讲座题目：Individualized No-show Predictions: Effect on Clinic Overbooking and

Appointment Reminders

主讲人：李喻天  特任副教授

主持人：李泳臻  博 士

时间 ：2019年7月2日 (周二)  

16：00 - 17：00

地点：3044am永利集团504室

讲座人介绍：

 Dr. Yutian Li is an Assistant Professor in School of Management at University of Science and Technology of China. He received his Ph.D. from School of Business, University of Miami in 2018. His research focuses on the value of information in supply chain management and healthcare management. His work has been accepted by POMS and presented in several conferences.

讲座内容：

Patient 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 no-show 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. 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.

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