  An important challenge confronting healthcare is the effective management of
access to primary care. Robust appointment scheduling policies/templates can
help strike an effective balance between the lead-time to an appointment
(a.k.a. indirect waiting time, measuring the difference between a patient's
desired and actual appointment dates) and waiting times at the clinic on the
day of the appointment (a.k.a. direct waiting time). We propose methods for
identifying effective appointment scheduling templates using a two-stage
stochastic mixed-integer linear program model. The model embeds simulation for
accurate evaluation of direct waiting times and uses sample average
approximation method for computational efficiency. The model accounts for
patients' no-show behaviors, provider availability, overbooking, demand
uncertainty, and overtime constraints. The model allows the scheduling
templates to be potentially updated at regular intervals while minimizing the
patient expected waiting times and balancing provider utilization. Proposed
methods are validated using data from the U.S. Department of Veterans Affairs
(VA) primary care clinics.
