Predict, then schedule: Prescriptive analytics approach for machine learning-enabled sequential clinical scheduling

Abstract

This research considers the design of an appointment system (AS) for sequentially scheduling patients in the presence of stochastic factors (no-show and service time uncertainty) and two patient classes. Although studies on AS design are prevalent in literature, most prior works do not consider heterogeneous patient characteristics (e.g., patient-specific no-show risk and service time duration) and sequential patient call-ins together. This research integrates patient-specific uncertainty estimates into the AS design and assess its impact on AS efficiency (measured as the weighted sum of patient waiting time, doctor idle time, and overtime). Specifically, the patients are scheduled as they sequentially call the clinic for appointments based on their risk of no-show and estimated consultation duration. For this setting, a predict-then-schedule framework is proposed. In the predict step, patient-specific no-show risk and service duration are estimated using a machine learning model. The schedule step determines each patient’s appointment time and interval by integrating the predictions with three scheduling decisions (allocation, sequencing, and overbooking). As a result, new sequencing rules are employed. Results indicate that adopting the predict-then-schedule approach in a sequential framework always dominates conventional approaches and could improve the efficiency by 60%.