About
Dr. Yongjia Song is an associate professor in the Department of Industrial Engineering at Clemson University. He received the Ph.D. degree in industrial engineering from University of Wisconsin-Madison in 2013. Prior to that, he got his B.S. degree in computational mathematics from Peking University, China, in 2009, and his M.S. degree in operations research and computer science from University of Wisconsin-Madison in 2012. Dr. Song’s research interests include computational stochastic programming, integer programming, and applications of optimization in transportation and logistics, health care, and power systems. Dr. Song is a recipient of the NSF CAREER Award 2021. His research has been supported by the National Science Foundation, Harvey L. Neiman Health Policy Institute, Department of Energy, Office of Naval Research, etc.
Visit Dr. Song's Department Profile.
How their research is transforming health care
Dr. Song’s main research focus in the health research has been on the following three aspects: Health-care delivery, in particular operating room scheduling and patient assignment problems. Her proposed joint operating room scheduling, and patient assignment optimization models can achieve significant cost reduction from the perspectives of both health care providers and patients.
Health care policy making, in particular the multi-agent multi-level decision making models. Her team’s analysis shows how hospital interventions of incentivization, training and nudging affect physician decisions and consequently hospital’s quality metrics. Their findings provide insights for policy makers on the multi-level effects of their policy decisions and provide guidance for hospitals on if and how they should influence physicians.
Clinical trials design for precision medication. The team’s proposed robust clinical trial design incorporates patients’ covariate information and achieves significant variance reduction in treatment effect model parameter estimations compared to traditional randomized designs. Many of my methodological research works on deterministic and stochastic optimization have great potentials to be applied in sequential decision making under uncertainty in health care applications, such as epidemic control, chronic disease monitoring and treatment, health care supply chain design and operations, real-time health care resource allocation, etc.
Health research keywords
Faculty Scholar, Health care delivery; health care policy making; clinical trial design; epidemic control; healthcare supply chain design and operations
