Dynamic Patient Scheduling for Multi-Appointment Health Care Programs

We investigate the scheduling practices of a multidisciplinary, multistage, outpatient health care program. Patients undergo a series of assessments before being eligible for elective surgery. Such systems often suffer from high rates of attrition and appointment no-shows leading to capacity underutilization and treatment delays. We propose a new scheduling model where the clinic assigns patients to an appointment day but postpones the decision of which assessments patients undergo pending the observation of who arrives. In doing so, the clinic gains flexibility to improve system performance. We formulate the scheduling problem as a Markov decision process and use approximate dynamic programming to solve it. We apply our approach to a dataset collected from a bariatric surgery program at a large tertiary hospital in Toronto, Canada. We examine the quality of our solutions via structural results and compare them with heuristic scheduling practices using a discrete-event simulation. By allowing multiple assessments, delaying their scheduling, and by optimizing over an appointment book, we show significant improvements in patient throughput, clinic profit, use of overtime, and staff utilization.