Optimal and approximate algorithms for sequential clinical scheduling with no-shows

Abstract

The accessibility and efficiency of outpatient clinic operations are largely affected by appointment schedules. Clinical scheduling is a process of assigning physician appointment times to sequentially calling patients. A significant problem in clinical operations is patient no-show, that is, scheduled patients not showing for their appointments. Overbooking can compensate revenue loss due to no-show, but naive overbooking can result in longer patient waiting times and uneven physician work loads. In the past few years, new overbooking methods have been developed for sequential scheduling that yield higher expected profit than simple scheduling rules, but these often fail to exploit information about the future call-in process (they are myopic). To fully use this important information, we develop a Markov Decision Processes (MDP) model for sequential clinical scheduling that books patients to optimize the performance of clinic operations. The model is solved by Dynamic Programming (DP) for small problems. Approximate Dynamic Programming (ADP) algorithms based on aggregation and simulation are developed to find schedules for larger problems. Our computational experiments indicate good improvement over myopic methods.