Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting

This study addresses the problem of scheduling magnetic resonance imaging examinations to reduce indirect waiting time—that is, the time that elapses between the patient’s call for an appointment and the scheduled appointment time. Two alternative scheduling approaches are proposed: online and offline characterized, respectively, by the absence or presence of a batch of patients waiting for an appointment. Specifically, with an online approach, patients are scheduled when they call for an appointment, and consequently there is no need to have a batch of patients. On the contrary, the offline approach assumes that patients are given an expected waiting time when they call; they are subsequently called back and assigned an appointment within few days. With an offline approach, patients are thus collected in a batch until a scheduling policy is run; clearly, the batch size or equivalently the frequency according to which patients are scheduled impacts on the performance of the scheduling policy. The offline approach allows a better planning with respect to the online approach where the decision regarding a patient is greatly affected by the schedule of patients who called before him. On the other side, the online approach allows a prompt accommodation of the patient. In an attempt at trade-off these two approaches, the offline one is experimented with three different scheduling frequencies: once per week, two times per week, and three times per week. The paper describes a novel MIP model for implementation of the offline approach and a greedy heuristic for implementation of the online one. Online and offline approaches are then compared in terms of effectiveness, equity, efficiency and discrimination power, using a rolling horizon of 52 weeks and assuming different demand patterns. The comparison includes an evaluation of the impact of two managerial practices—examination overlapping and radiologist cross-training—when using the two forms of scheduling. The key findings are that the offline approach achieves smaller, less variable values for tardiness and enables scheduling of a higher number of patients in situations where capacity is scarce. As to managerial practices, overlapping is found to be relatively more effective than cross-training when an offline approach is adopted, while for online scheduling, cross-training is more effective than overlapping. The results presented are based on an extensive experimental campaign based on real data coming from a leading Italian hospital.