A Decision-Support System to Schedule Rotations for Trainees

BACKGROUND: Each training program has its own internal policies and restrictions, which must be considered while developing trainee schedules. Designing these schedules is complex and time consuming, and the final schedules often contain undesirable aspects for trainees. OBJECTIVE: We developed a decision-support system (DSS) to optimally schedule daily assignments and monthly rotations for trainees. The proposed DSS aims to 1) reduce the schedule development time, 2) maximize trainee preferences for desired rotations and vacation times, and 3) ensure adaptability of the DSS across multiple graduate medical programs through a flexible design and intuitive graphical user interface. METHODS: Using mixed-integer linear programming, we developed a scheduling model that 1) maximized trainees’ preferences on specific rotations and vacation times and 2) ensured fairness by assigning equal numbers of vacation days and a balanced schedule of difficult versus easy rotations among trainees. The model was successfully implemented in the Mayo Clinic Division of Pulmonary and Critical Care for the academic year 2018–2019. RESULTS: Using the DSS, it took only a few minutes to produce a schedule versus several days of preparation time required by the manual process. Compared with the manually developed schedule, the DSS schedule satisfied 11% more rotation preferences and improved fairness by 19%. All trainees met duty hours in the DSS schedule compared with 83% in the manually developed schedule. CONCLUSION: The proposed DSS can dramatically reduce the schedule preparation time, accommodate more of trainees’ preferences, and improve fairness in assigning rotations.