structures in com- puter-mediated communication. In A. Jaworski & C. Thurlow (Eds.), Semiotic Landscapes: Language, Image, Space (pp. 151–167). London: Continuum. Patel, B., Patel,A., Jan, S., Usherwood, T., Harris, M., Panaretto, K., Zwar, N., Redfern, J., Jansen, J., Doust, J., Peiris, D. (2014). A multifaceted quality improve- ment intervention for CVD risk management in Australian primary healthcare: A protocol for a process evaluation. Implementation Science, 9(187), pp 1–12. Pearce, C., Dwan, K., Arnold, M., Phillips, C., & Trumble, S. (2009). Doctor, patient and computer – A framework for the new consultation. International Journal of Medical Informatics, 78, 32–38. Peiris, D., Joshi, R., Webster, R., Groenestein, P., Usherwood, T., Heeley, E., Turnbull, F., Lipman, A., Patel, A. (2009). An electronic clinical decision support tool to assist primary care providers in cardiovascular disease risk management: Development and mixed methods evaluation. Journal of Medical Internet Research, 11(4). Peiris,D., Usherwood, T., Panaretto, K., Harris, M., Hunt, J., Redfern, J., Zwar, N., Colagiuri,S., Hayman,N., Lo, S., Patel, B., Lyford, M., MacMahon, S., Neal, B., Sullivan, D., Cass, A., Jackson, R., Patel, A. (2015). Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: The treatment of cardiovascular risk using electronic deci- sion support cluster-randomized trial. Circulation: Cardiovascular Quality and Outcomes, 8(1), 87–95. Roberts, C., & Sarangi, S. (2005). Theme-oriented discourse analysis of medical encounters. Medical Education, 39, 632–640. Robinson, J. (1998). Getting down to business: Talk, gaze and body orienta- tion during openings of doctor-patient consultations. Journal of the Society for Human Communications Research, 25(1), 97–123. Sarangi, S., & Candlin, C. (2003). Categorization and explanation of risk: A dis- course analytical perspective. Health, Risk & Society, 5(2), 115–124. Sarangi, S., & Roberts, C. (1999). The dynamics of interactional and institutional orders in work-related settings. In S. Sarangi & C. Roberts (Eds.), Talk, Work and Institutional Order (pp. 1–57). Berlin: Mouton. Swingelhurst, D., Roberts, C., & Greenhalgh, T. (2011). Opening up the ‘black box’ of the electronic patient record: A linguistic ethnographic study in general practice. Communication & Medicine, 8(1), 3–15.
Appendix
Table A17.1 Transcription conventions
[ A square bracket indicates the point at which a current speaker’s utterance is overlapped by the talk of another. It’s just statistics ... 303
= Where the turns of two different speakers are connected by two equal signs, this indicates that the second followed the first with no discernable silence between them, or was ‘latched’ to it.
(.) A dot in parenthesis indicates a micro-pause that is hearable but not measurable. : : Colons indicate the stretching or prolonging of the sound that immediately precedes them. Yes: If the letters preceding a colon are underlined, this indicates that there is a falling intonation contour; you can hear the pitch turn downwards. Yes: If the colon itself is underlined, this indicates a rising intonation contour; you can hear the pitch turn upward. ↑ An arrow indicates a strong fall or rise in pitch in accordance with the direction of the arrow. (( )) Double parenthesis are used to mark descriptions of events e.g. ((telephone rings)). (word) Words in parenthesis indicates uncertainty on the transcriber’s part but represents a likely possibility. Chapter 7. Modelled cost-effectiveness of a multifaceted health information technology delivered within an Australian Primary Health Networks setting to improve cardiovascular disease prevention
7.1 Chapter overview
This chapter reports the cost effectiveness of HealthTracker over a 5-year period delivered by the Primary Health Networks (PHNs) in the state of New South Wales (NSW), Australia.
7.2 Introduction
Quality issues plague Australia’s primary healthcare system with large deficiencies in guideline recommended care.1 The need for high quality health systems is a major focus of the Australian government’s healthcare reform.2 The ultimate goal is to improve population health outcomes and address health inequities by providing continuous, effective, efficient and accessible care. The establishment of 31 government-funded PHNs Australia-wide to replace Medicare Locals in July of 2015 was a major effort of the Australian government’s healthcare reform. PHNs main objectives are to improve efficiency and effectiveness through coordination of care between the primary healthcare sector and local hospital networks (LHNs).3
A major role of the PHN is to act as a commissioning agency working closely with general practitioners and other primary healthcare services to adopt quality improvement (QI) initiatives with the aim of improving care delivery and health outcomes while reducing hospital costs.4,5 Booth and Boxall define commissioning as “strategic purchasing decisions based on local health needs, priorities and service availability and quality” with the main activities being strategic planning, contracting services and monitoring and evaluation (figure 7.1).6 PHNs receive funding from the Australian Federal government after they have approved the ‘needs assessment and service planning’ documents for their local regions. The primary emphasis is on the
212 delivery of high quality, low-cost services through acquiring funding for specific programmes from a competitive and limited pool of flexible funding.7
Figure 7.1: Commissioning elements8
Cardiovascular disease (CVD) is a leading cause of death and disability and one of the biggest health problems facing the Australian population and their health systems despite improvements in diagnosis and treatments in the last few decades.9,10 Aboriginal and Torres Strait Islander people experience rates of CVD hospitalisation and death that are twice as high compared to other Australians. Healthcare expenditure – for hospital services, out-of-hospital medical care and pharmacological
213 agents – resulting from cardiovascular diseases (CVDs) represents a level of economic burden that exceeds that of any other disease group in Australia (12% of all allocated healthcare expenditure).11,12 One study found, approximately one-fifth of the Australian population aged 45-74 years (~1.4 million people) were at high risk of developing a CVD event within the next five years.13 A large proportion of these events are preventable by interventions that target both the population and individuals in assessing and managing absolute CVD risk in primary healthcare settings.14
One of the major barriers to prevention of CVD is lack of evidence-based risk assessment and prescribing of recommended preventive medications to the high CVD risk population. Australian clinical guidelines recommend that assessment for CVD prevention and management should be based on a combination of risk factors (the “absolute risk” approach) rather than treating risk factors such as elevated blood pressure and cholesterol in isolation.15,16 This enables early identification and treatment for primary prevention of CVD for individuals at high risk at the primary healthcare level. In combination with well-established secondary prevention recommendations for people who have experienced a previous CVD event, the absolute risk approach offers an efficient strategy from a population perspective for reducing further CVD events (mortality and morbidity) with recommended medications.17
Adherence to clinical guidelines for CVD assessment and management at a primary healthcare level is suboptimal. Studies have found that only 50% of adults attending Australian primary healthcare are screened for CVD risk factors according to best practice, and only 40% of those identified as high risk for CVD are prescribed preventive medications optimally consistent with guideline recommendations.18,19 These studies indicate a large evidence practice gap in CVD management, and the inadequacy of current CVD prevention programs in Australia. Therefore, identifying cost-effective quality improvement strategies that encourage increased uptake of absolute risk management by healthcare providers are warranted in primary healthcare settings.
214 The US Community Preventive Services Task Force has demonstrated, through a systematic review, sufficient evidence that clinical decisions support systems (CDSSs) are effective in improving screening for CVD risk factors, clinical tests, and prescribing recommended treatments with larger effects when CDSS is combined with other interventions.20-22 However, the results are inconsistent in improving CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein (LDL), and haemoglobin A1C levels (HbA1C). This could be a result of sparse data collection and insufficient statistical power for clinical outcomes. Still, it is hypothesised that multifaceted CDSS preventive strategies could reduce CVD risk factors that would in turn reduce morbidity and mortality and increase the quantity and quality of years lived. Health information technology (HIT) is being recognised worldwide as a key enabler in transforming healthcare systems to control costs and improve outcomes.23,24
The Task Force also conducted a systematic economic review of CDSSs on CVD prevention and what drives intervention costs and benefits from published literature.25 Three US-based studies performed long term cost-effectiveness analysis of randomised controlled trials of CDSS interventions to improve diabetes care through improvements in clinical risk factors. The multifaceted interventions included the provision of personalised management recommendations in primary healthcare settings that improved intermediate risk factors, such as HbA1C, systolic blood pressure (SBP), and LDL, resulting in modest improvements in long-term health outcomes.26-28 Two interventions were considered cost-effective, with incremental cost-effectiveness ratios of $16,500 and $49,000 per quality-adjusted life year (QALY) saved, whilst another was not cost-effective at $143,000 per quality-adjusted life year saved.26-28 Similar time horizons were used for net cost and adjusted life years lived, and only one study did not include annual operating cost of the intervention.25,26 The interventions that demonstrated cost effectiveness also included financial incentives to the physicians in using the CDSS27 and diabetes consultation by practice nurse, recall system, and feedback on performance.28 However, the variation in value of the interventions could not be fully explained. These studies demonstrated potential of the multifaceted nature of the interventions to reduce
215 morbidity and mortality over large populations at relatively low cost,29,30 yet the cost- effectiveness of CDSS on CVD prevention in an Australian context has been limited.
To avoid repetition, the details of the intervention, HealthTracker, are presented in chapters 3-5. For relevance to this chapter, I present information necessary for the cost-effectiveness analysis. The intervention was evaluated in The Treatment of Cardiovascular Risk using Electronic Decision Support (TORPEDO) study, a cluster- randomised controlled trial in the Australian primary healthcare settings.31 Health services with the intervention demonstrated a 25% relative (10% absolute) improvement in CVD risk factor screening and a 33% relative (5% absolute) improvement in prescribing rates for individuals at high CVD risk who were not prescribed optimal recommended medicines at baseline. The study was not powered for effects on modifiable risk factor levels, however trials have consistently demonstrated that use of blood pressure and blood cholesterol lowering treatments lead to a relative risk reduction in both CVD morbidity and mortality.32 The intervention aimed at optimising recommended treatments in high CVD risk patients who attend primary healthcare.
The TORPEDO trial did not demonstrate significant improvements in prescribing to high CVD risk patients attending those health services that had the provision of HealthTracker compared to those that continued with usual care. In the post-hoc trial analysis, undertreated high CVD risk patients had a significant increase in prescribing in the intervention group as mentioned above. The findings in my previous chapters highlight several issues that contributed to this result; in particular the complexity of implementation and adoption of the intervention at the organisational, healthcare provider/staff and patient levels. With an appropriately implemented intervention, a modelled cost-effectiveness analysis will be able to provide the economic viability to inform health policy in Australia.
As I have mentioned in chapter 2, a priority for the Australian government is the commitment to investing in implementation of sustainable multifaceted HIT as an enabler for transforming healthcare delivery. However, there are limited cost- effectiveness analyses on large scale trials of HIT to improve CVD outcomes in 216 Australian primary healthcare. The basis of my economic evaluation provides a ‘linear approximation’33 on potential impact the intervention can have within a PHN population. We examine both the total costs of the intervention and its cost- effectiveness over a 5-year period, incorporating CVD related morbidity for the high CVD risk population.
7.2 Methods
Study design and setting
We conducted a modelled cost-effectiveness analysis of HealthTracker compared with usual care from the perspective of the Australian health system for individuals at high CVD risk. This perspective was chosen to assess long-term benefits of the intervention and the value of implementing the intervention in primary healthcare settings via the PHN. This will inform healthcare providers, health services and policy makers of the resource allocation and health benefits provided by implementing the intervention.
Definition of high CVD risk Based on Australian guidelines, high CVD risk was defined as (1) history of CVD (diagnosis of coronary heart disease, cerebrovascular disease, or peripheral vascular disease); (2) the presence of any guideline-stipulated clinically high risk conditions such as diabetes mellitus and age >60 years, diabetes mellitus and albuminuria, stage 3B chronic kidney disease, or extreme individual risk factor elevations: systolic BP ≥ 180 mm Hg, diastolic BP ≥ 110 mm Hg, total cholesterol > 7.5 mmol [290 mg/dL]); or (3) a calculated 5-year CVD risk of >15% based on the Framingham equation.16
Intervention
In brief, HealthTracker was integrated with healthcare providers’ electronic health record (EHR) and used for any regular patients attending the 30 intervention health services.
217 Comparator
The usual care primary healthcare services continued with their usual practice of their current systems, including any access to cardiovascular related tools or participation in continuous quality improvement programs already implemented as routine patient care.
Health outcomes
The health outcome of interest was the number of CVD events measured over a 5- year period. As per current CVD management guidelines,16 the Framingham risk equation was used at baseline to predict the number of CVD events over a 5-year period for both groups. Changes in 5-year CVD risk were based on reductions in intermediate risk factors (LDL-C and SBP) using results from the TORPEDO trial over a 17-month period.
Target population
The population of interest was the number of high CVD risk individuals within an average-sized PHN. The high CVD risk cohort was derived from the TORPEDO trial.31 In line with CVD prevention guidelines, adults aged 45 years and over (35 years and over for Aboriginal and Torres Strait Islanders) were included in the analysis. The prevalence of high CVD risk was 20.3% for non-Aboriginal and 6.3% for Aboriginal peoples in the TORPEDO population.32 We calculated the mean high CVD risk population for all NSW PHNs using the PHN demographic data in 2015.34
Economic model
A decision analytic model was developed to estimate the costs and benefits over a 5-year period. Individuals at high CVD risk, Framingham risk scores, prescribing patterns and changes in intermediate risk factors were based on TORPEDO end of trial data. We divided high risk individuals into primary (those without a prior CVD event) and secondary (those previously experiencing a CVD event) prevention
218 groups. The model ran in annual cycles and costs were discounted using a 3% discount rate.35,36
Efficacy and assumptions
The model was based on the effect of the intervention on improving prescribing patterns in the high CVD risk population compared to usual care at the end of the trial (Table 7.2) and applied over five years.
Baseline CVD risk assumptions
Baseline 5-year absolute CVD risk was calculated using Framingham risk scores based on the mean population characteristics: (1) for the primary prevention group the risk was estimated to be 20%, (2) for the secondary prevention group, 5-year CVD risk was conservatively estimated to be 30%. Reductions in 5-year absolute CVD risk were based on the point estimates of the differences in the change from baseline in LDL-C and SBP between the intervention and control groups, stratified by primary and secondary CVD prevention categories after a 17-month follow-up period in the TORPEDO trial (Table 7.2). Numerous trials and meta-analyses demonstrate relative risk reductions in LDL and SBP associated with pharmacological treatment leads to a reduction in CVD events and death.37,38 These are described in detail below.
Blood pressure lowering treatment effects
We used a recent meta-analysis of trials assessing blood pressure lowering treatment effects on reduction of total major CVD events or death based on individual’s absolute risk.39 For each 5mmHg reduction in SBP, an expected risk reduction of 15% was assumed.
219 Lipid lowering treatment effects
A meta-analysis of randomised trials assessed statin effects on reduction per in major vascular events. For each 1.0 mmol/L LDL cholesterol reduction a 22% reduction in major vascular events was assumed.40
Antiplatelet effects
A meta-analysis of aspirin in primary and secondary prevention found, a 19% risk reduction (RR = 0.81) in secondary prevention of any vascular events. This effect size was assumed for the evaluation.41 For primary prevention as aspirin is not currently routinely recommended in Australian guidelines, we took a conservative approach and did not include this in the modelled effects.
Adherence
It is well known that patients stop taking recommended medication or take it less than prescribed making them nonadherent.42 By focusing on blood pressure and lipid levels in the trial at baseline and 17-month follow-up, this in effect takes sub-optimal adherence into account. Adherence rates for individuals taking aspirin for secondary prevention were based on a large meta-analysis of 376,162 patients from 20 studies assessing adherence using prescription refill frequency.43 Based on these data, the adherence rate to aspirin was assumed to be 66% for the secondary prevention group.
Costs
Intervention costs were derived from the TORPEDO trial associated with the implementation and maintenance of the tool within general practices and ACCHSs and patient level costs (hospitalisation as a result of CVD and prescription of drugs) (Table 7.3). The costs of software licenses, implementation and maintenance are assumed to be borne by the PHN and were estimated at $500 per health service per year. The costs were calculated as long-term average costs of implementation and exclude development and research costs. For medication costs, CVD medications
220 were categorised into 3 groups: lipid lowering, blood pressure lowering and antiplatelets/anticoagulants. We used the dispensed price for maximum quantity (DPMQ) and number of dispensed scripts from the Pharmaceutical Benefits Schedule 44 to estimate the annual average weighted cost of each group for the 2015 calendar year. The estimated hospital costs related to major vascular events were based on Australian Institute for Health and Welfare from 2015-2016.45
Sensitivity analysis
A series of one-way sensitivity analyses were conducted on: the discount rate (0% and 7%); assumptions on the continuing lowering of medication costs as a result of the expiration of patent protection on statins (25% and 75% reduction); and the lower and upper limits of 95% confidence intervals (CI) for changes in LDL-C between the intervention arm compared to usual care for the two high CVD risk groups (primary and secondary prevention). Although I had planned to conduct a sensitivity analyses using the lower limits of the 95% CIs for SBP, the difference in the change from baseline was non-significant and consequently usual care is assumed to be dominant under this scenario.
7.3 Results
Table 7.1 describes our hypothetical population and different treatment categories based on TOPREDO data and assumptions based on literature. We estimated the average sized eligible NSW PHN population to be 62,723, with 15,963 (25.4%) and 15,398 (24.5%) classified as primary and secondary high CVD risk individuals, respectively. Optimal medication use was 55.3% and 48.5% in the primary and secondary intervention groups, compared to 48.4% and 44.3% in the control groups respectively.
The provision of HealthTracker intervention was hypothesised to increase guide- recommended prescribing, thereby, reducing LDL-C and SBP levels. The trial results, showed a small but significant LDL-C difference in the change from baseline for both primary (0.05; 95% CI: 0.00-0.09) and secondary prevention groups (0.05; 95% CI:
221 0.01-0.10) compared to usual care. For SBP the intervention was associated with a non-significant difference in the change from baseline (0.12 mmHg; 95% CI: -0.98 to 1.23) in primary and (1.09 mmHg; 95% CI: -0.03 to 2.21) in secondary prevention groups.
Table 7.4 summarises the results in the base case. It was estimated that 168 major CVD events would occur per 1000 persons in the primary prevention population over 5 years in PHNs using HealthTracker compared to 176 CVD events in those PHNs not using the intervention (8 major CVD averted events per 1000 persons). Similarly, for the secondary prevention population, it was estimated that 259 major CVD events would occur per 1000 persons in the PHNs using HealthTracker intervention compared to 267 in those PHNs not using the intervention (8 repeat CVD events averted per 1000 persons).
The costs for medication per person will be more in the intervention group compared to the usual care as a result of increased prescribing, however this is partially offset by the reduction in hospitalisation costs for both primary and secondary prevention. The net healthcare costs per person for those PHNs with the provision of HealthTracker intervention was estimated to be $59.25 and $142.11 for the primary and secondary prevention groups, respectively. The estimated incremental cost effectiveness ratio (ICER) is $7,406 and $17,988 per CVD event averted for the primary and secondary prevention groups, respectively.
The sensitivity analyses are summarised in Table 7.5. One-way sensitivity analysis showed ICER to be moderately sensitive to the change in LDL-C using lower limits of the 95% CI in the secondary prevention group. This impacted on the number of CVD events and subsequently, hospitalisation costs. The ICERs in the primary (ICER, $7,610.83) and secondary prevention (ICER, $27,667.72) groups if using the lower limit change in LDL-C. An assumed reduction in statin costs due to patent expiration of 75% reduced the ICER to $5,104 per CVD event averted in the primary group and $14,661 per CVD event averted in the secondary group.
222 7.4 Discussion
Modelling implementation of HealthTracker within a NSW PHN population indicated potential health benefits by preventing major CVD events that could lead to improvement in population health at under AU$50,000 per DALY threshold. The estimated ICER for HealthTracker was $7,406 and $17,988.16 per CVD event averted for the primary and secondary prevention groups, respectively. The findings are limited by the following: (1) the TORPEDO trial may not have been adequately powered for changes in risk factor levels; and (2) as stated in findings from chapters 4 to 6, there are several factors at the health service, healthcare provider and patient levels that influence implementation processes and adoption of the intervention.
The effects sizes reported here were small but given the low costs of the intervention when scaled to a larger population it does result in a potentially cost-effective intervention. Clearly, a more intense effect size could make the case more compelling. The TORPEDO trial did not identify significant increases in prescribing to the high CVD risk population overall, although there was a significant increase in prescribing in the post-trial phase. There are several potential reasons for this outcome as stated in previous chapters. Chapter 4 suggests that doctors may be cautious in prescribing immediately after assessment of risk. Doctors may opt for lifestyle change before commencement, and patient acceptance of taking medications may take time. Chapters 5 and 6 reveal the complexity of the primary healthcare organisations, implementation processes, the people and risk communication in implementing and adopting HealthTracker. The mixed methods process evaluation in chapter 5 found a complex interaction between contextual factors and implementation processes that produced suboptimal effects of the intervention with different factors at play during the trial and those needed to influence sustainability of the intervention. The discourse analysis in chapter 6 found the use of the intervention as a valuable entry point to discuss CVD risk which requires skilled knowledge of the tool and absolute risk by the healthcare provider. The impact of effective risk communication on the patient required the healthcare provider to understand the patient’s values and experiences in order to collaboratively discuss and negotiate CVD management or prevention options.
223 This chapter suggests that there is guarded potential for HealthTracker to be low cost and improve CVD outcomes, but this depends on it being implemented optimally. The results of this study, along with the findings from Chapters 4, 5 and 6 suggest that HealthTracker requires a comprehensive assessment of both quantitative and qualitative outcomes to enhance several factors in improving optimal medication uptake and reducing intermediary CVD risk factors. This will aid in allocating appropriate resources for successful implementation of HIT for prevention and management of CVD. In Australia, CVD accounts for a substantial share of national health expenditures11 which can be drastically reduced with effective HIT strategies to prevent and control major risk factors for CVD.46 HIT is being promoted around the world as an enhanced model to improve delivery of care and quality while reducing health care expenditures.47,48 This has resulted in OECD countries heavily investing in HIT,49 however there is paucity in data demonstrating business value from these investments.50 There is a significant need to understand the mechanisms around how HIT can generate business value.
The analyses have several limitations. As the perspective was taken from the health system, other costs such as lost productivity for individuals due to premature morbidity and mortality were not captured. The day to day ‘operating cost’ of the tool according to the Community Preventive Services Task Force requires staff time and other resources. These costs were not captured due to the complexity and variation of health services. However, the intervention could improve care processes and possibly reduce resources needed to care for patients once intervention is optimally implemented. Finally, the major limitation is the assumption that the observed improvements in trial would be sustained over time without any variation. The model assumes no changes in adherence and compliance to prescribing in the 5 years after the trial period. It is unknown how these assumptions will be maintained into the future especially within a complex health system that “is adaptive to changes in its local environment, is composed of other complex systems and behaves in a nonlinear fashion”.51
224 Conclusions
The five-year modelling of cost effectiveness demonstrates potential to increase the value of HealthTracker if implemented by PHNs optimally. This will result in benefits for the broader health system. To further increase the potential cost-effectiveness of the intervention, it is important to take into account macro, -meso and -micro health system level components that influence adoption and sustainability. Understanding the circumstances under which the intervention is or is not cost-effective will be an important determinant of efficient resource allocation in the implementation process.
225 Table 7.1 Input variables
Estimated NSW PHN population 62,723 High CVD risk (established CVD) – HealthTracker- Usual care Reference medication profile CVD Estimated PHN population 15398 15398 Information sourced 2094 (13.6%) from the TORPEDO No treatment 1817 (11.8%) trial Antiplatelets/anticoagulants 493 (3.2%) 585 (3.8%) Blood pressure lowering 354 (2.3%) 493 (3.2%) Blood pressure lowering & 1032 (6.7%) 816 (5.3%) antiplatelets/anticoagulants Lipid lowering 1216 (7.9%) 1540 (10.0%) Lipid lowering & 1355 (8.8%) 1355 (8.8%) antiplatelets/anticoagulants Lipid lowering and blood pressure 862 (5.6%) 832 (5.4%) lowering Optimal (All three medications) 8517 (55.3%) 7445 (48.5%) High CVD risk (> 15% and clinically high risk) Estimated PHN population 15963 15963 No treatment 3097 (19.4%) 3608 (22.6%) Blood pressure lowering 1596 (10.0%) 1516 (9.5%) Lipid lowering 3528 (22.1%) 3767 (23.6%) Optimal (lipid lowering and blood 7065 (44.3%) 7732 (48.4%) pressure lowering)
Assumptions Reduction Risk reduction The Blood Pressure 15% Lowering Treatment Systolic blood pressure (SBP) 5 mmHG Trialists’ Collaboration Lancet 2014 Cholesterol Treatment Trialists’ (CTT) LDL Cholesterol 1 mmol/L 22% Collaboration Lancet 2010 Newly Antithrombotic Trialists’ Aspirin commenced 19% (ATT) Collaboration treatment Lancet 2009 Baseline 5-year absolute risk – High 30% CVD Risk (with CVD) Baseline 5-year absolute risk – High 20% CVD risk (without CVD)
226 Table 7.2 TORPEDO trial prescribing rate and intermediate outcome mean differences
Prescribing high risk Usual care patient due to HealthTracker-CVD established CVD Statins 71.8% 65.9% BP lowering 77.3% 73.8% Antiplatelets/anticoagulants 71.8% 67.7%
Prescribing high risk patient due >15% + clinically high risk
Statins 58.5% 53.7% BP lowering 70.6% 67.9% Change in mean lab Difference Mean difference Mean difference values from baseline to (95% CI) from baseline (SD) from baseline (SD) end of study
High CVD risk due to CVD
Mean low-density lipoprotein 0.05 (0.01,0.10) cholesterol (LDL-C) 0.11 (0.81) 0.06 (0.63) 0.12 (-0.98, Mean Systolic BP (SBP) 1.23) mmHg 0.54 (18.8) 0.42 (19.7)
Total high risk due to >15% + clinical high risk
Mean low-density lipoprotein 0.05 (0.00, 0.09) cholesterol (LDL-C) 0.18 (0.80) 0.13 (0.74) Mean Systolic BP (SBP) 1.09 (-0.03-2.21) mmHg 4.01 (19.4) 2.92 (20.5)
227 Table 7.3 Costs of patient level and intervention implementation
Unit price Source Assumptions (2015 AUD$)
Pharmaceutical costs (annual costs)
Lipid-lowering $292.42 PBS cost and Average annual cost weighted by medications quantity in 2015 scripts for standard daily dose for all lipid lowering drugs (simvastatin, rosuvastatin, atorvastatin, pravastatin, fluvastatin, fenofibrate, gemfibrozil, simvastatin and ezetimibe, etc)
Blood pressure $217.11 Average annual cost weighted by lowering scripts for standard daily dose for all medications types of blood pressure lowering drugs (beta blockers, calcium channel blockers, ACE inhibitors, angiotensin II antagonists, diuretics, etc)
Antiplatelet $288.32 medications
Hospitalisation costs for CVD events
Average cost – all $10,872.29 AIHW 2015 Average weighted cost CVD events (per person per year) Intervention implementation (annual costs)
Software license per $500 Software Through discussion with software health service developer developer, the price of software license per year per health service. (average - 283 Health services per PHN)
228 Table 7.4 Costs, outcomes and the incremental cost-effectiveness ratio per CVD event averted compared to usual care
CVD disease events and HealthTracker- Usual Care Difference (% economic outcomes CVD reduction/gain)
Costs and outcomes per person
High CVD risk (with CVD)
CVD events per 1000 over 5 259 267 8 years
Medication costs $2,576,864.95 $2,411,612.23 $165,252.72
Hospital costs for CVD events $2,268,676.08 $2,337,768.38 ($69,092.30)
Intervention cost (based on 283 $45,946.04 ---- $45,946.04 health services per PHN)
ICER per CVD event averted $17,988.16
(3% discounting)
High CVD risk (without CVD)
CVD events per 1000 over 5 168 176 8 years
Medication costs $1,571,509.10 $1,486,612.44 $84,896.65
229 Hospital costs for CVD events $1,468,429.89 $1,538,396.77 ($69,966.88)
Intervention cost $44,321.23 --- $44,321.23
ICER per CVD event averted $7,406.38
(3% discounting) CVD events, coronary heart disease, cerebrovascular disease, peripheral vascular disease, or death; ICER, incremental cost-effectiveness ratio
230 Table 7.5 One-way sensitivity analysis for alternative scenarios
# of Medication CVD event ICER CVD costs difference costs difference events (hospitalisation) averted
High CVD risk (without CVD)
Base case analysis 8 $84,896.65 ($69,966.88) $7,406.38
Scenario 1 –95% CI of 8 $84,896.65 ($69,092.29) $7,610.83 the difference of LDL (lower limit)
Scenario 1 – 95% CI of 12 $84,896.65 ($103,201.15) $2,204.81 the difference LDL (upper limit)
Scenario 2 – 0% 8 $89,988.22 -$72,043.28 $7,783.27 discounting
Scenario 3 – 7% 8 $78,959.46 -$67,349.76 $6,991.37 discounting
Scenario 4 – 25% 8 $82,151.43 -$69,966.88 $7,063.22 reduction in statin costs
231 Scenario 5 – 75% 8 $66,477.86 -$69,966.88 $5,104.03 reduction in statin costs
High CVD risk (with CVD)
Base case analysis 8 $165,252.72 ($69,092.30) $17,988.16
Scenario 1 –95% CI of 7 $165,252.72 ($63,844.78) $27,667.72 the difference of LDL (lower limit)
Scenario 1 – 95% CI of 14 $165,252.72 ($124,191.121) $6,127.29 the difference of LDL (upper limit)
Scenario 2 – 0% 8 $175,163.53 -$69,341.66 $19,710.12 discounting
Scenario 3 – 7% 8 $153,695.88 -$64,824.14 $17,508.80 discounting
Scenario 4 – 25% 8 $161,536.88 -$67,343.12 $18,199.97 reduction in statin costs
Scenario 5 – 75% 8 $134,283.59 -$67,343.12 $14,660.59 reduction in statin costs
232 7.5 References
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233 8. O’Brien S. SA Health Clinical Commissioning Intentions. Department for Health and Ageing; 2013.Available from: http://www.sahealth.sa.gov.au/wps/wcm/connect/e32a3e8040b4d859a817f b809397f885/Clinical+Commissioning+Intentions-Plan%26Comm- Pol%26Comm- 20130812.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE- e32a3e8040b4d859a817fb809397f885-lmeZZLS. Accessed May 2018.
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13. Banks E, Crouch SR, Korda RJ, et al. Absolute risk of cardiovascular disease events, and blood pressure- and lipid-lowering therapy in Australia. Med J Aust. 2016;204(8):320.
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15. D'Agostino RB, Sr., Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-753.
16. Guidelines for the management of absolute cardiovascular disease risk. National Vascular Disease Prevention Alliance 2012.Available from: https://www.heartfoundation.org.au/images/uploads/publications/Absolute- CVD-Risk-Full-Guidelines.pdf. Accessed November 2012.
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18. Webster RJ, Heeley EL, Peiris DP, Bayram C, Cass A, Patel AA. Gaps in cardiovascular disease risk management in Australian general practice. Med J Aust. 2009;191(6):324-329.
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22. Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1):29-43.
235 23. To Err is Human: Building a Safer Health System. Washington (DC): Institute of Medicine 2000.Available from: http://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/1999/ To-Err-is- Human/To%20Err%20is%20Human%201999%20%20report%20brief.pdf. Accessed January 2018.
24. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Institute of Medicine; 2001.Available from: http://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/2001/ Crossing-the-Quality- Chasm/Quality%20Chasm%202001%20%20report%20brief.pdf. Accessed November 2017.
25. Jacob V, Thota AB, Chattopadhyay SK, et al. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review. J Am Med Inform Assoc. 2017;24(3):669-676.
26. O'Reilly D, Holbrook A, Blackhouse G, Troyan S, Goeree R. Cost- effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records. J Am Med Inform Assoc. 2012;19(3):341-345.
27. Gilmer TP, O'Connor PJ, Sperl-Hillen JM, et al. Cost-effectiveness of an electronic medical record based clinical decision support system. Health Serv Res. 2012;47(6):2137-2158.
28. Cleveringa FG, Welsing PM, van den Donk M, et al. Cost-effectiveness of the diabetes care protocol, a multifaceted computerized decision support diabetes management intervention that reduces cardiovascular risk. Diabetes Care. 2010;33(2):258-263.
236 29. Chattopadhyay SK, Jacob V, Mercer SL, Hopkins DP, Elder RW, Jones CD. Community Guide Cardiovascular Disease Economic Reviews: Tailoring Methods to Ensure Utility of Findings. Am J Prev Med. 2017;53(6s2):S155- s163.
30. Payne TH, Bates DW, Berner ES, et al. Healthcare information technology and economics. J Am Med Inform Assoc. 2013;20(2):212-217.
31. Peiris D, Usherwood T, Panaretto K, et al. The Treatment of cardiovascular Risk in Primary care using Electronic Decision supOrt (TORPEDO) study- intervention development and protocol for a cluster randomised, controlled trial of an electronic decision support and quality improvement intervention in Australian primary healthcare. BMJ open. 2012;2(6):e002177.
32. Peiris D, Usherwood T, Panaretto K, et al. Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: the treatment of cardiovascular risk using electronic decision support cluster-randomized trial. Circ Cardiovasc Qual Outcomes. 2015;8(1):87-95.
33. Shiell A, Hawe P, Gold L. Complex interventions or complex systems? Implications for health economic evaluation. BMJ. 2008;336(7656):1281- 1283.
34. HealthStats NSW: Population by Primary Health Networks. Sydney: NSW Department of Health Available from: http://www.healthstats.nsw.gov.au/Indicator/dem_pop_phnmap/dem_pop_p hn_age_trend?filter1ValueId=70975&LocationType=Primary Health Network&name=Population&code=dem_pop. Accessed November 2017.
35. Grosse SD. Assessing cost-effectiveness in healthcare: history of the $50,000 per QALY threshold. Expert Rev Pharmacoecon Outcomes Res. 2008;8(2):165-178.
237 36. Eichler HG, Kong SX, Gerth WC, Mavros P, Jonsson B. Use of cost- effectiveness analysis in health-care resource allocation decision-making: how are cost-effectiveness thresholds expected to emerge? Value Health. 2004;7(5):518-528.
37. Jackson R, Lawes CM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual's absolute cardiovascular risk. Lancet. 2005;365(9457):434-441.
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39. Collaboration TBPLTT. Blood pressure-lowering treatment based on cardiovascular risk: a meta-analysis of individual patient data. Lancet. 2014;384(9943):591-598.
40. Baigent C, Blackwell L, Emberson J, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376(9753):1670-1681.
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42. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care. 2005;43(6):521-530.
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50. Haddad P, Gregory M, Wickramasinghe N. Evaluating Business Value of IT in Healthcare in Australia: The Case of an Intelligent Operational Planning Support Tool Solution. BLED 2014 Proceedings. 2014;17.
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240 PART D IMPLICATIONS
241 Chapter 8: Discussion and conclusions
In this thesis I took a multimethod approach to understanding how and why a technology-enabled quality improvement intervention generated mixed outcomes when implemented in Australian general practices and Aboriginal Community Controlled Health Services (ACCHSs). Factors influencing uptake of technological innovations are often listed in terms of barriers and facilitators. While this is valuable, it does not explain the interplay between these factors and the environments in which they are tested. Greenhalgh’s seminal paper on diffusion of innovations in service organisations states, “it is not individual factors that make or break a technology implementation effort but the dynamic interaction between them”.1 The component studies in this thesis aimed to better understand what is already known about the implementation of technology based quality improvement interventions (Chapter 3), how a specific intervention (HealthTracker) was sustained in a post-trial setting (Chapter 4), how various interactions between the technology and the health service environments in which it was placed influenced adoption (Chapters 5 and 6), and what are the economic considerations for implementing the intervention at scale (Chapter 7).2
In this discussion, I use Greenhalgh and colleagues’ recently published framework, the Nonadaptation, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, to synthesise the main findings from the thesis.3 NASSS was derived from a narrative systematic review of theory-informed frameworks and aims to identify and address challenges of non-adoption and abandonment of health technologies over time (Figure 8.1). It consists of seven domains, each of which may be simple (few components, predictable), complicated (many components but still largely predictable) or complex (many components interacting in a dynamic and unpredictable way). NASSS was designed to be used both retrospectively to understand the degree to which complexity influenced uptake of technology and prospectively as a means of systematically appraising and potentially reducing complexity to enhance adoption and minimise non-adoption.
242 Figure 8.1. The NASSS framework for considering influences on the adoption, non- adoption, abandonment, spread, scale-up, and sustainability of health technologies.3
8.1 Findings
The component chapters of the thesis identified numerous complicated and complex factors that influenced the impact of the HealthTracker intervention. These are addressed below using questions that Greenhalgh has posed within each domain of the NASSS framework.
243 The Condition/Illness a) To what extent is the illness or condition predictable and straightforward? Does
the condition affect everyone in the same way? Does everyone receive the same
treatment? If not, are there tests that can stratify people reliably?
Cardiovascular disease (CVD) represents a cluster of illnesses with multiple interacting risk factors. The conditions and their common risk factors are well recognised by health professionals and there is a robust evidence base for what interventions (both drug and non-drug) are effective in reducing CVD disease burden.4 For primary prevention of CVD, however, the ‘condition’ is more complex to characterise and the multiplicative nature of the risk factors makes CVD risk detection more complex. Most guidelines internationally recognise that assessment of a person’s total or absolute CVD risk should be based on multiple risk factors and this approach is superior to assessment of single risk factors.5,6 Despite this strong evidence base, risk prediction equations are more complex to understand than simple single risk factor threshold-based definitions like hypertension. The difficult nature of assessing CVD risk generates different responses from health practitioners, some defaulting to the time-honoured single risk factor paradigm, others embracing risk-based approaches but finding it difficult to incorporate into routine practice,7-10 and others “eye-balling” the people most at risk. Taking all these factors into account the condition is complex but with appropriate tools and resources can be managed. The underlying premise of HealthTracker was that a decision support tool integrated with clinical software systems would remove some of the complicated steps of assessment and management of CVD risk. However, this was seen as an enabling factor only for some of the participating GPs. Many were not comfortable with all the features of the intervention. For example, one GP found the statistical concepts underpinning absolute CVD risk calculation too complicated to explain to patients (Chapter 5). Further, although patients seemed to appreciate the interactive features of the tools, meaningful communication required a high level of background proficiency on the part of both GP and the patient (Chapter 6).
244 b) What other illnesses may coexist with the one targeted? How are these likely to
affect management? In particular, would co-morbidities or sociocultural influences
affect use of the technology?
The presence of other comorbid conditions is common with CVD, particularly in the elderly, Aboriginal and Torres Strait Islander people, and those that experience socioeconomic hardship. CVD, diabetes and chronic kidney disease have shared common risk factors and often occur together.11 Some of these comorbidities are variably recorded in electronic medical records and consequently manual data entry fields for family history and other comorbidities were included in the decision support tool. However, general practitioners (GPs) found this time-consuming and there were difficulties with saving these data back into the primary electronic health record (EHR). This could lead to reduced provider engagement on two fronts; both because some GPs felt it did not capture all the necessary information for comprehensive management and because the workaround solution to address it was not fit for purpose (Chapter 5). c) How might (for example) the illness and its management be affected by age,
poverty, IT literacy, health literacy, system literacy, English proficiency or
citizenship status? How might these factors affect management? Is the standard
treatment available and acceptable to all social and cultural groups? If not, what
else needs to be taken into account?
For patients, concepts of CVD risk and risk stratification are also vague. This suggests that achieving a mutual understanding between provider and patient may require both to have high levels of general literacy and specifically health and digital literacy (Chapter 6). There are also challenges with applying a ‘one size fits all’ approach to heterogenous populations and the absence of validated equations for Aboriginal and Torres Strait Islander people, in particular, is seen as a barrier to using these equations more widely. Although this is a known limitation of current equations,
245 we did not observe this to be a major factor in influencing adoption in Aboriginal Community Controlled Health Services.
The technology a) What are the technology’s material features? Is it dependable? Is it easy to use?
Is it freestanding or already interoperable with other relevant technologies and
systems?
HealthTracker drew on two extensively studied technological components.12-15 It incorporated “in-consultation” tools (real-time decision support integrated with electronic medical records and an interactive risk communication tool) and “out of consultation” tools to support reflective practice (automated clinical audit tool and a web portal of peer-ranked performance trends). The major selling point of the system was its integration with clinical software thereby reducing data entry and providing quick access to tailored guideline recommendations at the time during the clinical encounter when management decisions needed to be made. There were several examples, however, of where the technology was difficult to install, health service information systems with low operating capacity slowed down on initial installation, and frequent technical fixes were required even when initial installation barriers were overcome (Chapter 5). In some sites this led to early abandonment as first impressions of the new technology were strong determinants of future use.
Additionally, it would appear that while certain staff gravitated to particular features of the software (clinic managers using the audit tools, GPs using the risk communication tools) there was a lack of cohesiveness within the health service to integrate these features collectively. This diluted the potential for different users reinforcing the value of the intervention to others in the service. It was rare that people used the intervention collectively e.g. team meetings looking at audit performance to formulate strategies to address performance gaps (Chapter 5). Achieving collective action is driven by factors such as leadership, strong managerial relations, readiness for change, a culture of staff training and resource availability.16
246 The mix of these organisational attributes in the services participating in TORPEDO were highly variable and this strongly influenced the level of engagement with HealthTracker. b) What knowledge or support is needed to use the technology? Could anyone
learn to use the technology with simple instructions or an introductory course?
Ongoing training and support was provided during the trial period by the research team, technical helpdesk and a clinical champion. During the post-trial phase, training was scaled back to an initial training and installation session and then ongoing support if requested (Chapter 4 and 5). Many expressed the need for ongoing support, refresher courses, troubleshooting and staged training to progressively understand the full capabilities of the software. This training would need to be flexibly delivered according to individual staff circumstances. In Chapter 7, I estimated the running costs for Primary Health Networks to support this program. Although I found the median support time during the trial to be quite low, it is likely that in a non-research setting additional support resources would need to be provided to cover ongoing training needs. c) Do the data generated by the technology reliably and transparently reflect changes
in the condition? Or are these data obscure, partial and/or contested by
clinicians? What kind of data does the technology NOT provide (or what data
does it implicitly over-ride)?
Most health professionals found the data generated to be reliable and trustworthy, particularly because it was developed by a university research institution. Many sites initially received pathology data in an unstructured format and when the audit tool was initially used resulted in management gaps larger than they actually were. This may have had an impact on perceived trustworthiness of the data, however, the project support team were able to explain this issue, implement the necessary technical fixes, and this was then reflected in improved performance on subsequent
247 data extractions. On balance, this was a simple problem to solve and had minimal impact on adoption as the solutions were pre-empted, easily explained, easy to fix and the results were obvious within a short time frame. A more complex issue, however, arose when practitioners actively rejected the guideline recommendations as not being appropriate for a particular patient. It was frustrating for them to be continually reminded via a red traffic light that something was missing. There was a need for greater flexibility in being able to dynamically alter recommendation prompts for specific individuals. The use of computer templates is known to render patients in a particular way and privileges the population over the individual.17 d) What is the technology supply model? Is the technology generic, ‘plug-and-play’
or customisable-off-the-shelf – or does it require a bespoke contract?
The technology supply was dependent on an existing commercial software platform that is inter-operable with commonly used Australian medical record systems. This meant that the fortunes of HealthTracker were intimately connected with those of the commercial software platform. There was initially great appeal in housing HealthTracker within this particular platform as the software vendor had entered into a partnership with the Royal Australian College of General Practitioners to provide the software to all its members. However, over the life of the trial, this partnership was abandoned and therefore the potential for easily reaching large numbers of GPs was diminished. As a consequence, a ‘software-agnostic’ approach is now being taken to work with multiple software vendors to diminish the risk of relying on any one system.
The Value Proposition a) What is proposed about the technology’s supply and demand side value? If the
technology is still being developed, does the business case rest on sound,
independently verifiable assumptions and predict a good return for investors
within a reasonable timescale? Is the technology something that patients are likely
248 to want and need? Has it been subject to health technology assessment and
shown to be both efficacious and cost-effective? Are clinicians agreed on the
robustness of the evidence base?
The perception of value of this type of technology product is likely to vary for different stakeholders including policy makers, regulatory agencies, public and private payers, healthcare providers and patients.18 The economic modelling study conducted in Chapter 7 assessed the potential value of the program if paid for and administered by a Primary Health Network. In terms of input costs, it is clear that quality improvement programs of this nature are cheap to implement at scale and could be reasonably accommodated within the budget allocations of these organisations. The cost-effectiveness of the program, however, was inconclusive and clearly depends on a stronger signal of effect than was achieved in the trial. Although the trial-based estimates of reductions in blood pressure and cholesterol were small, the modelling work from Chapter 7 found that if these small effect sizes could be sustained over time, interventions of this nature are highly cost-effective. Further work is needed to understand the potential value to other actors in the health care system.
Healthcare providers and staff generally valued the intervention and expressed interest in using it long term (with the caveat that amendments be made to technical aspects of the software and that ongoing training and support is provided). Some GPs talked about the need for government subsidised CVD risk assessments as occurs for other chronic diseases such as diabetes. Although additional program considerations such as these incur financial costs they are likely to raise perceived value to providers (Chapter 5).
249 Adopters (Staff, Patient, Caregivers)
What is expected of professional staff and the patient? Can the staff member use the technology within their capability, doing their existing job in the way they currently do it? Or will they be expected to fundamentally change what they do?
Most GPs found the tools fairly straightforward to use and it aligned with their current modes of practice. However, time constraints and lack of financial incentives was an issue in using the intervention regularly. In the interviews, GPs reported that the length of time they spent with the intervention ranged from a few minutes to ten minutes which is a substantial time commitment to incorporate into a standard clinical consultation. For other members of staff there were mixed levels of engagement. Practice nurses engaged minimally with the intervention despite it being designed such that the initial screening component could be completed by nurses. This suggests that the decision support was predominantly viewed as a GP tool and did not enhance team collective action.19
The case study analysis highlighted the need for staff with a dedicated quality improvement role in the organisation. This may require creation of new roles and realigning of responsibilities for non-GP staff such as practice managers and nurses to incorporate implementation of the intervention, coordination of training and support and use of audit and feedback in their daily activities. In sites where these roles already existed the intervention was easier to adopt (e.g. case 5 in Chapter 5). However, such sites tended to be larger and these models would not be possible to replicate in small general practices.
The acquisition of new knowledge, particularly in adopting the absolute risk approach, was influenced by ‘special people’ that included both clinical champions and non-clinical staff who could motivate and engage other staff (Chapter 5). It is well known that people learn faster and are more effective in spreading knowledge when working in a social group (Chapter 2).20,21 Identifying and recruiting these ‘special people’ as central members of these social groups is under-recognised. This resonates with Gabbay and Le May’s concept of ‘mindlines’ which are professionally
250 sanctioned codes of practice that may or may not relate to evidence-based guidelines.22 There were only a few examples of where health professional teams, both within and across health service sites, were involved in knowledge exchange activities. These were sporadic in nature and not systematically implemented. Greater emphasis on stimulating professional networks and use of top-down structural support combined with bottom-up participation is likely to have an impact on adoption.23
In terms of patient engagement, the findings from a single care consultation encounter in Chapter 6 provided some insight on what might be required for patients to engage in these tools. This was influenced by the manner in which the doctor deploys the intervention with the patient, the skills required by the doctor to introduce it strategically at multiple critical moments during the encounter, and the skills required by the patient to interpret the information and apply it to their circumstances. Although HealthTracker was designed for in consultation use, one key factor that might promote a higher degree of patient engagement is offline access to the tools post-consultation. A follow-on project is currently underway to assess the impact of a consumer portal that provides HealthTracker information and other content linked to a patient’s electronic health record.24
The Organisation(s) a) What is the organisation’s capacity to innovate? Does the organisation have
strong leadership, flat hierarchies, good managerial relations, slack resources that
can be channelled into new projects and a climate that encourages trying out
new things? What changes will be needed in organisational routines? Will the
technology fit with existing care pathways and linked organisational routines?
Chapter 5 illustrated the importance of a health service’s commitment to CQI programs, an engaged leader with an interest in trying new approaches, cohesive teams, and strong information technology infrastructure and support. In sites without these elements there was greater dependency on the research project officers to
251 sustain engagement and this was clearly a factor in how much the tools were used in the post-trial period.
Although most services participating in the trial saw value in the tools being tested, there was generally no sense of urgency for change within these services. In the preparatory work for commencing the trial, all services received an audit report highlighting baseline performance. With some exceptions, these reports demonstrated large evidence practice gaps that were not previously known by the organisation. Although certain individuals were highly motivated by these findings, on balance most services did not respond by making it a priority. Further, engagement with non-clinical managers was variable in the trial, some reluctantly participating because the GP was keen to be involved. This lack of interest toward participation was a factor in preventing adoption of the intervention (e.g., when software bugs needed attention the manager may have placed this low on the priority list)
As described above in the adoption domain, GPs were not expected to make many changes to their organisational routines, however this may not have been the case for other staff. In organisations that lacked existing processes for adopting new technology, use of audit tools and engaging in collaborative work to improve performance was a daunting and time-consuming task for some staff who had minimal exposure to such approaches. The lack of reflexive monitoring in almost all of the case sites studied in Chapter 5 suggests that such processes are challenging to incorporate into existing routine methods of work. In places where there is substantial organisational inertia to overcome, the benefits of the intervention are remote. Early wins with improvements in data quality were often used to increase engagement. While this was apparent in many sites, for others there was little capacity to evaluate and monitor improvements. This further demotivates people who were not necessarily convinced of the initial value of the intervention.
252 The wider context for the programme
What is the political/policy context? Is there a ‘policy push’ for this innovation? What is the regulatory and legal context? What is the position of professional bodies? What is the position of patients, citizens and the public?
The Australian primary health care system is making small, incremental steps to reform its current fee-for-service dominant model. This includes an increased focus on improving care for people with chronic and complex care needs, strategies to enhance integration of care between primary care and hospital sectors and large- scale deployment of a national personal health record system that is interoperable with electronic health record systems. Payment reforms are currently being made which reflect these changes and there is much emphasis on meso-tier organisations (Primary Health Networks and Local Hospital Networks) to support implementation of these reforms. All of these initiatives make ‘plug and play’ tools like HealthTracker appealing, however they should be viewed as only one enabling factor amongst many others that are needed to improve system performance.
There are potential regulatory issues with widespread implementation of decision support tools. However, the US Federal Drug Administration recently released guidance on clinical and patient decision support software suggests that rule-based tools that provide guideline-based recommendations do not constitute a device and therefore do not require regulatory oversight.25 This remains a grey area and it is not yet clear whether the Australian Therapeutic Goods Administration takes a similar view on this issue.
Professional bodies are unlikely to have major issues with supporting such tools. Although such organisations are commonly asked to endorse clinical guidelines, there do not exist clearly defined processes for formally endorsing clinical software. As mentioned above one of the motivations for building HealthTracker with one particular software vendor was related to their existing collaboration with the Royal Australian College of General Practitioners. Such connections could be considered a
253 ‘nice to have’ for supporting adoption but are likely to be less influential than the other domains discussed here.
Although this research focussed mainly on health service and provider factors supporting and hindering adoption, the patient interview data suggests that patients have at best a moderate interest in the use of such tools. However, as major initiatives are being implemented to support uptake of the national personal health record, it is possible that over time patients will start to become more engaged in such tools, particularly if they are interoperable.
Adaption Over Time
Can the technology and the programme evolve and adapt over time? Or is the technology ‘as supplied’, dependent on rigid roles and interactions, and likely to become obsolete before long?
During the randomised trial the technology and implementation strategy were somewhat static. This highlights the challenges of using this evaluation design for interventions that need to adapt as user feedback is gathered and the digital health environment changes.26 Since trial completion, however, there have been major changes to the software including expansion of scope of decision support to cover other conditions such as diabetes and atrial fibrillation. The software platform provided by the commercial partner has also undergone major changes in response to some of the technical barriers encountered during the trial. New implementation strategies are also being trialled including: (1) the program is being supported by a Primary Health Network as part of a CVD focussed quality improvement collaborative;27 (2) the intervention strategy is being supplemented with a pharmacy support program to maximise quality use of medicines for people at high CVD risk;28 and (3) a companion consumer portal (described above) is being trialled to make HealthTracker information more accessible to patients outside of the clinical encounter.24 Given the outcomes observed in TORPEDO and in the post-trial period were modestly positive, new strategies to refine the tools and the implementation
254 strategy will help build a stronger understanding of the factors driving adoption, scale and spread.
8.2. Strengths and limitations
The strength of this research was a theory-informed approach that drew on multiple data sources to understand the complexity of a technology intervention. While the trial-based evaluation provided an ‘on-average’ assessment of the impact of the intervention, it fails to understand the wide variation that occurred within individual services. This thesis tried to address this by providing insight on the factors influencing implementation and adoption at the patient, provider and organisational levels. Although I started with a somewhat simplified logic model that looked at particular ingredients, activities and outcomes, I finished with a more nuanced understanding that is less linear and predictable. By focussing on the messy narratives that occur within health services, it helped me to get to the important question of why things play out differently in some contexts compared to others. The rigorous multimethod process evaluations of a multifaceted health information technology are not routinely done, and my work is critically important to address this knowledge gap in the literature. My thesis provides novel and extensive evidence on the adoption and sustainability of technology interventions beyond a trial setting and sheds light on the conditions required for investing in such interventions to improve CVD outcomes. It will contribute to a broader conversation about the role of such quality improvement strategies for policymakers, administrators and health care providers.
The limitations associated with each study are discussed in each chapter. On a broader level, the research was only conducted in the Australian primary healthcare context with a particular emphasis on urban general practices and ACCHSs in two Australian states. While the findings are likely to be of relevance to other health systems, the specific health system constraints and opportunities in other countries will inevitably create different results (e.g., countries with different primary health care systems and differing levels of maturity with digital health). Another potential limitation was that my “insider-outsider” status in the research may have resulted in me taking 255 a particular lens to the evaluation that would be quite different to that of an independent evaluator. Working closely with the architects of the HealthTracker intervention may have fostered a certain degree of investment in its success and it was important to maintain a high degree of reflexivity in the process. The main strategy I took to mitigate this risk was engagement with a range of multidisciplinary researchers who were not involved in the development of the intervention. As mentioned in Chapter 5, most of the data collected for the process evaluation was done toward the end of the trial and therefore failed to capture the dynamic evolution of the intervention and how people reacted to it over time.
8.3 Conclusion
Implementation of digital health strategies are a policy priority in Australia and for most health systems internationally. Digital health has great potential to improve health system performance on may dimensions (e.g. access, quality, efficiency) however the benefits remain inconsistent and poorly understood. Even when digital health policies are strongly supported, there remain major adoption challenges. Without a detailed understanding of these challenges, the full potential of a digitally enabled health system is unlikely to be realised.
This thesis goes beyond a checklist approach to understanding factors that influence adoption of technology interventions. It highlighted the complex interaction of these interventions and the environments in which they are placed. It uncovered factors that are inherently not knowable or predictable but dynamic and emergent. In so doing, it provides important information on where investments need to be made both with the technical aspects of the intervention and the human elements that are critical to driving adoption.
Both bottom-up approaches within health service organisations and top-down enablement with support from the wider system is needed. This results in multiple overlapping and layered strategies that address different facets of the target health conditions, the technology, the adopter system (patients, providers, managers), organisational context and broader system enablers (policy, financing etc). Although
256 no one strategy is likely to be a magic bullet for success, collectively and incrementally these strategies may generate small influences that when scaled can lead to a large impact.
257 8.4 References
1. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581-629.
2. Braithwaite J, Runciman WB, Merry AF. Towards safer, better healthcare: harnessing the natural properties of complex sociotechnical systems. Quality & safety in health care. 2009;18(1):37.
3. Greenhalgh T, Wherton J, Papoutsi C, et al. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. J Med Internet Res. 2017;19(11):e367.
4. Cardiovascular diseases (CVDs). WHO; 2016.Available from: http://www.who.int/en/news-room/fact-sheets/detail/cardiovascular- diseases-(cvds). Accessed May 18, 2018.
5. D'Agostino RB, Sr., Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-753.
6. Jackson R, Lawes CM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual's absolute cardiovascular risk. Lancet. 2005;365(9457):434-441.
7. Heeley EL, Peiris DP, Patel AA, et al. Cardiovascular risk perception and evidence--practice gaps in Australian general practice (the AusHEART study). Med J Aust. 2010;192(5):254-259.
8. Webster RJ, Heeley EL, Peiris DP, Bayram C, Cass A, Patel AA. Gaps in cardiovascular disease risk management in Australian general practice. Med J Aust. 2009;191(6):324-329.
258 9. Bonner C, Jansen J, McKinn S, et al. General practitioners' use of different cardiovascular risk assessment strategies: a qualitative study. Med J Aust. 2013;199(7):485-489.
10. Jansen J, Bonner C, McKinn S, et al. General practitioners' use of absolute risk versus individual risk factors in cardiovascular disease prevention: an experimental study. BMJ open. 2014;4(5):e004812.
11. Dickens C, Cherrington A, McGowan L. Depression and health-related quality of life in people with coronary heart disease: a systematic review. Eur J Cardiovasc Nurs. 2012;11(3):265-275.
12. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223-1238.
13. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.
14. Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:f657.
15. Ivers N, Jamtvedt G, Flottorp S, et al. Audit and feedback: effects on professional practice and healthcare outcomes. The Cochrane database of systematic reviews. 2012;6:Cd000259.
16. McMullen H, Griffiths C, Leber W, Greenhalgh T. Explaining high and low performers in complex intervention trials: a new model based on diffusion of innovations theory. Trials. 2015;16:242.
259 17. Swinglehurst D, Greenhalgh T, Roberts C. Computer templates in chronic disease management: ethnographic case study in general practice. BMJ open. 2012;2(6).
18. Lehoux P, Miller FA, Daudelin G, Denis JL. Providing Value to New Health Technology: The Early Contribution of Entrepreneurs, Investors, and Regulatory Agencies. International journal of health policy and management. 2017;6(9):509-518.
19. Murray E, Treweek S, Pope C, et al. Normalisation process theory: a framework for developing, evaluating and implementing complex interventions. BMC Med. 2010;8:63.
20. Wells S, Tamir O, Gray J, Naidoo D, Bekhit M, Goldmann D. Are quality improvement collaboratives effective? A systematic review. BMJ quality & safety. 2017.
21. Nix M, McNamara P, Genevro J, et al. Learning Collaboratives: Insights And A New Taxonomy From AHRQ's Two Decades Of Experience. Health Aff (Millwood). 2018;37(2):205-212.
22. Gabbay J, le May A. Evidence based guidelines or collectively constructed "mindlines?" Ethnographic study of knowledge management in primary care. BMJ. 2004;329(7473):1013.
23. Dixon-Woods M, Bosk CL, Aveling EL, Goeschel CA, Pronovost PJ. Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167-205.
24. Neubeck L, Coorey G, Peiris D, et al. Development of an integrated e-health tool for people with, or at high risk of, cardiovascular disease: The Consumer Navigation of Electronic Cardiovascular Tools (CONNECT) web application. Int J Med Inform. 2016.
260 25. Clinical and Patient Decision Support Software: Draft Guidance for Industry and Food and Drug Administration Staff. U.S. Department of Health and Human Services: FDA; 2017.Available from: https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidanc e/guidancedocuments/ucm587819.pdf. Accessed June 2018.
26. Peiris D, Miranda JJ, Mohr DC. Going beyond killer apps: building a better mHealth evidence base. BMJ Global Health. 2018;3(1):e000676.
27. Get your Q pulse racing Sydney: Primary Health Network Central and Eastern Sydney; 2018.Available from: https://www.cesphn.org.au/news/latest- updates/57-enews/1992-get-your-q-pulse-racing. Accessed June 2018.
28. Hayek A, Joshi R, Usherwood T, et al. An integrated general practice and pharmacy-based intervention to promote the use of appropriate preventive medications among individuals at high cardiovascular disease risk: protocol for a cluster randomized controlled trial. Implementation science : IS. 2016;11(1):129.
261 APPENDICES
262 APPENDIX A: Policy, submission of PhD theses containing published work
Thesis with publications (Thesis and Examination of Higher Degrees by Research Policy 2015)
(1) The University will accept for examination a thesis which contains previously published material provided that:
Thesis and Examination of Higher Degrees by Research Policy 2015 Page 9 of 21
(a) the thesis makes an original and substantial contribution to the field of knowledge;
(b) the thesis forms a consistent, coherent and unified whole;
(c) the previously published material relates to research undertaken during the candidature and was published during the candidature; and
(d) in addition to the published material, the student provides, at the minimum: (i) an introduction which argues for the aim(s) of the thesis and contextualises the research problems it purports to address; and
(ii) a conclusion which draws together the findings of the studies in the context of the stated aims of the thesis.
(2) The student may also provide other separate chapters to supplement the published papers such as a literature review, background information, or description of the methodology used.
(3) Acceptable publications (including material already published, accepted for publication, or submitted for publication) include: (a) papers in refereed journals;
(b) book chapters;
263 (c) conference papers;
(d) a documentary record of an exhibition or installation mounted during candidature which is not part of the creative or artistic component of a thesis.
(4) A blog is not an acceptable publication.
(5) A collection of disparate publications, no matter what their quality, must not be approved for the award of a higher degree by research if they do not meet the criteria for the award.
(6) A thesis containing published material must be examined using the same criteria, and by the same process, as one which does not.
264 APPENDIX B: Ethics Approval Letters
a) University of Sydney Human Research Ethics Committee approval letters
b) Aboriginal Health and Medical Research Council Ethics Committee approval letters
265 RESEARCH INTEGRITY Human Research Ethics Committee Web: http://sydney.edu.au/ethics/ Email: [email protected]
Address for all correspondence: Level 6, Jane Foss Russell Building - G02 The University of Sydney NSW 2006 AUSTRALIA
Ref: MF/PE
7 April 2011
Dr David Peiris The George Institute for Global Health Missenden Road CAMPERDOWN NSW 2050 Email: [email protected]
Dear Dr Peiris
Thank you for your correspondence dated 6 April 2011 addressing comments made to you by the Human Research Ethics Committee (HREC).
I am pleased to inform you that with the matters now addressed your protocol entitled “Treatment of cardiovascular risk in primary care with electronic decision support - The TORPEDO study” has been approved.
Details of the approval are as follows:
Protocol No.: 13533
Approval Period: April 2011 to April 2012
Authorised Personnel: Dr David Peiris Associate Professor Kathryn Panaretto Professor Mark Harris Professor Tim Usherwood Dr Jenny Hunt Ms Faith Koh
Documents Approved: GP Health Professional Information and Consent Sheet Version 1 25/1/11 GP Patient Information and Consent Form Version 2 6/4/11 Interview Guide for Health Professionals Version 1 21/1/2011 Interview Guide for Clients/Patients Version 1 21/1/2011
The HREC is a fully constituted Ethics Committee in accordance with the National Statement on Ethical Conduct in Research Involving Humans-March 2007 under Section 5.1.29.
The approval of this project is conditional upon your continuing compliance with the National Statement on Ethical Conduct in Research Involving Humans. A report on this research must be submitted every 12 months from the date of the approval or on completion of the project, whichever occurs first. Failure to submit reports will result in withdrawal of consent for the project to proceed. Your report is due by 30 April 2012.
Manager Human Ethics Human Ethics Secretariat: ABN 15 211 513 464 CRICOS 00026A Dr Margaret Faedo Ms Patricia Engelmann T: +61 2 8627 8172 E: [email protected] T: +61 2 8627 8176 Ms Karen Greer T: +61 2 8627 8171 E: [email protected] E: [email protected] Ms Kala Retnam T: +61 2 8627 8173 E: [email protected]
Special Condition of Approval
Approval is given only for the General Practice component of the study. It is a condition of approval that letters of support are received from all participating Divisions of General Practice. (We note that letters of support have been received from Nepean Division of General Practice, South Eastern Sydney Division of General Practice Limited, Central Sydney GP Network Ltd and the University of New South Wales).
Chief Investigator / Supervisor’s responsibilities to ensure that:
1. All serious and unexpected adverse events should be reported to the HREC within 72 hours for clinical trials/interventional research.
2. All unforeseen events that might affect continued ethical acceptability of the project should be reported to the HREC as soon as possible.
3. Any changes to the protocol must be approved by the HREC before the research project can proceed.
4. All research participants are to be provided with a Participant Information Statement and Consent Form, unless otherwise agreed by the Committee. The following statement must appear on the bottom of the Participant Information Statement: Any person with concerns or complaints about the conduct of a research study can contact the Manager, Human Ethics, University of Sydney on +61 2 8627 8176 (Telephone); + 61 2 8627 8177 (Facsimile) or [email protected] (Email).
5. You must retain copies of all signed Consent Forms and provide these to the HREC on request.
6. It is your responsibility to provide a copy of this letter to any internal/external granting agencies if requested.
7. The HREC approval is valid for four (4) years from the Approval Period stated in this letter. Investigators are requested to submit a progress report annually.
8. A report and a copy of any published material should be provided at the completion of the Project.
Please do not hesitate to contact Research Integrity (Human Ethics) should you require further information or clarification.
Yours sincerely
Dr Margaret Faedo Manager, Human Ethics On behalf of the HREC
Copy: Ms Faith Koh [email protected]
Research Integrity Human Research Ethics Committee
Wednesday, 27 February 2013
Mr David Peiris The George Institute for Global Health; Sydney Medical School Email: [email protected]
Dear David
Your request to modify the above project submitted on 12 February 2013 was considered by the Executive of the Human Research Ethics Committee at its meeting on 20 February 2013.
The Committee had no ethical objections to the modification/s and has approved the project to proceed.
Details of the approval are as follows:
Project No.: 2012/2183
Project Title: Treatment of cardiovascular risk in primary care with electronic decision support - The TORPEDO study
Addition of Authorised Personnel: Ms. Shannon Mckinn
Approved Documents:
Date Uploaded Type Document Name 11/02/2013 Participant Consent Form ACCHSs Patient Information and Consent_clean 11/02/2013 Participant Consent Form ACCHSs Patient Information and Consent_tracked 11/02/2013 Interview Questions Approved Health Professional Interview Guide 11/02/2013 Interview Questions Approved Patient Interview Guide 11/02/2013 Questionnaires/Surveys End of study survey 11/02/2013 Participant Consent Form Health Professional Interview information and consent_clean 11/02/2013 Participant Consent Form Health Professional Interview information and consent_tracke 11/02/2013 Participant Consent Form Health Professional video information and consent_clean 11/02/2013 Participant Consent Form Health Professional video information and consent_tracke 11/02/2013 Participant Consent Form Patient Information and Consent form_clean 11/02/2013 Participant Consent Form Patient Information and Consent form_tracked 11/02/2013 Interview Questions Revised Health Professional Interview Guide 11/02/2013 Interview Questions Revised Patient Interview Guide
Research Integrity T +61 2 8627 8111 ABN 15 211 513 464 Research Portfolio F +61 2 8627 8177 CRICOS 00026A Level 6, Jane Foss Russell E [email protected] The University of Sydney sydney.edu.au NSW 2006 Australia
Please do not hesitate to contact Research Integrity (Human Ethics) should you require further information or clarification.
Yours sincerely
Dr Stephen Assinder Chair Human Research Ethics Committee
This HREC is constituted and operates in accordance with the National Health and Medical Research Council’s (NHMRC) National Statement on Ethical Conduct in Human Research (2007), NHMRC and Universities Australia Australian Code for the Responsible Conduct of Research (2007) and the CPMP/ICH Note for Guidance on Good Clinical Practice.
Page 2 of 2
Research Integrity Human Research Ethics Committee
Thursday, 8 August 2013
Dr David Peiris The George Institute for Global Health; Sydnney Medical School Email: [email protected]
Dear Dr David Peiris,
Your request to modify the above project submitted on 12 July 2013 was considered by the Executive of the Human Research Ethics Committee.
The Committee had no ethical objections to the modification/s and has approved the project to proceed.
Details of the approval are as follows:
Project No.: 2012/2183
Project Title: Treatment of Cardiovascular Risk in Primary Care with electronic decision support-The TORPEDO Study
Approved Documents:
Date Uploaded Type Document Name 31/07/2013 Participant Info Statement Approved Master GP Patient PIS, Version 3.0 31/07/2013 Participant Info Statement Patient Interview Only, Version1, 311July2013, clean 31/07/2013 Participant Info Statement Patient Interview Only, Version3, 111July2013, clean 12/07/2013 Participant Consent Form Qualitative Interview PIS and Conssent form Version 3.0 12/07/2013 Questionnaires/Surveys End of Study Survey for Intervention sites Version 2.0 12/07/2013 Questionnaires/Surveys End of Study Survey for Usual care sites Version 2.0
Please do not hesitate to contact Research Integrity (Human Ethics) shouldd you require further information or clarification.
Yours siincerely
Professor Glen Davis Chair Human Research Ethics Committee
This HREC is constituted and operates in accordance with the National Heealth and Medical Research Council’s (NHMRC) National Statement on Ethical Conduct in Human Research (2007), NHMRC and Universities Australia Australian Code for the Responsible Conduct of Research (2007) and the CPMP/ICH Note for Guidance on Good Cliniical Practice.
Research Integrity T +61 2 8627 8111 ABN 15 211 513 464 Research Portfolio F +61 2 8627 8177 CRICOS 00026A Level 2, Margaret Telfer E [email protected] The University of Sydney sydney.edu.au NSW 2006 Australia
AH&MRC ETHICS COMMITTEE 28th April 2014
Dr David Peiris Program Head - Primary Health Care Research The George Institute PO Box M201 Camperdown NSW 2050
Dear Dr Peiris,
RE: 778/11 - TORPEDO Study -Treatment of cardiovascular risk in primary care with electronic decision support (Health Tracker)
I refer to the correspondence received 8th March 2014 providing an Annual Progress Report and extension request for this project that has already been approved by the AH&MRC Ethics Committee.
The Committee has agreed to approve an extension for the study until 30th June 2015. If you require any further extension it will be subject to provision of an annual progress report prior to this date. Please find attached an Annual Progress Report pro forma for use at the end of the term.
Thank you also for submitting the manuscript titled, ‘Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: The TORPEDO cluster-randomised trial’ for review by the Committee.
The Ethics Committee has reviewed the manuscript and have no objection to its publication.
The conditions of approval contained in the original approval letter will continue to apply.
On behalf of the AH&MRC Ethics Committee,
Yours sincerely
Val Keed Chairperson AH&MRC Ethics Committee
APPENDIX C: Participant Consent Forms
a) Health professional information sheet and consent form
b) General practitioner video recording information sheet and consent form
c) Patient video-recording and interview information sheet and consent form
i. Participants recruited from general practice
ii. Participants recruited from ACCHS
iii. Participant opt out form
d) Survey information sheet and consent form
277 The TORPEDO Study Evaluation Health Professional Qualitative Interview Information and Consent Sheet
Participant Information Sheet
You are invited to take part in the evaluation of a new electronic decision support tool that has been implemented as a part of a research trial at your health service. Before you decide whether or not to participate, it is important for you to understand why the research is being done and what is involved. Please take time to read the following information carefully. You are entirely free to decide whether or not to take part in this study and you are free to discontinue your involvement with the study at any time by informing us. If there is anything that is not clear, or if you would like more information, please do not hesitate to ask. If you agree to take part, you will be asked to sign the attached Participant Consent Form.
What is the TORPEDO study? TORPEDO stands for the ‘Treatment of cardiovascular risk in primary health care using electronic decision support’. Several months ago your health service agreed to participate in this project. We provided an electronic decision support tool to assist health staff in the management of cardiovascular disease (CVD) risk. This tool is called HealthTracker. It aims to improve the uptake of best practice evidence by giving management advice to health staff and patients based on national guidelines.
How is HealthTracker being evaluated overall? The HealthTracker software has been randomly allocated to 30 health services with an additional 30 health services who did not receive HealthTracker acting as a comparison group. There are three parts to evaluating whether HealthTracker improves quality of care for CVD risk management. 1. Health service audits were done at baseline and again at the end of 12 months (±6 months). Anonymous data is collected on appropriate screening of cardiovascular risk factors and guideline based prescribing of recommended medicines for people at high risk of heart attack or stroke.
Comparisons will be made between the group who received HealthTracker and the group that did not. 2. Interviews with selected health staff and patients are being conducted to understand the usefulness and acceptability of HealthTracker. 3. Video recording of some healthcare consultations between doctors and patients will be conducted to better understand how HealthTracker is actually used in practice.
Who can participate in the interview?
Health professionals who have had access to the HealthTracker tool and/or Clinical Audit Tool (CAT) will be randomly selected to participate in the interview evaluation.
What will the interview involve? Interviews will be conducted by a research team member and will take approximately 40 to 60 minutes. If you participate in the interview, we will ask you about your experience in using HealthTracker and/or Clinical Audit Tool Health Professional Qualitative Interview Information and Consent_V2_10Feb2013 Page 1 of 4
The TORPEDO Study Evaluation Health Professional Qualitative Interview Information and Consent Sheet
(CAT). We seek to explore any impact it may have had on the care you provided. We will ask you what you found beneficial and to identify any problems associated with its use. We will explore with you the factors that might influence uptake of the tool as a part of your routine care with your patients. All interviews will be digitally recorded, professionally transcribed and your name and personal details will be removed for analysis purposes. You will be identified by a unique ID number.
The interview process is informal and flexible as our main aim is to encourage you to articulate your experiences and views. We appreciate your work commitments and will fit with your schedule
We will use the data received from the interviews for this study and any secondary analysis required in the future. At the conclusion of the research, data will be stored for 7 years minimum in accordance with ethics and governance regulations.
Costs Participation in the health professional qualitative interviews will not cost you anything, nor will you be paid.
Confidentiality We will ask you for your contact details in order to communicate with you if the need arises. This information will be treated as strictly confidential and not released to anyone outside the research team. The information provided to the researchers will not include any personal information and will be identified by a unique study number only.
Study management This is an independent study managed by The George Institute for Global Health, University of Sydney, in collaboration with several partner organisations. Funding is provided by the National Health and Medical Research Council.
Ethics Approval This study has been approved by The University of Sydney Human Ethics Administration Committee. Any person with concerns or complaints about the conduct of this study may contact The Manager, Human Ethics Administration on +61 2 8627 8176 (telephone); +61 2 8627 8177 (fax); or email [email protected]
This study has been approved by the Aboriginal Health & Medical Research Council (AH&MRC) of New South Wales. Any person with concerns or complaints about the conduct of this study may contact The Chairperson, on 9212 4777 and quote protocol number (778/11).
~~~ Thank you for your participation ~~~
Health Professional Qualitative Interview Information and Consent_V2_10Feb2013 Page 2 of 4
The TORPEDO Study Evaluation Health Professional Qualitative Interview Information and Consent Sheet
Consent Form
Before you sign this form please be sure that you understand what it means to be part of the study. Please read (or have read to you) the Participant Information Sheet (Version 2; dated 10Feb2013). Please ask the study team member to answer any questions you have.
It is important to understand: You may decide not to take part in the study. There will be no penalty against you at work. You can stop taking part at any time. Information you give will be used for this study and any post-study analysis required in the future. It will be stored in a secure place. Only study team members will have access. Your name and details will not be made public. Nothing written in reports will link you personally to the study. If interviewed, you agree that the interview will be audio taped and some words (not your name) can be used in the study reports.
Participant to complete:
I have a copy of the study information sheet and have yes no had an opportunity to ask questions about the study □ □
I agree to take part in an interview for the study. yes no □ □
I agree that the interview be audiotaped yes no □ □
I agree that some of my words (not my name) may be yes no used in the study reports □ □
I understand that I have the right to withdraw my yes no consent and cease involvement in the study at any □ □ time without penalty, either financially or personally
Health Professional Qualitative Interview Information and Consent_V2_10Feb2013 Page 3 of 4
The TORPEDO Study Evaluation Health Professional Qualitative Interview Information and Consent Sheet
Participant to complete:
I, ______(name), of
______(address), hereby consent to take part in this study.
Signature: Date:
Interviewer’s name:
I have explained the nature and purpose of the study to the above participant and have answered their questions.
Signature: Date:
Contact details: If you have any questions regarding this research project please contact:
Dr David Peiris (02) 9993 4513 The George Institute For Global Health Level 10, King George V Building Missenden Rd Camperdown NSW 2050
The Manager (02) 8627 8177 Human Ethics Administration The University of Sydney Level 6 Jane Foss Russell Building Camperdown NSW 2006
The Chairperson (02) 9698 1099 AH&MRC Ethics Committee PO Box 1565 Strawberry Hills NSW 2012
Health Professional Qualitative Interview Information and Consent_V2_10Feb2013 Page 4 of 4
The TORPEDO Study Evaluation Health Professional Video-recording Information and Consent Sheet
Participant Information Sheet
You are invited to take part in the evaluation of a new electronic decision support tool that has been implemented as a part of a research trial at your health service. Before you decide whether or not to participate, it is important for you to understand why the research is being done and what is involved. Please take time to read the following information carefully. You are entirely free to decide whether or not to take part in this study and you are free to discontinue your involvement with the study at any time by informing us. If there is anything that is not clear, or if you would like more information, please do not hesitate to ask. If you agree to take part, you will be asked to sign the attached Participant Consent Form.
What is the TORPEDO study? TORPEDO stands for the ‘Treatment of cardiovascular risk in primary health care using electronic decision support’. Several months ago your health service agreed to participate in this project. We provided an electronic decision support tool to assist health staff in the management of cardiovascular disease (CVD) risk. This tool is called HealthTracker. It aims to improve the uptake of best practice evidence by giving management advice to health staff and patients based on national guidelines.
How is HealthTracker being evaluated overall? The HealthTracker software has been randomly allocated to 30 health services with an additional 30 health services who did not receive HealthTracker acting as a comparison group. There are three parts to evaluating whether HealthTracker improves quality of care for CVD risk management. 1. Health service audits were done at baseline and again at the end of 12 months (±6 months). Anonymous data is collected on appropriate screening of cardiovascular risk factors and guideline based prescribing of
recommended medicines for people at high risk of heart attack or stroke. Comparisons will be made between the group who received HealthTracker and the group that did not. 2. Interviews with selected health staff and patients are being conducted to understand the usefulness and acceptability of HealthTracker. 3. Video recording of some healthcare consultations between doctors and patients will be conducted to better understand how HealthTracker is actually used in practice.
Who can participate in the interview? Doctors who have had access to the HealthTracker tool will be randomly selected to participate in the video recording of their patient consultation. Health care consultation recording requires informed consent from both you and your patient.
What will the video recording involve? If you agree to participate in the video recordings of healthcare consultations we will organise recording facilities to be set up at your health service over a maximum one week period. You will control when the recording starts and finishes. The analysis will focus GP Health Professional Video recording Information and Consent_V2_10Feb2013 Page 1 of 5
The TORPEDO Study Evaluation Health Professional Video-recording Information and Consent Sheet
on how you use the HealthTracker tool and communicate information to your patients. After the recording day is completed we will ask you a debrief question at the end of each day of how you felt about being video-taped. During this time some of the health care consultation recordings may be played back to you to explore further how you used the tool in practice.
Recording healthcare consultations may be uncomfortable to some individuals. To minimise any potential unwanted effects and to provide safeguards for you the following will be undertaken:
You and your patients will decide which aspects of the consultation are recorded and you will have the option of turning off the recording at any time. You and your patients will have the option of reviewing the recorded data and if you feel that any footage should not be used (for any reason) we will not use this for any analysis. The data will be analysed by a small team of experienced researchers and will be stored on a secure, password protected folder accessible only to the researchers. We seek permission to use quotes from your interview and/ or recorded consultations in our reports but none of this information will allow identification of an individual person. We will use the data received from the video recording for this study and any secondary analysis required in the future.
At the conclusion of the research, data will be stored for 7 years minimum in accordance with ethics and governance regulations.
We appreciate your work commitments and will fit with your schedule
Costs Participation in the health professional qualitative interviews will not cost you anything, nor will you be paid.
Confidentiality We will ask you for your contact details in order to communicate with you if the need arises. This information will be treated as strictly confidential and not released to anyone outside the research team. The information provided to the researchers will not include any personal information and will be identified by a unique study number only.
Study management This is an independent study managed by The George Institute for Global Health, University of Sydney, in collaboration with several partner organisations. Funding is provided by the National Health and Medical Research Council.
Ethics Approval This study has been approved by The University of Sydney Human Ethics Administration Committee and . Any person with concerns or complaints about the conduct of this study may contact The Manager, Human Ethics Administration on +61 2 8627 8176 (telephone); +61 2 8627 8177 (fax); or email [email protected].
GP Health Professional Video recording Information and Consent_V2_10Feb2013 Page 2 of 5
The TORPEDO Study Evaluation Health Professional Video-recording Information and Consent Sheet
This study has been approved by the Aboriginal Health & Medical Research Council (AH&MRC) of New South Wales. Any person with concerns or complaints about the conduct of this study may contact The Chairperson, on 9212 4777 and quote protocol number (778/11). ~~~ Thank you for your participation ~~~
GP Health Professional Video recording Information and Consent_V2_10Feb2013 Page 3 of 5
The TORPEDO Study Evaluation Health Professional Video-recording Information and Consent Sheet
Consent Form
Before you sign this form please be sure that you understand what it means to be part of the study. Please read (or have read to you) the Participant Information Sheet (Version 2; dated 10Feb2013). Please ask the study team member to answer any questions you have.
It is important to understand: You may decide not to take part in the study. There will be no penalty against you at work. You can stop taking part at any time. Information you give will be used for this study and any post-study analysis required in the future. It will be stored in a secure place. Only study team members will have access. Your name and details will not be made public. Nothing written in reports will link you personally to the study. If interviewed, you agree that the interview will be taped and some words (not your name) can be used in the study reports.
Participant to complete:
I have a copy of the study information sheet and have yes no had an opportunity to ask questions about the study □ □
I agree to take part in an interview for the study. yes no □ □
I agree that the interview be audiotaped yes no □ □
I agree that some of my words (not my name) may be yes no used in the study reports □ □
I understand that I have the right to withdraw my yes no consent and cease involvement in the study at any □ □ time without penalty, either financially or personally
GP Health Professional Video recording Information and Consent_V2_10Feb2013 Page 4 of 5
The TORPEDO Study Evaluation Health Professional Video-recording Information and Consent Sheet
Participant to complete:
I, ______(name), of
______(address), hereby consent to take part in this study.
Signature: Date:
Interviewer’s name:
I have explained the nature and purpose of the study to the above participant and have answered their questions.
Signature: Date:
Contact details:
If you have any questions regarding this research project please contact:
Dr David Peiris (02) 9993 4513 The George Institute For Global Health Level 10, King George V Building Missenden Rd Camperdown NSW 2050
The Manager (02) 8627 8177 Human Ethics Administration The University of Sydney Level 6 Jane Foss Russell Building Camperdown NSW 2006
The Chairperson (02) 9698 1099 AH&MRC Ethics Committee PO Box 1565 Strawberry Hills NSW 2012
GP Health Professional Video recording Information and Consent_V2_10Feb2013 Page 5 of 5
The TORPEDO Study Evaluation Patient Information and Consent Sheet
Participant Information Sheet
You are invited to take part in the evaluation of a new electronic decision support software tool that has been implemented as a part of a research trial at your health service. Before you decide whether or not to participate, it is important for you to understand why the research is being done and what is involved. Please take time to read the following information carefully. You are entirely free to decide whether or not to take part in this study and you are free to discontinue your involvement with the study at any time by informing us. If there is anything that is not clear, or if you would like more information, please do not hesitate to ask. If you agree to take part, you will be asked to sign the attached Consent Form.
What is the TORPEDO study? TORPEDO stands for the ‘Treatment of cardiovascular risk in primary health care using electronic decision support’. Several months ago your health service agreed to participate in this project. We provided an electronic decision support software tool to assist health staff in the management and prevention of heart disease and stroke. This software tool is called HealthTracker. It aims to promote best practice care by giving management advice to health staff and patients based on national guidelines.
How is HealthTracker being evaluated? The HealthTracker software has been randomly allocated to 30 health services with an additional 30 services who did not receive HealthTracker acting as a comparison group. There are three parts to evaluating whether HealthTracker improves the quality of care provided. 1. Health service audits were done at the beginning and again at the end of 12 months. Anonymous data is collected on how well heart disease risk factors are measured and whether people at high risk of heart attack or stroke are being prescribed the recommended medicines. Comparisions will be made between the group who received the HealthTracker software and the group that did not. 2. Interviews with selected health staff and patients are being conducted to understand the usefulness and acceptability of HealthTracker. 3. Video recording of some healthcare consultations between doctor and patients will be conducted to better understand how HealthTracker is actually used in practice.
We are inviting you to have your doctor consultation video recorded and participate in the interview evaluation. You have the option of doing either the video recording of your consultation and interview or just the video recording of your consultation, depending on your preference. Health care consultation recording requires informed consent from both you and your doctor.
What will the evaluation involve? Interviews will be conducted by a research team member and will take around 30 to 45 minutes. We will explore your opinions about the HealthTracker tool. We are interested in any impact it may have had on the care you received and whether it assisted you in making decisions about your health. We will ask you
GP Patient Information and Consent Form_Version 3_10Feb2013 Page 1 of 4
The TORPEDO Study Evaluation Patient Information and Consent Sheet
if there were any benefits, and if there were any problems associated with its use. Interviews will be digitally recorded, professionally transcribed and your name and personal details will be removed when we analyse the information you provide. You will be identified by a unique identification number.
Health care consultation video recording will take place at your doctor’s room or clinic. If you agree to participate in the video recording of your health care consultation, you and your doctor will decide when the recording starts and finishes. After the recording, a research team member will arrange a time and a place to conduct an interview with you. During this interview some of your health care consultation recording may be played back to explore further your opinions on how the tool was used.
Recording healthcare consultations may be uncomfortable to some individuals. To minimise any potential unwanted effects and to provide safeguards for you the following will be undertaken:
You and your doctor will decide which aspects of the consultation are recorded and you will have the option of turning off the recording at any time. You and your doctor will have the option of reviewing the recorded data and if you feel that any footage should not be used (for any reason) we will not use this for any analysis. The data will be analysed by a small team of experienced researchers and will be stored on a secure, password protected folder accessible only to the researchers. We seek permission to use quotes from your interview and/ or recorded consultations in our reports but none of this information will allow identification of an individual person, and will only be identified by a unique ID number.
At the conclusion of the research, the information collected will be securely stored for 7 years minimum in accordance with ethics regulations.
Costs Participation in the health care consultation video recording and/or interviews will not cost you anything, nor will you be paid. Confidentiality We will ask you for your contact details in order to communicate with you if the need arises. This information will be treated as strictly confidential and not released to anyone outside the research team. The information provided to the researchers will not include any personal information and will be identified by a unique study number only. Study management This is an independent study managed by The George Institute for Global Health, University of Sydney, in collaboration with several partner organisations. Funding is provided by the National Health and Medical Research Council. Ethics Approval This study has been approved by The University of Sydney Human Ethics Administration Committee. Any person with concerns or complaints about the conduct of this study may contact The Manager, Human Ethics Administration on +61 2 8627 8176 (telephone); +61 2 8627 8177 (fax); or email [email protected]
~~~ Thank you for your participation ~~~
GP Patient Information and Consent Form_Version 3_10Feb2013 Page 2 of 4
The TORPEDO Study Evaluation Patient Information and Consent Sheet
Consent Form
Before you sign this form please be sure that you understand what it means to be part of the study. Please read (or have read to you) the Participant Information Sheet (Version 3; dated 10February2013). Please ask the study team member to answer any questions you have.
It is important to understand: You may decide not to take part in the study. There will be no penalty against you at work. You can stop taking part at any time. Information from the video-recording and/or interviews will be used for this study and any additional post-study analysis required in the future. All data received from you (videotapes generated and interview data) will be stored in a secure place. Only study team members will have access. Your name and details will not be made public. Nothing written in reports will link you personally to the study.
If interviewed, you agree that the interview will be audio taped and
some of your words (not your name) can be used in study reports.
I have a copy of the study information sheet and have yes no had an opportunity to ask questions about the study. □ □
I agree to take part in an interview for the study. yes no
□ □
I agree that the interview be audiotaped. yes no
□ □
I agree to have my health care consultation with my yes no doctor video taped.
□ □
I agree that some of my words (not my name) may be yes no used in the study reports. □ □
I understand that I have the right to withdraw my yes no consent and cease involvement in the study at any time without penalty, either financially or personally. □ □
GP Patient Information and Consent Form_Version 3_10Feb2013 Page 3 of 4
The TORPEDO Study Evaluation Patient Information and Consent Sheet
Participant to complete:
I, ______(name), of
______(address), hereby consent to take part in this study.
Signature: Date:
Interviewer’s name:
I have explained the nature and purpose of the study to the above participant and have answered their questions.
Signature: Date:
Contact details If you have any questions regarding this research project please contact:
Dr David Peiris (02) 9993 4513 (Telephone) The George Institute For Global Health (02) 9993 4502 (Fax) Level 10, King George V Building Missenden Rd Camperdown NSW 2050
The Manager (02) 8627 8176 (Telephone) Human Ethics Administration (02) 86278177 (Fax) The University of Sydney [email protected] Level 6 Jane Foss Russell Building Camperdown NSW 2006
GP Patient Information and Consent Form_Version 3_10Feb2013 Page 4 of 4
The TORPEDO Study Evaluation Patient Information and Consent Sheet
Participant Information Sheet
You are invited to take part in the evaluation of a new electronic decision support software tool that has been implemented as a part of a research trial at your health service. Before you decide whether or not to participate, it is important for you to understand why the research is being done and what is involved. Please take time to read the following information carefully. You are entirely free to decide whether or not to take part in this study and you are free to discontinue your involvement with the study at any time by informing us. If there is anything that is not clear, or if you would like more information, please do not hesitate to ask. If you agree to take part, you will be asked to sign the attached Consent Form.
What is the TORPEDO study? TORPEDO stands for the ‘Treatment of cardiovascular risk in primary health care using electronic decision support’. Several months ago your health service agreed to participate in this project. We provided an electronic decision support software tool to assist health staff in the management and prevention of heart disease and stroke. This software tool is called HealthTracker. It aims to promote best practice care by giving management advice to health staff and patients based on national guidelines.
How is HealthTracker being evaluated? The HealthTracker software has been randomly allocated to 30 health services with an additional 30 services who did not receive HealthTracker acting as a comparison group. There are three parts to evaluating whether HealthTracker improves the quality of care provided. 1. Health service audits were done at the beginning and again at the end of 12 months. Anonymous data is collected on how well heart disease risk factors are measured and whether people at high risk of heart attack or stroke are being prescribed the recommended medicines. Comparisions will be made between the group who received the HealthTracker software and the group that did not. 2. Interviews with selected health staff and patients are being conducted to understand the usefulness and acceptability of HealthTracker 3. Video recording of some healthcare consultations between doctor and patients will be conducted to better understand how HealthTracker is actually used in practice.
We are inviting you to have your doctor consultation video recorded and participate in the interview evaluation. You have the option of doing either the video recording of your consultation and interview or just the video recording of your consultation, depending on your preference. Health care consultation recording requires informed consent from both you and your doctor.
What will the evaluation involve? Interviews will be conducted by a research team member and will take around 30-45 minutes. We will explore your opinions about the HealthTracker tool. We are interested in any impact it may have had on the care you received and whether it assisted you in making decisions about your health. We will ask you ACCHS Patient Information and Consent_Version 3_10Feb2013 Page 1 of 5
The TORPEDO Study Evaluation Patient Information and Consent Sheet
if there were any benefits and if there were any problems associated with its use. Interviews will be digitally recorded, professionally transcribed and your name and personal details will be removed when we analyse the information you provide. You will be identified by a unique identification number.
Health care consultation video recording will take place at your doctor’s office or health service. If you agree to participate in the video recording of your health care consultation, you and your doctor will decide when the recording starts and finishes. After the recording we will arrange a time and place to conduct an interview with you. During this interview some of your health care consultation recording may be played back to explore further your opinions on how the tool was used.
Recording healthcare consultations may be uncomfortable to some individuals. To minimise any potential unwanted effects and to provide safeguards for you the following will be undertaken:
You and your doctor will decide which aspects of the consultation are recorded and you will have the option of turning off the recording at any time. You and your doctor will have the option of reviewing the recorded data and if you feel that any footage should not be used (for any reason) we will not use this for any analysis and will erase the recorded data. The data will be analysed by a small team of experienced researchers and will be stored on a secure, password protected folder accessible only to the researchers. We seek permission to use quotes from your interview and/ or recorded consultations in our reports but none of this information will allow identification of an individual person, and will only be identified by a unique ID number.
At the conclusion of the research, the information collected will be securely stored for 7 years minimum in accordance with ethics regulations.
Costs Participation in the health care consultation video recording and/or interviews will not cost you anything, nor will you be paid. Confidentiality We will ask you for your contact details in order to communicate with you if the need arises. This information will be treated as strictly confidential and not released to anyone outside the research team. The information provided to the researchers will not include any personal information and will be identified by a unique study number only. Study management This is an independent study managed by The George Institute for Global Health, University of Sydney, in collaboration with several partner organisations including the Aboriginal Health & Medical Research Council and the Queensland Aboriginal and Islander Health Council. Funding is provided by the National Health and Medical Research Council. Ethics Approval This study has been approved by the Aboriginal Health & Medical Research Council (AH&MRC) of New South Wales. Any person with concerns or complaints about the conduct of this study may contact The Chairperson, on 9212 4777 and quote protocol number (778/11).
ACCHS Patient Information and Consent_Version 3_10Feb2013 Page 2 of 5
The TORPEDO Study Evaluation Patient Information and Consent Sheet
~~~ Thank you for your participation ~~~
ACCHS Patient Information and Consent_Version 3_10Feb2013 Page 3 of 5
The TORPEDO Study Evaluation Patient Information and Consent Sheet
Consent Form
Before you sign this form please be sure that you understand what it means to be part of the study. Please read (or have read to you) the Participant Information Sheet (Version 3; dated 10February2013). Please ask the study team member to answer any questions you have.
It is important to understand: You may decide not to take part in the study. There will be no penalty against you at work. You can stop taking part at any time. Information from the video-recording and/or interviews will be used for this study and any additional post-study analysis required in the future. All data received from you (video tapes and interview data) will be stored in a secure place. Only study team members will have access. Your name and details will not be made public. Nothing written in reports will link you personally to the study.
If interviewed, you agree that the interview will be audio taped and
some of your words (not your name) can be used in the study reports.
I have a copy of the study information sheet and have yes no had an opportunity to ask questions about the study. □ □
I agree to take part in an interview for the study. yes no
□ □
I agree that the interview be audiotaped. yes no
□ □
I agree to have my health care consultation with my yes no care provider video taped
□ □
I agree that some of my words (not my name) may be yes no used in the study reports? □ □
I understand that I have the right to withdraw my yes no consent and cease involvement in the study at any time without penalty, either financially or personally. □ □
ACCHS Patient Information and Consent_Version 3_10Feb2013 Page 4 of 5
The TORPEDO Study Evaluation Patient Information and Consent Sheet
Participant to complete:
I, ______(name), of
______(address), hereby consent to take part in this study.
Signature: Date:
Interviewer’s name:
I have explained the nature and purpose of the study to the above participant and have answered their questions.
Signature: Date:
Contact details:
If you have any questions regarding this research project please contact:
Dr David Peiris (02) 9993 4513 The George Institute For Global Health Level 10, King George V Building Missenden Rd Camperdown NSW 2050
The Chairperson (02) 9698 1099 AH&MRC Ethics Committee PO Box 1565 Strawberry Hills NSW 2012
ACCHS Patient Information and Consent_Version 3_10Feb2013 Page 5 of 5
The Treatment of Cardiovascular Risk in Primary Care Using Electronic Decision Support, TORPEDO project
Request to not include information in audits of health records
I, ______(name), of
______(address), have reviewed the information about this project.
I understand that my health service will conduct audits of health
records to look at the quality of health care provided. These
audits are computer generated and will not include any personal details such as names and addresses.
I do not wish to have my health information included in these
audits. I understand that my decision will not have any effect on the health care that I receive.
Signature______
If you have any further questions regarding this project please contact:
Dr David Peiris (02) 9993 4513 The George Institute For Global Health Level 10, King George V Building Missenden Rd Camperdown NSW 2050
The Chairperson (02) 9698 1099 AH&MRC Ethics Committee
PO Box 1565 Strawberry Hills NSW 2012
TORPEDO STUDY A research study to see if an electronic decision support tool (HealthTracker-CVD) prevents and treats Cardiovascular Disease
TORPEDO Study: Team Climate Inventory and Job Satisfaction Surveys
INFORMATION FOR PARTICIPANTS
Introduction You are invited to take part in two surveys that are trying to understand if team environment and job satisfaction at general practices or Aboriginal Community Controlled Health Services predict better management and prevention of cardiovascular disease. The surveys will be undertaken as part of the TORPEDO study that is testing whether HealthTracker- CVD tool, an electronic decision support tool, used by healthcare providers helps people manage and prevent cardiovascular disease.
What is required? If you participate you will be asked to complete two surveys. Survey 1: Team Climate Inventory questionnaire There are a series of questions which ask about the climate or atmosphere in your practice or health service. It asks about how people tend to work together in your practice/health service, how frequently you interact, the organisation’s aims and objectives, and how much practical support and assistance is given towards the implementation of new and improved ways of doing things. Survey 2: Job Satisfaction questionnaire This section has questions about how satisfied you are with your job. Both questionnaires together usually take around 15-20 minutes to complete. There are no right or wrong answers, and no-one other than the researchers will see your answers. The information you give will be de-identified.
Ethical considerations Any personal information that is collected will be de-identified (i.e. no names will be used and the practice/ health service will not be identified in any analyses). Your responses will remain confidential and will be available only to authorised research staff working on the TORPEDO study. Participation is entirely optional. You do not have to take part in it. If you do take part, you can withdraw at any time without having to give a reason. Whatever your decision, please be assured that it will not affect your work/job. Please sign the consent form only if you want to participate in the surveys and only after you have had a chance to ask your questions and have received answers that you are happy with.
MASTER TORPEDO GP and Staff_Information for Participants and Consent Form Version 1.0 November 26, 2012 Page 1 of 3
Who can help you with further information? Investigator: Associate Professor David Peiris The George Institute for Global Health PO Box M201 Missenden Rd, Sydney NSW 2050 Tel: 02 9993 4500 Fax: 02 9993 4502
Ethics Approval The surveys have been approved by The University of Sydney Human Ethics Administration Committee, and Aboriginal Health and Medical Research Council Ethics Committee. Any person with concerns or complaints about the conduct of this study may contact The Manager, Human Ethics Administration on 02 8627 8176 or the AH&MRC Executive Officer on 02 9212 4777.
This information sheet is for you to keep.
MASTER TORPEDO GP and Staff_Information for Participants and Consent Form Version 1.0 November 26, 2012 Page 2 of 3
TORPEDO STUDY
A research study to see if an electronic decision support tool (HealthTracker-CVD) prevents and treats Cardiovascular Disease
TORPEDO Study: Team Climate Inventory and Job Satisfaction Surveys
PARTICIPANT CONSENT FORM
I have read the participant information sheet
I feel free to accept or refuse to participate in the surveys
I have had a chance to ask questions and all of my questions have been answered to my satisfaction
I have been given and I understand the information on the surveys concerning its nature, purpose, and duration
By signing this form, I give my free and informed consent to take part in these surveys as outlined in the information sheet and this consent form. I understand that I am free to withdraw from the surveys at any given time. I have been given a copy of this consent form. By signing this form I have not given up my legal rights.
PARTICIPANT NAME …………………………………………………………......
PARTICIPANT SIGNATURE: …………………………………………………………...Date:…..……………………
I have explained the nature and purpose of the study to the above participant and have answered their questions.
INTERVIEWER NAME: …….………………………………………………………………………………….…………
INTERVIEWER SIGNATURE: …………………………………………………………...Date:…..……………………
MASTER TORPEDO GP and Staff_Information for Participants and Consent Form Version 1.0 November 26, 2012 Page 3 of 3 APPENDIX D: Instruments and Tools
a) Health professional interview guide (see chapter 5)
b) Patient interview guide
c) Surveys
300
TORPEDO Study Patient interview guide (Intervention)
Introductions
Thank you for agreeing to be interviewed.
We are studying doctor-patient communication, especially in relation to how heart disease risk, management and treatment are communicated, and how patients may understand this information.
Everything you say will be strictly confidential. We will make a transcript from the audio recording of this interview, but your name and identifying details will be removed. You will be identified by a unique ID number. The interview will take around 30-45 minutes. If you would not prefer to answer some questions, or would like to stop the interview at any time, that’s fine, just let me know.
Do you have any questions before we start?
Review of video- I’d like to play back a few segments from your consultation with the doctor recording of GP consultation
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 1 of 9
Area of Interest Initial Broad Possible Probing Questions Descriptive Questions (These are a guide only. Depending on what the patient tells you, you do not have to ask all these questions or use the words exactly as written.)
HealthTracker-CVD Has HealthTracker had - HealthTracker is a decision support tool that your GP used on the computer during you consultation tool and shared any impact on the o Here are two screenshots of HealthTracker decision making decisions you made about your health? If - Lifestyle changes yes, what were they? - use of medicines - involvement of other care providers - impact on family - social life
How do you prefer your - To what extent would you like to be involved? doctor to make decisions about your heart disease risk and treatment?
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 2 of 9
Experience using What was your opinion of - How useful did you find the tool? HealthTracker tool HealthTracker the doctor How did you find the recommendations given to you from the tool? used during your visit? - o Reliable o Helpful - How easy was it to understand the information in HealthTracker? o How did you find the colour coded information? - Did the information given to you from the tool have an impact on your health care consultation? - How did you find the Heart Age risk graph? o Was the graph easy to understand? If yes, what was it that made it easy? If no, what was that made it difficult or confusing? o How was your experience in being presented the heart disease risk graph in this format? Heart age Risk projection % risk of heart attack Use of colour coded risk categories Traffic light recommendations Summary print out Recommended guidelines
Management of How could your health - How does your GP care for the health of your heart? Heart disease care be improved? o Blood pressure, cholesterol, smoking, diet, or exercise
- Have you had any discussions with your doctor about heart disease risk? If yes, what type of discussions did you What about specifically have? heart health? - Treatment/mediations, diet, smoking, exercise?
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 3 of 9
CVD risk and heart What has the doctor said - Cardiovascular disease also means heart disease and some terms he may have used are high blood pressure, high age about your risk of cholesterol, stroke, heart attack. communication cardiovascular disease? - How did your doctor explain your risk of heart disease to you? - Did the doctor tell you that you need to be on medication for heart disease? If yes, how did you feel about starting new treatment? - Do you remember the percentage risk your doctor told you? o
How would you describe - Would you say you are low risk, medium risk, or high risk? Why your chance/risk of Have you heard about heart age, and if yes, what do you know about it? having heart disease? - o
Shared Decision How do you prefer your - To what extent would you like to be involved in the decision making? making doctor to make decisions about your heart disease risk and treatment?
Actions Did your doctor - Smoking recommend any changes Control of BP or Cholesterol by medications to reduce your heart risk? - Lifestyle Are you planning to make - any (lifestyle) changes as a result of this consultation?
Health care Do you feel that your - How is your health in general? experience health needs were What are some of the good/bad things about your health care? adequately met in the - time you spent with your - Why did you go visit your doctor? What were your health needs? GP? - Did you discuss heart disease, such as high blood pressure and cholesterol? If yes, is this discussed regularly at your doctor visit?
For Patients aged Please complete Vulnerable Elders Survey (VES-13) 75+
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 4 of 9
Ongoing What types of resources - What would you like from your GP to improve management and prevention of heart disease? management of would you like your Would you like to see a tool like this for use in your home on your computer? heart disease risk doctor to provide to help - manage your heart disease?
Note: Some questions in the guidelines will be modified in response to participant’s answers and themes emerging from the data.
I would like to finish this interview by asking a few general questions about you. This will be used to describe the whole participant group.
1. What is your age?
2. What is your postcode?
3. Gender Male Female
4. Ethnicity Aboriginal (tick as many as apply to you) Torres Strait Islander Both Aboriginal and Torres Strait Islander Other (please specify):
5. What language(s) do you speak at home?
6. Where are you currently living? Own home/ flat A home/flat owned by family A rented home/flat A friend’s house/flat A hostel/ other temporary accommodation Other (please specify):
7. What is your current relationship status? Married Divorced/ Separated Widowed MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 5 of 9
De facto/ long term partner Single Other relationship (please specify):
8. What is your highest level of school Year 10 or below education? Year 11-12 College diploma or similar Undergraduate university degree Postgraduate university degree
9. What is your employment status? Full-time work (tick as many as apply) Part-time/ Casual work Full or part-time study Unpaid/ Volunteer work Unemployed Other (please specify):
10. Do you have regular access to the following: Yes No Phone Car Computer Internet
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 6 of 9
For patients aged 75+ (VES-13)
The Vulnerable Elders Survey (VES-13) is a simple function-based tool for screening community-dwelling populations to identify older persons at risk for health deterioration. The VES considers age, self-rated health, limitations in physical function, and functional disabilities.
1. Age 1 POINT FOR AGE 75-84 3 POINTS FOR AGE ≥ 85 2. In general, compared to other people your Poor,* (1 POINT) age, would you say that your health is: Fair,* (1 POINT) Good, Very good, or Excellent
3. How much difficulty, on average, do you SCORE: 1 POINT FOR EACH * RESPONSE have with the following physical activities: IN Q3a THROUGH f . MAXIMUM OF 2 POINTS.
No A little Some A lot of Unable to difficulty difficulty difficulty difficulty do a. stooping, crouching or kneeling? * * b. lifting, or carrying objects as heavy as * * 10 pounds? c. reaching or extending arms above * * shoulder level? d. writing, or handling and grasping small * * objects? e. walking a quarter of a mile? * * f. heavy housework such as scrubbing floors or * * washing windows?
4. Because of your health or a physical SCORE: 4 POINTS FOR ONE OR MORE * RESPONSES IN Q4a condition, do you have any difficulty: THROUGH Q4e a. shopping for personal items (like toilet items or medicines)? Yes do you get help with shopping? Yes * No No Don’t do is that because of your health? Yes * No b. managing money (like keeping track of expenses or paying bills)? MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 7 of 9
Yes do you get help with managing money? Yes * No No Don’t do is that because of your health? Yes * No c. walking across the room? USE OF CANE OR WALKER IS OK. Yes Do you get help with walking? Yes * No No Don’t do is that because of your health? Yes * No d. doing light housework (like washing dishes, straightening up, or light cleaning)?
Yes Do you get help with light housework? Yes * No No Don’t do is that because of your health? Yes * No e. bathing or showering? Yes Do you get help with bathing or showering? Yes * No No Don’t do is that because of your health? Yes * No
Thank you for your participation.
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 8 of 9
Interview Staff only
Participant category Regular client with CVD/CKD/Diabetes Regular client without CVD/CKD/Diabetes Family of Client Non-user/ infrequent user of the health service
Notes:
MASTER_TOPREDO Patient Interview Guidelines Version_Intervention_ 2.1_7September2013 Page 9 of 9
TORPEDO Study Patient interview guide (Usual Care)
Introductions
Thank you for agreeing to be interviewed.
We are investigating doctor-patient communication, especially in relation to how heart disease risk, management and treatment are communicated, and how patients may understand this information
Everything you say will be strictly confidential. We will make a transcript from the audio recording of this interview, but your name and identifying details will be removed. You will be identified by a unique ID number.
The interview will take around 30-45 minutes. If you would not prefer to answer some questions, or would like to stop the interview at any time, that’s fine, just let me know. Do you have any questions before we start?
Area of Interest Initial Broad Possible Probing Questions Descriptive Questions (These are a guide only. Depending on what the patient tells you, you do not have to ask all these questions or use the words exactly as written.) I’d like to play back a few segments from your consultation with the doctor Review of video- recording of GP consultation
MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 1 of 7
Management of How could your health - How does your GP care for the health of your heart? Heart disease care be improved? o Blood pressure, cholesterol, smoking, diet, or exercise
- Have you had any discussions with your doctor about heart disease risk? If yes, what type of discussions did you What about specifically have?* heart health? - Treatment/mediations, diet, smoking, exercise?
CVD risk and heart What has the doctor said - Cardiovascular disease also means heart disease and some terms he may have used are high blood pressure, high age about your risk of cholesterol, stroke, heart attack. communication cardiovascular disease? - How did your doctor explain your risk of heart disease to you? - Did the doctor tell you that you need to be on medication for heart disease? If yes, how did you feel about starting new treatment? - Do you remember the percentage risk your doctor told you? o
How would you describe - Would you say you are low risk, medium risk, or high risk? Why your chance/risk of Have you heard about heart age, and if yes, what do you know about it? having heart disease? -
Shared Decision How do you prefer your - To what extent would you like to be involved in the decision making? making doctor to make decisions about your heart disease risk and treatment?
Actions Did your doctor - Smoking recommend any changes Control of BP or Cholesterol by medications to reduce your heart risk? - Lifestyle Are you planning to make - any (lifestyle) changes as a result of this consultation?
MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 2 of 7
Health care Do you feel that your - How is your health in general? experience health needs were What are some of the good/bad things about your health care? adequately met in the - time you spent with your - Why did you go visit your doctor? What were your health needs? GP? - Did you discuss heart disease, such as high blood pressure and cholesterol? If yes, is this discussed regularly at your doctor visit?
Ongoing What types of resources - What would you like from your GP to improve management and prevention of heart disease? management of would you like your Would you like to see a tool like this for use in your home on your computer? heart disease risk doctor to provide to help - manage your heart disease?
For Patients aged Please complete Vulnerable Elders Survey (VES-13) 75+
Note: Some questions in the guidelines will be modified in response to participant’s answers and themes emerging from the data.
I would like to finish this interview by asking a few general questions about you. This will be used to describe the whole participant group.
1. What is your age?
2. What is your postcode?
3. Gender Male Female
4. Ethnicity Aboriginal (tick as many as apply to you) Torres Strait Islander Both Aboriginal and Torres Strait Islander Other (please specify):
5. What language(s) do you speak at home?
MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 3 of 7
6. Where are you currently living? Own home/ flat A home/flat owned by family A rented home/flat A friend’s house/flat A hostel/ other temporary accommodation Other (please specify):
7. What is your current relationship status? Married Divorced/ Separated Widowed De facto/ long term partner Single Other relationship (please specify):
8. What is your highest level of school Year 10 or below education? Year 11-12 College diploma or similar Undergraduate university degree Postgraduate university degree
9. What is your employment status? Full-time work (tick as many as apply) Part-time/ Casual work Full or part-time study Unpaid/ Volunteer work Unemployed Other (please specify):
10. Do you have regular access to the following: Yes No Phone Car Computer Internet MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 4 of 7
For patients aged 75+ (VES-13)
The Vulnerable Elders Survey (VES-13) is a simple function-based tool for screening community-dwelling populations to identify older persons at risk for health deterioration. The VES considers age, self-rated health, limitations in physical function, and functional disabilities.
1. Age 1 POINT FOR AGE 75-84 3 POINTS FOR AGE ≥ 85 2. In general, compared to other people your Poor,* (1 POINT) age, would you say that your health is: Fair,* (1 POINT) Good, Very good, or Excellent
3. How much difficulty, on average, do you SCORE: 1 POINT FOR EACH * RESPONSE have with the following physical activities: IN Q3a THROUGH f . MAXIMUM OF 2 POINTS.
No A little Some A lot of Unable to difficulty difficulty difficulty difficulty do a. stooping, crouching or kneeling? * * b. lifting, or carrying objects as heavy as * * 10 pounds? c. reaching or extending arms above * * shoulder level? d. writing, or handling and grasping small * * objects? e. walking a quarter of a mile? * * f. heavy housework such as scrubbing floors or * * washing windows?
4. Because of your health or a physical SCORE: 4 POINTS FOR ONE OR MORE * RESPONSES IN Q4a condition, do you have any difficulty: THROUGH Q4e a. shopping for personal items (like toilet items or medicines)? Yes do you get help with shopping? Yes * No No Don’t do is that because of your health? Yes * No b. managing money (like keeping track of expenses or paying bills)? MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 5 of 7
Yes do you get help with managing money? Yes * No No Don’t do is that because of your health? Yes * No c. walking across the room? USE OF CANE OR WALKER IS OK. Yes Do you get help with walking? Yes * No No Don’t do is that because of your health? Yes * No d. doing light housework (like washing dishes, straightening up, or light cleaning)?
Yes Do you get help with light housework? Yes * No No Don’t do is that because of your health? Yes * No e. bathing or showering? Yes Do you get help with bathing or showering? Yes * No No Don’t do is that because of your health? Yes * No
Thank you for your participation.
MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 6 of 7
Interview Staff only
Participant category Regular client with CVD/CKD/Diabetes Regular client without CVD/CKD/Diabetes Family of Client Non-user/ infrequent user of the health service
Notes:
MASTER_TOPREDO Patient Interview Guidelines Version_usual care_ 2.1_22May13 Page 7 of 7
GP and Non-GP Staff: Treatment of cardiovascular risk in primary care with electronic decision support (TOPREDO)
WARR-COOK-WALL SCALE OF JOB SATISFACTION
Name: ...... Date of Birth: ...... Today’s date: ......
How long have you been working in your present job? …………………………… (years) Please answer the following questions about your job. There are no right or wrong answers no-one other than the researchers will see your answers. The information you give will be de-identified to keep it completely CONFIDENTIAL, so please be as honest as you can. Do not confer with anyone else when completing the questionnaire. Looking at your current job, how satisfied are you with each of the following? Please fill in (using a black or blue pen) the appropriate box (for example: ) on the
scale, ranging from "extremely dissatisfied” to "extremely satisfied":
LOOKING AT YOUR CURRENT JOB, HOW d SATISFIED ARE YOU WITH EACH OF THE
FOLLOWING?
Very Dissatisfied Moderately Dissatisfied Not sure Moderately Satisfie Very Satisfied
Extremely Dissatisfied
Extremely Satisfied 1. The amount of responsibility you are given 2. The freedom to choose your own method of working
3. The amount of variety in your job
4. Your colleagues and fellow workers
5. The physical work conditions
6. Your opportunity to use your abilities
7. Your income
8. The recognition you get for your work
9. Your hours of work
10. Taking everything into consideration, how do you feel about your job?
Researcher ID: Practice ID #: Staff ID #: Date Sent: Data Checked? Date entered OFFICE USE ONLY:
WARR-COOK-WALL Job Satisfaction Scale_November 26, 2012
Role of GP and Non-GP staff at General Practice: Treatment of cardiovascular risk in primary care with electronic decision support (TOPREDO)
Team Climate Inventory Questionnaire
NAME:
GENERAL PRACTICE NAME:
DATE OF BIRTH: ______/______/______D D M M M Y Y Y Y
TODAY’S DATE: ______/______/______D D M M M Y Y Y Y
GENDER: Male Female
ROLES (checks one & answer any respective questions for your role, if applicable):
General Practitioner Country of Graduation ______Year of Graduation ______Do you consult in language other than English? No Yes If Yes, what language ______
Nurse – Are you involved in patient care? No Yes Country of Graduation ______Year of Graduation ______Do you consult in language other than English? No Yes If Yes, what language ______
Aboriginal Health Worker – Are you involved in patient care? No Yes Practice Manager Receptionist IT staff Other ______
Years at current General Practice: ______……………………………………………………………………………………………………………………………………………………. INSTRUCTIONS This questionnaire asks about the climate or atmosphere in your work group or practice. It asks about how people tend to work together in your practice, how frequently you interact, the practice’s aims and objectives, and how much practical support and assistance is given towards the implementation of new and improved ways of doing things. There are no ‘right’ or ‘wrong’ answers to any of the questions – it is more important that you give an accurate and honest response to each question. Do not spend too long on any one question. First reactions are usually best. For each question consider how your practice tends to be in general or how you feel in general about the climate within your practice. Please fill in the appropriate box using a black or blue pen: for example
OFFICE USE Researcher ID: Practice ID #: Staff ID #: Date Checked: Date entered: ONLY:
Team Climate Inventory_TOPREDO Version 1.0_26Nov2012
Team Climate Inventory Questionnaire
PART I COMMUNICATION AND Strongly Disagree Neither Agree Strongly INNOVATION disagree agree nor agree disagree 1 We share information generally in the practice rather than keeping it to ourselves.
The programme or area in which I work 2 functions well and does not have any aspects which need changing Assistance in developing new ideas is 3 readily available. 4 We all influence each other. The practice always functions to the best 5 of its capability. We keep in regular contact with each 6 other. In this practice we take the time needed to 7 develop new ideas. 8 There’s nothing that I really need to change about the way I do my job to be more efficient. People feel understood and accepted by 9 each other. Everyone’s view is listened to, even if it is 10 in a minority. People in the practice never feel tense 11 with one another. The practice is open and responsive to 12 change. People in the practice co-operate in order 13 to develop and apply new ideas. Being part of the practice team is the most 14 important thing at work for the people who work here. 15 I’ve been thinking that I might want to help change something about the programme or area in which I work. 16 We have a ‘we are in it together’ attitude.
17 We interact frequently.
The practice is significantly better than 18 any other in its field. People keep each other informed about 19 work-related issues in the practice. 20 I plan to be involved in changing the programme or area in which I work. 2
Team Climate Inventory Questionnaire
21 Members of the practice provide and share resources to help in the application of new ideas. There are consistently harmonious 22 relationships between people in the practice.
There is a lot of give and take. 23 24 I am working hard to help improve aspects of the programme or area in which I work. We keep in touch with each other as a 25 practice.
People in this practice are always searching for fresh, new ways of looking 26 at problems. The practice consistently achieves the 27 highest targets with ease. There are real attempts to share 28 information throughout the practice. This practice is always moving towards 29 the development of new answers.
Practice members provide practical support for new ideas and their 30 application. Members of the practice meet frequently 31 to talk both formally and informally. We are trying to make sure we keep changes/improvements my program/area 32 has made.
3
Team Climate Inventory Questionnaire
PART II OBJECTIVES Not at all Somewhat Completely
33 How clear are you about what your practice objectives are?
34 To what extent do you think they are useful and appropriate objectives?
35 How far are you in agreement with these objectives?
36 To what extent do you think other practice members agree with these objectives?
37 To what extent do you think your practice’s objectives are clearly understood by other members of the practice?
38 To what extent do you think your practice’s objectives can actually be achieved?
39 How worthwhile do you think these objectives are to you?
40 How worthwhile do you think these objectives are to the organisation?
41 How worthwhile do you think these objectives are to the wider society?
42 To what extent do you think these objectives are realistic and can be attained?
43 To what extent do you think members of your practice are committed to these objectives?
4
Team Climate Inventory Questionnaire
PART III TASK STYLE To a very To some To a very little extent extent great extent
44 Do your practice colleagues provide useful ideas and practical help to enable you to do the job to the best of your ability?
45 Do you and your colleagues monitor each other so as to maintain a higher standard of work?
46 Are practice members prepared to question the basis of what the practice is doing?
47 Does the practice critically appraise potential weaknesses in what it is doing in order to achieve the best possible outcome?
48 Do members of the practice build on each other’s ideas in order to achieve the best possible outcome?
49 Is there a real concern among practice members that the practice should achieve the highest standards of performance?
50 Does the practice have clear criteria which members try to meet in order to achieve excellence as a practice?
Published by The NFER-NELSON Publishing Company Ltd, Darville House, Oxford Road East, Windsor, Berkshire, SL DF, UK First Published 996. Revised edition 999 Copyright © Neil Anderson and Michael West, ASE 996, 999
Reproduced with permission
5
TORPEDO Study End of Study Evaluation (Intervention)
Name of General Practitioner: ______
Name of Participating Health Service (GP/ACCHS):
We are collecting this information to evaluate our electronic decision support tool, HealthTracker-CVD. Your responses to the questions below will give us important background information about you and your practice and will take about 15 minutes to complete. All responses are private and confidential. Your assistance is greatly appreciated.
Section 1: Your Background
1.1 What is your age?
20-29 30-39 40-49 50-59 60 or over
1.2 What is your gender? Male Female
1.3 What is the primary language you speak at home?
1.4 What country were you born in?
1.5 From which university did you obtain your medical degree?
Year graduated:
1.6 Are you vocationally registered? Yes No
1.7 How many sessions per week do you work at this practice?
1.8 How many sessions per week do you work elsewhere? Never Sometimes Often Very often
1.9 How often do you participate in research?
1.10 How often do you conduct your own research?
Researcher Practice ID GP ID #: Date Data Checked? Date entered OFFICE USE ID#: #: Sent: ONLY:
TORPEDO_End of Study GP Evaluation_Intervention_Version 2.0_July 10, 2013_Clean
TORPEDO Study End of Study Evaluation (Intervention)
Section 2: Opinions on HealthTracker-CVD: Electronic Decision Support Tool
For patients over 45 years (or over 35 years for Indigenous patients), on average how often would you use 2.1 HealthTracker-CVD? More than Less than Always 50% of the time 50% of time Never
2.2 Please indicate your agreement or disagreement with the following statements about HealthTracker-CVD tool.
Strongly Disagree Disagree Neutral Agree Strongly agree
Overall, HealthTracker-CVD was easy to use
Calculation of the cardiovascular risk score helped me to improve the quality of care
The recommendations for screening helped me to improve the quality of care
The recommendations for treatment helped me to improve the quality of care
The CVD Risk projection graph increased patient’s awareness of the health of their heart
Overall, HealthTracker-CVD increased the quality of my interaction with the patient during the consult
I would like to use HealthTracker-CVD after the study finishes
Section 3: Practice Characteristics
3.1 Which of the following best describes access to bulk- (please choose one) billing at your practice? Exclusively bulk-billing
Selective bulk-billing (e.g. children,
seniors, concession card holders)
No bulk-billing Other billing arrangements (please specify):
TORPEDO_End of Study GP Evaluation_Intervention_Version 2.0_10July2013_Clean 2 TORPEDO Study End of Study Evaluation (Intervention)
Section 4: Use of Information Technology
Several times a day
Daily 4.1 Weekly
Monthly
How often do you use the Internet for personal and/or professional use, including e-mail from home, work, or another Less than month or not at all location?
4.2 Overall how satisfied are you with Very Very the computer systems at your Unsatisfied Unsatisfied Neutral Satisfied Satisfied practice?
4.3 How much of a barrier is each of the following to successful implementation of computer systems at your practice? Not a barrier Minor barrier Major barrier
Staff training
Privacy/ Security concerns
Medical software limitations Technical limitations (e.g. slow response time of computers, poor technical support
Costs (e.g. additional RAM, server, computer (s), upgrade, etc.)
Frequency of technology changes (e.g. technology adaptation such as EMRs, SideBar, Clinical Audit Tool, Doctor’s Control Panel, PCEHR, etc.)
4.4 Please indicate how positive the impact of computer systems has been for each of the areas below.
Very Somewhat Somewhat negative negative No effect positive Very positive
The practice of evidence based medicine
Patient-doctor communication
Patient privacy TORPEDO_End of Study GP Evaluation_Intervention_Version 2.0_10July2013_Clean 3 TORPEDO Study End of Study Evaluation (Intervention)
Practice cost efficiencies
Overall patient safety (e.g. reduction in medication errors)
If there is anything further you would like to discuss or clarify please contact:
Bindu Patel Project Manager PO Box M201 Missenden Rd, NSW 2050 [email protected]; Telephone: 02 99934546; Mobile: 0420317195
Thank you for your participation!
TORPEDO_End of Study GP Evaluation_Intervention_Version 2.0_10July2013_Clean 4
TORPEDO Study End of Study Evaluation (Usual Care)
Name of General Practitioner: ______
Name of Participating Health Service (GP/ACCHS):
We are collecting this information to evaluate our electronic decision support tool, HealthTracker-CVD. Your responses to the questions below will give us important background information about you and your practice and will take about 15 minutes to complete. All responses are private and confidential. Your assistance is greatly appreciated.
Section 1: Your Background
1.1 What is your age?
20-29 30-39 40-49 50-59 60 or over
1.2 What is your gender? Male Female
1.3 What is the primary language you speak at home?
1.4 What country were you born in?
1.5 From which university did you obtain your medical degree?
Year graduated:
1.6 Are you vocationally registered? Yes No
1.7 How many sessions per week do you work at this practice?
1.8 How many sessions per week do you work elsewhere? Never Sometimes Often Very often
1.9 How often do you participate in research?
1.10 How often do you conduct your own research?
Researcher Practice ID GP ID #: Date Data Date OFFICE USE ID#: #: Sent: Checked? entered ONLY:
TORPEDO_End of Study GP Evaluation_Usual Care_Version 2.0_July 10, 2013_clean
TORPEDO Study End of Study Evaluation
(Usual Care)
Section 2: Practice Characteristics
2.1 Which of the following best describes access to bulk- (please choose one) billing at your practice? Exclusively bulk-billing
Selective bulk-billing (e.g. children,
seniors, concession card holders)
No bulk-billing Other billing arrangements (please specify):
Section 3: Use of Information Technology
Several times a day
3.1 Daily
Weekly
Monthly How often do you use the Internet for personal and/or professional use, including e-mail from home, work, or another location? Less than month or not at all
3.2 Overall how satisfied are you with Very the computer systems at your Unsatisfied Unsatisfied Neutral Satisfied Very Satisfied practice?
3.3 How much of a barrier is each of the following to successful implementation of computer systems at your practice? Not a barrier Minor barrier Major barrier
Staff training
Privacy/ Security concerns
Medical software limitations Technical limitations (e.g. slow response time of computers, poor technical support
Costs (e.g. additional RAM, server, computer (s), upgrade, etc.)
Frequency of technology changes (e.g. technology adaptation such as EMRs, SideBar, Clinical Audit Tool, Doctor’s Control Panel, PCEHR, etc.)
TORPEDO_End of Study GP Evaluation_Usual Care_Version 2.0_10July2013_clean 2 TORPEDO Study End of Study Evaluation
(Usual Care)
3.4 Please indicate how positive the impact of computer systems has been for each of the areas below.
Very Somewhat Somewhat negative negative No effect positive Very positive
The practice of evidence based medicine
Patient-doctor communication
Patient privacy
Practice cost efficiencies
Overall patient safety (e.g. reduction in medication errors)
If there is anything further you would like to discuss or clarify please contact:
Bindu Patel Project Manager PO Box M201 Missenden Rd, NSW 2050 [email protected]; Telephone: 02 99934546; Mobile: 0420317195
Thank you for your participation!
TORPEDO_End of Study GP Evaluation_Usual Care_Version 2.0_10July2013_clean 3 APPENDIX E: Papers Arising From, But Not Contained Within This Thesis
Peiris D, Usherwood T, Panaretto K, Harris M, Hunt J, Redfern J, Zwar N, Colagiuri S, Hayman N, Lo S, Patel B, Lyford M, MacMahon S, Neal B, Sullivan D, Cass A, Jackson R, Patel A. Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: the treatment of cardiovascular risk using electronic decision support cluster-randomized trial. Circ Cardiovasc Qual Outcomes, 2015; 8(1): 87-95.
Peiris D, Usherwood T, Panaretto K, Harris M, Hunt J, Patel B, Zwar N, Redfern J, Macmahon S, Colagiuri S, Hayman N, Patel A. The Treatment of cardiovascular Risk in Primary care using Electronic Decision suppOrt (TORPEDO) study-intervention development and protocol for a cluster randomised, controlled trial of an electronic decision support and quality improvement intervention in Australian primary healthcare. BMJ Open, 2012; 2(6): e002177
Peiris D, Agaliotis M, Patel B, Patel A. Validation of a general practice audit and data extraction tool. Aus Fam Physician, 2013; 42(11): 816-9.
Redfern J, Hyun K, Atkins E, Chow C, Briffa T, Patel B, Zwar N, Usherwood T, Li Q, Patel A, Peiris D. Utilisation of Medicare-funded schemes for people with cardiovascular disease. Aust J Prim Health, 2017; 23: 482-488.
330 Original Article
Effect of a Computer-Guided, Quality Improvement Program for Cardiovascular Disease Risk Management in Primary Health Care The Treatment of Cardiovascular Risk Using Electronic Decision Support Cluster-Randomized Trial
David Peiris, MBBS, MIPH, PhD; Tim Usherwood, MBBS, MD; Kathryn Panaretto, MBBS, MPH; Mark Harris, MD; Jennifer Hunt, MBBS, PhD; Julie Redfern, PhD; Nicholas Zwar, MBBS, PhD; Stephen Colagiuri, MD; Noel Hayman, MBBS, MPH; Serigne Lo, PhD; Bindu Patel, MPH; Marilyn Lyford, BHSc;
Downloaded from Stephen MacMahon, DSc; Bruce Neal, MBChB, PhD; David Sullivan, MBBS; Alan Cass, MBBS, PhD; Rod Jackson, PhD; Anushka Patel, MBBS, SM, PhD
Background—Despite effective treatments to reduce cardiovascular disease risk, their translation into practice is limited. Methods and Results—Using a parallel arm cluster-randomized controlled trial in 60 Australian primary healthcare centers,
http://circoutcomes.ahajournals.org/ we tested whether a multifaceted quality improvement intervention comprising computerized decision support, audit/ feedback tools, and staff training improved (1) guideline-indicated risk factor measurements and (2) guideline-indicated medications for those at high cardiovascular disease risk. Centers had to use a compatible software system, and eligible patients were regular attendees (Aboriginal and Torres Strait Islander people aged ≥35 years and others aged ≥45 years). Patient-level analyses were conducted using generalized estimating equations to account for clustering. Median follow-up for 38 725 patients (mean age, 61.0 years; 42% men) was 17.5 months. Mean monthly staff support was <1 hour/site. For the coprimary outcomes, the intervention was associated with improved overall risk factor measurements (62.8% versus 53.4% risk ratio; 1.25; 95% confidence interval, 1.04–1.50; P=0.02), but there was no significant differences in recommended prescriptions for the high-risk cohort (n=10 308; 56.8% versus 51.2%; P=0.12). There were significant treatment escalations (new prescriptions or increased numbers of medicines) for antiplatelet (17.9% versus 2.7%; P<0.001), lipid-lowering by guest on September 11, 2017 (19.2% versus 4.8%; P<0.001), and blood pressure–lowering medications (23.3% versus 12.1%; P=0.02). Conclusions—In Australian primary healthcare settings, a computer-guided quality improvement intervention, requiring minimal support, improved cardiovascular disease risk measurement but did not increase prescription rates in the high- risk group. Computerized quality improvement tools offer an important, albeit partial, solution to improving primary healthcare system capacity for cardiovascular disease risk management. Clinical Trial Registration—URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336630. Australian New Zealand Clinical Trials Registry No. 12611000478910. (Circ Cardiovasc Qual Outcomes. 2015;8:87-95. DOI: 10.1161/CIRCOUTCOMES.114.001235.) Key Words: cardiovascular diseases ◼ primary health care ◼ quality improvement
ardiovascular diseases (CVDs) are the greatest contribu- to use vascular disease preventive drug therapy should be on the Ctors to the global burden of disease, and finding ways to basis of a patient’s overall or absolute cardiovascular risk.2 The reduce this burden are a major challenge faced by health sys- broader application of risk-based care with safe, effective treat- tems worldwide.1 Most guidelines recommend that the decision ments has the potential to reduce disease burden substantially
Received June 25, 2014; accepted December 5, 2014. From The George Institute for Global Health, University of Sydney, Sydney, New South Wales, Australia (D.P., J.R., S.L., B.P., M.L., S.M., B.N., A.P.); Westmead Clinical School (T.U.), The Boden Institute (S.C.), and Sydney Medical School (D.S.) University of Sydney, New South Wales, Sydney, Australia; Queensland Aboriginal and Islander Health Council, Brisbane, Queensland, Australia (K.P.); Centre for Primary Health Care and Equity (M.H.) and School of Public Health and Community Medicine (N.Z.) University of New South Wales, Sydney, New South Wales, Australia; Aboriginal Health and Medical Research Council, Sydney, New South Wales, Australia (J.H.); Inala Indigenous Health Service, Queensland Health, Brisbane, Queensland, Australia (N.H.); Menzies School of Health Research, Darwin, Northern Territory, Australia (A.C.); and School of Population Health, University of Auckland, Auckland, New Zealand (R.J.). The Data Supplement is available at http://circoutcomes.ahajournals.org/lookup/suppl/doi:10.1161/CIRCOUTCOMES.114.001235/-/DC1. Correspondence to David Peiris, MBBS, MIPH, PhD, PO Box M201 Missenden Rd, New South Wales 2050 Australia. E-mail [email protected] © 2015 American Heart Association, Inc. Circ Cardiovasc Qual Outcomes is available at http://circoutcomes.ahajournals.org DOI: 10.1161/CIRCOUTCOMES.114.001235 87 88 Circ Cardiovasc Qual Outcomes January 2015
The Treatment of Cardiovascular Risk using Electronic WHAT IS KNOWN Decision Support (TORPEDO) study was a cluster randomized trial that tested whether a computer-guided QI intervention • Effective treatments to reduce cardiovascular disease comprising point-of-care electronic decision support, audit and risk exist, but their use in routine clinical practice is feedback tools, and clinical workforce training improved CVD limited, and as few as 50% of people at high car- risk management when compared with usual care. diovascular disease risk are prescribed appropriate treatments. Methods • Computerized clinical support tools are a promising strategy to improve healthcare quality. Study Design • Clinical trials in this area are variable in quality, Parallel arm cluster-randomized controlled trial in 60 Australian pri- tend to lack data on clinical parameters, and are not mary healthcare centers. scalable. Included Patients and Health Services WHAT THE STUDY ADDS Health services were eligible to participate if there was exclusive use of 1 of the 2 compliant software systems to record risk factor informa- tion, pathology test results and prescribe medications and a willing-
Downloaded from • This Australian cluster-randomized trial, involving >38 000 people and 60 health services, tested a deci- ness from all clinical staff to use the intervention. The eligible patient population was based on Australian guideline vascular risk screening sion support system, combined with audit and feed- 19 back strategies. recommendations and defined as all Aboriginal and Torres Strait Islander people ≥35 years and all others ≥45 years (no upper age limit) • The intervention results in a 10% absolute improve- who had attended the service ≥3× in the previous 24-month period and ment in screening for cardiovascular disease risk. at least once in the previous 6-month period. The outcome evaluation http://circoutcomes.ahajournals.org/ • However, there were no significant improvements cohort included patients who met these criteria at both baseline and in prescribing recommended medicines to people at end of study data extractions. high cardiovascular disease risk although there were significant improvements in treatment escalation Study Setting (new prescriptions or increased numbers of medi- General practices were recruited from the Sydney region with as- cines) of recommended medicines. sistance from primary healthcare organizations known as Medicare • The findings suggest that computerized tools may Locals. ACCHSs were recruited through collaboration with 2 state play an important role in preventative treatments; representative bodies from NSW and Queensland and included urban, however, there is an important opportunity to rural, and remote services. A $500AUD reimbursement to all partici- improve clinical management further. pating sites was made to assist with study-related activities. All license costs and technical support associated with the intervention were pro-
by guest on September 11, 2017 vided free to intervention sites. The costs associated with patient care occurred as per usual practice. Australia has a universal health insur- and has been shown to be highly cost-effective.3,4 However, ance scheme (Medicare), which subsidizes primary healthcare consul- there has been a failure to implement such a strategy adequately tations on a predominantly fee for service basis. General practices can for both primary and secondary CVD prevention globally.5–7 charge patients above the Medicare rebate at their discretion. ACCHS do not charge above the rebate and receive additional state and fed- Even in high-income countries, the use of recommended medi- eral funding for provision of other primary healthcare services beyond cines in people with established CVD may be as low as 50% general practice care. after 6 months of therapy, with only around one third of people achieving treatment goals.8,9 In Australian general practice and Randomization and Allocation Concealment Aboriginal Community Controlled Health Service (ACCHS) Randomization was in a 1:1 allocation to the intervention or usual care settings, ≈50% of routinely attending adults lacked sufficient stratified at 3 levels: (1) ACCHS versus general practices; (2) service recorded information to evaluate vascular risk and only ≈40% size (<500 patients meeting eligibility criteria versus ≥500); and (3) to 50% of people at high CVD risk were prescribed optimal current participation in a national or state QI program. Permuted block 10,11 randomization was performed centrally, and outcome analyses were guideline-indicated medicines. conducted blinded to randomized allocation. Participating services Strategies to address these gaps in care are generally did not make any special provisions to advertise the trial and their al- complex, multifaceted, and target barriers at the system, location status to patients; however, it would be reasonable to assume provider, and patient levels.12 Quality improvement (QI) that when the tools were used during a consultation patients may have interventions can take many forms and most of the evi- been aware of the intervention. dence about effectiveness is based on observational stud- ies, which have major limitations. Two main strategies that Intervention have been more extensively evaluated are first, clinical Full details of the intervention have been published and are also sum- marized in the Appendix in the Data Supplement.20 In brief, a sin- decision support systems and second, audit and feedback gle screening and management algorithm were developed and then systems. Although both systems have been demonstrated validated, based on a synthesis of recommendations from several to confer modest improvements in practitioner perfor- screening and management guidelines for CVD, kidney disease, and mance,13–17 few trials have targeted CVD risk management diabetes mellitus.21 The algorithm interfaces with 2 clinical practice software systems that together comprise ≈80% of primary healthcare and most of these have focused on single risk factors with record systems in Australia. Data from the patient record prepopulate varying results and with little attention to patient outcomes the tool. Point-of-care recommendations based on that patient’s ab- or intervention costs.15,18 solute CVD risk are provided. If the patient is receiving suboptimal Peiris et al The TORPEDO Cluster-Randomized Trial 89
screening or management, a series of traffic light prompts alert the in the control arm as a result of study participation, an average clus- practitioner to suggested recommendations. A risk communication ter size of 750 patients with 30% of these at high CVD risk, base- tool also assists patients to understand their CVD risk, including line rates of risk factor measurement and appropriate prescribing of how overall risk is affected by changes in individual risk factors.22 50%,10 2α=0.05 and an intraclass correlation coefficient of 0.05. The Identification of screening and management gaps for the whole patient intraclass correlation coefficient was based on data from 3 recent population was also built into a commonly used audit tool. This tool cross-sectional studies in Australian general practices and ACCHSs allows health services to audit health records, identify performance conducted by our group.10,11 gaps, and establish recall/reminder prompts rapidly. It also allows for deidentified data to be exported to a Web-based portal where health services can view peer-ranked performance data benchmarked against Data Analysis other participating trial sites. Patient-level data analysis was performed using SAS enterprise guide Clinical staff were trained in use of the tools and received access 5.1 (SAS Institute Inc, Cary, NC) on an intention-to-treat basis us- to a technical support desk. One face-to-face training visit was sup- ing generalized estimating equations with an exchangeable correla- plemented with ad hoc visits to resolve technical issues as required. tion structure to account for clustering of patients within services. The Bimonthly Webinars were offered with a focus on the practical dem- population defined for the primary analyses was a cohort of eligible onstrations of the tools. Sites allocated to the control arm continued patients whose health record data were extracted at both randomization usual care without access to the intervention tools or training. Services and end-of-study periods. Analyses were conducted using Gaussian in both arms participating in existing QI initiatives continued with and log-binomial generalized estimating equation regressions for con- these programs at their discretion. Intervention was for a minimum tinuous and binary outcomes, respectively. The intervention effects are Downloaded from of 12 months. expressed as unadjusted rate ratios for binary end points and mean dif- ference for continuous end points with 95% confidence intervals (CIs) and P values. Subgroup analyses were performed using the 3 random- Data Collection ization strata. For each subgroup, the primary analysis was repeated Deidentified data extracts were obtained for all patients who met the with the addition of the subgroup variable along with its interaction eligibility criteria with an encrypted identifier code attached to each with treatment. Heterogeneity was assessed based on the significance of the interaction term. Although formal adjustments for multiple tests
http://circoutcomes.ahajournals.org/ patient’s data to allow for longitudinal comparisons. Data extraction was performed using a validated extraction tool at 1 month before were not made, findings are interpreted in the light of the number of randomization to check data quality, at randomization and at the end comparisons made and the level of significance of the result.25 of the study.23 Ethical Considerations Outcomes The study was approved by the University of Sydney Human Research Coprimary outcomes were defined as follows: Ethics Committee and the Aboriginal Health and Medical Research Council Human Research Ethics Committee. Individual consent waiver was granted, given data collection was based on deidentified 1. The proportion of eligible patients who received appropriate extracts from the electronic health record system. Signed agreements screening of CVD risk factors by the end of study. This was defined with participating sites were obtained. as having recorded: smoking status at least once, systolic blood
by guest on September 11, 2017 pressure (BP) in the previous 12 months, total cholesterol and high- density lipoprotein cholesterol in the previous 24 months. Results 2. The proportion of eligible patients defined at baseline as being at high CVD risk, receiving recommended medication prescriptions Recruitment at the end of study. This was defined as (1) current prescription for Sixty-four services were recruited from September 2011 to ≥1 BP-lowering drugs and a statin for people at high risk with- May 2012 (Figure 1) with 61 randomized (31 to intervention out established CVD, (2) current prescription for ≥1 BP-lowering drugs and a statin and an antiplatelet agent (unless contraindicated and 30 to usual care). One small intervention general practice by oral anticoagulant use) for people with established CVD, or (3) site (n=152 eligible patients) withdrew from the study shortly lowering of calculated 5-year CVD risk to ≤15%. after randomization. This left 60 randomized services and on outcome evaluation cohort of 38 725 eligible patients that High CVD risk is defined in Australian guidelines as (1) history of included 10 308 patients defined as high CVD risk at baseline. CVD (diagnosis of coronary heart disease, cerebrovascular disease, Median follow-up for intervention and control arms was 17.3 peripheral vascular disease); (2) the presence of any guideline-stip- ulated clinically high-risk conditions (diabetes mellitus and age >60 and 17.7 months, respectively. Almost all intervention services years, diabetes mellitus and albuminuria, stage 3B chronic kidney dis- (27 sites) used the audit tool to conduct data extractions and ease, or extreme individual risk factor elevations: systolic BP ≥180 submissions to the Web portal ≥50% of the time (ie, on average mm Hg, diastolic BP ≥110 mm Hg, total cholesterol >7.5 mmol [290 data were submitted at least bimonthly). Intervention practices 19 mg/dL]) ; or (3) a calculated 5-year CVD risk of >15% using the received an average of 48-minute support per month compris- 1991 Anderson Framingham equation.24 Secondary outcome measures included (1) measurements of indi- ing on-site training, remote clinical Webinars, and helpdesk vidual CVD risk factors (smoking status, BP, lipids, body mass index, services. A detailed description of this support is provided in estimated glomerular filtration rate, and albuminuria); (2) escalation the Appendix in the Data Supplement. Table 1 shows the ser- of drug prescription among patients at high CVD risk (either newly vice level characteristics, and Table 2 shows the baseline car- prescribed or additional numbers of antiplatelet, BP-lowering and diovascular risk profile of the sample. lipid-lowering agents); (3) BP and serum lipid levels among people at high CVD risk; and (4) newly recorded CVD-related diagnoses. Primary and Secondary Outcomes During follow-up, patients in intervention sites were more Sample Size likely to receive appropriate screening for CVD risk (62.8% Randomization of 60 services (30 per arm) was calculated to provide 90% power to detect a ≥10% absolute higher occurrence in each pri- versus 53.4% risk ratio [RR], 1.25; 95% CI, 1.04–1.50; P=0.02; mary study outcome among services receiving the intervention. This Figure 2). Improvements were mainly driven by improvements assumed for the coprimary outcomes a 10% absolute improvement in total/high-density lipoprotein cholesterol measurement and 90 Circ Cardiovasc Qual Outcomes January 2015
64 health services agreed to participate 2 services decided not to participate on further consideration 62 health services assessed for software requirements and had pre-randomisation data extractions performed 1 service did not meet technical software requirements 61 health services randomised
•31 sites randomised to intervention •30 sites randomised to usual care (21GPs, 10 ACCHS) •Ps, 10 ACCHS) •28 sites received the intervention as randomised •30 sites received usual care as randomised •2 sites used only the audit tool and quality improvement portal •Data extraction cohort •1 site (GP) withdrew from the study shortly after •19,340 patients at baseline (median 495 /site randomisation (152 patients) (IQR 349-962)) •4916 patient at high CVD risk-(median 135 /site •Data extraction cohort (IQR 74-219)) •19,385 patients median 498 /site (IQR 326-755)) Downloaded from •5392 patients at high CVD risk- median 139 /site (IQR 93 - 238))
•30 sites analysed at end of study •30 sites analysed at end of study http://circoutcomes.ahajournals.org/ •Median follow-up 17.3 months (IQR 15.3-18.0) •Median follow-up 17.7 months (IQR 14.3-18.3)
Figure 1. Study flow diagram. ACCHS indicates Aboriginal Community Controlled Health Service; CVD, cardiovascular disease; GP, general practice; and IQR, interquartile range.
BP recording. There was a trend to heterogeneity of effect prescription rates of recommended medications were 46.7% based on whether these risk factors were measured at base- (intervention) and 52.8% (control; Table 2). At end-of-study line (Figure 3). For the high-risk cohort (n=10 308), baseline comparison, there were no statistically significant improve- ments in prescription of recommended medications (56.8% Table 1. Baseline Service Characteristics versus 51.2%; RR, 1.11; 95% CI, 0.97–1.27; P=0.12). The by guest on September 11, 2017 Intervention Usual Care intervention was most strongly associated with escalation (n=30; n=19 385) (n=30; n=19 340) of medications for patients at high risk (new prescriptions Eligible population or increased numbers of medications) with respect to anti- platelet medications (17.9% versus 2.7%; RR, 4.80; 95% CI, <500 15/30 (50%) 15/30 (50%) 2.47–9.29; P<0.001), lipid-lowering medications (19.2% ver- ≥500 15/30 (50%) 15/30 (50%) sus 4.8%; RR, 3.22; 95% CI, 1.77–5.88; P<0.001), and BP- Type of service lowering medications (23.3% versus 12.1%; RR, 1.89; 95% ACCHS 10/30 (33%) 10/30 (33%) CI, 1.08–3.28; P=0.02). General Practice 20/30 (67%) 20/30 (67%) For the intervention arm site that withdrew from the study, Current participation in a QI initiative a sensitivity analysis was conducted assuming that there was No 17/30 (57%) 16/30 (53%) no improvement in the coprimary outcomes at end of study for Yes 13/30 (43%) 14/30 (47%) this site and this had negligible effect on the findings. Because Medical software used of likely effect modification relating to initial levels of the Best Practice 10/30 (33%) 11/30 (37%) coprimary outcome, we did not conduct adjusted analyses for baseline differences. As is more appropriate in the presence of Medical Director 20/30 (67%) 19/30 (63%) effect modification, we interpreted the effect of the interven- IT support tion based on stratified results. Both local and 5/30 (17%) 11/30 (37%) In the high-risk cohort, there were no clear effects on external mean systolic BP (−2.3 versus −1.5 mm Hg; difference, −0.8 External 17/30 (57%) 14/30 (47%) mm Hg; 95% CI, −2.0 to 0.4; P=0.20) and low-density lipo- Local 8/30 (27%) 5/30 (17%) protein cholesterol (−0.14 versus −0.09 mmol/L; difference, Staff currently using data extraction tools −0.05 mmol/L; 95% CI, −0.12 to 0.01; P=0.08). There was a Most 1/30 (3%) 1/30 (3%) higher proportion attaining guideline BP targets in the inter- Some 18/30 (60%) 19/30 (63%) vention group versus control (61.0% versus 55.0%; RR, 1.10; None 11/30 (37%) 10/30 (33%) 95% CI, 1.00–1.20; P=0.05). There were no differences in the ACCHS indicates Aboriginal Community Controlled Health Service; IT, proportion attaining lipid targets (P=0.61). There were also no information technology; and QI, quality improvement. significant differences in prescribing rates for BP, statin, and Peiris et al The TORPEDO Cluster-Randomized Trial 91
Table 2. Baseline Patient Characteristics
Intervention (Sites=30; n=19 385) Usual Care (Sites=30; n=19 340) Available Data n (%) or Mean (SE) Available Data n (%) or Mean (SE) P Value Age, y, mean (SD) 19 382 60.7 (12.4) 19 339 61.3 (12.7) 0.66 Men 19 377 7729 (40.0%) 19 305 8536 (44.0%) 0.03 Aboriginal/Torres Strait Islander 19 385 3624 (18.7%) 19 340 3292 (17.0%) 0.66 Current smoker/ex-smoker in the past 12 mo 16 539 3524 (21.4%) 16 464 3537 (21.4%) 0.94 Systolic blood pressure, mm Hg, mean (SD) 17 497 129.9 (17.5) 18 092 129.9 (16.4) 0.61 Total cholesterol, mmol, mean (SD) 16 383 5.00 (1.08) 14 544 5.00 (1.13) 0.40 High-density lipoprotein, mmol, mean (SD) 15 422 1.40 (0.43) 12 761 1.40 (0.41) 0.69 HbA1c for those with recorded diagnosis of 3224 8.0 (4.6) 2942 7.5 (1.8) 0.16 diabetes mellitus, %, mean (SD) Body mass index >30 kg/m2 12 981 4949 (36.3%) 13 647 4900 (37.8%) 0.32 Albuminuria* 3942 1025 (25.7%) 3996 1181 (30.0%) 0.79 Downloaded from Estimated glomerular filtration 16 415 1476 (9.9%) 14 876 1896 (11.6%) 0.87 rate† <60 mL/min per 1.73 m2 Recorded diagnoses Coronary heart disease 19 385 2170 (11.2%) 19 340 1914 (9.9%) 0.31 Cerebrovascular disease 19 385 570 (2.9%) 19 340 525 (2.7%) 0.92 http://circoutcomes.ahajournals.org/ Peripheral vascular disease 19 385 160 (0.8%) 19 340 206 (1.1%) 0.51 Diabetes mellitus 19 385 3555 (18.3%) 19 340 3250 (16.8%) 0.95 Left ventricular hypertrophy 19 385 34 (0.2%) 19 340 95 (0.5%) 0.01 Atrial fibrillation 19 385 724 (3.7%) 19 340 657 (3.4%) 0.85 Heart failure 19 385 354 (1.8%) 19 340 287 (1.5%) 0.84 CVD risk information 5-y CVD risk‡ Missing information 19 385 5678 (29.3%) 19 340 7101 (36.7%) 0.19 <10% 19 385 7197 (37.1%) 19 340 6493 (33.6%) 0.40 by guest on September 11, 2017 10%–15% 19 385 1118 (5.8%) 19 340 830 (4.3%) 0.29 >15% 19 385 505 (2.6%) 19 340 398 (2.06%) 0.30 Clinically high risk condition§ 19 385 2249 (11.6%) 19 340 2094 (10.8%) 0.94 Established CVD║ 19 385 2638 (13.6%) 19 340 2424 (12.5%) 0.61 Primary outcomes at baseline Patients with appropriate CVD risk screening 19 385 10 110 (52.2%) 19 340 8558 (44.3%) 0.47 Patients at high CVD risk with appropriate 5392 2516 (46.7%) 4916 2598 (52.8%) 0.17 medical management HbA1c indicates glycated hemoglobin. *Urinary albumin:creatinine ratio >2.5 men and >3.5 women. †Calculated using the Chronic Kidney Disease Epidemiology Collaboration formula. ‡Calculated using the 1991 Anderson Framingham risk equation. §Any of the following based on Australian guidelines: diabetes mellitus and age >60 year, diabetes mellitus and albuminuria, estimated glomerular filtration rate <45 mL/min per 1.73 m2, systolic blood pressure (BP) ≥180 mm Hg, diastolic BP ≥110 mm Hg, total cholesterol >7.5 mmol/L. ║Any of the following: coronary heart disease, cerebrovascular disease, and peripheral vascular disease.
antiplatelet medicines for those at low risk of CVD (<10% at not prescribed medicines (n=5090) showing a large improvement 5-year risk; all P>0.55). There were no differences in the pro- (38.3% versus 20.9%; RR, 1.59; 95% CI, 1.19–2.13; P<0.001). portion with newly recorded CVD diagnoses (P=0.72). There were greater improvements in risk factor screening in Discussion smaller when compared with larger health services (P interac- TORPEDO contributes new evidence on the effect of tech- tion=0.02), but no other significant differences were observed nology-assisted interventions to improve healthcare qual- for either primary outcome for any prespecified subgroup ity. It address a recent US Community Prevention Services (Figure 3). Taskforce recommendation that multicomponent service In a post hoc analysis, there was a significant heterogeneity delivery interventions, combining electronic health record– of effect according to whether patients were prescribed recom- integrated decision support with performance feedback, are mended medicines at baseline (interaction P=0.03) with those needed for CVD prevention.17 TORPEDO demonstrated that a 92 Circ Cardiovasc Qual Outcomes January 2015
Favours Favours Risk ratio InterventionUsual care Usual care Intervention (95% CI)pvalue- ICC
CVD risk screening (n=38,275)
Primary outcome Proportion receiving appropriate and timely measurement of CVD risk factors 12164/19385 (62.8%) 10317/19340 (53.4%) 1.25 (1.04, 1.50) 0.02 0.08
Secondary outcomes Smoking status recorded 17596/19385 (90.8%) 17227/19340 (89.1%) 1.04 (0.96, 1.13) 0.35 0.08
Systolic BP recorded in previous 12 months 16433/19385 (84.8%) 15587/19340 (80.6%) 1.08 (0.99, 1.18) 0.09 0.05
Total & HDL cholesterol recorded in the 14641/19385 (75.5%)12855/19340 (66.5%) 1.19 (1.03, 1.37) 0.02 0.09 previous 24 months
BMI measurement in previous 12 months 9780/19385 (50.5%) 9559/19340 (49.4%) 0.97 (0.77, 1.23) 0.79 0.15
Urinary ACR measured in the previous 24 months 5196/19385 (26.8%) 4281/19340 (22.1%) 1.23 (0.84, 1.80) 0.29 0.11
eGFR measured in the previous 24 months 16230/19385 (83.7%)15494/19340 (80.1%) 1.06 (0.97, 1.15) 0.20 0.06
Downloaded from Medication management for people at high CVD risk (n=10,308)
Primary outcome
Proportion receiving guideline recommended 3030/5335 (56.8%)2483/4846 (51.2%) 1.11 (0.97, 1.27) 0.12 0.09 medication prescriptions
Secondary outcomes Current prescription for at least one BP lowering medicine and a statin for people at high risk without CVD 1571/2697 (58.3%)1311/2422 (54.1%) 1.09 (0.97, 1.22) 0.16 0.06 http://circoutcomes.ahajournals.org/
Current prescription for at least one BP medicine, a statin 1459/2638 (55.3%)1172/2424 (48.4%) 1.14 (0.97, 1.35) 0.10 0.12 and an antiplatelet medicine for people with CVD
Escalation of antiplatelet medicines for people 470/2638 (17.8%) 65/2424 (2.7%) 4.79 (2.47, 9.29) <.001 0.57 with CVD 0.49 Escalation of lipid-lowering medicines 1026/5335 (19.2%) 226/4846 (4.7%) 3.22 (1.77, 5.88) <.001 Escalation of BP-lowering medicines 1243/5335 (23.3%) 586/4846 (12.1%) 1.89 (1.09, 3.28) 0.02 0.42
Figure 2. Cardiovascular disease (CVD) risk factor screening and medication management end points. ACR indicates albumin:creatinine ratio; BMI indicates body mass index; BP, blood pressure; CI, confidence interval; eGFR, estimated glomerular filtration rate; HDL, high- density lipoprotein; and ICC, intraclass correlation coefficient. by guest on September 11, 2017 computer-guided intervention comprising point-of-care deci- of audit and feedback, a systematic review of 49 studies (not sion support, audit and feedback tools, training and support CVD specific) found a median absolute improvement in per- improved cardiovascular risk factor screening. The interven- formance of 4.3%.17 TORPEDO demonstrated 3-fold greater tion did not improve the prescription of appropriate preven- improvements than these for screening and test ordering. Key tive drugs in the overall high-risk cohort. The improvements features of the TORPEDO interventions that are known to be identified in the high-risk individuals inadequately treated at drivers of change included work flow integration, alignment baseline suggest that this is the group most likely to benefit with usual decision-making processes in the patient consulta- from the intervention; however, this was a post hoc analy- tion, provision of treatment recommendations rather than just sis. The escalation in guideline-based care indicates that the assessments, and repeated audit and feedback with explicit intervention was effective in reducing practitioner therapeutic recommendations.14,17 inertia (the failure to initiate or increase therapy when treat- Importantly, however, TORPEDO was less successful ment goals are not being met) although it must be emphasized in shifting prescribing behavior, which is consistent with that absolute rates of treatment remained unacceptably low.26 small intervention effect sizes found in the US Community The observed improvements in care were equally apparent in Prevention Services Taskforce systematic review (only a 2% ACCHSs and in general practices; this is especially pertinent absolute improvement).18 Consequently, there remains much to addressing Indigenous health inequities in Australia, which scope for further improvements if such QI strategies are to are largely driven by excess CVD burden. translate into tangible health benefits. Berwick27 has com- Despite more than a decade of CVD guidelines recommend- mented that QI is not a single, testable answer. Rather it is a ing medical management on the basis of overall cardiovascu- complex process driven by a range of factors at the level of lar risk, most implementation strategies have focused on the the patient, provider, health service, and the broader health management of single risk factors and there are few strategies system.28 For diabetes mellitus care, QI strategies that have that have been shown to be effective in changing practitioner targeted both prescriber and patient behavior change in combi- behavior toward risk-based management. The US Community nation seem to be associated with greater success.29 Similarly, Prevention Services Taskforce review of 44 randomized for CVD risk management, patient-focused strategies may be a controlled trials on effectiveness of cardiovascular decision critically important additional element to improving outcomes. support systems found median absolute improvements of Despite the bold promise of consumer-focused technologies to 3.2% for screening and 4.0% for test ordering.18 In the area increase patient engagement, few trials have been conducted Peiris et al The TORPEDO Cluster-Randomized Trial 93
Favours Favours Risk ratio p-value Intervention Usual care Usual care Intervention (95% CI) interaction ICC
CVD risk screening by subgroup (n=38,275) (Proportion receiving appropriate and timely measurement of CVD risk factors)
Type of service ACCHS 2904/4812 (60.4%)1579/3459 (45.7%) 1.29 (0.98, 1.70)0.76 0.13 General Practice 9260/14573 (63.5%)8738/15881 (55.0%) 1.22 (0.97, 1.55) 0.07 Service size at baseline Small (<500) 2652/4436 (59.8%)1620/4343 (37.3%) 1.63 (1.17, 2.26)0.02 0.14 Large (>=500) 9512/14949 (63.6%)8697/14997 (58.0%) 1.03 (0.87, 1.23) 0.06
Participation at baseline in a national quality improvement program No 7576/11839 (64.0%)5113/9843 (52.0%) 1.35 (1.00, 1.82)0.41 0.11 Yes 4588/7546 (60.8%)5204/9497 (54.8%) 1.16 (0.94, 1.43) 0.06
Medication management for people at high CVD risk (n=10,308) by subgroup
Downloaded from (Proportion receiving guideline recommended medication prescriptions)
Type of service ACCHS 1112/1622 (68.6%)787/1195 (65.9%) 1.05 (0.97, 1.14)0.45 0.01 General Practice 1918/3713 (51.7%)1696/3651 (46.5%) 1.14 (0.96, 1.35) 0.08 Service size at baseline Small (<500) 757/1315 (57.6%)800/1390 (57.6%) 1.04 (0.86, 1.24)0.35 0.05 http://circoutcomes.ahajournals.org/ Large (>=500) 2273/4020 (56.5%)1683/3456 (48.7%) 1.18 (0.98, 1.41) 0.09 Participation at baseline in a QI program No 1583/3151 (50.2%)1021/2209 (46.2%) 1.13 (0.92, 1.39)0.91 0.09 Yes 1447/2184 (66.3%)1462/2637 (55.4%) 1.11 (0.98, 1.26) 0.07
Figure 3. Screening and medication management end points by prespecified subgroups. ACCHS indicates Aboriginal Community Controlled Health Service; CI, confidence interval; CVD, cardiovascular disease; ICC, intraclass correlation coefficient; and QI, quality improvement. by guest on September 11, 2017 to examine their effectiveness, costs, and optimal delivery nature of the trial and a focus on increasing prescription of mechanisms. New studies are required that combine such con- treatments of known efficacy, which is a critical first step in sumer-focused approaches with provider-focused approaches, maximizing the full benefits of such treatments. Data link- each designed in a way that takes careful account of health age studies with national hospitalization and mortality data- service and system characteristics. Given the particularly large bases are currently being planned and will help to ascertain unmet need for quality improvement interventions in low- and the effect on hard outcomes. Although not a limitation per se, middle-income countries, such regions should also be a major the intervention is ideally suited for implementation in set- focus for attention.30 tings where there are high adoption rates of electronic health The strengths of this study include the pragmatic implemen- records. Australia has among the highest rates of electronic tation of a randomized study within usual day-to-day prac- health record adoption in the world (>90%); however, uptake tice, the large sample size, the clinical outcome data, scalable is increasing internationally with the majority of high-income intervention components, and the low level of implementation countries in Europe now achieving rates in excess of 80% support required. Another strength is the representativeness of and substantial implementation occurring in North America, participating general practices and ACCHSs. All general prac- spearheaded by the Medicare and Medicaid meaningful use tices included in TORPEDO were recruited from urban set- program.33,34 Intervention programs such as that tested by tings (in which ≈70% of all Australian general practices are TORPEDO are therefore well placed for large-scale imple- based) and had site characteristics that were broadly represen- mentation in high-income countries. Indigenous governed tative of general practice in Australia.31 The ACCHSs repre- community health services operating within other high-income sented urban, rural, and remote regions and comprised ≈20% country settings, such as United States and Canada, may also of all ACCHSs that provide medical services in Australia. The be well suited to adopting this intervention. These findings TORPEDO ACCHS sites also demonstrated service character- may also have broader relevance to the management of cardio- istics that were similar to the sector at large.32 Furthermore, the vascular risk in other resource-poor settings and in other rural baseline rates for key outcome measures were similar to those and remote communities. found in previous Australian studies in both general practice The implications of effective QI tools and strategies are and ACCHSs.10,11 substantial. Improving health system performance by even a The main study limitation is that it was not powered for clin- small margin has the potential to make a major effect on dis- ical outcomes. This needs to be balanced against the pragmatic ease burden if improvements can be delivered at scale. Taking 94 Circ Cardiovasc Qual Outcomes January 2015
a conservative estimate that 5% of people in Australia have at S, Barrero LH, Bartels DH, Basáñez MG, Baxter A, Bell ML, Benjamin least a 20% 5-year CVD risk,35 and using published data on EJ, Bennett D, Bernabé E, Bhalla K, Bhandari B, Bikbov B, Bin Abdulhak 36–38 A, Birbeck G, Black JA, Blencowe H, Blore JD, Blyth F, Bolliger I, risk reductions from treatment interventions, a 2 mm Hg Bonaventure A, Boufous S, Bourne R, Boussinesq M, Braithwaite T, mean systolic BP reduction, 0.1 mmol low-density lipoprotein Brayne C, Bridgett L, Brooker S, Brooks P, Brugha TS, Bryan-Hancock reduction, and a 10% increase in aspirin adherence together C, Bucello C, Buchbinder R, Buckle G, Budke CM, Burch M, Burney P, could lead to around a 10% relative risk reduction and ≈20 000 Burstein R, Calabria B, Campbell B, Canter CE, Carabin H, Carapetis J, Carmona L, Cella C, Charlson F, Chen H, Cheng AT, Chou D, Chugh fewer events >5 years. Such improvements highlight the great SS, Coffeng LE, Colan SD, Colquhoun S, Colson KE, Condon J, Connor potential for the primary healthcare sector to make a larger MD, Cooper LT, Corriere M, Cortinovis M, de Vaccaro KC, Couser W, contribution to reduction of the CVD burden. Scalable and Cowie BC, Criqui MH, Cross M, Dabhadkar KC, Dahiya M, Dahodwala effective systems that require minimal support to implement N, Damsere-Derry J, Danaei G, Davis A, De Leo D, Degenhardt L, Dellavalle R, Delossantos A, Denenberg J, Derrett S, Des Jarlais DC, could make major improvements in primary healthcare system Dharmaratne SD, Dherani M, Diaz-Torne C, Dolk H, Dorsey ER, Driscoll performance and health outcomes globally. T, Duber H, Ebel B, Edmond K, Elbaz A, Ali SE, Erskine H, Erwin PJ, Espindola P, Ewoigbokhan SE, Farzadfar F, Feigin V, Felson DT, Ferrari A, Ferri CP, Fèvre EM, Finucane MM, Flaxman S, Flood L, Foreman Acknowledgments K, Forouzanfar MH, Fowkes FG, Fransen M, Freeman MK, Gabbe BJ, We gratefully acknowledge the support of the general practices and Gabriel SE, Gakidou E, Ganatra HA, Garcia B, Gaspari F, Gillum RF, Aboriginal Community Controlled Health Services involved in this Gmel G, Gonzalez-Medina D, Gosselin R, Grainger R, Grant B, Groeger J, Downloaded from study. We also acknowledge the support of the Queensland Aboriginal Guillemin F, Gunnell D, Gupta R, Haagsma J, Hagan H, Halasa YA, Hall W, and Islander Health Council, Aboriginal Health and Medical Research Haring D, Haro JM, Harrison JE, Havmoeller R, Hay RJ, Higashi H, Hill Council, Western Sydney Medicare Local, Inner West Sydney C, Hoen B, Hoffman H, Hotez PJ, Hoy D, Huang JJ, Ibeanusi SE, Jacobsen Medicare Local, South Eastern Sydney Medicare Local, Eastern KH, James SL, Jarvis D, Jasrasaria R, Jayaraman S, Johns N, Jonas JB, Sydney Medicare Local, South Western Sydney Medicare Local and Karthikeyan G, Kassebaum N, Kawakami N, Keren A, Khoo JP, King CH, Nepean-Blue Mountains Medicare Local. We acknowledge the fol- Knowlton LM, Kobusingye O, Koranteng A, Krishnamurthi R, Laden F, http://circoutcomes.ahajournals.org/ lowing project staff for their role in supporting implementation of Lalloo R, Laslett LL, Lathlean T, Leasher JL, Lee YY, Leigh J, Levinson D, the study: Maria Agaliotis, Sharon Parker, Genevieve Coorey, Lyn Lim SS, Limb E, Lin JK, Lipnick M, Lipshultz SE, Liu W, Loane M, Ohno Anderson, Melvina Mitchell. We also thank Pen Computer Systems SL, Lyons R, Mabweijano J, MacIntyre MF, Malekzadeh R, Mallinger L, for their support in developing the software tools and the Improvement Manivannan S, Marcenes W, March L, Margolis DJ, Marks GB, Marks Foundation for their support in developing and hosting the quality im- R, Matsumori A, Matzopoulos R, Mayosi BM, McAnulty JH, McDermott provement portal. Dr Peiris had full access to all of the data in the MM, McGill N, McGrath J, Medina-Mora ME, Meltzer M, Mensah GA, study and takes responsibility for the integrity of the data and the ac- Merriman TR, Meyer AC, Miglioli V, Miller M, Miller TR, Mitchell PB, curacy of the data analysis. Drs Peiris and Patel conceptualized the Mock C, Mocumbi AO, Moffitt TE, Mokdad AA, Monasta L, Montico M, intervention and the study. Dr Peiris, Dr Usherwood, Dr Panaretto, Moradi-Lakeh M, Moran A, Morawska L, Mori R, Murdoch ME, Mwaniki MK, Naidoo K, Nair MN, Naldi L, Narayan KM, Nelson PK, Nelson Dr Harris, Dr Hunt, Dr Redfern, Dr Zwar, Dr Colagiuri, Dr Hayman, RG, Nevitt MC, Newton CR, Nolte S, Norman P, Norman R, O’Donnell Dr Cass, S. MacMahon, Dr Sullivan, Dr Neal, Dr Jackson, Dr Patel M, O’Hanlon S, Olives C, Omer SB, Ortblad K, Osborne R, Ozgediz D, contributed to study design. Drs Patel, Lyford, and Peiris contribut- Page A, Pahari B, Pandian JD, Rivero AP, Patten SB, Pearce N, Padilla
by guest on September 11, 2017 ed to data collection. Dr Peiris, Dr Patel, Dr Lo, S. MacMahon, Dr RP, Perez-Ruiz F, Perico N, Pesudovs K, Phillips D, Phillips MR, Pierce Usherwood, Dr Panaretto, Dr Harris, Dr Hunt, Dr Cass, Dr Redfern, K, Pion S, Polanczyk GV, Polinder S, Pope CA III, Popova S, Porrini E, Dr Zwar, Dr Colagiuri, Dr Hayman, Dr Sullivan contributed to data Pourmalek F, Prince M, Pullan RL, Ramaiah KD, Ranganathan D, Razavi interpretation. Drs Peiris, Patel, and Lo contributed to preparation of H, Regan M, Rehm JT, Rein DB, Remuzzi G, Richardson K, Rivara FP, initial draft article. All authors contributed to or reviewed subsequent Roberts T, Robinson C, De Leòn FR, Ronfani L, Room R, Rosenfeld LC, drafts, approved the final version of the submitted article, and agree to Rushton L, Sacco RL, Saha S, Sampson U, Sanchez-Riera L, Sanman E, be accountable for all aspects of the work in ensuring that questions Schwebel DC, Scott JG, Segui-Gomez M, Shahraz S, Shepard DS, Shin H, related to the accuracy or integrity of any part of the work are appro- Shivakoti R, Singh D, Singh GM, Singh JA, Singleton J, Sleet DA, Sliwa priately investigated and resolved. K, Smith E, Smith JL, Stapelberg NJ, Steer A, Steiner T, Stolk WA, Stovner LJ, Sudfeld C, Syed S, Tamburlini G, Tavakkoli M, Taylor HR, Taylor JA, Taylor WJ, Thomas B, Thomson WM, Thurston GD, Tleyjeh IM, Tonelli Sources of Funding M, Towbin JA, Truelsen T, Tsilimbaris MK, Ubeda C, Undurraga EA, van The National Health and Medical Research Council of Australia and der Werf MJ, van Os J, Vavilala MS, Venketasubramanian N, Wang M, the New South Wales Department of Health funded this study. Both Wang W, Watt K, Weatherall DJ, Weinstock MA, Weintraub R, Weisskopf institutions had no role in the design and conduct of the study; col- MG, Weissman MM, White RA, Whiteford H, Wiebe N, Wiersma ST, lection, management, analysis, and interpretation of the data; prepa- Wilkinson JD, Williams HC, Williams SR, Witt E, Wolfe F, Woolf AD, ration, review, or approval of the article; and decision to submit the Wulf S, Yeh PH, Zaidi AK, Zheng ZJ, Zonies D, Lopez AD, AlMazroa article for publication. Dr Peiris was supported by a National Health MA, Memish ZA. Disability-adjusted life years (DALYs) for 291 dis- and Medical Research Council (NHMRC) Translating Research into eases and injuries in 21 regions, 1990-2010: a systematic analysis for the Practice fellowship and now a NHMRC postdoctoral fellowship Global Burden of Disease Study 2010. Lancet. 2012;380:2197–2223. doi: (1054754). Dr Patel is supported by an NHMRC Senior Research 10.1016/S0140-6736(12)61689-4. Fellowship (632938). Dr Redfern is funded by a NHMRC Career 2. Jackson R, Lawes CM, Bennett DA, Milne RJ, Rodgers A. Treatment with Development Fellowship (1061793) cofunded with a National Heart drugs to lower blood pressure and blood cholesterol based on an individu- Foundation Future Fellowship (G160523). al’s absolute cardiovascular risk. Lancet. 2005;365:434–441. doi: 10.1016/ S0140-6736(05)17833-7. 3. Jackson R, Wells S, Rodgers A. Will screening individuals at high risk of Disclosures cardiovascular events deliver large benefits? Yes. BMJ. 2008;337:a1371. None. 4. Gaziano TA, Opie LH, Weinstein MC. Cardiovascular disease prevention with a multidrug regimen in the developing world: a cost-effectiveness anal- ysis. Lancet. 2006;368:679–686. doi: 10.1016/S0140-6736(06)69252-0. References 5. Kotseva K, Wood D, De Backer G, De Bacquer D, Pyörälä K, Keil U; 1. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, Ezzati EUROASPIRE Study Group. EUROASPIRE III: a survey on the life- M, Shibuya K, Salomon JA, Abdalla S, Aboyans V, Abraham J, Ackerman style, risk factors and use of cardioprotective drug therapies in coronary I, Aggarwal R, Ahn SY, Ali MK, Alvarado M, Anderson HR, Anderson patients from 22 European countries. Eur J Cardiovasc Prev Rehabil. LM, Andrews KG, Atkinson C, Baddour LM, Bahalim AN, Barker-Collo 2009;16:121–137. doi: 10.1097/HJR.0b013e3283294b1d. Peiris et al The TORPEDO Cluster-Randomized Trial 95
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Effect of a Computer-Guided, Quality Improvement Program for Cardiovascular Disease Risk Management in Primary Health Care: The Treatment of Cardiovascular Risk Using Electronic Decision Support Cluster-Randomized Trial David Peiris, Tim Usherwood, Kathryn Panaretto, Mark Harris, Jennifer Hunt, Julie Redfern, Downloaded from Nicholas Zwar, Stephen Colagiuri, Noel Hayman, Serigne Lo, Bindu Patel, Marilyn Lyford, Stephen MacMahon, Bruce Neal, David Sullivan, Alan Cass, Rod Jackson and Anushka Patel
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Intervention components Component 1: Point of care decision support for use in clinical consultations
A risk assessment score and traffic light dashboard would appear when the HealthTracker assessment was opened. If essential information was missing to calculate risk then the risk bar would be in greyscale, an alert would state that risk cannot be calculated because essential information is missing and a traffic light prompt would alert the practitioner to the variables that were missing.
Pen Computers Sidebar application automatically extracts data from the primary care health record
Health Tracker- CVD screening and management panel.
Risk of heart attack or stroke in the next 5 years
Recommended screening tests based on national guidelines
Traffic light treatment recommendations based on national guidelines
Risk factor information The patients GP health extracted from the patient’s A resource link providing record access to clinical guidelines health record Component 2: Risk communication tool
This tool enables health care providers to discuss the risk score with patients and to perform “what if” scenarios to illustrate changes in risk score and trajectory with particular risk factors available. It draws on a similar approach taken in a New Zealand risk communication tool and incorporates a “heart age” calculation to show the user at what age their risk score would be similar to a person with “optimal” risk factor control. The tool was only used for primary prevention and would not appear for those with established cardiovascular disease. Component 3: Clinical Audit Tool
This data extraction tool allowed clinic staff to monitor performance and identify patients that were not being appropriately managed according to guidelines. Four sets of graphs were provided (screening gaps, risk profile, prescribing gaps for the high risk population and meeting guideline targets for blood pressure and lipids). For each of the graphs the practitioner can click on the red section of the column (eg. those without a cholesterol test performed) and the patients in that section would pop up as a list. Customised bubble alerts can then be set each time that patient record is opened or clinic administration staff can set up recall and reminder alerts/ letters to notify the patient to attend for a consultation. Component 4: Peer-ranked performance portal
Each month, clinics were asked to send de-identified aggregated data extracts for six performance indicators to a central secure facility. For each indicator the clinic would be able to securely log on to this facility and identify on a histogram their performance (in red) against all other participating sites for that reporting period. They would also be able to access a trend graph of their changes over time compared to the average. Component 5: Staff training and support
All contact time to support intervention implementation was logged on a standardised reporting template. There were three types of intervention support:
1. On site- support. All intervention arm sites received an initial face-to-face visit either at randomisation or shortly after randomisation to familiarize themselves with the intervention. Additional on-site support was provided at the request of the participating service with most sites receiving at least one follow-up visit. The median support time per site for the whole intervention period was 300 minutes (interquartile range (IQR) 172-510 minutes)
2. Remote clinical support was provided via training webinars, remote desktop connection and phone support. This was ad hoc and provided either at the request of the participating service or via a training webinar which was advertised to particular services. Only 14 of the 30 intervention arm services used remote clinical support. The median remote support time for these 14 sites for the whole intervention period was 38 minutes (IQR 16-112 minutes)
3. Technical support comprised helpdesk support provided by the software company for installation, assistance with user registration and password recoveries, software updates and any general issues with software performance. All of this support was provided by phone and remote desktop log in. The median technical support time for the whole intervention period for 28 sites with data available was 320 minutes (IQR 209-386 minutes) Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
Open Access Protocol The Treatment of cardiovascular Risk in Primary care using Electronic Decision suppOrt (TORPEDO) study: intervention development and protocol for a cluster randomised, controlled trial of an electronic decision support and quality improvement intervention in Australian primary healthcare
David Peiris,1 Tim Usherwood,2 Katie Panaretto,3 Mark Harris,4 Jenny Hunt,5 Bindu Patel,1 Nicholas Zwar,4 Julie Redfern,1 Stephen MacMahon,1 Stephen Colagiuri,2 Noel Hayman,6 Anushka Patel1
To cite: Peiris D, ABSTRACT ARTICLE SUMMARY Usherwood T, Panaretto K, Background: Large gaps exist in the implementation et al . The Treatment of of guideline recommendations for cardiovascular cardiovascular Risk in Primary Article focus disease (CVD) risk management. Electronic decision care using Electronic Decision ▸ The development of a multifaceted decision suppOrt (TORPEDO) study: support (EDS) systems are promising interventions to support tool and quality improvement (QI) intervention development and close these gaps but few have undergone clinical trial intervention. protocol for a cluster evaluation in Australia. We have developed ▸ The methods to test the effectiveness of this inter- randomised, controlled trial of HealthTracker, a multifaceted EDS and quality vention in improving guideline-recommended an electronic decision support improvement intervention to improve the management screening for cardiovascular risk and management and quality improvement of CVD risk. for individuals identified at high risk. intervention in Australian Methods/design: It is hypothesised that the use of BMJ Open primary healthcare. HealthTracker over a 12-month period will result in: Key messages 2012;2:e002177. doi:10.1136/ (1) an increased proportion of patients receiving ▸ This study tests a novel intervention that incor- bmjopen-2012-002177 guideline-indicated measurements of CVD risk factors porates point of care decision support, risk and (2) an increased proportion of patients at high risk communication and resources for patients, ▸ Prepublication history for will receive guideline-indicated prescriptions for health service audit tools and use of data for this paper are available lowering their CVD risk. Sixty health services supporting QI initiatives. online. To view these files (40 general practices and 20 Aboriginal Community ▸ In addition to assessing practitioner perform- please visit the journal online ance on indicators correlated with improved (http://dx.doi.org/10.1136/ Controlled Health Services (ACCHSs) will be health outcomes, the study also includes bmjopen-2012-002177). randomised in a 1:1 allocation to receive either the intervention package or continue with usual care, detailed process and economic evaluations. Received 3 October 2012 stratified by service type, size and participation in Strengths and limitations of this study Accepted 18 October 2012 existing quality improvement initiatives. The ▸ The strengths of the study are that it assesses an intervention consists of point-of-care decision This final article is available innovative complex intervention that is implemen- support; a risk communication interface; a clinical ted in routine primary healthcare settings. It will for use under the terms of audit tool to assess performance on CVD-related the Creative Commons provide rigorous evidence on process, clinical and indicators; a quality improvement component Attribution Non-Commercial economic outcomes and addresses an important 2.0 Licence; see comprising peer-ranked data feedback and support to issue facing health systems worldwide—namely http://bmjopen.bmj.com develop strategies to improve performance. The control scalable interventions that are able to achieve arm will continue with usual care without access to improvements in performance. these intervention components. Quantitative data will ▸ The main limitation is that it is conducted in one be derived from cross-sectional samples at baseline country, Australia, and thus its generalisability and end of study via automated data extraction. may be influenced by the prevailing health Detailed process and economic evaluations will also For numbered affiliations see system context. end of article. be conducted.
Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 1 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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Ethics and dissemination: The general practice component of the Despite these guidelines now becoming available, there study is approved by the University of Sydney Human Research remain substantial challenges in effectively implement- Ethics Committee (HREC) and the ACCHS component is approved by ing their recommendations. We have found that the Aboriginal Health and Medical Research Council HREC. Formal CVD-risk assessment and treatment work best when agreements with each of the participating sites have been signed. negotiated as part of a shared decision-making In addition to the usual scientific forums, results will be approach, taking an average of 15 min even where only disseminated via newsletters, study websites, face-to-face feedback 25 forums and workshops. one guideline needs to be consulted. Trial registration: The trial is registered with the Australian Clinical Trials Registry ACTRN 12611000478910. The role of electronic decision support in closing evidence practice gaps Electronic decision support (EDS) systems are among the most promising interventions to improve uptake of guideline-based recommendations in clinical practice. In BACKGROUND five systematic reviews on the effectiveness of EDS, Cardiovascular disease burden in Australia around two-thirds of studies demonstrated improvement Despite recent gains, cardiovascular disease (CVD) 26–30 ’ in practitioner performance. One systematic review remains Australia s biggest killer accounting for 18% identified four decision support system features asso- of the total disease burden and 11% of health system 1 ciated with improved performance: incorporation in expenditure in Australia. Aboriginal and Torres Strait routine work flow, provision at the time and location of Islander peoples experience around five times greater 2 patient consultation, use of computer-based tools and CVD burden than other Australians. Current estimates provision of treatment recommendations rather than project that by 2030 annual CVD expenditure will rise by 28 3 just assessments. Of 32 systems that incorporated all of around 100% to $16 billion. Primary care-based strat- these elements, significant improvements in perform- egies that improve the uptake of best-practice recom- ance were noted in 30. There are relatively few con- mendations could substantially reduce both the trolled evaluations of EDS systems that are integrated Indigenous and non-indigenous CVD burden and help with electronic health records (EHRs) in the area of fi – improve health system ef ciencies. CVD.31 35 Effect sizes vary greatly depending on the vari- ables studied and the type of EDS system. In one system- Evidence-practice gaps in CVD prevention atic review of on-screen point-of-care reminder systems In addition to lifestyle modification, a number of drug the absolute improvements ranged from 1% to 24% for therapies have been shown to be highly effective in pre- test ordering and from 3% to 28% for medication pre- venting cardiovascular events, primarily through modifi- scribing.27 In New Zealand, an EDS system that is fully 4–8 cation of blood pressure, lipids and platelet function. integrated with the country’s most popular primary care However, there is compelling evidence of the failure of software has been successfully implemented.36 To date, current clinical practice to adequately implement such we are unaware of an EDS system aimed at assisting com- treatments, and to translate current knowledge into prehensive cardiovascular risk management based on maximally improved health outcomes. Three recently Australian guidelines. Furthermore, we are not aware of completed cross-sectional studies of CVD risk manage- any randomised evaluations of such systems in Australian ment in Australian general practice and Aboriginal primary care settings. Globally, few examples exist and Community Controlled Health Service (ACCHS) set- – the evidence base remains poor. tings9 11 demonstrated that 50% of routinely attending adults lacked sufficient recorded information to compre- hensively evaluate vascular risk. For those identified at INTERVENTION DEVELOPMENT high vascular risk, only around 40% were prescribed HealthTracker is a novel EDS system to facilitate guideline-indicated medicines. Similar findings have guideline-based assessment and management of CVD – been noted in other Australian studies.12 15 These risk. Outlined below are the key steps taken in the devel- surveys have demonstrated failure to adequately imple- opment of the intervention. ment the ‘absolue risk’ paradigm for CVD prevention. Numerous tools are now available to estimate an indivi- Algorithm development and validation dual’s 5-year or 10-year absolute risk of coronary heart A single screening and management algorithm was – disease and/or CVD.16 21 Despite their availability, only developed based on a synthesis of recommendations a minority of Australian general practitioners (GPs) use from several primary care screening and management these risk assessment tools, and then primarily for guidelines (table 1). The algorithm calculates a person’s patient education, rather than to guide management 5-year absolute CVD risk based on the Framingham risk decisions.92223Australia’s first absolute risk assessment equation and NVDPA recommendations 17 20 and pro- guideline was released in 2009 by the National Vascular vides management recommendations based on the Disease Prevention Alliance (NVDPA)17 and in 2012 this guidelines listed in table 1. In 2008–2009, a β-version of was augmented by a single management guideline.24 HealthTracker was developed in a stand-alone software
2 Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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Table 1 Guidelines used in the HealthTracker algorithm Professional organisation Guideline National Vascular Disease Prevention Alliance Guidelines for the Assessment of Absolute Cardiovascular Disease Risk 2009 National Heart Foundation Reducing Risk in Heart Disease 2008 Guide to Management of Hypertension 2008- Updated Aug 2009 Aspirin for cardiovascular disease prevention 2003 National Heart Foundation/ Cardiac Society of The Lipid Position Statement 2005 Australia and New Zealand National Stroke Foundation Clinical Guidelines for Stroke Management 2010 Royal Australian College of General Guidelines for preventive activities in general practice 2009 Practitioners Diabetes Australia Diabetes Management in General Practice 2010/2011 NHMRC Evidence Based Guidelines for Type 2 Diabetes 2009- Case Detection and Diagnosis NHMRC Evidence Based Guidelines for Type 2 Diabetes 2009- Diagnosis, Prevention and Management of Chronic Kidney Disease Kidney Health Australia Chronic Kidney Disease Management (CKD) in General Practice 2007 Department of Health and Aging Schedule of Pharmaceutical Benefits Scheme 2011 General Statement for Lipid Lowering Drugs
system and independently validated for accuracy and changes were incorporated into the final version of compliance with the prevailing guidelines.22 In 2011, the tool. The level 2 testing process was repeated the algorithm was extensively revised to incorporate following these changes and perfect correlation recommendations from newly published guidelines. between HealthTracker and the independently pro- A similar validation process was then conducted consist- grammed version was achieved for all variables. ing of three levels: ▸ Level 1 was an iterative process where each of the cal- culations programmed in the algorithm were tested Integration of HealthTracker with the primary care to ensure they were consistent with recommendations electronic health record and quality improvement tools from the guidelines. This was conducted using dei- HealthTracker interfaces with the two Australian clinical dentified data from 337 patients involved in the pilot. practice software systems most commonly used in general Programming modifications were made where neces- practice and ACCHS settings (Medical Director and Best sary and all variables were retested to ensure they Practice). There are four components to the system: were programmed correctly. ▸ Point-of-care decision support: HealthTracker is built in ▸ Level 2 involved giving a plain language summary of the Pen Computer Systems PrimaryCareSidebar, the algorithm to a research fellow who had not been third-party software that interacts with the primary involved in the development of the algorithm. She EHR system. Figure 1 shows the HealthTracker user independently programmed the algorithm into a statis- interface and its integration with the tical software package. Using data from 9077 patients PrimaryCareSidebar and the EHR. A prompt function from three representative cross-sectional general prac- is used to encourage health professionals to conduct – tice surveys,9 11 we then assessed whether the outputs a cardiovascular assessment if guideline recom- from HealthTracker correlated with those generated mended. Where possible, the tool populates with from the independently programmed version. For 60 information from the patient’s record. If essential of the 63 output variables HealthTracker achieved information required for the calculation of absolute perfect correlation with the independently pro- risk is missing or out-of-date, a traffic light prompt grammed version. For the remaining three variables alerts the health professional and updated informa- minor programming errors were identified and tion can be entered. If the patient is receiving subopti- corrected. mal treatment then a traffic light recommendation is ▸ Level 3 involved user acceptance testing and scrutiny made to consider initiation of treatment or additional of the algorithm by the study investigators, 20 health agents. Information about eligibility for the Australian professionals working in both General Practice Government Pharmaceutical Benefits Scheme subsidy and ACCHSs and three national professional is provided if lipid lowering medicines are recom- organisations—the NVDPA, the Royal Australian mended. All outputs are qualified by statements College of General Practitioners and the National emphasising that the final decision to commence or Prescribing Service. Following this feedback, a change therapy should be made by the health profes- number of minor algorithm and user interface sional based on all available information.
Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 3 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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Figure 1 HealthTracker_user interface.
▸ A patient-oriented risk communication interface:Akey extracts of clinical performance are securely exported finding from the pilot evaluation was the role of the to a web-based central repository managed by the output in risk communication. GPs wanted to inter- IFA. This repository provides access to site-specific actively alter current risk factors and perform ‘what feedback reports on performance compared with if’ scenarios to demonstrate to patients the effects of other anonymised sites. Figure 4 shows an example of current and altered risk over time. This functionality how this information is presented. has been built into HealthTracker and uses the concept of ‘Heart Age’ to demonstrate to patients the discrepancy between current risk and an ideal risk based on well-controlled risk factor levels. Figure 2 STUDY OBJECTIVES ’ shows an example of how a patient’s heart age The TORPEDO study will test HealthTracker s perform- changes with the effect of smoking cessation. ance in assisting health professionals and patients in ▸ A data extraction tool: This provides health professionals making evidence-based management decisions to help with immediate feedback on their performance on prevent heart attack, stroke and related conditions. screening and management of CVD risk for their entire patient population. Figure 3 shows an example Hypotheses of screening performance for a range of CVD risk Using a cluster randomised, controlled trial design, two factors. Practitioners can use this tool to identify specific hypotheses will be tested. Compared with specific patients in whom there may be a particular control practices, those practices randomised to receive risk factor measurement missing or a potential pre- HealthTracker will have: scribing gap. Customised point-of-care prompts can 1. An increased proportion of patients receiving appro- then be created. When a patient record is opened an priate (guideline-indicated) measurements of their alert is provided to notify the practitioner of the par- CVD risk factors. ticular management issue and this can then be 2. An increased proportion of patients at high risk actioned. receiving appropriate (guideline-indicated) prescrip- ▸ A quality improvement (QI) component: This is aligned tions for the management of their CVD risk. with the methods of the Improvement Foundation of These aims will be augmented by formal economic Australia (IFA) Australian Primary Care and process evaluations to provide crucial information Collaboratives (APCC) programme. Deidentified data on large-scale implementation and sustainability.
4 Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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Figure 2 HealthTracker—cardiovascular disease-risk communication interface.
STUDY DESIGN 2. Attendance at the general practice or ACCHS at least HealthTracker will be evaluated using a cluster three times in the previous 24-month period AND at randomised, controlled trial design. At end of study, least once in the previous 6-month period. HealthTracker will be made available to both the inter- vention and control arms for a further 12 months free of Site recruitment any licence fees. The study schema including site and Participating general practices have been recruited patient eligibility criteria are highlighted in figure 5. from the Sydney region in collaboration with primary healthcare organisations known in Australia as Medicare Eligibility criteria Locals. Participating ACCHSs have been recruited in Health service partnership with two state representative bodies for 1. Use of Medical Director or Best Practice for EHR ACCHSs, the Aboriginal Health & Medical Research management. Council (AH&MRC) of NSW and the Queensland and 2. Exclusive use of these systems to record risk factor Aboriginal Islander Health Council (QAIHC). information, pathology test results and prescribe A $500AUD reimbursement to participating sites will be medications. made to partially compensate for health service staff 3. Agreement by all GPs and other designated staff to time commitment to study-related activities. Sites rando- use HealthTracker. mised to the intervention will receive training support in Services that do not have a compliant software system use of the system predominantly via face-to-face visits will be excluded from participation. Services using and webinars. All licence costs and technical support ‘hybrid’ paper and electronic systems for recording risk associated with the intervention will be provided free to factor information, pathology results and medication the intervention sites in the first 12 months and to all prescription will also not be eligible to participate. sites for the following 12 months after completion of the trial. A newsletter and networking web site will be Patients provided to participating sites. Royal Australian College 1. Aboriginal and Torres Strait Islander people 35+ of General Practitioners Quality Assurance and years and all others 45+ years (age criteria are based Continuing Professional Development points will be on NVDPA guideline screening recommendations37). offered to participating GPs in both arms of the trial.
Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 5 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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Figure 3 Sample output of performance in cardiovascular disease-risk factor screening.
Randomisation discontinued. Reasons for discontinuation will be out- Sixty services (40 general practices and 20 ACCHSs) will lined and all analyses will be conducted on an be randomised in a 1:1 allocation to use HealthTracker intention-to-treat basis (see below). or ‘usual care’ for 12 months. Clusters will be stratified at three levels: Control group 1. ACCHS versus general practices. 2. Service size (<500 patients meeting eligibility criteria Sites allocated to this arm will continue usual practice vs >=500). with their current systems without the implementation 3. Participation in existing QI programmes (current of HealthTracker. As the George Institute holds exclusive involvement in one of five national and state pro- rights to the distribution of the system, there is no possi- grammes involving regular audit and feedback versus bility of control sites having access to HealthTracker. If past or never involved in these programmes). these sites already routinely use data extraction tools for A site assessment survey will be administered to all assessing their quality of care then this will continue as sites to assess for service eligibility and these stratifying normal. As with the intervention arm, services participat- variables. Permuted block randomisation will be cen- ing in any QI initiatives will continue participation as trally performed using a web-based form. As this is a usual. For those sites not routinely using data extraction pragmatic trial, allocation will be single blinded with tools, the automated data extraction tool will be tempor- outcome analyses conducted blinded to treatment arily installed for data collection purposes only and then allocation. uninstalled that same day. A feedback report on per- formance will be provided at study completion only. Intervention group The intervention arm will receive the four components Quantitative data collection of the system described above (point-of-care decision Cross-sectional data will be collected in an automated support software, risk communication tools, data extrac- manner for all patients who satisfy the eligibility criteria tion tools and access to the QI portal). Clinical staff will at each service (figure 5). These data will then be sent be given training in use of the tools and a support securely to the George Institute via an export function service will be available for any technical queries. One for the analysis of primary and secondary outcomes. initial face-to-face training visit and subsequent site visits Prerandomisation: 1 month prior to randomisation, dei- and webinars targeting strategies to improve quality of dentified data will be collected from all sites. These data care will be provided. Unless requested by health ser- will be fed back to all sites as a formal report highlight- vices the intervention will not be modified or ing areas where data quality issues may occur.
6 Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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Figure 4 Sample display from the quality improvement portal.
Randomisation: Baseline data will be collected and sites explicitly recorded, diagnoses of diabetes or left ven- will be randomised to intervention or control. tricular hypertrophy will be assumed to be absent. End of intervention period: At the end of 12 months, High CVD risk is defined as a calculated 5-year CVD data will be collected in both study arms. risk of >15%, a history of CVD or the presence of any clinically high-risk conditions (as per NVDPA recom- mendations). Based on audit data, this is expected to ∼ 10 11 Primary outcomes comprise 30% of the patient population. fi ▸ Change in the proportion of eligible patients receiv- Appropriate prescriptions is de ned as a prescription and ing appropriate measurements of their CVD risk in for one or more BP lowering drugs a statin for the previous 12 months (measured at randomisation people at high risk without CVD; or a prescription for and and and at 12 months). one or more BP lowering drugs a statin an anti- ▸ Change in the proportion of eligible patients assessed platelet agent (unless contraindicated by oral anticoagu- at high CVD risk receiving appropriate prescriptions lant use) for people with established CVD. for their CVD risk factors in the previous 12 months (measured at randomisation and 12 months). Appropriate measurement of CVD risk factors is Secondary outcomes defined as having recorded or updated all the essential ▸ Change in the measurement of individual risk factors risk factors for the measurement of CVD risk (smoking separately (smoking status, BP, cholesterol, other status, blood pressure (BP) in the previous 12 months, non-Framingham risk factors—BMI, chronic kidney total cholesterol and high-density lipoprotein (HDL) disease (CKD) screening with urinary Albumin cholesterol in the previous 24 months) among those in to Creatinine ratio, estimated Glomerular Filtration whom risk assessment is guideline indicated. Unless Rate);
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prescription of appropriate medicines to high-risk patients (second primary outcome) is 50%. This is based on our published data on drug prescriptions – for individuals with and without established CVD.9 11 4. An intraclass correlation coefficient of 0.05 for both – primary outcomes based on our audit data.9 11 5. Two-sided α=0.05 Data analysis will be performed on an intention-to- treat basis using generalised estimating equations.38 Subgroup analyses will be carried out using the three prespecified strata: (1) ACCHS versus general practices, (2) service size (small vs large) and (3) current partici- pation in QI programmes vs past or no involvement in these programmes.
Economic evaluation The economic evaluation will have a trial-based compo- nent and a modelled evaluation of long-term costs and outcomes. The incremental cost will be based on soft- ware, training and other costs incurred with transition- ing practices to using HealthTracker. This will help determine the cost barriers experienced by different practices in adopting the system. Data on medications, laboratory tests, consultations and newly recorded diag- noses of CVD events incurred by eligible patients during the trial will be obtained from the data extraction tools. Costs will be calculated from prevailing Medicare rates and standard Australian National Diagnosis Related Groups cost weights for CVD hospitalisations. The incre- mental cost consequences of the HealthTracker system in achieving each of the primary outcomes will then be estimated, for example, cost per eligible patients Figure 5 TORPEDO study schema. assessed at high CVD risk receiving appropriate prescrip- tions. Trial-based data, however, cannot capture costs ▸ Intensification of existing medication regimes among and outcomes beyond the trial. To address this, a mod- patients at high CVD risk (additional BP and elled economic evaluation will enable quality of life and lipid-lowering agents); survival to be examined and allow incremental cost- ▸ Changes in mean systolic BP, total cholesterol, LDL effectiveness ratios to be calculated in terms of cost per cholesterol and HDL cholesterol; Quality Adjusted Life Years gained. Using a Markov ▸ New CVD and CKD diagnoses. model, the eligible patient population in both study arms will be hypothetically tracked over an extended Statistical considerations period. Transition between various defined health states, Randomisation of 60 services (30 per arm) will provide costs and quality of life attached to various health states 90% power to detect a ≥10% absolute higher occur- and the projected long-term intervention effects from rence in each primary study outcome among practices that observed in the trial will be based on published evi- receiving HealthTracker. The following assumptions are dence. With appropriate discounting, estimates of long- based on our three audits in ACCHSs and mainstream term costs and outcomes will fold out of the model. – general practices9 11 and include an assumed improve- Sensitivity analyses will be conducted on discount rate, ment of 10% in the two primary outcomes in control uncertainty in outcome estimates and assumptions made practices as a result of study participation. in costing (eg, varying efficiencies with different patient 1. Cluster size of eligible population will range from 200 practice ratios to those of the trial setting). This will in a small service through to 2000 in a large service. better inform policy makers as to the resource conse- An average cluster size of 750 is assumed. quences of rolling out this programme to scale. 2. Recording rates of essential risk factors needed for risk assessment in the target group (first primary Process evaluation outcome) average 50%.10 11 The qualitative evaluation of the β-version of 3. Thirty per cent of the cluster will be either be at high HealthTracker suggested that a critical factor affecting CVD risk or have established CVD (n=250) and the uptake of EDS interventions is whether and how
8 Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
The TORPEDO study they become embedded in routine healthcare.22 In the contemporaneously with data collection. This method TORPEDO trial we will build on this observation allows for interview content to be refined for subsequent through a detailed process evaluation to better appreci- data collection and to actively pursue emergent themes ate the factors that might influence sustainability beyond of interest. Although interviewing will continue until the trial setting. Two qualitative methods will be used to thematic saturation is achieved and therefore the exact explore these factors. number of interviews is unknown, we anticipate from prior experience that around 80 interviews (40 patients Semistructured interviews with health professionals and staff and 40 staff) will provide sufficiently rich data to meet A maximum variation sample will be taken to ensure our objectives. diverse opinions are gained from patients, clinical and Interview data will be digitally recorded, and profes- managerial health staff and sites with low and high sionally transcribed. NVivo 9 (QSR International uptake of the intervention.39 Key issues to be explored Melbourne, Victoria, Australia) will be used to assist with will include (1) how practitioners use HealthTracker; data organisation and coding for key themes. Video data (2) what effects it has on organisational practices and will be directly analysed and coded for key themes personnel and (3) what are patients’ experiences of within NVivo. Feedback of findings to participants will being presented with HealthTracker outputs and what be provided by a variety of methods, including work- impact does this have on the healthcare encounter. shops, summary reports, newsletters and via the study Individual informed consent will be sought and data will website. be collected towards the end of the intervention period so as not to unduly influence trial outcomes. ETHICAL CONSIDERATIONS Audio/video ethnography The general practice component of the study is A key component of understanding barriers/enablers to approved by the University of Sydney Human Research use of HealthTracker is a better appreciation of how Ethics Committee (HREC) and the ACCHS component practitioners and patients use it at the point of care. is approved by the Aboriginal Health and Medical Data collection using audio/video recording will capture Research Council HREC. Formal agreements with each how technological innovations are actually used in prac- of the participating sites have also been signed. tice.40 Ethnographic analysis will greatly augment the Quantitative data will be obtained from deidentified clin- interview accounts and will particularly shed important ical audits. Ethical approval to grant waiver of the usual light on (1) how the intervention impacts on the flow of requirement to obtain individual patient consent has the clinical encounter; (2) how risk information is com- been obtained. In participating ACCHS sites, eligible municated between health professional and patient and patients can request to ‘opt out’ from having data in the (3) how the patient receives and interprets the informa- clinical audit data extracts exported. Data exports will be tion and the role it may play in shared decision-making compliant with privacy legislation, centrally managed by processes. Although audio/video recorded clinical the George Institute and held in strict confidence. Some encounters are commonly used for primary care teach- individual health professionals (GPs, practice nurses, ing purposes, such a technique can be potentially sensi- etc) and patient participants will have their informed tive and therefore will be restricted to a small number of consent taken at the site to allow data collection sites. Recordings will be conducted toward the end of through semistructured in-depth interviews and/or the intervention period when both health staff and the use of audio/videotaped healthcare encounters. patients are thoroughly familiar with the system. This Participation in this component will be optional. Patient will occur over a 1-week period at each site. Participants information statements and consent forms have been who are approached for an interview will be invited to approved by each ethics committee and formatted in participate in this component. They will be given the accordance with their own guidelines and requirements. option of having their healthcare encounter audio or The study will be conducted in accordance with the video recorded. A follow-up interview will be arranged principles set out in the National Health and Medical with these participants (both staff and patients) where Research Council and the NSW Aboriginal Health and the recording is played back for participant interpret- Medical Research Council guidelines. Specific effort will ation of the data. be taken to respect the autonomy and governance of These data will be supplemented by project officer participating ACCHSs. The intellectual property rights field notes to identify any key processes, events, staffing of ACCHSs will be recognised and preserved. It is also and other resource issues occurring during the recognised that ACCHSs have rights and responsibilities intervention period that may be relevant in gaining a regarding the use of health-related information for their better understanding of barriers and facilitators to attending clients. Collaborators on the TORPEDO study implementation. will be encouraged to disseminate information from the A multidisciplinary research team will guide the ana- project in a manner that supports health improvement lysis process. As is common with qualitative inquiry, data for Aboriginal and Torres Strait Islander peoples and analysis will commence early and be conducted local benefit to participating ACCHSs.
Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 9 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
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DISCUSSION funded by a Postdoctoral Fellowship cofunded by the NHMRC and National The TORPEDO study will seek to provide reliable evi- Heart Foundation. dence about the effectiveness of QI interventions HealthTracker Steering Committee David Peiris (Co-Chair), Anushka Patel incorporating EDS in Australian primary care settings. (Co-Chair), Mark Harris, Tim Usherwood, Nicholas Zwar, Katie Panaretto, The implications of use of such systems for CVD risk Jenny Hunt, Julie Redfern, Stephen Colagiuri, Stephen McMahon, Rod Jackson, Bruce Neal, David Sullivan, Fiona Turnbull, Alan Cass, Noel Hayman, management extend well beyond being a point-of-care Alex Brown, Jessica Stewart, Bindu Patel clinical resource. Improving health system performance TORPEDO Study Project Staff Marilyn Lyford, Maria Agaliotis, Sharon Parker, is central to the aims of this initiative and this is espe- Lyn Anderson, Melvina Mitchell, Chris Henaway, Catriona McDonnell, cially pertinent to addressing Aboriginal health inequi- Olly Shestowsky ties where the CVD burden is five-fold greater. There is Pen Computer Systems for their support in developing the HealthTracker potential for substantially better health outcomes software and the data extraction tool. Improvement Foundation of Australia for from CVD in Australia with improved implementation of their support in developing and hosting the quality improvement portal. existing evidence in primary healthcare, where most of Competing interests None. the opportunity to manage cardiovascular risk occurs. The strategy proposed is the first of its kind in Australia Provenance and peer review Not commissioned; externally peer reviewed. and is strongly aligned with national strategy recom- Data sharing statement No additional data are available. mendations for health system reform. If effective, HealthTracker could have widespread applicability for the prevention and management of other chronic REFERENCES diseases. 1. Australian Institute of Health and Welfare. Australia’s Health 2012. Australia’s health series no.13. Cat. no. AUS 156. Canberra: AIHW, 2012. 2. Vos T, Barker B, Stanley L, et al. The burden of disease and injury Strengths and limitations of this study in Aboriginal and Torres Strait Islander peoples 2003. Brisbane: The strengths of the study are that it assesses an in- University of Queensland, 2007. 3. Australian Institute of Health and Welfare. Australia’s Health 2008. novative complex intervention that is implemented in Cat. no. 99. 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New Engl J Med Australia 2000;342:145–53. 4 School of Public Health and Community Medicine, University of New South 9. Heeley E, Peiris D, Patel A, et al. Cardiovascular risk perception and Wales, Sydney, New South Wales, Australia the evidence practice gap in Australian General Practice (the 5Aboriginal Health and Medical Research Council, Sydney, New South Wales, AusHEART study). Med J Aust 2010;192:254–9. Australia 10. Peiris D, Patel A, Cass A, et al. Cardiovascular disease risk management for Aboriginal and Torres Strait Islander peoples in 6Inala Indigenous Health Service, Queensland Health, Brisbane, Queensland, primary health care settings—findings from the Kanyini Audit. Med J Australia Aust 2009;191:304–9. 11. Webster R, Heeley E, Peiris D, et al. Identifying the gaps in Acknowledgements We gratefully acknowledge the support of the general cardiovascular risk management. Med J Aust 2009;191:324–9. practices and Aboriginal Community Controlled Health Services involved in 12. Reid C, Nelson MR, Shiel L, et al. Australians at risk: management of cardiovascular risk factors in the REACH Registry. Heart Lung this study. We also acknowledge the support of the Queensland Aboriginal Circ 2008;17:114–18. and Islander Health Council, Aboriginal Health and Medical Research Council, 13. Wan Q, Harris MF, Jayasinghe UW, et al. Quality of diabetes care Western Sydney Medicare Local, Inner West Sydney Medicare Local, South and coronary heart disease absolute risk in patients with type 2 Eastern Sydney, Eastern Sydney Medicare Local, South Western Sydney diabetes mellitus in Australian general practice. Qual Saf Health Medicare Local and Nepean-Blue Mountains Medicare Local. Care 2006;15:131–5. 14. Steven I, Wing L. Control and cardiovascular risk factors of Contributors DP, AP and TU developed the original concept of this hypertension. An assessment of a sample of patients. Aust Fam study. All authors contributed to the study design. DP, KP, BP, MH, Physician 1999;28:45–8. TU and JH are involved in the implementation of the project. DP wrote 15. Vale MJ, Jelinek MV, Best JD. How many patients with coronary heart disease are not achieving their risk-factor targets? Experience the first draft of the protocol and the final manuscript was reviewed by in Victoria 1996–1998 versus 1999–2000. Med J Aust all the authors. 2002;176:211–15. — Funding This study is funded by an Australian National Health and Medical 16. Diabetes Trials Unit The Oxford Centre for Diabetes Endocrinology and Metabolism. UKPDS Risk Engine . 2009. http://www.dtu.ox.ac. Research Council (NHMRC) Project grant (ID 1010547) and NSW Department uk/index.php?maindoc=/riskengine/ (accessed 2 Oct 2012). of Health. DP is supported by a NHMRC Translating Research into Practice 17. National Vascular Disease Prevention Alliance. Guidelines for the Fellowship. AP is supported by an NHMRC Senior Research Fellowship. 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Peiris D, Usherwood T, Panaretto K, et al. BMJ Open 2012;2:e002177. doi:10.1136/bmjopen-2012-002177 11 Downloaded from bmjopen.bmj.com on June 14, 2014 - Published by group.bmj.com
The Treatment of cardiovascular Risk in Primary care using Electronic Decision suppOrt (TORPEDO) study: intervention development and protocol for a cluster randomised, controlled trial of an electronic decision support and quality improvement intervention in Australian primary healthcare
David Peiris, Tim Usherwood, Katie Panaretto, et al.
BMJ Open 2012 2: doi: 10.1136/bmjopen-2012-002177
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Validation of a general practice audit and data extraction tool David Peiris Maria Agaliotis Bindu Patel Anushka Patel
Background There is growing interest in the use of Electronic Decision SuppOrt (TORPEDO) study. We assessed how accurately a primary healthcare data for multiple TORPEDO is a cluster randomised controlled trial common general practitioner (GP) purposes, including health professional examining the effectiveness of a multi-faceted audit tool extracts data from two audits, quality improvement programs and quality improvement intervention to improve software systems. research. A critical enabling factor is the cardiovascular disease (CVD) risk screening Methods availability of data extraction tools to audit and management. Sixty health services are First, pathology test codes were patient databases for information such participating (40 general practices and 20 audited at 33 practices covering nine as patient demographics, data quality, ACCHSs) in New South Wales and Queensland. companies. Second, a manual audit of disease profiles, risk factor measurements, Full details of the study protocol have been chronic disease data from 200 random pathology testing, immunisation and published elsewhere.12 patient records at two practices was cancer surveillance, use of medicines and compared with audit tool data. Medicare item uptake. Several extraction Methods Results tools are now available that interface TORPEDO involves extracting clinical data for all Pathology review: all companies with the major primary healthcare regularly attending patients (three visits in the assigned correct codes for cholesterol, software systems. These tools are used previous 2 years and one visit in the previous 6 creatinine and glycated haemoglobin; by healthcare providers for practice months) in whom national guidelines recommend four companies assigned incorrect audits and by many organisations such as CVD risk screening (>35 years if Aboriginal and codes for albuminuria tests, precluding Medicare Locals, Aboriginal Community Torres Strait Islander and >45 years for all others). accurate detection with the audit tool. Controlled Health Services (ACCHSs), Participating sites need to be exclusively using Case record review: there was strong the Australian Primary Care Collaborative either Medical DirectorTM or Best PracticeTM for agreement between the manual audit (APCC) program and the National their electronic health records (EHR) without any and the tool for all variables except 1–3 chronic kidney disease diagnoses, Prescribing Service. These tools are use of paper or hybrid paper/electronic recording. which was due to a tool-related also being used for national indicator These software products currently comprise programming error. programs, including the former Divisions the majority of EHR systems used in Australia. of General Practice indicator program,4 Extractions are performed at baseline and end of Discussion the Queensland Aboriginal and Islander study. Two de-identified data files are generated: The audit tool accurately detected Health Council indicator program,5 the (1) an individual patient data file that is sent most chronic disease data in two GP record systems. The one exception, Northern Territory and the National to the coordinating research institute for trial 6,7 however, highlights the importance Key Performance Indicator programs. analyses via a secure file transfer protocol; and of surveillance systems to promptly Increasingly, researchers are also using (2) an aggregated data file that is sent to a web- identify errors. This will maximise these tools to facilitate data collection.8 based portal via an identical mechanism as that potential for audit tools to improve Few studies have assessed the validity used in the APCC program. Intervention arm sites healthcare quality. of these tools in Australia and generally can perform these extractions monthly and can Keywords these have been secondary considerations view peer-ranked performance feedback data. 9–11 cardiovascular diseases; mass to the main objectives. Variables are extracted on patient screening; clinical coding; data linkage demographics, recorded diagnoses of chronic In this study, we assessed how accurately diseases, chronic disease risk factors, pathology one of the most commonly used audit tools tests and medications. We assessed the validity extracted data from patient record systems. of the data extraction tool in detecting these The study forms part of the Treatment Of variables from the two previously described EHR cardiovascular Risk in Primary care using systems via a two-stage process.
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Stage 1: Pathology review Results Manual record audit For extraction tools to accurately collect Table 1 highlights the patient characteristics Pathology audit pathology data, laboratories must submit test for the 200 records audited. Table 2 outlines results in a standard format (Health Level Thirty-three sites had pathology data audited the level of agreement/correlation for the Seven [HL-7]) and assign a unique code per covering nine laboratories in New South Wales and 23 variables manually audited. Overall, the test (Logical Observation Identifiers Names Queensland. All laboratories were using the correct majority of variables achieved perfect or near and Codes [LOINC]). Prior to validating the tool LOINC codes for total cholesterol, high-density perfect agreement/correlation. There was, itself, we reviewed pathology reporting for lipoprotein cholesterol, low-density lipoprotein however, one notable exception related to plasma cholesterol, creatinine and glycated cholesterol, triglycerides, creatinine and HbA1c. There chronic kidney disease (CKD). Criteria for CKD haemoglobin (HbA1c), and urinary albumin were, however, marked variations in codes used were based on national guideline definitions to creatinine ratio (UACR). As a condition of for UACR. Four laboratories used incorrect codes, and included a recorded diagnosis of CKD or participating in TORPEDO we mandated that precluding accurate extraction of results for these proteinuria or an estimated glomerular filtration all sites have their pathology reported in HL-7 tests. All pathology companies were notified and rate (eGFR) < 45ml/min/1.73m2. There were format. We then reviewed pathology extracts instructed on the correct codes to use for this test. several cases where the extraction tool seemed from a sample of health services covering all the laboratories in the TORPEDO study. These Table 1. Patient characteristics obtained from the manual case record extracts identified what LOINCs were being audit tool (n = 200) reported for the tests in question and from Risk factor measurement Mean (SD) or No of records these data we determined whether they were n (%) in those with available concordant with those searched for with the with available information data extraction tool. information (% total) Stage 2: Manual case record Demographic information audit at two general practices Age (years) 66.2 (12.5) 200 (100) Male 93 (47) 200 (100) Two hundred records (100 per practice) from Risk factor measurements patients in the eligible age range for TORPEDO Current smoker 29 (20) 145 (73) were randomly selected. The two sites were BMI (kg/m2) 31.5 (8.8) 97 (49) urban Sydney practices participating in the Systolic blood pressure (mmHg) 129.1 (15.4) 179 (90) intervention arm of TORPEDO. One site used Medical DirectorTM and the other used Best Diastolic blood pressure (mmHg) 79.1 (10.0) 179 (90) PracticeTM. Audits were conducted as part of a Pathology laboratory measurements 12-month follow-up visit. Both sites had shown Total cholesterol (mmol/L) 5.1 (1.2) 157 (79) some mild improvement in data quality over the HDL cholesterol (mmol/L) 1.4 (0.4) 155 (78) intervention period. A research assistant reviewed LDL cholesterol (mmol/L) 2.8 (0.9) 153 (77) the following sections of the record: demographic Triglycerides (mmol/L) 1.9 (1.2) 157 (79) details, diagnoses, past medical history, physical Urinary albumin to creatinine ratio (mg/ 6.8 (24.3) 93 (47) risk factor measurements, current medications mmol) and pathology results, encompassing 23 chronic Creatinine (umol/L) 78.7 (28.3) 147 (74) disease-related variables. Free text entries in HbA1c (%) 6.4 (1.3) 112 (56) progress notes were not reviewed. A fresh data Past medical history n (%) extraction was simultaneously performed and Coronary heart disease 30 (15) securely sent to the research institute. Data Ischaemic stroke/transient ischaemic 15 (7.5) entered from the manual record audit and that attack obtained from the individual patient data extract Peripheral vascular disease 6 (3) were compared. Kappa statistics were used Left ventricular hypertrophy 0 (0) for categorical variables and Bland–Altman Atrial fibrillation 11 (5.5) plots were constructed for continuous variables Heart failure 11 (5.5) with the mean differences and 95% limits of Diabetes 41 (20.5) agreement reported.13 Analyses were conducted using SAS Enterprise Guide (v. 4.5; SAS Institute, Abbreviations: BMI = body mass index; HbA1c = glycated haemoglobin; HDL = high-density lipoprotein; LDL = low-density lipoprotein; SD = standard deviation Cary, NC, USA)
REPRINTED FROM Australian Family Physician Vol. 42, No. 11, november 2013 817 research Validation of a general practice audit and data extraction tool to inappropriately assign a diagnosis of CKD also low agreement with UACR tests. This is as a result of this study, was identified to be a where no such criteria could be identified from likely to not be a problem related to the tool per programming error and subsequently fixed. We the manual record review. This was subsequently se, but related to incorrect LOINC coding by one also found wide discrepancies in the coding of identified to be a problem in the way the tool of the pathology companies used at that practice. CKD-related test results by various laboratories. extracted eGFR values from one pathology This affects the ability of any data extraction tool company. If the result reported a non-integer Discussion to accurately detect pathology test results for this value (eg. >90 ml/min), this defaulted to a zero In this study we found that data extraction using condition. value and hence met one of the CKD criteria one of the most commonly used tools in Australia An important study limitation is that it was (eGFR <45 ml/min). The software company was was highly consistent with manual reviews of the ‘fit for purpose’ for the chronic disease-related notified and a fix was put in place. There was data for all variables except CKD recording which, variables used in TORPEDO and for the population recommended for CVD risk screening. We cannot Table 2. Agreement/correlation statistics between the audit tool and make any assumptions, therefore, about validity manual case record audit for 200 patient records from two general for other disease areas. Another limitation is that practices we looked at interaction between one extraction Variable Agreement (kappa) (95% CI where applicable) tool and two GP EHR systems. Although these are Aboriginal status 1.00 the most commonly used systems in Australia, we cannot extrapolate findings to other systems. Smoking status 1.00 A final limitation is that we only included data Recorded diagnoses that could potentially be extracted from the Coronary heart disease 0.95 (0.90–1.00) EHR. If a practitioner is making free text entries Peripheral vascular disease 1.00 rather than in codable sections of the record Stroke 0.96 (0.89–1.00) then neither the extraction tool nor our manual Atrial fibrillation 1.00 case record reviewer would have detected those Left ventricular hypertrophy 1.00 entries. The Australian health care system is Congestive heart failure 0.95 (0.85–1.00 rapidly evolving to incorporate an information Diabetes 1.00 communication technology (ICT) infrastructure Gestational diabetes 1.00 that will enable data to be shared across Chronic kidney disease 0.24 (0.00–0.48) multiple platforms. The primary health care Chronic disease risk factor Mean difference* (95% No of readings sector is a leading driver of this change. GPs measurements limits of agreement where difference > and ACCHSs were early adopters of EHRs and where applicable) 2SD (%)† are now leading developments in use of data for Weight (kg) 0.001 (–0.51–0.51) 2 (1.0) quality improvement, key performance indicator Height (cm) 0 0 (0) reporting, secure messaging, and the personally Waist circumference (cm) 0 0 (0) controlled e-health record. The ability to extract data reliably and consistently is central to Systolic blood pressure (mmHg) 0.06 (–1.8–1.6) 3 (1.5) supporting these activities. Diastolic blood pressure (mmHg) 0.14 (-4.9–5.2) 2 (1) There are two clear recommendations from Total cholesterol (mmol/l) 0.02 (–0.37–0.34) 6 (3) the study. First, there is a critical need for HDL (mmol/l) 0.002 (–0.03–0.04) 0 (0) consistent reporting of pathology tests across LDL (mmol/l) 0.03 (–0.52–0.46) 2 (1) all laboratories. Second, the lack of agreement Triglycerides (mmol/l) 0.02 (–0.65–0.62) 6 (3) for variables associated with CKD recording, Creatinine (µmol/l) 0.7 (–10.1–8.8) 5 (2.5) although in retrospect was able to be explained, highlights the importance of establishing regular HbA1c (%) 0.001 (–0.18–0.19) 2 (1) surveillance procedures that are independent of Urinary albumin to creatinine 9.7 (–71.7–91.0) 0 (0) any validation work conducted by the software ratio (mg/mmol) vendors themselves. Whenever new code is Abbreviations: SD = standard deviation written, either for the extraction tools or within *The mean difference between the readings obtained from the manual audit vs the the EHR system itself, there is potential for errors audit tool with zero indicating perfect agreement for all readings in the data extraction process. National programs †Derived from constructing Bland–Altman plots for each variable such as the APCC are well placed to establish
818 REPRINTED FROM Australian Family Physician Vol. 42, No. 11, november 2013 Validation of a general practice audit and data extraction tool research such surveillance systems. This program receives by an NHMRC Translating Research into Practice Fellowship and now holds a NHMRC Early Career monthly aggregated data from a large number Fellowship. Maria Agaliotis is supported by a of practices. Automated warning systems for NHMRC Senior Research Fellowship. unexpected deviations in these routinely collected data would enable early detection of potential References problems. A sentinel network of practices that 1. Office of Aboriginal and Torres Strait Islander Health. Online Community Health Reporting routinely extracted data could complement such a Environment- OCHREStreams. Available at www. surveillance system. As we become increasingly ochrestreams.org.au/pages/welcome.aspx reliant on ICT systems for both clinical care and [Accessed 1 April 2013]. 2. Knight AW, Caesar C, Ford D, Coughlin A, Frick C. system improvements, it is important that action Improving primary care in Australia through the be taken to ensure these systems are robust. Australian Primary Care Collaboratives Program: a quality improvement report. BMJ Qual Saf Implications for general 2012;21:948–55. 3. NPS MedicineWise. MedicineInsight. Available at practice www.nps.org.au/about-us/what-we-do/medici- • The audit tool used in this study was reliable neinsight [Accessed 1 April 2013]. 4. Department of Health and Ageing. Divisions of for most chronic disease-related measurements General Practice Program - National Performance with the exception of CKD diagnoses. Indicators 2009–10. Available at www.phcris. • Inaccurate pathology data extracted from audit org.au/divisions/reporting/documents/techde- tools is due to laboratories assigning incorrect tails/NPI_Technical_Details_10_May_2010.pdf [Accessed 1 April 2013]. codes to test results. 5. Queensland Aboriginal and Islander Health • National initiatives to independently and Council. Core Performance Indicators – Version 2. regularly review the quality of data obtained Available at www.qaihc.com.au/resources/publi- cations/ [Accessed 1 April 2013]. from data extraction tools are needed. 6. Northern Territory Aboriginal Health Forum. NT Aboriginal Health Key Performance Information Authors System. Available at www.nt.gov.au/health/ahkpi/ David Peiris MBBS, MIPH, PhD, FRACGP, FARGP, [Accessed 1 April 2013]. Head of Primary Health Care Research, The 7. Australian Institute of Health and Welfare. George Institute for Global Health, University of Indigenous primary health care key performance Sydney, Camperdown, NSW. dpeiris@georgein- indicators. Available at http://meteor.aihw.gov.au/ stitute.org.au content/index.phtml/itemId/457994 [Accessed 1 April 2013]. Maria Agaliotis BSocSci, MPH, Project Officer, 8. university of Melbourne. GRHANITE Health Cardiovascular Division, The George Institute Informatics Unit. Available at www.grhanite.com/ for Global Health, University of Sydney, projects/ [Accessed 1 April 2013]. Camperdown, NSW 9. liljeqvist G, Staff M, Puech M, Blom H, Torvaldsen S. Automated data extraction from general Bindu Patel BS, MPH, Project Manager, practice records in an Australian setting: Trends in Cardiovascular Division, The George Institute influenza-like illness in sentinel general practices for Global Health, University of Sydney, and emergency departments. BMC Public Health Camperdown, NSW 2011;11:435. Anushka Patel MBBS, SM, PhD, FRACP, FCSANZ, 10. schattner P SM, Stanger L, Speak M, Russo K. Chief Scientist, The George Institute for Global Clinical data extraction and feedback in general practice: a case study from Australian primary Health, University of Sydney, Camperdown, NSW. care. Inform Prim Care 2010;18:205–12. Ethics approval: The study was approved by the 11. schattner P SM, Stanger L, Speak M, Russo K. University of Sydney Human Research Ethics Data extraction and feedback - does this lead Committee (Reference number: 2012/2/2183) and to change in patient care? Aust Fam Physician permission was obtained from all 2011;40:623–28. participating services to audit records. 12. Peiris D, Usherwood T, Panaretto K, et al. The Treatment of cardiovascular Risk in Primary care Competing interests: None declared. using Electronic Decision suppOrt (TORPEDO) Provenance and peer review: Not commissioned; study: intervention development and protocol for externally peer reviewed. a cluster randomised, controlled trial of an elec- tronic decision support and quality improvement intervention in Australian primary healthcare. Acknowledgements BMJ Open 2012;2:6. We gratefully acknowledge the support of Dr 13. bland JM, Altman DG. Statistical methods for Cedric Meyerowitz and Dr John Hillman in the assessing agreement between two methods of conduct of this study. David Peiris was supported clinical measurement. Lancet 1986;8476:307–10.
REPRINTED FROM Australian Family Physician Vol. 42, No. 11, november 2013 819 CSIRO PUBLISHING Australian Journal of Primary Health, 2017, 23, 482–488 Research http://dx.doi.org/10.1071/PY16166
Utilisation of Medicare-funded schemes for people with cardiovascular disease
Julie Redfern A,B,G, Karice Hyun A,B, Emily Atkins A,B, Clara Chow A,B,C, Tom Briffa D, Bindu Patel A,B, Nick Zwar E, Tim Usherwood A,B,F, Qiang Li A, Anushka Patel A and David Peiris A,B
AThe George Institute for Global Health, UNSW Sydney, NSW 2052, Australia. BSydney Medical School, University of Sydney, NSW 2006, Australia. CCardiology Department, Westmead Hospital, NSW 2145, Australia. DSchool of Population Health, University of Western Australia, WA 6009, Australia. ESchool of Medicine, University of Wollongong NSW 2522, Australia. FDepartment of General Practice, Sydney Medical School Westmead, University of Sydney, Sydney, NSW 2006, Australia. GCorresponding author. Email: [email protected]
Abstract. The aim of this study is to investigate the utilisation of Medicare Benefit Scheme items for chronic disease in the management of cardiovascular disease (CVD) in general practice and to compare characteristics of CVD patients with and without a General Practice Management Plan (GPMP). Subgroup analysis of Treatment of Cardiovascular Risk using Electronic Decision Support (TORPEDO) baseline data was collected in a cohort comprising 6123 patients with CVD. The mean age (s.d.) was 71 (Æ13) years, 55% were male, 64% had a recorded diagnosis of coronary heart disease, 31% also had a diagnosis of diabetes and the mean number of general practice (GP) visits (s.d.) was 11 (Æ9) in 12 months. A total of 1955/6123 (32%) received a GPMP in the 12 months before data extraction; 1% received a Mental Health Plan. Factors associated with greater likelihood of receiving a GPMP were: younger age, had a diagnosis of diabetes, BMI > 30 kg m–2, prescription of blood pressure-lowering therapy and more than ten general practice visits. Enhancing utilisation of existing schemes could augment systematic follow up and support of patients with CVD.
Received 21 December 2016, accepted 30 May 2017, published online 27 July 2017
Introduction However, once people leave hospital, only ~50% adhere to Cardiovascular disease (CVD), including coronary heart disease recommended medicines (Rasmussen et al. 2007) and, at best, (CHD), is a leading cause of morbidity and mortality globally 30% achieve lifestyle modification (Chow et al. 2010). Therefore, (World Health Organization 2014). In recent decades, mortality a more systematic approach is needed to improve the quality of associated with CHD has decreased substantially in developed ongoing care for people living with CVD. countries due to the improving pharmacological therapies, Primary health care can be described as the frontline of revascularisation procedures and prevention programs (World Australia’s healthcare system, and that CVD is a chronic disease Health Organization 2014). However, patients with prior CVD requiring lifelong management; this offers the ideal environment are at high risk of recurrent events and approximately one- for closing evidence-practice gaps. Research suggests that a quarter will be readmitted to hospital within 1 year of an index strong primary care system is associated with reduced costs, event (Briffa et al. 2011). In 2010, more than 25 000 Australian increased efficiency, lower rates of hospitalisations, increased hospital separations were associated with repeat cardiovascular patient satisfaction and better health outcomes (Department of admissions at a cost of more than A$600 million in direct Health 2013). In 2016, the Australian Government identified the costs alone (Deloitte Access Economics 2011). The associated need for development of a ‘Medicare payment system to reward challenge is that more people are surviving cardiovascular and encourage best practice and quality improvement in chronic events, hospital stays are becoming much shorter and there disease prevention and management’ (Department of Health are growing numbers of people living with CVD requiring 2016). Later in 2016, a consultation paper was released by the ongoing secondary prevention (Redfern et al. 2011). Secondary Department of Health that outlines how and why the practice prevention strategies (including lifestyle change and adherence incentive payment (PIP) could be redesigned with a greater focus to evidence-based medication) provide an effective way of on quality improvement with better use of data (Department of reducing CVD morbidity and mortality (Clark et al. 2005). Health 2016). The redesign aligns with previous calls from the
Journal compilation La Trobe University 2017 www.publish.csiro.au/journals/py Utilisation of Medicare-funded schemes for CVD Australian Journal of Primary Health 483
MBS Items that support access to psychological care and What is known about the topic? professionals with expertise in behaviour modification among others. Previous research has suggested that care plans can * Despite the potential value and importance of government- improve outcomes for people with chronic conditions including funded primary care chronic disease management items, diabetes (Zwar et al. 2007; Mitchell et al. 2008; Comino et al. their rate of utilisation in a CVD population remains 2015). Increasing our understanding about the utilisation of these unexplored. Items for people living with CVD could inform opportunities for fl What does this paper add? system-level improvements that may also in uence outcomes. The aim of this study is to conduct a subanalysis of existing * Only one-third of patients with diagnosed CVD received baseline study data to investigate the utilisation of MBS Items to a GPMP, and enhancing utilisation of existing schemes support the management of CVD in people presenting to primary could facilitate systematic follow up and support for care and to compare demographic and clinical characteristics patients living with CVD. of those with and without a GPMP.
Methods House of Representatives identifying the need for a Medicare This study is a subanalysis of Treatment of Cardiovascular payment system to reward and encourage quality in chronic Risk using Electronic Decision Support (TORPEDO) Study disease prevention and management (Department of Health data relating to use of chronic disease Medicare Items to support 2016). These discussions are likely to see opportunities for chronic disease management. Details of the methods and primary changes in how chronic conditions such as CVD are supported results of the TORPEDO Study are reported elsewhere (Peiris and managed, and hence understanding existing models and et al. 2012, 2015) In brief, the TORPEDO Study was a parallel services and their utilisation is important at this time. arm, cluster-randomised controlled trial involving 40 general In Australia, the publically funded universal health insurance practices and 20 Aboriginal Medical Services across NSW, scheme, known as the Medicare Benefits Scheme (MBS), is the Australia. Primary care services were eligible to participate if primary funding mechanism for primary health care. The scheme there was exclusive use of one of the two compliant software includes subsidies for treatment and services (identified through systems to record MBS Items numbers claimed, risk factor Item Numbers) provided by suitably qualified professionals such information, pathology test results and prescribed medications, as (but not limited to) medical practitioners, nurse practitioners and a willingness from all staff to use the intervention (Peiris and allied health providers (Department of Health 2015). The et al. 2012). The TORPEDO Study aimed to assess whether scheme includes several Item Numbers intended to support a multifaceted quality-improvement intervention comprising ongoing management of people with chronic medical conditions. point-of-care electronic decision support, audit and feedback These Item Numbers provide an opportunity to plan and monitor tools, and clinical workforce training, improved CVD risk care in an ongoing and regular way. The Chronic Disease management when compared with usual care (Peiris et al. Management (formerly Enhanced Primary Care) MBS Items 2012). The full TORPEDO cohort comprised 38 725 patients enable GPs to plan and coordinate the health care of patients with (10 308 high CVD risk at baseline). At follow up, the proportion chronic or terminal medical conditions, including patients with of patients with recommended CVD risk factor measurements these conditions who require multidisciplinary, team-based was higher in the intervention than control arm (63 v. 53%, care from a GP and at least two other health or care providers P = 0.02) (Peiris et al. 2015). There were also significant (Department of Health 2015). To be eligible, patients must treatment escalations (new prescriptions or increased numbers have a chronic or terminal medical condition (with or without of medicines) for antiplatelet (17.9 v. 2.7%; P < 0.001), lipid- multidisciplinary care needs), where a ‘chronic medical condition’ lowering (19.2 v. 4.8%; P < 0.001) and blood pressure-lowering is one that has been or is likely to be present for at least 6 months medications (23.3 v. 12.1%; P = 0.02) (Peiris et al. 2015). The (Department of Health 2015). The Chronic Disease Management TORPEDO Study has ethics approval from the University of Items include a GP Management Plan (GPMP) and Team Care Sydney Human Research Ethics Committee (HREC) and the Arrangements (TCA), which are designed to support those with Aboriginal Health and Medical Research Council HREC. complex conditions who require coordinated and multidisciplinary Individual consent waiver was granted given data collection was care. They are intended to be provided and coordinated by the based on de-identified extracts from electronic health records patient’s usual GP. However, despite these Items being introduced (Peiris et al. 2012, 2015). with the aim of improving care of people with chronic disease, their rate of implementation and use in a CVD population remains unexplored. Participants with CVD and Medicare data available Additional MBS Items support access to allied health The eligible population for this subanalysis, of baseline data and mental health services. These include services from was defined as all Aboriginal and Torres Strait Islander people individual allied health providers, including (but not limited 35 years and all others 45 years (based on vascular risk to) physiotherapists, Indigenous health workers, occupational screening recommendations; Anderson et al. 1991) who had therapists and psychologists (Department of Health 2015). The a documented diagnosis of established CVD (diagnosis of TCA Item supports the GP in working with other providers CHD, cerebrovascular disease, peripheral vascular disease) and for the care provided by those providers to be renumerated and attended the primary care service at least three times in the through the MBS (Department of Health 2015). There are also previous 24 months, including at least once in the previous 484 Australian Journal of Primary Health J. Redfern et al.
6-months. All MBS Items claimed for the 12 months before analysed using SAS, ver. 9.4, for Windows (SAS Institute Inc. the date of data extraction were available from 44 of the primary Cary, NC, USA). care sites (16 sites did not use compliant software billing systems). The total number of participants from these sites with Results documented CVD provided a cohort of 6123 participants for the present subanalysis. Importantly, only allied health service Demographic and clinical characteristics of those usage could be obtained for those services claimed via MBS with and without care plans where a service was delivered within the practice. Demographic and clinical characteristics for those with (n =1955) and without (n = 4168) a GPMP are presented in Table 1. The Data collection mean age (Æs.d.) of the total cohort (n = 6123) was 71 Æ 13.1 For the TORPEDO Study, de-identified data extractions from years; 55% were male, 64% had a documented diagnosis of databases at participating practices were completed using a CHD and 31% had a documented diagnosis of diabetes. The Æ validated data extraction tool for all patients who met the mean number of GP visits in the 12 months was 11.4 8.8 eligibility criteria (Peiris et al. 2013). These extracts were (Table 1). Regarding CVD risk factors, 17% were current or Æ Æ À1 securely uploaded to the coordinating research institute’s study recent smokers, the mean TC ( s.d.) was 4.36 1.11 mmol L , Æ Æ database (Peiris et al. 2015). An encrypted identifier code was the mean SBP ( s.d.) was 132.2 18.3 mm Hg and 29% had > –2 attached to each patient extract to allow for longitudinal a BMI 30 kg m . A diagnosis of CHD, peripheral vascular > –2 comparisons and subsequent extraction of MBS data from GP disease, diabetes, BMI 30 kg m and prescription of lipid- electronic records using third-party software. One month before lowering, antiplatelet medication, BP lowering medication, primary data collection, an extraction was performed to assess as well as those who saw their GP more frequently, were fi data quality. A brief report was sent to all services highlighting associated with a signi cantly higher rate of receiving a care plan areas where data quality issues may have been occurring. than not receiving a care plan (Table 1). Following this quality check, data were extracted for all eligible patients at all participating practices. The extraction provided Utilisation of MBS items clinical and demographic data at an individual level. A total of 1955 (32%) patients with CVD had a GPMP prepared in the 12 months before data extraction. For the Statistical analysis Indigenous subgroup (n = 1296), a total of 458 (35%) of the Data extracted at baseline for the TORPEDO Study were patients had a GPMP in the 12 months prior (Table 1). We used for this subanalysis. Descriptive statistics are means also calculated the number of patients who had a GPMP or (standard deviations), frequencies or proportions as appropriate. a review and found it was 2676/6123 (44%). Although the Independent t-test or Chi-Square tests were computed to compare categories are not mutually exclusive, the majority of recorded demographic and clinical characteristics. Patient- and practice- GP consultations were of standard length (87%) rather than level data analysis was performed to explore the factors extended (12%) or short (20%) (Table 2). Patients with a associated with increased likelihood of receiving a GPMP (Item GPMP compared to those without a GPMP were significantly #721), in the previous 12 months, for CVD patients using more likely to have standard (GPMP: 93% v. no GPMP: 84%, multiple adjusted multilevel logistic regression model with P < 0.001) and long consultations (GPMP: 18% v. no GPMP: random intercept to account for clustering of patients within 10%, P < 0.001). A total of 24% of patients received a GPMP services. A list of independent variables was chosen to be review and 28% received a TCA (Table 2). There were very included in the multivariable model if they were statistically few claims for the suite of mental health services (Table 2)and significant on univariate testing at an a-level of 0.2, and then also very few in-practice consultation claims with an allied according to the clinical significance, some variables were health provider (exercise physiologist, dietitian, occupational included or excluded regardless of their statistical significance. therapist, physiotherapist all <1%) through the Medicare system The patient-level variables included in the model were: age where the most commonly claimed service was for podiatry divided into four quartiles (<61, 61–71, 72–81 and >81 years), at 3%. gender, CHD, diabetes, Aboriginal or Torres Strait Islander We also compared utilisation of GPMPs based on practice status, body mass index (BMI) (>30 kg m–2 v. not), systolic level characteristics. We found that there was no significant blood pressure (SBP) (>140 mm Hg v. not), total cholesterol (TC) difference in the proportion of patients receiving a GPMP (>4.5 mmol LÀ1 v. not), anticoagulant, antiplatelet, blood pressure based on whether they attended an ACHS or a primary care (BP)-lowering statin or other lipid-lowering medication and GP practice (GPMP: 500/1510, 33% v. no GPMP: 1455/4613, 31%, visit (>10 v. 10 visits), and the practice-level variable included P = 0.256) and the size of the practice (large: 1020/3247, 31% v. was participation in a structured quality improvement initiative small: 935/2876, 33%, P = 0.3582). Practice-level variation outside the context of the study. Examples of structured quality in GPMP rate ranged from 0 to 75% (Mean (s.d.): 34% (19.3); improvement initiatives included (but were not limited to) median (IQR): 33% (17.5, 47.5)). However, in this unadjusted the Australian Primary Care Collaboratives Program, Divisions analysis, we found that practices who had participated in of General Practice Indicator Programs and the Audit and quality improvement had a significantly greater proportion of Best Practice for Chronic Disease Extension Project. The odds patients with GPMPs (709/1955, 36%) than practices that ratios and the corresponding 95% confidence intervals from had not participated in quality improvement (1182/4168, 28%, the multivariable model were plotted on a forest plot. Data were P < 0.001). Utilisation of Medicare-funded schemes for CVD Australian Journal of Primary Health 485
Table 1. Demographic and clinical characteristics of the cohort with established cardiovascular disease GPMP, General Practice Management Plan (MBS Item #721); s.d., standard deviation; HDL, high-density lipoprotein; TG, triglyceride; LDL, low-density lipoprotein; HbA1c, glycosylated haemoglobin ; BMI, body mass index; SBP, systolic blood pressure; GP, general practice. P-values are the difference between those with a GPMP and those without a GPMP
Variable GPMP (N = 1955) No GPMP (N = 4168) Total cohort (N = 6123) P value Age, mean (s.d.) (years) 70.2 (12.3) 71.1 (13.4) 70.8 (13.1) 0.014 Male, N (%) 1093 (56) 2254 (54) 3347 (55) 0.169 Aboriginal or Torres Strait Islander, N (%) 458 (23) 838 (20) 1296 (21) 0.003 Coronary heart disease diagnosis, N (%) 1283 (66) 2625 (63) 3908 (64) 0.045 Cerebrovascular disease diagnosis, N (%) 367 (19) 750 (18) 1117 (18) 0.462 Peripheral vascular diagnosis, N (%) 154 (8) 223 (5) 377 (6) <0.001 Diagnosis of diabetes, N (%) 746 (38) 1174 (28) 1920 (31) <0.001 Atrial fibrillation diagnosis, N (%) 453 (23) 975 (23) 1428 (23) 0.849 Diagnosis of chronic heart failure, N (%) 207 (11) 446 (11) 653 (11) 0.894 Cardiovascular risk factors Current smoker, N (%) 313 (18) 596 (17) 909 (17) 0.414 SBP (mm Hg), mean (s.d.) 131.6 (17.9) 132.5 (18.5) 132.2 (18.3) 0.089 SBP > 140 mm Hg, N (%) 480 (26) 1032 (28) 1512 (27) 0.148 Total cholesterol (mmol), mean (s.d.) 4.35 (1.1) 4.37 (1.1) 4.36 (1.11) 0.598 Total cholesterol > 4.5, N (%) 593 (37) 1237 (38) 1830 (37) 0.539 HDL (mmol), mean (s.d.) 1.26 (0.4) 1.30 (0.4) 1.28 (0.41) 0.005 TG (mmol), mean (s.d.) 1.68 (1.02) 1.60 (1.4) 1.63 (1.3) 0.041 LDL (mmol), mean (s.d.) 2.36 (1.1) 2.38 (0.9) 2.38 (1.0) 0.466 HbA1c for those with diabetes, mean (s.d.) 7.44 (4.5) 7.39 (4.5) 7.41 (4.5) 0.803 BMI > 30 kg m–2, mean (s.d.) 694 (35) 1072 (26) 1766 (29) <0.001 Cardiovascular medications prescribed Statins or other lipid lowering, N (%) 1500 (77) 2919 (70) 4419 (72) <0.001 Antiplatelet, N (%) 1336 (68) 2701 (65) 4037 (66) 0.007 Blood pressure-lowering, N (%) 1738 (89) 3462 (83) 5200 (85) <0.001 Anticoagulant, N (%) 335 (17) 678 (16) 1013 (17) 0.394 GP visits Mean number of visits (s.d.) 13.7 (9.0) 10.4 (8.5) 11.4 (8.8) <0.001 >10 visits in 12 months, N (%) 1092 (56) 1602 (38) 2694 (44) <0.001
Table 2. Health service utilisation according to MBS item claims by Factors associated with the likelihood of receiving the cohort with established CVD a GPMP fi CVD, cardiovascular disease; MBS, Medicare Bene ts Scheme; GP, General The adjusted odds ratios (ORs) and 95% confidence intervals Practitioner; GPMP, General Practice Management Plan; TCAs, Team Care (CIs) describing the likelihood of receiving a GPMP are presented Arrangements in Fig. 1. Patient-level factors independently associated with Total cohort, N (%) greater likelihood of receiving a GPMP were younger age, – (Practices = 44, n = 6123) diagnosis of diabetes, BMI 30 kg m 2, prescription of blood General practice consultations pressure-lowering therapy and more frequent GP consultations. MBS Item 3 – Brief GP consult 1224 (20) Following adjustment, factors such as gender, diagnosis of MBS Item 23 – Standard GP consult 5304 (87) CHD, SBP and TC were not associated with receipt of a MBS Item 36 – Long GP consult 3321 (54) GPMP. With adjusted analyses, practice-level characteristics, MBS Item 44 – Extended GP consult 764 (12) including quality improvement, also were not significantly Chronic disease management and team care arrangements associated with receipt of a GPMP (Fig. 1). MBS Item 721 – Preparation of a GPMP 1955 (32) MBS Item 732 – Review of GPMP or TCA 1453 (24) Discussion – MBS Item 723 Coordination of TCA 1687 (28) In this analysis of Medicare data from 6123 patients diagnosed Mental health and psychology items with CVD, we found that only one-third had received a GPMP MBS Item 2713 – Mental Health Treatment 159 (3) in the preceding 12 months. We did, however, find that 44% Consultation of the cohort had either a GPMP or a review in the 12-month – MBS Items 2700, 2701, 2715, 2717, 2712 77 (1) period. We also found that factors such as diagnosis of CHD Preparation or review of a GP Mental (compared to other CVD-related diagnoses) and high levels of Health Treatment Plan risk factors such as cholesterol and BP were not associated with 486 Australian Journal of Primary Health J. Redfern et al.
Odds ratio (95% CI)
Age (4 Groups) Group 1 v. Group 4 1.14 (0.90, 1.45)
Age (4 Groups) Group 2 v. Group 4 1.38 (1.11, 1.71)
Age (4 Groups) Group 3 v. Group 4 1.27 (1.03, 1.56)
Gender 1.03 (0.89, 1.19)
Indigenous 1.01 (0.74, 1.38)
Statin or other lipid lowering therapy 1.18 (0.99, 1.42)
Antiplatelet 1.08 (0.91, 1.30)
Blood pressure lowering therapy 1.46 (1.17, 1.83)
Anticoagulant 1.05 (0.84, 1.32)
Body mass index > 30 kg m–2 1.17 (1.01, 1.37)
Total cholesterol > 4.5 mmol L–1 1.04 (0.89, 1.21)
Systolic blood pressure > 140 mm Hg 0.90 (0.76, 1.05)
Diagnosis of coronary heart disease 1.04 (0.88, 1.21)
Diagnosis of diabetes 1.49 (1.27, 1.74)
GP visit >10 2.25 (1.94, 2.61)
Quality improvement 1.14 (0.58, 2.27)
.5 1 23
Fig. 1. Factors associated with increased likelihood of receiving a General Practice Management Plan (GPMP) plan (after adjustment for all other included variables). an increased likelihood of receipt of a GPMP. Overall, there (Comino et al. 2015). However, secondary prevention of CVD appears to be a significant gap in the utilisation of Medicare- in primary care is not specifically incentivised in a similar way funded Items for this population of Australian patients with to schemes available for other conditions such as diabetes. CVD who were attending primary care. In the case of CVD, ongoing secondary prevention is Our results align with previous Australian chronic disease recommended in all the major national and international studies. One study reported only 50% of patients with chronic guidelines (Aroney et al. 2006; Steg et al. 2012). These guidelines disease were receiving optimal management and that a change recommend use of evidence-based medications, risk factor in the health system, both in policies and in attitudes, was management, lifestyle modification (including physical activity, required (Infante et al. 2004). Further studies have reported that diet and smoking cessation), screening for psychosocial factors, GPs were often discouraged from using the chronic disease as well as education and provision of monitoring and support management (CDM) items. Reported barriers have included (Aroney et al. 2006; Steg et al. 2012). Clearly, if the World time constraints and reported difficulty in staying up to date Health Organization global targets are to be met, an overall with changing eligibility criteria (Blakeman et al. 2002). Other ‘strengthening’ of prevention is needed in terms of planning, reported barriers include inadequate infrastructure, lack of items service delivery and policy (Perel et al. 2015). Initiatives aimed that nurses can use to assist with implementation and lack of at supporting primary care to better enable delivery of ongoing available allied health staff, particularly in rural areas (Holden prevention are of pivotal importance (Redfern and Chow et al. 2012). Importantly, a systematic review of comprehensive 2013). The Bettering the Evaluation and Care of Health primary healthcare models found that CDM items were program (BEACH) report in Australia found that 12% of current associated with patients reporting increased quality of care, problems managed by GPs are cardiovascular related (Britt improved clinical measures (e.g. BP and blood cholesterol) and et al. 2015). An Australian study in a small acute coronary increased knowledge of conditions and management, as well as syndrome (ACS) cohort (n = 108) found the majority of GPs reporting greater patient satisfaction (McDonald et al. patients visited their GP on at least five occasions (85%) and 2006). A recent study of diabetic patients (n = 20 433) found a cardiologist at least once (63%) in the 12 months after that claims for annual cycles of care and review of GPMPs or hospital discharge (Redfern et al. 2010). Similar results have TCAs were associated with reduced likelihood of hospitalisation been reported based on the CONCORDANCE Registry, where Utilisation of Medicare-funded schemes for CVD Australian Journal of Primary Health 487
96% of patients visited their GP at least once within the 6 months to extrapolate the findings to infrequent attenders who may after their index event, and 35% visited their GP more than five exhibit different demographic, health and healthcare seeking times during the same period (Hyun et al. 2016). These studies characteristics. Third, we were unable to capture attendance at indicate that the majority of patients present frequently to their privately funded and non-practice allied health services such GP and each of these visits offers an opportunity to support as physiotherapy and dietetics for the management of CVD risk CVD prevention and management. factors. However, despite a significant proportion potentially It is difficult to pinpoint exactly why patients with CVD are attending non-Medicare-funded allied health providers, our not receiving more systematic care via existing MBS Items. It results highlight opportunities to improve access to care and is likely to be a multidimensional issue. The case is somewhat Medicare-funded services provided by dietians, physiotherapists different for diabetes, where there are complimentary activities and exercise physiologists, etc. We also acknowledge that some available to GPs including PIPs, annual cycles of care and of the independent variables included in the regression could be specific roles for Diabetes Educators. Historically, management somewhat interdependent, and our data do not enable specific of diabetes has required these services to enable access to determination of the relationships. This includes the potential subsidised services for such aspects of care such as foot care and that unobserved variables may be driving practice-level variation glycaemic control. However, people with CVD also require risk such as practice management structure, team environment factor management for aspects such as blood pressure and and provider leadership. Further research, including collection cholesterol management and access to allied health services of qualitative data, would enhance our understanding of such as dietary and physical activity advice and support. This why provision of systematic care appears to be suboptimal for present research highlights an evidence-practice gap for CVD patients with CVD and to explore the rate of allied health service management, and it is anticipated that the results will help utilisation. facilitate primary care strengthening in the area of CVD management. It is possible that GPs assume that CVD Conclusion management is the role of cardiologists, leaving some patients Only one-third of patients with diagnosed CVD and who to miss out receiving a GPMP. All of these together may be frequently attend primary care had received a chronic disease important drivers of why CVD management of patients lacks management plan from their GP at a practice studied in TORPEDO systematic care. Qualitative research could also help inform in the preceding 12 months. This study provides unique insights current knowledge. into the utilisation of MBS Items related to chronic disease Our results show that despite the availability of MBS Items management and highlights significant opportunities for enhanced to support chronic disease management, these are not commonly utilisation of existing health service-funded management plan utilised for patients with CVD. Perhaps one strategy for making systems. Findings also suggest that those with CHD and who CVD management more systematic is better use of routinely have diabetes or are overweight are most likely to receive a collected data with support for practice-level quality improvement management plan. Enhancing utilisation of existing services (Department of Health 2016). For example, electronic medical funded through Medicare could reduce the CVD burden and record systems and third party software systems usually have enhance systematic follow up and support for these patients. built-in ability to set recall and reminder alerts that enable practices to identify missed claiming opportunities and be Conflicts of interest proactive about managing patients with chronic diseases such fl as CVD. Such an approach, which aligns with the current The authors declare there are no con icts of interest. government focus on strengthening primary care, and the potential for improved service delivery through increased Acknowledgements utilisation of existing MBS Items, is a potential strategy for The National Health and Medical Research Council of Australia (NHMRC) improving management of CVD (Department of Health 2016). and the NSW Department of Health funded this study. Julie Redfern is funded The concept also identifies potential importance for strategies by a NHMRC Career Development Fellowship (1061793), co-funded with a such as Health Care Homes, which will provide bundled upfront National Heart Foundation Future Fellowship (G160523). Karice Hyun is and quarterly payments to recognise the time and effort that funded by a University of Sydney Postgraduate Award. Karla Santo is funded GPs and nurses invest into patients with chronic conditions. by a University of Sydney International Postgraduate Research Scholarship. There are several study limitations to this research. First, the Clara Chow is funded by a Career Development Fellowship, co-funded by the NHMRC and National Heart Foundation and Sydney Medical Foundation services recruited for the TORPEDO study did not represent a Chapman Fellowship. Anushka Patel is supported by a NHMRC Senior random sample of primary healthcare services in Australia. All Research Fellowship. 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