An analysis of overlapping appointment scheduling model in an outpatient clinic

This research addresses an overlapping appointment scheduling (OLAS) model to minimize patient waiting time and doctor idle time in an outpatient healthcare clinic when a stochastic service time is considered. In general, outpatient clinics should determine proper appointment schedules for their patients to maximize doctor utilization and patient satisfaction. As a result, the OLAS model has been proposed to find the optimal overlap period between patient appointment and allocated service times. A mathematical model is developed to minimize the total cost of patient waiting and doctor idle time, which has been analyzed with the assumption that the service time is followed by a uniform distribution. In addition, a Monte Carlo simulation model is developed to verify the optimal overlap period driven from the proposed OLAS model and to evaluate the effect of implementing an overlap period in clinics with different service distributions, overtime, and no-shows. The experimental results indicate that the optimal environment to apply an OLAS model is an outpatient clinic with a high no-show rate, long appointment lengths, and a high coefficient of variation. The results indicate that the utilization of overlapping scheduling can lead to a 40%–70% reduction in total costs.