Appointment scheduling at outpatient clinics using two-stage stochastic programming approach

Abstract

Clinics with a large volume of patients are often burdened with limited resources such as nurses and providers. Also, an efficient health system seeks short wait times for the patients to see the provider (indirect wait time) and within the clinic (direct wait time) during the day of the appointment. Additionally, the appointment duration, volume of patients, no-show behavior are uncertain. The direct and indirect wait times, stochastic parameters, rising treatment costs, and increased demand of patients motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop a two-stage stochastic mixed-integer linear programming model (SMILP) integrated with a simulation model to generate a scheduling template for the providers to schedule individual patient appointments and resources. The model minimizes the expected wait times for the patients with a fair and equitable utilization of the resources. Computational experiments were conducted using a data-driven simulation model, and the results indicate that the proposed approach can significantly decrease patients' direct and indirect wait times when compared to a deterministic indexing policy used for scheduling appointments.