Comparing the Effectiveness of Different Appointment Reminder Methods

NCT ID: NCT06767423

Last Updated: 2025-04-04

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 32925 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-11-05
• Study Completion Date: 2025-02-05

Brief Summary

Penn Medicine is continually trying to optimize operations and decrease number of patients who do not show up for their appointments. This has included new changes to text message reminders, implemented as usual care. At a baseline, less than 75% of scheduled outpatient appointments are actually completed. This results in longer wait times and decreased access for patients and operational inefficiencies. The goal of the project is to test whether supplementing standard text message appointment reminders with targeted outreach using an automated phone call to patients with increased risk of not showing up for their appointment (>15% per Epic's Risk of Patient No-Show Model) reduces no show rate (the study's primary outcome) and increases patient appointment completion rate (% of appointments that were completed during scheduled appointment time, a secondary outcome). Participants will be randomized in a 1:1 ratio to receive either the standard text message or standard text message plus the automated caller. Eligible patients have already consented to receiving text message reminders from Penn Medicine and must have an in-person appointment scheduled during the study period. The Access Optimization Group at Penn will be monitoring the randomization and outcomes reporting of whether a patient confirmed, cancelled, or no showed at the scheduled appointment. All eligible outpatient appointments over a two week period will be included in this operational evaluation. The Access Optimization Group will then make a decision on which approach to implement as usual care based on the results of this operational evaluation.

Conditions

Appointment Reminders

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Text Message (Usual Care)

Standard text message appointment reminders to English and Spanish-speaking patients. Text reminders will go out 3-weeks, 3 days, and 24 hours before the appointment.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Text Message + Automated Caller (Intervention)

Standard text message appointment reminders to English and Spanish-speaking patients. Text reminders will go out 3-weeks, 3 days, and 24 hours before the appointment. If patient has not confirmed their appointment after the 3-week and 3-day reminders, they'll receive an automated caller.

Group Type: ACTIVE_COMPARATOR

Text Message + Automated Caller (Intervention)

Intervention Type: BEHAVIORAL

Standard text message appointment reminders to English and Spanish-speaking patients. Text reminders will go out 3-weeks, 3 days, and 24 hours before the appointment. If patient has not confirmed their appointment after the 3-week and 3-day reminders they will receive an automated caller.

Interventions

Text Message + Automated Caller (Intervention)

Standard text message appointment reminders to English and Spanish-speaking patients. Text reminders will go out 3-weeks, 3 days, and 24 hours before the appointment. If patient has not confirmed their appointment after the 3-week and 3-day reminders they will receive an automated caller.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• in-person appointments
• English or Spanish speakers
• 18 years old or older

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

M. Kit Delgado, MD

OTHER

Sponsor Role: lead

Responsible Party

M. Kit Delgado, MD

Associate Professor of Emergency Medicine

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

Penn Medicine

Philadelphia, Pennsylvania, United States

Countries

United States

References

Tarabichi Y, Higginbotham J, Riley N, Kaelber DC, Watts B. Reducing Disparities in No Show Rates Using Predictive Model-Driven Live Appointment Reminders for At-Risk Patients: a Randomized Controlled Quality Improvement Initiative. J Gen Intern Med. 2023 Oct;38(13):2921-2927. doi: 10.1007/s11606-023-08209-0. Epub 2023 May 1.

Reference Type: BACKGROUND

PMID: 37126125 (View on PubMed)

Related Links

https://www.epicshare.org/share-and-learn/metrohealth-no-show-predictive-model

Helping Patients Make It to Their Appointments Using Predictive Analytics and Personalized Phone Calls

Other Identifiers

IRB # 855257

Identifier Type: -

Identifier Source: org_study_id