Appointment scheduling with unscheduled arrivals and reprioritization

Inspired by the real life problem of a radiology department in a Dutch hospital, we study the problem of scheduling appointments, taking into account unscheduled arrivals and reprioritization. The radiology department offers CT diagnostics to both scheduled and unscheduled patients. Of these unscheduled patients, some must be seen immediately, while others may wait for some time. Herein a trade-off is sought between acceptable waiting times for appointment patients and unscheduled patients’ lateness. In this paper we use a discrete event simulation model to determine the performance of a given appointment schedule in terms of waiting time and lateness. Also we propose a constructive and local search heuristic that embeds this model and optimizes the schedule. For smaller instances, we verify the simulation model as well as compare our search heuristics’ performance with optimal schedules obtained using a Markov reward process. In addition we present computational results from the case study in the Dutch hospital. These results show that a considerable decrease of waiting time is possible for scheduled patients, while still treating unscheduled patients on time

Emergency Operating Room or Not?

Hospital operating theaters often face the problem of unscheduled emergency arrivals that should be treated as soon as possible. In practice different policies are used to allocate these emergency patients to the operating rooms. These policies are (1) keeping operating rooms empty and available for emergency arrivals; (2) treating emergency patients in elective operating rooms, postponing elective patients; and (3) a mix of these two policies. The use of a specific policy affects performance (e.g., utilization, waiting times, overtime). Currently, these effects are not clear, and there is no agreement on what works ‘best’ for a specific hospital. Using discrete-event simulation, we evaluate the policies for many case characteristics such as hospital size, patient case mix, and fraction of (emergency) patients. We gathered the simulation results in a tool called OR analyzer. This tool is made available online and allows healthcare practitioners to gain insight into the effects of the scheduling policies in settings similar to their specific hospital setting. In addition, this tool allows others researching emergency scheduling policies to frame their hospital settings and compare results