A decision support system for physician scheduling during a public health crisis: a mathematical programming model

With the occurrence of a public health crisis, the demand for healthcare services increases, which leads to an increase in the workload of hospitals. To overcome this predicament, hospitals should increase the number of their medical staff. Adding new medical staff, especially physicians, is a time-consuming process, and in such a situation, when the society is facing a shortage of physicians, it is almost impossible. Physician scheduling can be a practical solution to overcome this problem. Scheduling physicians without adding new physicians increases the workload of physicians, and this may affect their productivity and the service quality. To solve this problem, in addition to financial incentives, non-financial incentives such as increasing physicians' satisfaction should also be considered. Hence, by applying a novel mixed-integer linear programming (MILP) model, this study configures a decision support system for scheduling physicians by considering physicians' satisfaction during a public health crisis. The purpose of the proposed model is to maximize the fairness in the distribution of workload among physicians by considering their preferences. It should be noted that the satisfaction of physicians is considered using two indicators including equitable shifts distribution and physicians' preferences. The effectiveness of the proposed MILP model is examined using data from a hospital in Iran during the outbreak of the coronavirus disease (COVID-19). The investigated hospital consists of 15 regular departments that are served by 79 physicians. With the spread of COVID-19 pandemic, three departments are added to the existing departments to serve the COVID-19 patients. Finally, the proposed MILP model is implemented with and without considering physicians' preferences, and the effect of considering preferences on physician scheduling is shown.