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.

OBJECTIVE

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.

STUDY DESIGN

Retrospective cross-sectional analysis.

SETTING

Outpatient clinics of the Royal Hospital in Muscat, Oman.

PARTICIPANTS

All outpatient clinic appointments scheduled between January 2014 and February 2021 (n=947 364).

PRIMARY AND SECONDARY OUTCOME MEASURES

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.

RESULTS

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.

CONCLUSIONS

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.