Automated medical resident rotation and shift scheduling to ensure quality resident education and patient care
At academic teaching hospitals around the country, the majority of clinical care is provided by resident physicians. During their training, medical residents often rotate through various hospitals and/or medical services to maximize their education. Depending on the size of the training program, manually constructing such a rotation schedule can be cumbersome and time consuming. Further, rules governing allowable duty hours for residents have grown more restrictive in recent years (ACGME 2011), making day-to-day shift scheduling of residents more difficult (Connors et al., J Thorac Cardiovasc Surg 137:710–713, 2009; McCoy et al., May Clin Proc 86(3):192, 2011; Willis et al., J Surg Edu 66(4):216–221, 2009). These rules limit lengths of duty periods, allowable duty hours in a week, and rest periods, to name a few. In this paper, we present two integer programming models (IPs) with the goals of (1) creating feasible assignments of residents to rotations over a one-year period, and (2) constructing night and weekend call-shift schedules for the individual rotations. These models capture various duty-hour rules and constraints, provide the ability to test multiple what-if scenarios, and largely automate the process of schedule generation, solving these scheduling problems more effectively and efficiently compared to manual methods. Applying our models on data from a surgical residency program, we highlight the infeasibilities created by increased duty-hour restrictions placed on residents in conjunction with current scheduling paradigms.
KeywordsPhysician scheduling Integer programming Rotation assignment Decision support
We would like to express our thanks to Dr. Keith Delman and Dr. Jeffrey Jopling for their assistance and support of this research. We are also grateful to Dr. Ozlem Ergun, Busra Ergun, Lesley-Anne Harris, and Kevin Yee for their contributions in the earlier stages of this study.
This work was supported by the William B. George Health Systems Institute Fellowship, the Mary Anne and Harold R. Nash Endowment, the Joseph C. Mello Endowment, and the Smalley Endowment at Georgia Tech.
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