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IIR 17-134 – HSR Study

IIR 17-134
Using Behavioral Economics to Enhance Appointment Reminders and Reduce Missed Visits
Alan R. Teo, MD MS
VA Portland Health Care System, Portland, OR
Portland, OR
Funding Period: November 2018 - September 2022


“No-shows,” or missed visits are a persistent problem in all health care systems. They compromise patient access, lengthen wait times, increase health care inefficiencies, and worsen clinical care. The VA’s no-show rate has shown no improvement in years, resulting in a staggering nine million ambulatory no-shows in 2015 and perhaps over $1 billion in additional annual costs. Appointment reminders are an essential element to addressing no-shows but major research gaps exist. Behavioral economics (BE) suggests that “nudges” can be designed to make it easier for people to do the “right” thing while retaining individual autonomy. We draw from BE, psychology and related fields to identify concepts that can be applied to innovating the field of appointment reminders. One concept is social norms, which suggests that Veterans are likely to attend appointments if they sense that it is the norm for Veterans to do so. Another concept is based on the idea that providing clear instructions and an implementation plan increases the target behavior. A third approach is to highlight potential harms or losses to the Veteran from missing an appointment, while a fourth approach instead highlights potential negative consequences of no- showing to other Veterans. Each of these concepts can be transformed into messages that can be incorporated into appointment reminders. We will design and evaluate four intervention groups varying in type of nudge included in appointment reminder letters. We will address the following Specific Aims: 1) Develop and iteratively refine BE-informed messages based on Veterans’ perceptions, and incorporate them into enhanced appointment reminders; 2) Determine the effect of four versions of enhanced appointment reminders on measures relevant to treatment access, compared with usual reminders; 3) Evaluate differences in treatment effect associated with four versions of enhanced appointment reminders; 4) Characterize potential barriers and facilitators to widespread implementation of enhanced appointment reminder messages. Applying insights from BE to enhance appointment attendance in VA is highly significant and innovative. VA leaders describe reducing no-shows as critical to efforts to improve access, the top priority in VA. Appointment reminders are especially important in mental health (where no-show rates are high) and primary care (where appointments are scheduled far in advance). With the exception of a few studies in the U.K.’s National Health Service, BE has not been incorporated and evaluated in the design and content of appointment reminders. After iterative refinement of nudges to include in enhanced appointment reminders, we will conduct a cluster randomized controlled trial to test four interventions. Intervention arms will vary by what type of nudge and messages are added to appointment reminders. Appointments will be randomly allocated at the provider-level to one of the four interventions or a control group of usual appointment reminders. The study will be conducted at VA Portland Health Care System in primary care and mental health clinics. We will evaluate effect on a variety of access-related metrics, including appointment no-show, attendance, and cancelation rates. We will also explore impact on wait times. Finally, we will also conduct a qualitative assessment consisting of semi- structured interviews with key informants (Veterans and VA leaders) to inform future implementation of the intervention. Together, we believe this will result in a reduction in no-show rates, increased attendance and access by Veterans, decreased wait times for scheduled appointments, and information to prepare to extend the results to other VA facilities. Study results will have direct implications for VA at local and national levels. Individual VA facilities interested in reducing their no-show rates could benefit from implementation of the most effective study intervention, and VA leaders nationally, including our operational partners working on access and no- show issues, could utilize our results for future changes to appointment reminder systems.

External Links for this Project

NIH Reporter

Grant Number: I01HX002449-01A2

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Journal Articles

  1. Zikmund-Fisher BJ, Tuepker A, Metcalf EE, Strange W, Teo AR. Applying user-centered design in the development of nudges for a pragmatic trial to reduce no-shows among veterans. Patient education and counseling. 2022 Jun 1; 105(6):1620-1627. [view]
  2. Teo AR, Metcalf EE, Strange W, Call AA, Tuepker A, Dobscha SK, Kaboli PJ. Enhancing Usability of Appointment Reminders: Qualitative Interviews of Patients Receiving Care in the Veterans Health Administration. Journal of general internal medicine. 2021 Jan 1; 36(1):121-128. [view]
  3. Lafferty M, Strange W, Kaboli P, Tuepker A, Teo AR. Patient Sense of Belonging in the Veterans Health Administration: A Qualitative Study of Appointment Attendance and Patient Engagement. Medical care. 2022 Sep 1; 60(9):726-732. [view]
  4. Teo AR, Niederhausen M, Handley R, Metcalf EE, Call AA, Jacob RL, Zikmund-Fisher BJ, Dobscha SK, Kaboli PJ. Using Nudges to Reduce Missed Appointments in Primary Care and Mental Health: a Pragmatic Trial. Journal of general internal medicine. 2023 Jul 1; 38(Suppl 3):894-904. [view]
Government Briefings

  1. Teo AR, Call AA, Metcalf EE, Kaboli P, Dobscha SK. Current Practices Regarding Appointment Reminder Letters for Patients at the VA Portland Health Care System: Briefing before the Office of Veterans Access to Care; 2020 Sep 1; Portland, Oregon. [view]
  2. Teo AR, Call AA, Metcalf EE, Strange W, Tuepker A, Dobscha SK, Kaboli P. Recommendations to improve the usability of VA appointment reminders for Veterans: Briefing before the Office of Veterans Access to Care; 2020 Mar 1; Portland, Oregon. [view]

DRA: Health Systems
DRE: Treatment - Observational, TRL - Applied/Translational
Keywords: Models of Care
MeSH Terms: None at this time.

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