Title

Decision Support In The Emerging Pay-For-Value World:

An Essential Element Of Market Place Success

Decision Support In The Emerging Pay-For-Value World 1 What Is Driving The Need For Decision Support? The Increasingly Pay-For-Value World Of Health & Human Service Organizations

The Patient Protection and (PPACA) has sparked a revolution: health care is shifting from a fee-for-service (“pay-for-volume”) model to one in which services are reimbursed based upon the health value they bring to the consumer (“pay-for-value”). In this new market, payers and consumers are evaluated for value against the yardstick of “The Triple Aim:” better care, reduced costs, and improved patient satisfaction.i

Transition From A Fee-for-Service to Pay-for-Performance Reimbursement

Case Rates & Capitation & Pay-For- Fee-For-Service Bundled Population Performance Payments Payments (P4P)

While the U.S. health and human services system has seen change before, a perfect storm of four market factors resulting from this revolution drives change in the competitive market positioning of every stakeholder organization in the field: ii

• PPACA’s limitations on health plan profitability • Consumer and payer search for “value” in health purchases • The shift to value-based reimbursement • The merging roles of care management and service delivery organizationsiii

Each of these factors alone presents strategic challenges. Combined, these factors have created a new market environment that demands new management team skills and organizational infrastructure of provider organizations to remain competitive and viable.

The PPACA limits health insurance premium dollars spent on infrastructure or profit by any health plan or at-risk care management organization; the remaining dollars are targeted for directly improving health, a responsibility that falls to the health care provider.iv In addition, the PPACA disallows annual and lifetime limits for consumer care, bringing with this paradigm shift even more exposure and responsibility for the health of consumers to provider organizations. The health of consumers is now directly tied to

Decision Support In The Emerging Pay-For-Value World 2 provider reimbursement, and therefore presents a new kind of financial risk. Gone are the days when prior authorization and utilization processes were sufficient to manage the care of consumers with higher health care needs. Efficient, effective solutions must be implemented with better and less labor intensive tools.

In addition, Federal and state governments, employers, and consumers – health care payers – have been hard hit with rising health care expenses. A combination of more advanced (and more expensive) health care technologies, the extended lifespan of Americans, and a rise in the prevalence of chronic diseases, have pushed U.S. health care spending to 17% of the Gross Knowledge is power – v Domestic Product. At that macroeconomic level, health but only if it can be care spending cannot increase or competitiveness is in analyzed quickly and jeopardy – which has touched off the search for value in health care purchases. The question – how to get more and efficiently from a better outcomes without spending more money? deluge of data.

Accountable care organizations in and health homes in started just five years ago. Today there are over 700 accountable care organizations (ACOs) and more the 8,000 primary care practices that are accredited as medical homes in the U.S.vi – and these new care management systems span all payers, from Medicare and Medicaid to most commercial plans. What these new systems share is the move to integrated “whole person” care management and the use of value-based reimbursements.vii Both integration and value-based reimbursement have changed not only how the delivery system is organized, but what state-of-the-art health and human service organizations need for an information infrastructure to remain competitive.

Last, but certainly not least, is an unintended consequence of the pursuit of value – the merging roles of health plans – traditionally were only care management organizations – and service provider organizations – traditionally were financed in a pay-for-volume model.viii As provider organizations move to more advanced pay-for-value arrangements, they assume more of the financial risk that was traditionally held by health plans and, by necessity, assume more of the functions that were traditionally held by care management organizations. This traditional specialty-specific horizontal ‘carve out’ model of health care financing is being replaced by a complex integrated model with a complicated and cascading set of gainsharing and risk-sharing arrangements.ix

What is needed for organizations – traditional health plans, care management organizations, and service provider organizations – to succeed in this new pay-for-value- world? The future belongs to the organizations with the most data the fastest – data that are organized for rapid management decision-making and action.

Decision Support In The Emerging Pay-For-Value World 3 EHRs, Data Analytics & Decision Support: Best Practice Informatics For A Value-Based World

So how do organizations develop the information infrastructure they need for success in a value-based world? To get data fast – in a way that provides insight for management – forward-looking organizations need an informatics infrastructure with three key elements: a fully-functional electronic health recordkeeping system, a data analytics platform, and decision support tools.x

Actionable Insights Driven By EHR, Data Analytics & Decision Support Tools Working Together

Full EHR Functionality

Data Analytics Platform

Decision Support Tools

The executive teams of many provider organizations struggling to get EHR functionality to where it needs to be for management of service delivery will be surprised to learn that an EHR is not enough for a pay-for-value environment – unless your organization is not going to participate in pay-for-value contracting. Understanding the analytic capabilities and the decision support tools that are needed – and their relationship to the EHR system – is key to success in the emerging service delivery models.

Decision Support In The Emerging Pay-For-Value World 4 Electronic Health Recordkeeping Systems The EHR is essential infrastructure for consumer care delivery. The focus of the EHR is to provide a longitudinal record of consumer care delivery within a health care provider organization or health care system – and with interoperability, connecting the health care records for a single consumer across health care provider organizations. EHRs create a record of each consumer encounter with the health care systemxi – and with it, the ability to submit an invoice for that encounter – exactly what is needed in a pay-for-volume, fee- for-service health care system.

What EHRs do not have is the native ability to manage value-based financing or value- based reimbursement arrangements. With that regard, EHRs fall short in three key areas:

• EHRs do not provide population health aggregation of data for identification of trends nor the ability to compare agency-based consumer data comparatively across other populations outside of the agency based system • Most EHRs do not have the ability to conduct statistical analysis of a wide array of data, to identify trends, to track current activity patterns, or to project future scenarios (the analytics) • Few EHRs have tools that link current research on best practices, evidence-based practices, and comparative effectiveness to facilitate proactive management decision making (the decision support tools)

In the new value-based world, the ability to use the data in an EHR, with other systems’ EHR data and data from other sources, is the factor that will differentiate the successful from the non-competitive health and human service organizations.xii

Data Analytics Platforms If you have data, do you have analytics? The answer is no. Analytics is the ability to use the data – to conduct statistical analysis of the data to identify meaningful patterns and generate insights from that data.xiii And for the many provider organizations moving to some type of care management role with value-based reimbursement, the next step in their information evolution is to add the ability to analyze data.

To move ahead with the most fundamental of metrics-based management, management teams need analytics to summarize their existing data – whether clinical, administrative, and/or financial. And, as management teams get more skilled using analytics, their ‘view’ of the information moves from retrospective (what happened) to proactive (what is happening) to predictive (what is going to happen). With each step in this evolution, the competitive advantage of the organization increases.

Decision Support In The Emerging Pay-For-Value World 5 Evolution Of Analytics Use Among Management Teams

Retrospective Proactive Predictive

• What • What is • What is happened happening going to happen

So how are competitive health and human service organizations using data analytics? Here are a few examples.

Flagging Non-Standard Care Non-standard care often signifies waste, as well as treatment practices for which there is no evidence of effectiveness. Identifying patterns of non-standard care that can be improved at the population level and treatment anomalies at the individual level permits agencies and clinicians to further evaluate practices in the context of population health outcomes as well as specific patient’s health care needs and to make adjustments accordingly.

Improving The Efficiency Of Care Delivery In a value-driven health care environment, payer and provider organizations need to collect data on the time it takes to do tasks, the time it takes to get resources to where they are needed, and the implications of certain decisions on care outcomes. The insights gained through aggregating and analyzing this data guide action to increase efficiencies, redirecting and making judicious use of clinical time and resources based on impactable opportunities/best care for risk reduction, all while providing the same or better quality of care.

Health Trend Analysis Of Specific Populations Trend analysis helps payer and provider organizations find areas of low or substandard performance and take action to improve. It also helps clinicians identify at-risk patients for preventative treatment. Preventative care often fends off increases in acuity and chronicity, the treatment of which is associated with increased cost and reduced health.

Using Near Real-Time Claims & Clinical Data Analysis To Speed Decision-Making Analyzing accurate claims and clinical data offers a snapshot of the larger patient population served by the health care provider. Armed with insights through this analysis, provider organizations can tailor health strategies to their community; for

Decision Support In The Emerging Pay-For-Value World 6 example, by offering health education courses or other health services that will benefit their patient population.

Proactively Managing Quality & Outcomes Analytics offer the use of historical data to predict future events and risks. This includes readmissions, preventive care management, tools to identify gaps and variations in care, and the effects of specific treatments on outcomes. Analytics also help identify what courses of treatment work best for different health populations and for individuals within those populations. Such insight and action lead to higher quality of care and better patient and population health outcomes.

In any pay-for-value model, the key to financial sustainability is the overall improvement in population health. EHRs cannot provide the population needed to demonstrate provider value. Health care analytics allow management teams to know what is happening – and what is going to happen -- in a way that facilitates management action and organizational performance improvement across many domains.

Decision Support Tools If you have data and analytics, do you have decision support? The answer is ‘sort of’. Having accurate, timely data does support management teams in making more accurate timely decisions. But while data and analytics provide the platform for management teams using their professional knowledge and judgement in making decisions, decision support tools guide organizational decision-making activities by identifying the best decisions given the complexity of the data.

In particular, clinical decision support (CDS) tools provide clinical professionals, administrative managers, and consumers with enhanced decision-making in the clinical workflow. These tools include, but are not limited to: • Computerized alerts and reminders to professionals and consumers • Clinical guidelines and condition-specific order sets • Documentation templates • Diagnostic support • Contextually relevant reference information

Decision Support In The Emerging Pay-For-Value World 7 Successful Clinical Decision Support Requires Five Elements The Right Information ... Evidence-based guidance, response to clinical need

... To The Right People ... Entire care team – including the patient

... Through The Right Channels ... e.g., Medical Managementor or CDS Platform, EHR, mobile device, patient portal

... In The Right Intervention Formats ... e.g., desk top intervention tools, flow-sheets, dashboards, patient lists

... At The Right Points In Workflow For decision making or action

Decision support tools are sophisticated technology tools that require computable biomedical knowledge, person-specific data, and a reasoning mechanism that combines knowledge and data to generate recommendations to professionals and consumers as care is being delivered. The tools filter, organize, and present the data, the analysis, and the recommendations in a way that supports the current workflow, allowing the user to make an informed decision quickly and take action. For obvious reasons, decision support tools need to be customized for different consumer demographics, for different processes of care, and for different care settings.

The benefits of decision support tools are many – from increased quality of care and enhanced health outcomes to avoidance of errors and adverse events to improved professional and consumer satisfaction to improved efficiency. In a health and human service market where payers are seeking value – and stakeholders are reimbursed based on the value they deliver – these are the competencies of competitive advantage.

Decision Support In The Emerging Pay-For-Value World 8 The Competitive Advantage Of The Data-Informed Management Team: Decision Support In Action In The Health & Human Service Market

If decision support is critical to success in the future health and human service market, why do so few organizations in the field have decision support tools up and running? The answer is all about timing. Until just five years ago, only the largest health plans had analytic tools, and few service provider organizations had fully-functional EHRs. It is the shifts in the payer financing and the shift in provider organization reimbursement that have increased the need for metrics-based decision support – informatics at a whole new level.

But there were early adopters of decision

support in the market. For example, Care Missouri Behavioral Health Homes Management Technologies (CMT) is the Powered by CMT’s analytic solution, the first-in- behavioral health data analytics partner for nation behavioral health home for Missouri Missouri’s State Medicaid Agency, Medicaid beneficiaries: • Reduced costs by $23.1 million over 18 months MoHealthNet, which was the first Medicaid • Reduced hospitalizations by 9.1% in the first Behavioral Health Home in the nation. CMT’s year sophisticated clinical knowledge and expertise • Lowered cholesterol by 28% over 2 years combined with its state-of-the-art information • Lowered blood pressure by 30% over 2 years technology and “big data” analytics helped • Lowered blood sugar by 39% over 2 years Missouri achieve a 25% reduction in emergency room visits and hospitalizations, and an average annual savings per intervened patient of up to $1,500 in pharmacy and services costs. Results also included a $23+ million reduction (net costs) in care expenses in the first 18 months of the program – with both reduced hospitalization and improved health status.xiv

MoHealthNet also sought to improve opioid prescribing to limit potential abuse, misuse and diversion. Care Management Technologies’ Using Opioid Prescription Intervention Analytics, decision support tools compared a physician’s Missouri’s State Medicaid Agency MoHealthNet prescriptions with the patient’s diagnoses and achieved: the research on recommended treatment. If the • 36.8% reduction in hospital admissions treatment was not consistent with best practices • 14.8% - 19.1% reduction in the average adult based upon those data, practitioners were monthly dose of opioids dispensed alerted through CMT’s Quality IndicatorsTM and directed to the research for further consideration of the intervention. The results included a double digit reduction in the number of doses of opioids prescribed, and an impressive 36.8% drop in hospital admissions of patients utilizing or at risk for needing opioids.

Decision Support In The Emerging Pay-For-Value World 9 In addition, CMT’s subsidiary, Comprehensive NeuroScience™ of

Canada (CNSC), has been working Analytics At Work: in collaboration with the Provincial Manitoba IMPRᵪOVE Program Department of Manitoba Health, Using CMT analytics, Manitoba HealthCare Policy conducted an Healthy Living and Seniors in independent landmark prospective randomized clinical trial on Manitoba, Canada since 2009 to the prescribing behavior of 1,147 physicians. 571 who received a CMT mailed intervention were compared to 576 physicians who improve the quality of prescribing did not (control group). Six quality indicators involved commonly of psychotropic medicines for the misused benzodiazepines or hypnotic (insomnia) drugs, classes of Manitoban Provincial Health medication that may result in respiratory depression and fatal beneficiaries using CMT/CNSC’s overdose when combined with opioids. • decision support tools. In a A statistically significant decrease of up to 40% in triggering rate of prescribing was noted in the randomized study of the program intervention group compared to the control group on conducted by the Manitoba Centre five of the six quality indicators involving for Health Policy, it was found that benzodiazepines or hypnotic drugs. doctors who received CMT’s • Reduced prescribing of benzodiazepines to adults by educational packages swiftly and 0.25 per physician per month. • Reduced prescribing of long-acting benzodiazepines to significantly reduced potentially older adults by 0.4 per physician per month. inappropriate prescribing for sleeping pills and benzodiazepines, drugs statistically associated with hospital readmissions and unnecessary emergency room visits.xv The improvements noted resulted in an estimated 4.5:1 ROI for the Manitoba Province.

Decision Support In The Emerging Pay-For-Value World 10 The CMT Advantage

For organizations ready for decision support analytics, Care ProAct Will Help Your Organization Start Management Technologies’ ProAct Improving Health & Lowering Costs Today! provides the data analytics and • 200+ Validated Rules with Clinical Briefs decision support tools necessary • Evidence-Based Behaviorally Driven Analytics to begin improving population • Prospective Risk Analysis health and reducing costs • Predictive Analysis Built on State of the Art immediately. CMT is the behavioral Statistical Modeling health answer to population health • Identification of Tractable Conditions for management. Behavioral Health is Outreach a primary driver of cost of care for • Desktop Intervention Tools complex needs populations. • Workforce and Workflow Efficiency • Documenting Cost and Quality Improvements Powered by an unrivaled analytics • Performance Metric Measurement warehouse and next-generation • Data Documented Value Proposition technology, CMT helps payer and provider organizations serving complex needs populations transform complex data—behavioral and medical--into actionable insights that improve care quality, clinical outcomes, patient safety, and organizational performance. Care Management Technologies brings state-of-the-art population health management solutions to high-needs consumer groups with comorbid and multimorbidity health disorders and complex support needs. From meeting the goals of The Triple Aim, to meeting the standards of pay-for-performance contracts, CMT’s innovative “behavioral health first” approach empowers the data-informed decision-making needed to go beyond standard care management and optimize the “value” of care.

Powerful Data. Powerful Analytics. ProAct Contact Care Management Technologies Today!

Decision Support In The Emerging Pay-For-Value World 11 References

i Berwick, D.M., Nolan, T.W. and Whittington, J. (2008). Health Affairs. The triple aim: care, health and cost. Retrieved June 17, 2015 from http://content.healthaffairs.org/content/27/3/759.full ii Gamble, M. and Herman, B. (2013). Hospital Review Weekly. 3 years of PPACA: the five biggest changes in healthcare since the law’s passage. Retrieved June 15, 2015 from http://www.beckershospitalreview.com/hospital-management-administration/3-years-of- ppaca-the-5-biggest-changes-in-healthcare-since-the-laws-passage.html iii Porter, M.E. and Lee, T.H. (2013). Harvard Business Review. The strategy that will fix health care. Retrieved June 25, 2015 from https://hbr.org/2013/10/the-strategy-that-will-fix- health-care ivKirchhoff, S.M. (2014). Medical loss ratio requirements under the patient protection and affordable care act (ACA): Issues for congress. Retrieved June 15, 2015 from https://fas.org/sgp/crs/misc/R42735.pdf v Squires, D.A. (2012). Issues in International Health Policy. Explaining high health care spending in the united states: An international comparison of supply, utilization, prices, and quality. Retrieved June 15, 2015, from http://www.commonwealthfund.org/~/media/Files/Publications/Issue%20Brief/2012/May/ 1595_Squires_explaining_high_hlt_care_spending_intl_brief.pdf vi Robeznieks, A. (2014). Modern Healthcare. Reform update: Medical-home adoption growing; evidence of effectiveness still elusive. Retrieved June 15, 2015, from http://www.modernhealthcare.com/article/20140818/NEWS/308189963/reform-update- medical-home-adoption-growing-evidence-of vii Choudhury, J.S., Subramanian, S., D’Sa, S. and Rajamani, G. (2013). Booze and Company/Strategy&. Healthcare for complex populations: the power of whole person care models. Retrieved June 25, 2015 from http://www.strategyand.pwc.com/media/file/Strategyand_Healthcare-for-Complex- Populations.pdf viii Eggbeer, W.T. (2015). BDC Advisors. Organizing for the second curve: combining health plans and healthcare provider system. Retrieved June 17, 2015 from http://www.bdcadvisors.com/insight/organizing-second-curve-combining-health-plans- healthcare-provider-systems/ ix Surpin, J. and Stanowski, A. (2014). Becker’s Hospital CFO. Six essential differences between gainsharing and shared savings programs. Retrieved June 17, 2015, from http://www.beckershospitalreview.com/finance/6-essential-differences-between- gainsharing-and-shared-savings-programs.html x Porter, M.W. and Lee, T.H. (2013). Harvard Business Review. The strategy that will fix health care. Retrieved June 17, 2015 from https://hbr.org/2013/10/the-strategy-that-will- fix-health-care. xi HealthIT.gov Frequently Asked Questions (n.d.) Retrieved June 17, 2015, from http://www.healthit.gov/providers-professionals/frequently-asked-questions/334#id2 xii Payne, T.H. et al. (2015). Journal of the American Medical Informatics Association. Report of the amia ehr 2020 task force on the status and future direction of ehrs. Retrieved June 17, 2015 from http://jamia.oxfordjournals.org/content/early/2015/05/22/jamia.ocv066

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xiii Ingram Micro Advisor. (2015). Understanding big data: is it the same as analytics? Retrieved June 17, 2015 from http://www.ingrammicroadvisor.com/big- data/understanding-big-data-is-it-the-same-as-analytics xiv Gorman, J.M. (2015, February). CMT Newsletter. A population health approach to improving opioid prescribing. 6, 1, p. 3. xv Gorman, J.M. (2015, February). CMT Newsletter. A population health approach to improving opioid prescribing. 6, 1, p. 2.

Decision Support In The Emerging Pay-For-Value World 13 Disease Management Indicators Master List

Number Short Long Description Description DM1 Use of inhaled corticosteroid medications by persons with a history of COPD (chronic obstructive pulmonary disease) or Asthma DM2 Use of ARB (angiotensin II receptor blockers) or ACEI (angiotensin converting enzyme inhibitors) medications by persons with a history of CHF congestive heart failure) DM3 Use of beta-blocker medications by persons with a history of CHF (congestive heart failure) DM4 Use of statin medications by persons with history of CAD (coronary artery disease) DM5 Use of H2A (histamine 2-recptor antagonists) or PPI (proton pump inhibitors) medications for no more than 8 weeks by persons with a history of GERD (gastroesophageal reflux disease) DM6 Presence of a fasting lipid profile within the past 12 months for patients with CAD (coronary artery disease) DM7 Presence of a DRE (dilated retinal exam) within the past 12 months for patients with diabetes mellitus DM8 Presence of a urinary microalbumin test within the past 12 months for patients with diabetes mellitus DM9 Presence of at least 2 hemoglobin A1C tests within the past 12 months for patients with diabetes mellitus DM10 Presence of a fasting lipid profile within past 12 months for patients with diabetes mellitus DM11 LDL Control % of patients 18-75 years and older with a diagnosis of CAD Cardio (A) with lipid level adequately controlled (LDL <100 mg/dL). DM12 Persistence of Beta-Blocker treatment after a heart attack DM14 BP Control HTN % of patients 18 years and older with a diagnosis of (A) hypertension with a blood pressure <140/90 mmHg during the most recent office visit within a 12 month period DM16 Presence of Retinal Disease Eye Screening for Patients with Diabetes DM17 Diabetes LDL % of patients 18-75 years of age with a diagnosis of diabetes Control (A) (type 1 or type 2) who had LDL <100 mg/dL. DM18 Presence of Nephropathy Screening for Patients with Diabetes DM19 Diabetes BP % of patients 18-75 years of age with a diagnosis of diabetes Control (A) (type 1 or type 2) who had a blood pressure <140/90 mmHg. DM20 Asthma Med (A) % of patients who were identified as having persistent asthma and were appropriately prescribed mediation during the measurement period DM21 Asthma Med (C) % of patients 5-17 years of age who were identified as having persistent asthma and were appropriately prescribed mediation during the measurement period DM22 Diabetes A1c % of patients with a diagnosis of diabetes (type 1 or type 2) Control (A) who had an HbA1c <8.0% DM23 Diabetes A1c % of patients under 18 years of age with a diagnosis of Control (C) diabetes (type 1 or type 2) who had an HbA1c <8.0% DM24 BMI Control (C) % of children with documented BMI between 18.5-24.9 DM25 BMI Control (A) % of adults with documented BMI between 18.5-24.9

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Disease Management Indicators Master List

Number Short Long Description Description DM26 No Tobacco Use % of children reporting tobacco use in previous 12 months (C) DM27 No Tobacco Use % of adults reporting tobacco use in previous 12 months (A) DM28 Metabolic % of children that had a Metabolic Screening in the previous Screen (C) 12 months DM29 Metabolic % of adults that had a Metabolic Screening in the previous 12 Screen (A) months

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