Robust Mask-Based Appointment Scheduling in Primary Care Practices

In most health care systems, a primary care physician (PCP) is both the first instance consulted by patients with medical concerns and the instance coordinating patients' continued access to medical care. Due to the PCP's pivotal role, we address challenges of a high-quality primary care service by interday appointment scheduling on a tactical decision level.

Our study considers three different types of patients, including walk-ins who complicate the PCP's schedule planning as they forgo scheduling an appointment and seek immediate care by walking into the practice without prior notice.

We study appointment scheduling systems based on so-called masks and focus on the balanced workload of the PCP in form of the mask design problem.

To account for different uncertainties in demand for treatment, we extend the mask design problem to the robust mask design and the robust multimask design problem.

For all three problems, we provide a combinatorial interpretation by a network flow and design model. We develop a solution approach that combines binary search with compact formulations (of extensions) of minimum cost flow problems. Finally, we conduct an extensive case study by agent-based simulation in which we evaluate the mask-based appointment scheduling systems and compare them with five appointment scheduling systems from the literature.