Objective
The resident 80-hour work week requires that programs now schedule duty hours. Typically, scheduling is performed in an empirical “trial-and-error” fashion. However, this is a classic “scheduling” problem from the field of operations research (OR). It is similar to scheduling issues that airlines must face with pilots and planes routing through various airports at various times. The authors hypothesized that an OR approach using iterative computer algorithms could provide a rational scheduling solution.
Methods
Institution-specific constraints of the residency problem were formulated. A total of 56 residents are rotating through 4 hospitals. Additional constraints were dictated by the Residency Review Committee (RRC) rules or the specific surgical service. For example, at Hospital 1, during the weekday hours between 6 am and 6 pm, there will be a PGY4 or PGY5 and a PGY2 or PGY3 on-duty to cover Service “A.” A series of equations and logic statements was generated to satisfy all constraints and requirements. These were restated in the Optimization Programming Language used by the ILOG software suite for solving mixed integer programming problems.
Results
An integer programming solution was generated to this resource-constrained assignment problem. A total of 30,900 variables and 12,443 constraints were required. A total of man-hours of programming were used; computer run-time was 25.9 hours. A weekly schedule was generated for each resident that satisfied the RRC regulations while fulfilling all stated surgical service requirements. Each required between 64 and 80 weekly resident duty hours.
Conclusions
The authors conclude that OR is a viable approach to schedule resident work hours. This technique is sufficiently robust to accommodate changes in resident numbers, service requirements, and service and hospital rotations.
Institution of the 80-hour work week for residency programs poses many challenges for general surgery residency programs. Although it is imperative that residency programs are viewed as educational experiences rather than service functions, a wide variety of stakeholders draw on the available 80 hours. Therefore, constructing a schedule for a group of residents that will comply with the requirements of the Residency Review Committee (RRC) and the needs of various hospitals, general surgery, and specialty surgical services can prove to be extremely challenging and frustrating. Typically, a schedule is made empirically via “trial-and-error” and multiple iterations are constructed weekly or monthly, as gaps arise in residency hours or patient care responsibilities. Fortunately, the scheduling of the resident 80-hour work week is an example of the classic “scheduling” problem in the discipline of operations research or management science and is commonly taught to first year students in M.B.A. programs.
Operations research (OR) is the science of decision making. Operations research originated before World War II with the establishment of scientific teams to study strategic and tactical problems in military operations. The objective was to find the most effective utilization of limited military resources by the use of quantitative techniques, such as linear and nonlinear programming, network analysis, Markov processes, or stochastic programming.1 In our setting, resident scheduling is a classic OR problem, typically termed “staff scheduling.” Given a set of employees, assign them to a schedule such that they are working when most needed, while ensuring that certain constraints (such as work hours) are maintained. With the advent of more powerful personal computing resources over the last 15 years, optimization methodologies that combine ideas from OR with techniques from logic and artificial intelligence are available to a wide array of users. Given the history of successful implementation of OR techniques in the realm of business decision making, the authors hypothesized that OR techniques would be applicable for scheduling the resident 80-hour work week. Equally important, the lack of an OR solution would indicate that the problem is overly constrained, suggesting too many rotations, service requirements, and/or hospitals or, conversely, too few available residents. Finally, this technique could be generalized to set schedules while changing the number of residents, make-up of the resident teams, number of participating hospitals, number of rotations, or work-hour requirements.
The authors conclude that OR is a viable approach to schedule resident work hours. This technique is sufficiently robust to accommodate changes in resident numbers, service requirements, and service and hospital rotations.
