Prescribing of potentially unsafe medications for older adults is extremely common; benzodiazepines, antipsychotics, anticholinergics, and sedative hypnotics are four key drug classes frequently implicated in adverse health consequences for vulnerable older adults, such as confusion or sedation, leading to hospitalizations, falls, and fractures. Fortunately, most of these consequences are preventable. Physicians? lack of awareness of alternatives, ambiguous practice guidelines, and perceived pressure from patients or caregivers are among the reasons why these drugs are used more than might be optimal. Reducing inappropriate use of these drugs may be achieved through decision support tools for providers that are embedded in electronic health record (EHR) systems. While EHR strategies are widely used to support the informational needs of providers, these tools have demonstrated only modest effectiveness at improving prescribing. The effectiveness of these tools could be enhanced by leveraging principles of behavioral economics and related sciences. In specific, three behavioral economic principles, such as salience effects, social norming, and default bias, have successfully changed behavior in other settings but have had very limited application in EHRs and, more specifically, for prescribing in older adults. To this end, we propose three cluster randomized controlled trials of novel EHR decision support tools that seek to reduce inappropriate prescribing for these drug classes and their associated adverse drug events and health outcomes. This proposal builds on many years of research by our group on interventions to engage providers and patients in clinical-decision making, behavior change, and evaluating novel interventions in real- world delivery systems. The EHR decision support tools will be designed using promising behavioral economic principles such as salience effects, social norming, and default bias.
The specific aims of this study are to: (1) design and pilot test multiple EHR decision support tools constructed using behavioral economics principles; (2) rapidly identify the potential effectiveness of numerous EHR tools at reducing inappropriate prescribing using a novel randomized adaptive design; (3) examine whether these most potentially promising EHR tools from Aim 2 reduce inappropriate prescribing and adverse drug events when using a randomized parallel group trial; and (4) evaluate the effectiveness of the EHR tools in a different clinical environment. Using rigorous randomized designs, we have proposed a pragmatic and scalable approach to optimizing and evaluating EHR tools aimed at provider behavior change for prescribing for older adults. We will also be able to rigorously test a large number of EHR tools as well as replicate and validate the effectiveness of the best performing tools in a different healthcare system. The expected overall impact of this innovative proposal is that it will fundamentally advance how behavioral economics can be used to optimize decision support to reduce inappropriate prescribing and ultimately improve patient outcomes.
We propose multiple pragmatic randomized trials to evaluate whether electronic health record (EHR)-based tools that are optimized using behavioral economic principles, such as salience effects, social norming, and default bias, can reduce inappropriate prescribing of potentially harmful drugs and clinically-significant adverse health outcomes among older adults. We will identify which of the many potential EHR tools have the most promise at changing provider behavior using rapid-cycle testing and then further evaluate the tools in parallel group randomized trials. These findings will provide important insight into whether incorporating behavioral economics into EHR decision support can reduce adverse health consequences associated with inappropriate prescribing, including adverse drug events that are major contributors to poor health among older adults.
