Chapter 3.4e Informatics interventions Samir Gupta1and K. Ann McKibbon2 1University of Toronto, Toronto, ON, Canada 2McMaster University, Hamilton, ON, Canada Key learning points Knowledge translation (KT) and informatics domains share many of the same basic components of collecting, summarizing, packaging, and delivering knowledge. KT concentrates on implementing pub- lished evidence while informatics interventions focus on providing patient- or population-specific knowledge and data. Many informatics applications can be effective KT tools, delivering evidence to health professionals, patients, and informal caregivers. Informatics interventions that speed KT can be found in the areas of patient and physician education, mobile health, communication and support, reminder systems, and computerized clinical decision sup- port systems. They have been shown to change knowledge and behavior, improve adherence through reminders, efficiently collect and present data from multiple sources, and effectively support deci- sion making. Their effects on health care costs and health outcomes have been less well demonstrated. Many of these effective informatics applications exist as demonstra- tion projects or on a small scale. We have yet to harness the full potential of integration of the KT process with informatics applications. Knowledge Translation in Health Care: Moving from Evidence to Practice, Second Edition. Sharon E. Straus, Jacqueline Tetroe and Ian D. Graham. Ó 2013 by John Wiley & Sons, Ltd. Published 2013 by John Wiley & Sons, Ltd. 189 190 Knowledge translation in health care Knowledge translation (KT) deals with the collection, summarization, and packaging of (research) knowledge and its delivery in a timely and appropriate format to those who can use it caring for patients and populations. Informatics does the same with (patient or population) information: collecting, summarizing, packaging, and delivering. Both domains share the theoretical foundation of epistemology: understand- ing and knowing the limits and validity of knowledge [1, 2]. KT and informatics are natural partners and the question for this chapter is how do, and which, informatics applications best support KT. Infor- matics interventions can support or implement knowledge use by mak- ing data collection and analysis easier and faster; enhancing communication with new devices; improving educational projects through multifaceted, individualized programs; and providing clinical support through reminders, clinical decision support, and order entry systems. What sources of data can be used for planning and evaluating KT projects? Electronic medical records (EMR), personal health records and other large clinical systems have data which can be analyzed to show evi- dence–practice gaps (needs assessment) and evaluate KT interventions. These systems can be used in audit and feedback, quality improvement, and many other KT projects. Hynes and colleagues [3] describe how the US Veterans Affairs health systems use informatics resources including EMR data in quality improvement. Quality improvement (see Chap- ter 4.5) may not be completely under the purview of KT but we can learn much from their work. Mobile health (e.g., cell phones and tablets and their apps and medical devices such as automated glucometers and step counters) is also fast becoming an important KT tool for both delivering care and data collection before, during, and after KT imple- mentations [4]. These mobile devices are described further below. Per- sonal health records systems are collections of health and wellness data kept by patients. Their roots are in paper records for patients, especially in areas such as charting and monitoring pregnancies and data related to children (e.g., immunizations and other health milestones). Personal health records systems, especially those tethered (e.g., can send data to and receive data from) clinician-kept or institutional EMR systems also provide opportunities for data collection and behavior based interven- tions [5, 6]. Informatics interventions 191 What informatics interventions might be effective in achieving KT? Multifaceted educational interventions with new informatics tools (mobile health) One of the areas where informatics may have the greatest effect on KT interventions is the use of the internet to educate and support clinicians, patients, and families in relation to health and wellness. (See also Chap- ter 3.4b for more information on education.) Pletneva and colleagues [4] report that half of their survey participants in Europe in 2011 used the internet at least weekly to seek health information. North American data are similar. The most effective use of the internet for educating and chang- ing behavior is if the intervention has multiple components and includes such things as goal setting, individualized support or tutoring, communica- tion with real or “electronic” personnel, and if it is ongoing. This pattern of success is shown in reviews by Neve and colleagues [7] on the effects of web based interventions on weight loss and maintenance, by Ramadas and col- leagues [8] on web based interventions for patients with diabetes, and by Krebs and colleagues [9] who summarize the evidence on the effects of behavior targeted informatics projects on smoking cessation, healthy eating, physical activity, and mammography screening. Mobile health is defined as systems (often cell phones, tablets, or monitoring devices) with wireless connectivity that are consumer centered; record, monitor, and transmit health or wellness data; and often direct actions based on analyzed data. Mobile health is new and evolving and early evidence supports its spread and usefulness [4]. The caveat across these electronic knowledge domains, however, is that although the studies uniformly show important improve- ments, their methods are often weak and contain problems. Computerized Clinical Decision Support Systems (CDSSs) Computers are excellent at storing, synthesizing, and presenting data in an efficient and user-friendly format. CDSSs are electronic systems that aid clinical decision making by generating patient-specific assessments and rec- ommendations through software algorithms that match individual patient data to a computerized knowledge database [10]. Such systems can “push” information to clinicians through alerts or reminders at the point-of-care, or through system-wide approaches such as evidence-based order sets. Alerts or prompts can either be active (requiring users to act on them) or passive (appearing without requiring user action) [11]. Alternatively, CDSSs can act as simple information repositories from which clinicians can “pull” context-specific knowledge as required [10]. 192 Knowledge translation in health care CDSSs are superior to paper-based resources because they are more flexi- ble and can rapidly retrieve vast amounts of data (e.g., test results), perform time-consuming calculations, and navigate complex care algorithms [12]. They can also present information “just-in-time,” without overloading pro- viders with unnecessary data. For example, British Columbia’s PharmaNet is a simple CDSS which provides physicians with patients’ prior prescrip- tion data at the point-of-care [13]. In a more sophisticated CDSS, the UK’s National Institute for Health and Clinical Excellence (described in Chap- ter 2.2), has developed tagging specifications for guidelines so that their content can be electronically “matched” to individual patients in EMRs and suggestions can be presented to clinicians during clinical decision making [14]. Finally, CDSSs can improve care by giving clinicians performance feedback on quality indicators, enabling them to identify and bridge their own practice gap [10, 15]. CDSSs can address diagnostic, prevention or screening, drug dosing, and disease management decisions. They may be stand-alone systems functioning in parallel to an existing paper or EMR system, or may be integrated into an EMR, enabling direct and automated patient data import. They can also work on mobile devices, thus being well suited to clinicians delivering care in diverse locations. With the convergence of laptop, tablet, and Smartphone computing capabilities, and the fact that nearly two-thirds of physicians now own a Smartphone [12], bar- riers to introducing portable computing devices into the care milieu have been reduced. Not only do physicians already use Smartphones to access information to guide patient care, but many medical apps have rendered this task more efficient and user friendly [16]. This portabil- ity, ease of use, speed, accessibility, and abilities to support both patients and their families and clinicians are perceived by physicians to improve productivity and care [12]. For example, a handheld com- puter-based CDSS for patients with suspected pulmonary embolism increased clinicians’ use of evidence-based pretest probability calcula- tions, appropriateness of diagnostic testing, and guideline adherence, when compared to paper-based guidelines [17]. In a systematic review of the effectiveness of CDSSs, Bright and col- leagues reported a significant improvement in process measures [10]. However, effects on clinical outcomes and cost-effectiveness were meas- ured in a minority of studies and were inconclusive. Other caveats include potential for decreased clinician efficiency and increased work- load, clinician “deskilling” due to task automation, and inadvertent deleterious effects on clinician performance and patient safety due to flawed system design