A Network‐Based Formulation for Scheduling Clinical Rotations

We investigate the scheduling practices of a medical school that must assign a cohort of students to a series of clinical rotations, while respecting both operational and quality‐of‐service requirements. Students become available to start clerkship progressively throughout the year and can complete rotations at hospitals in different geographic regions. Each hospital may offer a subset of the clinical rotations, with different start dates, capacities, and cost rates. We propose a novel network‐flow model based on decision diagrams, a graphical structure that compresses the state space of a dynamic program, to model feasible schedules. We demonstrate that our network model has several interesting structural features, is computationally superior as compared to a classical mixed‐integer linear program, and can be used to generate useful insights that can aid in managerial decision‐making. Using a dataset collected from the American University of the Caribbean, we perform a counterfactual analysis which shows that had our scheduling approach been implemented, a cost reduction of approximately 19% on average could have been achieved. To understand how assignment decisions can affect future costs, we develop a discrete‐event simulation of the licensing examination and clerkship scheduling process. We then compare our exact scheduling approach with current practice and achieve an average cost reduction of 25%. We also show that this cost reduction is robust with respect to estimation and forecast uncertainty, specifically, the licensing exam failure rate and the future cohort size.