Rochester, Minnesota




The research of Feifan Wang, Ph.D., is focused on automation in health care and continuous improvement of health care delivery systems. Dr. Wang applies methods, such as operations research, simulation and machine learning, to evaluate performance of complex processes and optimize decision-making. His research is aimed at improvement of system efficiency and care quality. Specifically, Dr. Wang develops mathematical models and software tools to identify bottlenecks in health care delivery and support coordination of patients, doctors, nurses and technicians. His research projects include creating tools for physician, technician, and procedure scheduling and records review for outpatient clinical triage.

Focus areas

  • Personnel scheduling in health care delivery systems. Proper coordination of health care professionals is necessary to deliver quality care, but hospitals are often faced with uncertain demand due to unknown case numbers, varying case duration and patient no-shows. Dr. Wang is studying a personnel scheduling method with the aim of maximizing health care service capacity and health care professional satisfaction.
  • Real-time monitoring and control of smart health care. The objective of this research is to build novel tools for real-time monitoring and control of smart health care technologies. Dr. Wang studies algorithms that use real-time data and support real-time medical decision-making. One application of this research is proton therapy. Dr. Wang is studying a method that controls proton therapy operation according to real-time system state to improve treatment experience without sacrificing proton therapy system throughput.
  • Outpatient clinical triage. The aim of this research is to create a triage system that accounts for patient medical records to triage patient referrals, thus decreasing human effort spent on record reviewing.

Significance to patient care

Health care delivery is a complex system. Dr. Wang's research provides tools, supported by operations research, simulation and machine learning, to effectively manage such a complex system. Improved system efficiency increases patient access to care. Further, Dr. Wang's research allows health care providers to fully consider each patient's medical requirements when making decisions about the patient's care.

Professional highlights

  • Member, Institute of Electrical and Electronics Engineers (IEEE), 2017-present
  • Member, Institute for Operations Research and the Management Sciences, 2015-present
  • Best Design and Manufacturing Paper Award, IISE Transactions Journal, Institute of Industrial and Systems Engineers, 2021
  • Best Healthcare Automation Paper Award finalist, 17th International Conference on Automation Science and Engineering, IEEE, 2021
  • Best Student Paper Award, 15th International Conference on Automation Science and Engineering, IEEE, 2019


Academic Rank

  1. Assistant Professor of Health Care Systems Engineering


  1. Ph.D. - Industrial Engineering Arizona State University
  2. MS - Industrial Engineering Zhejiang University
  3. BS - Industrial Engineering Zhejiang University of Technology

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