LAUSR

P atient no-shows and late cancellations lead to clinic inefficiency, high clinic costs and low patient satisfaction. The two main strategies clinics employed to alleviate the adverse effects of no-shows are overbooking and patient appointment reminders. Developing effective overbooking schedules depends on accurately predicting each patient’s noshow probability, while developing effective reminder systems requires a patient-level estimate of communication sensitivity. Current methods of estimating no-show probabilities do not produce such patient-level predictions. To remedy this, we develop a Bayesian nested logit model which utilizes appointment confirmation data and estimates individual-level coefficients for patient-specific predictors. A log-likelihood comparison of model fit on 12 months of appointment data shows that the Bayesian model outperforms the standard logit model by about 30% improvement in model fit. Additionally, our Bayesian model allows categorization of patients based on their appointment confirmation behavior. Finally, using patient-specific no-show probabilities as an input to a simulated appointment scheduler we find that the Bayesian model improves clinic profit over the standard logit model. The benefit comes mainly from waiting cost reduction when no-show probability is low and from physician overtime and idle time cost reduction when no-show probability is high. Our study has two managerial implications. First, the Bayesian method allows customizing appointment reminder effort based on patient’s confirmation behavior. Second, the Bayesian method also allows improved overbooking scheduling especially in clinics that experience large patient throughput.