Static and dynamic appointment scheduling to improve patient access time

Appointment schedules for outpatient clinics have great influence on efficiency and timely access to health care services. The number of new patients per week fluctuates, and capacity at the clinic varies because physicians have other obligations. However, most outpatient clinics use static appointment schedules, which reserve capacity for each patient type. In this paper, we aim to optimise appointment scheduling with respect to access time, taking fluctuating patient arrivals and unavailabilities of physicians into account. To this end, we formulate a stochastic mixed integer programming problem, and approximate its solution invoking two different approaches: (1) a mixed integer programming approach that results in a static appointment schedule, and (2) Markov decision theory, which results in a dynamic scheduling strategy. We apply the methodologies to a case study of the surgical outpatient clinic of the Jeroen Bosch Hospital. We evaluate the effectiveness and limitations of both approaches by discrete event simulation; it appears that allocating only 2% of the capacity flexibly already increases the performance of the clinic significantly.