Development and evaluation of prediction models to improve the hospital appointments overbooking strategy at a large tertiary care hospital in the Sultanate of Oman: a retrospective analysis.

Abstract

Missed hospital appointments are common among outpatients and have significant clinical and economic consequences. The purpose of this study is to develop a predictive model of missed hospital appointments and to evaluate different overbooking strategies. Retrospective cross-sectional analysis. Outpatient clinics of the Royal Hospital in Muscat, Oman. All outpatient clinic appointments scheduled between January 2014 and February 2021 (n=947 364). Predictive models were created using logistic regression for the entire cohort and individual practices to predict missed hospital appointments. The performance of the models was evaluated using a holdout set. Simulations were performed to compare the effectiveness of predictive model-based overbooking and organisational overbooking in optimising appointment utilisation. Of the 947 364 outpatient appointments booked, 201 877 (21.3%) were missed. The proportion of missed appointments varied by clinic, ranging from 13.8% in oncology to 28.3% in urology. The area under the receiver operating characteristic curve (AUC) for the overall predictive model was 0.771 (95% CI: 0.768 to 0.775), while the AUC for the clinic-specific predictive model was 0.845 (95% CI: 0.836 to 0.855) for oncology and 0.738 (95% CI: 0.732 to 0.744) for paediatrics. The overbooking strategy based on the predictive model outperformed systematic overbooking, with shortages of available appointments at 10.4% in oncology and 25.0% in gastroenterology. Predictive models can effectively estimate the probability of missing a hospital appointment with high accuracy. Using these models to guide overbooking strategies can enable better appointment scheduling without burdening clinics and reduce the impact of missed appointments.