A distributionally robust optimization approach for coordinating clinical and surgical appointments

Abstract

In this article, we address a two-stage scheduling problem that requires coordination between clinical and surgical appointments for specialized surgeries. First, patients have a clinical appointment with a surgeon to determine whether they are an appropriate candidate for the surgical procedure. Subsequently, if the decision to pursue the surgery is made the patient undergoes the procedure on a later date. However, the scheduling process aims to book both the clinical and surgical appointments for a patient at the time of the initial appointment request. Two sources of uncertainty make this scheduling process challenging: (i) the patient may or may not need surgery after the clinical appointment and (ii) the surgery duration for each patient and procedure is unknown. We present a Distributionally Robust Optimization (DRO) approach for coordinating clinical and surgical appointments under these uncertainties. A case study of the Transcatheter Aortic Valve Replacement procedure at Mayo Clinic, Rochester, MN is presented. Numerical results include comparisons with the current practice and four heuristic scheduling policies from the literature. Results show that the DRO-based scheduling policies lead to lower total surgeon idle-time and overtime per day. The proposed policies also restrict the under and over utilization of clinical capacity.

Keywords:

• OR in health services
• appointment scheduling
• surgery scheduling
• distributionally robust optimization
• coordinated care

Additional information

Notes on contributors

Ankit Bansal

Ankit Bansal obtained his PhD in industrial engineering from North Carolina State University. He is currently working as a postdoctoral associate at the Institute for Mathematics and its Applications, University of Minnesota. His research focuses on solving complex scheduling and resource allocation problems encountered within healthcare delivery and production systems.

Bjorn Berg

Bjorn Berg is an assistant professor in the Division of Health Policy and Management in the School of Public Health at the University of Minnesota. His research interests include optimization, stochastic modeling, scheduling, and healthcare operations management. He received a PhD in industrial engineering from North Carolina State University and a BA in mathematics from St. Olaf College.

Yu-Li Huang

Yu-Li Huang is an assistant professor of health care systems engineering for the College of Medicine at Mayo Clinic. He received his PhD, MSE, and BSE in industrial and operations engineering from the University of Michigan. His role and research interests with Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery at Mayo Clinic focuses on practice process improvement applying operations research, data science, and system engineering principles.