A Behavioral Economics-Electronic Health Record Module to Promote Appropriate Diabetes Management in Older Adults: Protocol for a Pragmatic Cluster Randomized Controlled Trial

Hayley M Belli, Andrea B Troxel, Saul B Blecker, Judd Anderman, Christina Wong, Tiffany R Martinez, Devin M Mann, Hayley M Belli, Andrea B Troxel, Saul B Blecker, Judd Anderman, Christina Wong, Tiffany R Martinez, Devin M Mann

Abstract

Background: The integration of behavioral economics (BE) principles and electronic health records (EHRs) using clinical decision support (CDS) tools is a novel approach to improving health outcomes. Meanwhile, the American Geriatrics Society has created the Choosing Wisely (CW) initiative to promote less aggressive glycemic targets and reduction in pharmacologic therapy in older adults with type 2 diabetes mellitus. To date, few studies have shown the effectiveness of combined BE and EHR approaches for managing chronic conditions, and none have addressed guideline-driven deprescribing specifically in type 2 diabetes. We previously conducted a pilot study aimed at promoting appropriate CW guideline adherence using BE nudges and EHRs embedded within CDS tools at 5 clinics within the New York University Langone Health (NYULH) system. The BE-EHR module intervention was tested for usability, adoption, and early effectiveness. Preliminary results suggested a modest improvement of 5.1% in CW compliance.

Objective: This paper presents the protocol for a study that will investigate the effectiveness of a BE-EHR module intervention that leverages BE nudges with EHR technology and CDS tools to reduce overtreatment of type 2 diabetes in adults aged 76 years and older, per the CW guideline.

Methods: A pragmatic, investigator-blind, cluster randomized controlled trial was designed to evaluate the BE-EHR module. A total of 66 NYULH clinics will be randomized 1:1 to receive for 18 months either (1) a 6-component BE-EHR module intervention + standard care within the NYULH EHR, or (2) standard care only. The intervention will be administered to clinicians during any patient encounter (eg, in person, telemedicine, medication refill, etc). The primary outcome will be patient-level CW compliance. Secondary outcomes will measure the frequency of intervention component firings within the NYULH EHR, and provider utilization and interaction with the BE-EHR module components.

Results: Study recruitment commenced on December 7, 2020, with the activation of all 6 BE-EHR components in the NYULH EHR.

Conclusions: This study will test the effectiveness of a previously developed, iteratively refined, user-tested, and pilot-tested BE-EHR module aimed at providing appropriate diabetes care to elderly adults, compared to usual care via a cluster randomized controlled trial. This innovative research will be the first pragmatic randomized controlled trial to use BE principles embedded within the EHR and delivered using CDS tools to specifically promote CW guideline adherence in type 2 diabetes. The study will also collect valuable information on clinician workflow and interaction with the BE-EHR module, guiding future research in optimizing the timely delivery of BE nudges within CDS tools. This work will address the effectiveness of BE-inspired interventions in diabetes and chronic disease management.

Trial registration: ClinicalTrials.gov NCT04181307; https://ichgcp.net/clinical-trials-registry/NCT04181307.

International registered report identifier (irrid): DERR1-10.2196/28723.

Keywords: behavioral economics; clinical decision support; diabetes; electronic health records; pragmatic; randomized controlled trial.

Conflict of interest statement

Conflicts of Interest: None declared.

©Hayley M Belli, Andrea B Troxel, Saul B Blecker, Judd Anderman, Christina Wong, Tiffany R Martinez, Devin M Mann. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.10.2021.

Figures

Figure 1
Figure 1
Flow chart of the study design. A total of 66 clinics across New York University Langone Health were randomized 1:1 to the behavioral economics–electronic health record (BE-EHR) module + standard care (intervention) or standard care only (control) after meeting eligibility criteria and informed consent requirements. *The number of providers and patients per arm is an initial estimate as of October 12, 2020, based on eligible patient-provider encounters from the prior 18 months. Due to the dynamic nature of the study being embedded within the EHR, we expect providers and patients to enter and leave the study over the 18-month duration window. CW: Choosing Wisely.

References

    1. Ariely D. Predictably Irrational. New York, NY: HarperCollins; 2008.
    1. Camerer C, Issacharoff S, Loewenstein G, O'Donoghue T, Rabin M. Regulation for Conservatives: Behavioral Economics and the Case for "Asymmetric Paternalism". Univ PA Law Rev. 2003 Jan;151(3):1211. doi: 10.2307/3312889.
    1. Connolly T, Butler D. Regret in economic and psychological theories of choice. J Behav Decis Making. 2006 Apr;19(2):139–154. doi: 10.1002/bdm.510.
    1. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science. 1974 Sep 27;185(4157):1124–31. doi: 10.1126/science.185.4157.1124.185/4157/1124
    1. Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica. 1979 Mar;47(2):263. doi: 10.2307/1914185.
    1. Loewenstein G, John L, Volpp KG. Using decision errors to help people help themselves. In: Shafir Eldar., editor. The Behavioral Foundations of Public Policy. Princeton, NJ: Princeton University Press; 2012. pp. 361–379.
    1. Mullainathan S, Thaler R. Behavioral Economics. In: Smelser NJ, Baltes PB, editors. International Encyclopedia of the Social & Behavioral Sciences. Oxford, UK: Oxford University Press; 2001. pp. 1094–1100.
    1. Thaler RH, Sunstein CR. Libertarian Paternalism. Am Econ Rev. 2003 Apr 01;93(2):175–179. doi: 10.1257/000282803321947001.
    1. Thaler RH, Tversky A, Kahneman D, Schwartz A. The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test. Q J Econ. 1997 May 01;112(2):647–661. doi: 10.1162/003355397555226.
    1. Tversky A, Kahneman D. Loss Aversion in Riskless Choice: A Reference-Dependent Model. Q J Econ. 1991 Nov 01;106(4):1039–1061. doi: 10.2307/2937956.
    1. Chapman GB, Coups EJ. Emotions and preventive health behavior: worry, regret, and influenza vaccination. Health Psychol. 2006 Jan;25(1):82–90. doi: 10.1037/0278-6133.25.1.82.2006-01035-011
    1. Marti J, Bachhuber M, Feingold J, Meads D, Richards M, Hennessy S. Financial incentives to discontinue long-term benzodiazepine use: a discrete choice experiment investigating patient preferences and willingness to participate. BMJ Open. 2017 Oct 06;7(10):e016229. doi: 10.1136/bmjopen-2017-016229. bmjopen-2017-016229
    1. Meeker D, Linder JA, Fox CR, Friedberg MW, Persell SD, Goldstein NJ, Knight TK, Hay JW, Doctor JN. Effect of Behavioral Interventions on Inappropriate Antibiotic Prescribing Among Primary Care Practices: A Randomized Clinical Trial. JAMA. 2016 Feb 09;315(6):562–70. doi: 10.1001/jama.2016.0275. 2488307
    1. Thaler R, Sunstein C. Nudge: Improving Decisions about Health, Wealth, and Happiness, 2nd ed. New York, NY: Penguin Books; 2009.
    1. Loewenstein G, Brennan T, Volpp KG. Asymmetric paternalism to improve health behaviors. JAMA. 2007 Nov 28;298(20):2415–7. doi: 10.1001/jama.298.20.2415.298/20/2415
    1. Volpp KG, Loewenstein G, Troxel AB, Doshi J, Price M, Laskin M, Kimmel SE. A test of financial incentives to improve warfarin adherence. BMC Health Serv Res. 2008;8:272. doi: 10.1186/1472-6963-8-272. 1472-6963-8-272
    1. Kimmel SE, Troxel AB, Loewenstein G, Brensinger CM, Jaskowiak J, Doshi JA, Laskin M, Volpp K. Randomized trial of lottery-based incentives to improve warfarin adherence. Am Heart J. 2012 Aug;164(2):268–74. doi: 10.1016/j.ahj.2012.05.005. S0002-8703(12)00321-3
    1. Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc. 2007;14(2):141–5. doi: 10.1197/jamia.M2334. M2334
    1. Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc. 2011 May 01;18(3):327–34. doi: 10.1136/amiajnl-2011-000094. amiajnl-2011-000094
    1. Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, Lobach D. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012 Jul 3;157(1):29–43. doi: 10.7326/0003-4819-157-1-201207030-00450.1206700
    1. Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V, Rigon G, Vaona A, Ruggiero F, Mangia M, Iorio A, Kunnamo I, Bonovas S. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health. 2014 Dec;104(12):e12–22. doi: 10.2105/AJPH.2014.302164.
    1. Khalifa M, Zabani I. Improving Utilization of Clinical Decision Support Systems by Reducing Alert Fatigue: Strategies and Recommendations. Stud Health Technol Inform. 2016;226:51–4.
    1. Patel MS, Volpp KG, Small DS, Wynne C, Zhu J, Yang L, Honeywell S, Day SC. Using Active Choice Within the Electronic Health Record to Increase Influenza Vaccination Rates. J Gen Intern Med. 2017 Jul;32(7):790–795. doi: 10.1007/s11606-017-4046-6. 10.1007/s11606-017-4046-6
    1. Patel MS, Volpp KG, Small DS, Wynn C, Zhu J, Yang L, Honeywell S, Day SC. Using active choice within the electronic health record to increase physician ordering and patient completion of high-value cancer screening tests. Healthc (Amst) 2016 Dec;4(4):340–345. doi: 10.1016/j.hjdsi.2016.04.005. S2213-0764(16)30068-9
    1. Patel MS, Kurtzman GW, Kannan S, Small DS, Morris A, Honeywell S, Leri D, Rareshide CAL, Day SC, Mahoney KB, Volpp KG, Asch DA. 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 Netw Open. 2018 Jul 06;1(3):e180818. doi: 10.1001/jamanetworkopen.2018.0818. 2688535
    1. Choosing Wisely: Promoting conversations between patients and clinicians. ABIM Foundation. 2019. [2019-07-20].
    1. American Geriatrics Society. Choosing Wisely/ABIM Foundation. 2017. [2019-07-20].
    1. American Geriatrics Society Expert Panel on Care of Older Adults with Diabetes Mellitus. Moreno G, Mangione CM, Kimbro L, Vaisberg E. Guidelines abstracted from the American Geriatrics Society Guidelines for Improving the Care of Older Adults with Diabetes Mellitus: 2013 update. J Am Geriatr Soc. 2013 Nov;61(11):2020–6. doi: 10.1111/jgs.12514.
    1. AGS Choosing Wisely Workgroup American Geriatrics Society identifies another five things that healthcare providers and patients should question. J Am Geriatr Soc. 2014 May;62(5):950–60. doi: 10.1111/jgs.12770.
    1. Lipska KJ, Ross JS, Miao Y, Shah ND, Lee SJ, Steinman MA. Potential overtreatment of diabetes mellitus in older adults with tight glycemic control. JAMA Intern Med. 2015 Mar;175(3):356–62. doi: 10.1001/jamainternmed.2014.7345. 2089233
    1. Belli HM, Chokshi SK, Hegde R, Troxel AB, Blecker S, Testa PA, Anderman J, Wong C, Mann DM. Implementation of a Behavioral Economics Electronic Health Record (BE-EHR) Module to Reduce Overtreatment of Diabetes in Older Adults. J Gen Intern Med. 2020 Nov;35(11):3254–3261. doi: 10.1007/s11606-020-06119-z.10.1007/s11606-020-06119-z
    1. R Core Team . R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. [2021-09-30].
    1. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. doi: 10.1016/0021-9681(87)90171-8.
    1. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, Januel J, Sundararajan V. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011 Mar 15;173(6):676–82. doi: 10.1093/aje/kwq433.kwq433
    1. DuGoff EH, Canudas-Romo V, Buttorff C, Leff B, Anderson GF. Multiple chronic conditions and life expectancy: a life table analysis. Med Care. 2014 Aug;52(8):688–94. doi: 10.1097/MLR.0000000000000166.00005650-201408000-00003
    1. Chokshi SK, Belli HM, Troxel AB, Blecker S, Blaum C, Testa P, Mann D. Designing for implementation: user-centered development and pilot testing of a behavioral economic-inspired electronic health record clinical decision support module. Pilot Feasibility Stud. 2019;5:28. doi: 10.1186/s40814-019-0403-z. 403
    1. Chokshi SK, Troxel A, Belli H, Schwartz J, Blecker S, Blaum C, Szerencsy A, Testa P, Mann D. User-Centered Development of a Behavioral Economics Inspired Electronic Health Record Clinical Decision Support Module. Stud Health Technol Inform. 2019 Aug 21;264:1155–1158. doi: 10.3233/SHTI190407. SHTI190407
    1. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981 Jan 30;211(4481):453–8. doi: 10.1126/science.7455683.
    1. Berkowitz A, Perkins H. Resident advisers as role models: a comparison of drinking patterns of resident advisers and their peers. J Coll Student Personn. 1986;27(2):146–153.
    1. Steele C. The psychology of self-affirmation: sustaining the integrity of the self. Adv Exp Soc Psychol. 1988;21:261–302. doi: 10.1016/S0065-2601(08)60229-4.
    1. Seligman ME, Csikszentmihalyi M. Positive psychology. An introduction. Am Psychol. 2000 Jan;55(1):5–14. doi: 10.1037//0003-066x.55.1.5.
    1. Yacoub M. Emotional Framing: How Do Emotions Contribute to Framing Effects? (Masters thesis) Chapel Hill, NC: University of North Carolina at Chapel Hill; 2012.
    1. Lerner JS, Tetlock PE. Accounting for the effects of accountability. Psychol Bull. 1999 Mar;125(2):255–75. doi: 10.1037/0033-2909.125.2.255.
    1. Tetlock PE, Skitka L, Boettger R. Social and cognitive strategies for coping with accountability: Conformity, complexity, and bolstering. J Pers Soc Psychol. 1989 Oct;57(4):632–640. doi: 10.1037//0022-3514.57.4.632.
    1. Sedikides C, Herbst KC, Hardin DP, Dardis GJ. Accountability as a deterrent to self-enhancement: The search for mechanisms. J Pers Soc Psychol. 2002 Sep;83(3):592–605. doi: 10.1037//0022-3514.83.3.592.
    1. Johnson EJ, Goldstein D. Medicine. Do defaults save lives? Science. 2003 Nov 21;302(5649):1338–9. doi: 10.1126/science.1091721.302/5649/1338
    1. Madrian BC, Shea DF. The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior. Q J Econ. 2001 Nov 01;116(4):1149–1187. doi: 10.1162/003355301753265543.
    1. Thaler R, Benartzi S. Save More Tomorrow™: Using Behavioral Economics to Increase Employee Saving. J Polit Econ. 2004 Feb;112(S1):S164–S187. doi: 10.1086/380085.
    1. Read JL, Shortell SM. Interactive games to promote behavior change in prevention and treatment. JAMA. 2011 Apr 27;305(16):1704–5. doi: 10.1001/jama.2011.408.jama.2011.408
    1. Blecker S, Pandya R, Stork S, Mann D, Kuperman G, Shelley D, Austrian JS. Interruptive Versus Noninterruptive Clinical Decision Support: Usability Study. JMIR Hum Factors. 2019 Apr 17;6(2):e12469. doi: 10.2196/12469. v6i2e12469
    1. Collier R. Rethinking EHR interfaces to reduce click fatigue and physician burnout. CMAJ. 2018 Aug 20;190(33):E994–E995. doi: 10.1503/cmaj.109-5644. 190/33/E994
    1. Fant C, Adelman D. Too many medication alerts: How alarm frequency affects providers. Nurse Pract. 2018 Nov;43(11):48–52. doi: 10.1097/01.NPR.0000544279.20257.4b.00006205-201811000-00009
    1. Hussein H, Zaccardi F, Khunti K, Davies MJ, Patsko E, Dhalwani NN, Kloecker DE, Ioannidou E, Gray LJ. Efficacy and tolerability of sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 receptor agonists: A systematic review and network meta-analysis. Diabetes Obes Metab. 2020 Jul;22(7):1035–1046. doi: 10.1111/dom.14008.

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