An Optimization Model on Patient Appointment Scheduling of MRI Diagnostic Examination with Prioritization

Purchasing additional MRI machines may not always be a feasible solution to increase diagnostic services capacity. Healthcare systems make use of appointment scheduling to optimize the use of their limited resources as an alternative to investing in new machines. A properly planned appointment scheduling system will allow for the efficient use of the MRI machine and accommodate patients effectively. The challenge in appointment scheduling comes in the various uncertainties that could disrupt the planned schedules. Current research and literature on appointment scheduling for MRI scans have not tackled the appointment scheduling problem while considering the occurrence of unpunctual patients, no show patients, and unscheduled arrivals, at the same time. This study develops a scheduling model that considers the aforementioned uncertainties in addition to different patient types. A two stage solution is utilized in order to create a weekly appointment schedule. The first stage determines the total number of patients to be accommodated for the week. This is then used as an input for the second model to determine the time slot assignment of patients. The latter minimizes the total penalty contributed by the priority levels and the cost of rearranging from the original order of requests. A neighborhood search heuristic is used to develop a prioritization rule when such uncertainties are realized. From the results and analysis, it was found that the model returns a schedule that generates a lower value of the objective function compared to ad hoc scheduling practices such as that of scheduling according order of request only or according to priority level only.