An artificial intelligence-based model to reduce the no-show rate in outpatient clinics of an academic hospital

Abstract

Abstract Purpose non-attendance of patients for outpatient appointments, known as "no-shows," poses a persistent challenge for healthcare facilities, with significant repercussions for both patients and healthcare systems. This study aimed to investigate whether targeting high-risk individuals with interventions could effectively reduce the rate of no-shows within reasonable resource allocation. Methods we developed an artificial intelligence (AI) algorithm-based prediction model to estimate the likelihood of an appointment resulting in a no-show. Utilizing retrospective data from 24 outpatient clinics, a machine learning (ML) model was constructed and trained to identify patients at high risk of no-show. Subsequently, over a 6-month period, 35% of the highest-risk patients were randomly assigned to either the intervention group (receiving a reminder phone call three workdays before their appointment) or the control group (no reminder call). Results following the intervention, the intervention group experienced a notable 26.2% reduction in no-shows. This reduction translates to a 14.3% decrease in the overall number of no-shows, demonstrating the efficacy of the reminder service. Moreover, this intervention led to additional benefits, including the ability to schedule new patients on previously avoided no-show slots, enhanced patient experience, reduced staff preparation time for missed appointments, and a decrease in administrative burden associated with rescheduling no-shows. Conclusions Our AI-powered model proved to be an effective tool for identifying high-risk patients prone to missing their outpatient appointments. This allowed for targeted interventions, such as reminder phone calls, to be implemented. The substantial reduction in no-show rates underscores the potential impact of this approach on optimizing healthcare resource allocation and improving patient attendance.