Modeling, Computation, Nonlinearity, Randomness and Waves Seminar

The explosion of patient data is changing the way and the extent to which healthcare organizations capture data, analyze information, and make decisions. In this study, we develop an adaptive capacity planning model for ambulatory surgery centers (ASCs) that coordinate many activities connected closely, posing a significant challenge for delivering a smooth patient flow. When scheduling surgeries, ASCs face a trade-off between the need to be responsive to patients' demand and the need to efficiently schedule surgeries to maximize capacity utilization. Based on actual patient flow data, we first propose an approach to classify patients into a smaller number of groups using descriptive analytics, which significantly reduces the complexity of the capacity planning problem and improves the model’s practicality. Next, we develop several new mathematical formulations as prescriptive analytics models that derive capacity decisions. An extensive computational study demonstrates the effectiveness of our approach and provides implications under the uncertain nature of several business parameters. Considering more than 5,000 ASCs in the U.S. performing 23 million surgeries annually, we hope this study guides practitioners to make appropriate investments that will improve ASC operations via capacity adjustment and patient scheduling.

Bio: Seokjun Youn joined the Eller MIS Department this Fall 2019 as an assistant professor after earning his PhD in Operations and Supply Chain Management from Texas A&M University. His research to date focuses on healthcare operations and supply chain management. Specifically, he studies healthcare payment models, capacity planning and scheduling in healthcare, and logistics optimization for food safety. He holds a master’s degree from Texas A&M University and a bachelor’s degree from Seoul National University, both in Industrial Engineering. Prior to living in the U.S., Seokjun worked as an Air Force officer from South Korea.