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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.

To improve coverage against vaccine-preventable diseases for children and adults, and to aid caretakers and providers in making appropriate and timely vaccination decisions, Georgia Institute of Technology collaborated with the Centers for Disease Control and Prevention to develop decision support tools for creating optimized catch-up immunization schedules for four target groups: children through age 6, adolescents ages 7 through 18, adults ages 19 and over in the United States, and children and adolescents through age 19 in Canada. Our solution to the catch-up scheduling problem for each targeted group determines the best coverage schedule for each individual given his (her) vaccination history and age. If an individual misses one or more doses of a recommended vaccine, a health-care professional is typically responsible for generating a feasible catch-up schedule that optimizes the person's coverage against vaccine-preventable diseases, a task that is often challenging and time consuming. Inappropriate schedules could prevent some individuals from being vaccinated in a timely manner, potentially increasing their risk of contracting a disease. Each decision support tool uses a dynamic programming algorithm to construct recommended immunization schedules in an optimized manner. These tools simplify the tedious process of manually constructing immunization schedules, expedite the process, and eliminate errors.

A physician schedule that maximizes continuity (i.e., reduces instances of patients being treated by multiple physicians) could improve the efficiency of handoffs—the transfer of patients from the care of one physician to another. We present a modeling and solution approach for assigning physicians to service and call shifts in the pediatric intensive care unit (PICU) at Children’s Healthcare of Atlanta at Egleston (Children’s). We developed the handoff continuity score (HCS) for measuring the continuity of a schedule. We combined the HCS with a mixed-integer programming model (MIP) with the objective of maximizing the HCS, while minimizing violations of physician preferences. For a 51-week horizon and a physician pool of 16 physicians, no feasible solution to this MIP is found within 48 hours using CPLEX 12.4. However, an iterative heuristic incorporating modified versions of the MIP produces a schedule (3.42 percent optimality gap) for the scheduling instance faced by Children’s for this period. Our solution approach facilitates resource optimization, and automated scheduling requires less time than manually constructing such a schedule. We generated six-month schedules that were implemented in the PICU at Children’s in 2011, 2012, and 2013. Such automated schedule construction allows for creation of schedules that maximize continuity.