Automated Calls Added to SMS Reminders Reduce Missed Appointments among High-Risk Patients

Area of Science:

• Health Informatics
• Healthcare Management
• Patient Engagement

Background:

• Missed outpatient appointments (no-shows) disrupt healthcare delivery, reduce access to care, and waste resources.
• Traditional interventions for no-shows have limited scalability and mixed effectiveness.
• Predictive modeling can identify high-risk patients, but requires effective outreach strategies.

Purpose of the Study:

• To understand reasons for appointment no-shows and develop a scalable intervention.
• To implement and evaluate an automated outreach program using interactive voice response (IVR) technology.
• To reduce no-show rates and improve appointment completion through targeted, low-effort interventions.

Main Methods:

• A text message survey identified patient reasons for no-shows, with "I did not know I had an appointment" being the most common.
• An automated IVR outreach campaign was developed, leveraging electronic health record (EHR) no-show predictive models.
• A rapid randomized trial compared text reminders alone versus text reminders plus IVR calls for high-risk patients.

Main Results:

• The combined IVR and text message intervention reduced the no-show rate by 1.7 percentage points (from 11.3% to 9.6%).
• Appointment completion rates increased by 1.9 percentage points (from 75.9% to 77.8%) with the intervention.
• The intervention showed the greatest impact on reducing no-shows among the highest-risk quartile and improved equity for Black patients.

Conclusions:

• Integrating predictive modeling with automated IVR outreach is a low-cost, scalable solution for improving appointment completion.
• This approach enhances clinic efficiency, promotes health equity, and can generate revenue for health systems.
• Sustained improvements in appointment completion rates were observed post-implementation, demonstrating long-term effectiveness.