Personalized Nudges Aren’t Just for Patients; They’re for Clinicians Too
In my previous article, I discussed personalized nudging in healthcare specifically in the context of improving patient health behavior. Although a recent trend, it’s a rather widely known concept that has gained a lot of traction. Here, I explore how the same concept of personalization could possibly be applied to improving clinician behavior.
Over the last decade, several studies have demonstrated the effectiveness of nudges for improving clinician decision-making. However, very few have focused on how different physician characteristics may be associated with varying levels of nudge effectiveness. Furthermore, electronic health record (EHR) usage and activity patterns can provide valuable data that can be used to identify suboptimal decision-making by clinicians. The combination of these insights could aid in personalizing nudges, but why might we not simply deliver nudges to all clinicians?
Before I dive into why personalizing nudges might be important for clinicians, I want to discuss two specific groups of studies to help contextualize the discussion. I focus on these two particular examples for a few reasons:
- They are both examples of nudges targeted at improving clinical decision-making by aligning physician behavior with established treatment guidelines;
- The interventions in the studies work by disrupting clinical workflow, unlike other forms of nudges such as setting default choices; and
- Concurrent research has been conducted on the clinician characteristics associated with suboptimal decision-making patterns of interest.
References
1. Almusalam N, Oh J, Terzaghi M, Maurino J, Bakdache F, Montoya A, et al. Comparison of Physician Therapeutic Inertia for Management of Patients With Multiple Sclerosis in Canada, Argentina, Chile, and Spain. JAMA Netw Open. 2019;2: e197093–e197093.
2. Saposnik G, Mamdani M, Montalban X, Terzaghi M, Silva B, Saladino ML, et al. Traffic Lights Intervention Reduces Therapeutic Inertia: A Randomized Controlled Trial in Multiple Sclerosis Care. MDM Policy Pract. 2019;4: 2381468319855642.
3. Ma J, Sehgal NL, Ayanian JZ, Stafford RS. National trends in statin use by coronary heart disease risk category. PLoS Med. 2005;2. doi:10.1371/journal.pmed.0020123
4. Patel MS, Kurtzman GW, Kannan S, Small DS, Morris A, Honeywell S, et al. Effect of an Automated Patient Dashboard Using Active Choice and Peer Comparison Performance Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized Clinical Trial. JAMA Network Open. 2018;1. doi:10.1001/jamanetworkopen.2018.0818
5. Kannan S, Asch DA, Kurtzman GW, Honeywell S Jr, Day SC, Patel MS. Patient and physician predictors of hyperlipidemia screening and statin prescription. Am J Manag Care. 2018;24: e241–e248.
6. Gregory ME, Russo E, Singh H. Electronic Health Record Alert-Related Workload as a Predictor of Burnout in Primary Care Providers. Appl Clin Inform. 2017;8: 686.
7.Changolkar S, Rewley J, Balachandran M, Rareshide CAL, Snider CK, Day SC, et al. Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study. PLoS One. 2020;15: e0232895.
8. Hsiang EY, Mehta SJ, Small DS, Rareshide CAL, Snider CK, Day SC, et al. Association of Primary Care Clinic Appointment Time With Clinician Ordering and Patient Completion of Breast and Colorectal Cancer Screening. JAMA Netw Open. 2019;2: e193403–e193403.
9. Patel MS, Volpp KG, Small DS, Wynn C, Zhu J, Yang L, et al. USING ACTIVE CHOICE WITHIN THE ELECTRONIC HEALTH RECORD TO INCREASE PHYSICIAN ORDERING AND PATIENT COMPLETION OF HIGH-VALUE CANCER SCREENING TESTS. Healthcare (Amsterdam, Netherlands). 2016;4: 340.
About the Author
Sanketh Andhavarapu
Sanketh is an undergraduate student at the University of Maryland: College Park studying Health Decision Sciences (individual studies degree) and Biology. He is the co-Founder and co-CEO of Vitalize, a digital wellness platform for healthcare workers, and has published research on topics related to clinical decision-making, neurology, and emergency medicine and critical care. He is also currently leading business development for a new AI innovation at PediaMetrix, a pediatric health startup, and previously founded STEPS, an education nonprofit. Sanketh is interested in the applications of behavioral and decision sciences to improve medical decision-making, and how digital health and health policy serve as a scalable channel to do so.
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