HCM 440 Module 4 Project One Example

Project One: Evaluating Two-Way Text Reminders at Fairhaven Medical Group, Proposal Part One

[Student Name]

Southern New Hampshire University

HCM 440: Healthcare Research and Evaluation Methodologies

Project One

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Project One: Evaluating Two-Way Text Reminders at Fairhaven Medical Group, Proposal Part One

Fairhaven Medical Group has invested in two-way text reminders for six clinics and plans to extend them to all fourteen. This proposal describes how the group will learn whether the investment reduces missed appointments compared with the automated phone calls it replaces. Part one, presented here, covers background, purpose, questions and hypotheses, the program's logic, the evidence, the design, the setting and the outcomes. Part two, due in Module Seven, will add measurement, analysis, ethics and dissemination.

Background and Significance

Of roughly 310,000 appointments Fairhaven schedules each year, about 17.6% end with the patient absent and no advance cancellation, more than 54,000 empty slots. Using the group's average net revenue per visit of about $150, the finance office estimates $8.1 million in forgone revenue, though some slots are backfilled with walk-ins. The access cost is harder to price. In behavioral health, where one in four visits is missed, patients now wait an average of 23 days for the third available new-patient appointment. Every no-show there is a visit someone else needed.

Problem Statement

Fairhaven does not know whether its new reminders work better than the old ones. The pilot clinics' no-show rate dropped after the switch, but the same clinics added Saturday hours, the comparison spans winter to summer and several volunteered after an unusually bad season. Leadership is about to commit roughly $140,000 a year to expand the service. Without a credible evaluation, the group could pay for a program that adds little, or abandon one that helps, based on a misleading comparison.

Purpose

The purpose of this study is to estimate how much two-way texting changes missed appointments and slot refill, relative to the robocalls it replaces, across Fairhaven's clinics over twelve months, and to understand how front-desk staff and patients experience the change. Results will inform the expansion decision and the design of reminder messages for groups with the highest no-show rates.

Questions and Hypotheses

Question 1: Do two-way text reminders reduce the weekday no-show rate compared with automated phone reminders? Hypothesis: clinics using texts will show a no-show rate at least 2 percentage points lower than expected from their prior trend and the comparison clinics. Question 2: Do texts increase the share of canceled slots refilled before the appointment time? Hypothesis: the refill rate will rise, because cancellations arrive earlier. Question 3, exploratory: Is the benefit larger or smaller for particular ages, for Spanish speakers or in certain specialties? No direction is predicted, but the literature suggests younger patients may respond more.

How the Program Is Expected to Work

Skivington et al. (2021) advise that evaluations of complex interventions begin with a program theory explaining how and why the intervention should produce its effects. For two-way texts, the theory has two paths. The first is memory: a message at 72 and 24 hours reminds patients who would otherwise forget. The second is ease of cancellation: patients who cannot come can reply to cancel in seconds rather than calling during business hours, which converts a no-show into a cancellation that schedulers can refill. Table 1 sets out the logic model.

Table 1. Logic Model for Two-Way Text Reminders

Note. Developed by the author with the clinic operations team.

Evidence: Do Text Reminders Work?

The evidence on effectiveness is fairly consistent. Guy et al. (2012) pooled randomized trials and reported attendance odds roughly half again as high for texted patients (1.48), an effect that held across clinic types and message timing. Gurol-Urganci et al. (2013) reached a similar conclusion in a Cochrane review, adding that text and phone reminders performed about equally in the few head-to-head comparisons and that texts cost less. These findings justify the hypothesis for Question 1 but also warn that the gain over phone calls may be modest.

Evidence: Who Misses and What It Costs

Dantas et al. (2018) reviewed research on no-shows and found an average rate near 23% across studies, with prior missed visits, younger age and long booking lead times among the strongest predictors. These factors must be accounted for when comparing clinics whose patient mix differs. Kheirkhah et al. (2016), working at a large Veterans Affairs center, documented the substantial cost of missed visits, supporting the proposal's attention to financial as well as access outcomes.

Evidence: Lessons for Design

Methods literature shapes the design. Hemming et al. (2015) show that a stepped wedge rollout gives randomized evidence when every cluster will eventually receive a program. Lopez Bernal et al. (2017) explain that time series analyses should model seasonality and look for competing events near the start date. Dimick and Ryan (2014) caution that comparisons between groups over time depend on the groups following similar trends beforehand. The design below reflects all three lessons.

Design Overview

The study uses two linked designs. In part one, the six pilot clinics are compared with the eight phone-reminder clinics using a controlled interrupted time series with 24 months of data before March 2026 and the months after until the comparison clinics begin switching. In part two, the eight remaining clinics switch in pairs, one pair every other month, in an order drawn by lottery, forming a stepped wedge. A small qualitative component, interviews with front-desk staff and a patient survey, will explore how the change works in practice.

Setting and Population

Fairhaven is a physician-owned multispecialty group with 14 clinics in one metropolitan area and its suburbs, covering primary care, pediatrics, behavioral health, cardiology, orthopedics and women's health. About 38% of adult patients are under 40, and 16% prefer Spanish. The study population includes all scheduled appointments for patients 18 and older during the study period. Excluded are same-day appointments, telehealth visits, procedures scheduled by hospital staff and patients who opted out of all reminders.

Outcomes

The primary outcome is the weekday no-show rate: weekday bookings where the patient never arrived and gave under two hours of notice, or none, divided by all scheduled weekday appointments, calculated monthly by clinic. Secondary outcomes are the refill rate, meaning canceled slots booked by another patient before the appointment time, the late cancellation rate and patient satisfaction with reminders. All appointment outcomes come directly from the scheduling system, which timestamps every booking, cancellation and check-in.

Timeline

The study runs over fourteen months, including preparation and reporting. Table 2 shows the major phases.

Table 2. Study Timeline

Note. Planned schedule; step timing may shift if the vendor delays setup.

Expected Contribution

For Fairhaven, the study will answer a spending question with far better evidence than the pilot's before-and-after figure. It will also show which patient groups benefit least, guiding message changes such as Spanish-language texts. Beyond Fairhaven, it will add a rare head-to-head comparison of two-way texts and automated phone calls in a US multispecialty setting, one of the gaps identified in the literature.

Limitations Preview

Part one cannot fully separate texts from other changes at pilot clinics, even with adjustments. The stepped wedge depends on clinics switching on schedule. And a single group's results may not generalize to settings with different patients or phone habits. Part two of the proposal will describe how measurement and analysis address these limits.

Conclusion

The proposal turns a promising pilot into a study that can support a confident decision. It defines a clear purpose and testable hypotheses, explains how the program should work, builds on consistent evidence and uses a design that exploits the rollout Fairhaven already plans. Part two will complete the plan with measures, analysis and protections for patients and staff.

References

Dantas, L. F., Fleck, J. L., Cyrino Oliveira, F. L., & Hamacher, S. (2018). No-shows in appointment scheduling: A systematic literature review. Health Policy, 122(4), 412-421. https://doi.org/10.1016/j.healthpol.2018.02.002

Dimick, J. B., & Ryan, A. M. (2014). Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA, 312(22), 2401-2402. https://doi.org/10.1001/jama.2014.16153

Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., Atun, R., & Car, J. (2013). Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database of Systematic Reviews, 2013(12), Article CD007458. https://doi.org/10.1002/14651858.CD007458.pub3

Guy, R., Hocking, J., Wand, H., Stott, S., Ali, H., & Kaldor, J. (2012). How effective are short message service reminders at increasing clinic attendance? A meta-analysis and systematic review. Health Services Research, 47(2), 614-632. https://doi.org/10.1111/j.1475-6773.2011.01342.x

Hemming, K., Haines, T. P., Chilton, P. J., Girling, A. J., & Lilford, R. J. (2015). The stepped wedge cluster randomised trial: Rationale, design, analysis, and reporting. BMJ, 350, Article h391. https://doi.org/10.1136/bmj.h391

Kheirkhah, P., Feng, Q., Travis, L. M., Tavakoli-Tabasi, S., & Sharafkhaneh, A. (2016). Prevalence, predictors and economic consequences of no-shows. BMC Health Services Research, 16, Article 13. https://doi.org/10.1186/s12913-015-1243-z

Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348-355. https://doi.org/10.1093/ije/dyw098

Skivington, K., Matthews, L., Simpson, S. A., Craig, P., Baird, J., Blazeby, J. M., Boyd, K. A., Craig, N., French, D. P., McIntosh, E., Petticrew, M., Rycroft-Malone, J., White, M., & Moore, L. (2021). A new framework for developing and evaluating complex interventions: Update of Medical Research Council guidance. BMJ, 374, Article n2061. https://doi.org/10.1136/bmj.n2061