As decision makers in the healthcare sector look for new ways to impact their patient populations, many have taken a creative approach by borrowing insights from the social sciences. Behavioral insights, specifically, have been effective at motivating certain behaviors through incentive schemes dubbed “nudges”. In one example, behavioral economics researchers were able to show that paying somebody to go to the gym could help develop the intrinsic motivation to work out. In other words, even after participants stop receiving payments they may still face the incentive to exercise (1). Examples like this show that, when used correctly, behavioral interventions have the potential to impact people’s health.

One area of healthcare that has made a comfortable home for behavioral science is population health. Population health is an interdisciplinary approach to healthcare that attends to health outcomes within groups of patients, rather than focusing on individual patients. The population health approach is valuable because it allows decision makers to identify health trends that would not be obvious from looking at patient data on a small scale

Many behavioral scientists believe that population health is key to incorporating effective behavioral interventions in healthcare. In fact, past research has shown that the most “promising applications of behavioral insights in [healthcare] involve… far-reaching and systemic interventions” (2), rather than small-scale nudges. This is because systemic approaches are more effective at addressing “deep-seated behaviors” as well as deeply-rooted “cultural and social norms” (ibid). 

For example, a past study showed that Medicare patients are often not enrolled in the cheapest prescription plans available to them (7). The reason for this is may be patients must choose among a large number of prescription plans, each with a large amount of information associated with them (3). In response to this, researchers at the Virginia Commonwealth University conducted a laboratory experiment in which they reduced the amount of information available to participants regarding these types of plans. The results showed that when confronted with less information, patients were actually able to make more informed decisions about choosing the right prescription plan (4).

In another example, it was noted that a large percentage of those eligible for Medicaid insurance actually failed to sign up. Behavioral scientists theorized that this irrational behavior was due to both a lack of awareness about programs as well as a system of overwhelmingly “complex bureaucratic procedures” (2). Policy makers at the federal level responded by implementing system-wide, multi-pronged solutions that drastically removed administrative barriers. These included automatically enrolling individuals, providing hands-on enrollment assistance, and expanding communication of program benefits. As a result, enrollment increased by 1.2 million members (6).

One of the reasons that these interventions were able to be executed successfully was that decision makers could track their patient populations on a large scale in order to identify patterns. Without a population health tool that could enable this type of view, it would have been difficult to identify and tackle these system-wide issues with a behavioral approach. Juxly offers a valuable solution in this domain of population health with our application Juxly Portal. Juxly Portal is a population health tool that allows payers and providers to get an overview of the risk adjustment status of their patient population, thereby enabling more informed RAF-scoring decision making.

Moving forward, population health and behavioral science are likely to become even more important as the US healthcare system dedicates more resources to Social Determinants of Health (SDOH). SDOH are the conditions of one’s social (e.g education, income, relationship status) and physical (e.g. living space, public transport system) environment that affects their health. Understanding the complex relationships that comprise SDOH requires decision makers to understand their patient population, both as a whole and within segments. In understanding these relationships and applying interventions, behavioral science has proven to be a valuable supplementary tool (5). Future attempts at targeting large member groups with system-wide solutions may yield far-reaching, lasting impacts, but only if decision makers are able to understand their patient population from a bird’s eye view.

By Harrison MacDonald, Operations Coordinator

harrison.macdonald@juxly.com

References

  1. Charness, Gary, and Uri Gneezy. “Incentives to Exercise.” Wiley Online Library, John Wiley & Sons, Ltd, 21 May 2009, onlinelibrary.wiley.com/doi/abs/10.3982/ECTA7416. 
  2. Loewenstein, George, et al. Behavioral Insights for Healthcare Policy. Carnegie Mellow University, 2017, www.cmu.edu/dietrich/sds/docs/loewenstein/BehInsightsHealthCare.pdf. 
  3. Konrad, Walecia. “Nearly 65? Time for the Medicare Maze.” The New York Times, The New York Times, 14 Oct. 2009, www.nytimes.com/2009/10/15/your-money/15CARE.html?scp=3&sq=medicare%2Bpart%2Bd%2BWALECIA%2BKONRAD&st=nyt. 
  4.  Barnes, Andrew, et al. “One Fish, Two Fish, Red Fish, Blue Fish: Effects of Price Frames, Brand Names, and Choice Set Size on Medicare Part D Insurance Plan Decisions.” VCU Scholars Compass, Virginia Commonwealth University, 6 Feb. 2012, scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1003&context=hcpr_pubs. 
  5.  Short, Susan E, and Stefanie Mollborn. “Social Determinants and Health Behaviors: Conceptual Frames and Empirical Advances.” Current Opinion in Psychology, U.S. National Library of Medicine, 5 Oct. 2015, www.ncbi.nlm.nih.gov/pmc/articles/PMC4511598/. 
  6.  DHS, Health and Human Services, Agency U.S. Department of Human & Health Services. “Obama Administration Awards Nearly $300 Million to States for Enrolling Eligible Children in Health Coverage.” The National Law Review, 29 Dec. 2011, www.natlawreview.com/article/obama-administration-awards-nearly-300-million-to-states-enrolling-eligible-children-health-. 
  7. Gruber, Jonathan. “Choosing a Medicare Part D Plan: Are Medicare Beneficiaries Choosing Low-Cost Plans?” KFF, The Henry J. Kaiser Family Foundation, 27 Feb. 2009, www.kff.org/medicare/report/choosing-a-medicare-part-d-plan-are/.