Rochester, Minnesota




Yu-Li Huang, Ph.D., specializes in health care process improvement and operational decision-making using operations research, systems engineering, and data science principles to improve health outcomes and delivery. His research interest focuses on outpatient scheduling redesign and staffing and space utilization optimization to improve patient access to care and treatment quality as well as financial performance. His recent work has combined predictive analytics with simulation and optimization models to maximize real-time scheduling decision-making.

Focus areas

  • Outpatient scheduling systems redesign. Dr. Huang's work in this area focuses on the design of optimal scheduling templates to minimize the cost of patient wait time, provider idle time, overtime and resource allocation. He seeks to develop optimal scheduling policies including overbooking and override, cancellation and urgent care and to account for uncertainty such as patient no-show and appointment mix. Dr. Huang also seeks to determine optimal appointment duration and its associated patient groups according to patient characteristics and conditions. Dr. Huang's efforts led to chemotherapy scheduling template designs for chemotherapy units across Mayo Clinic and Mayo Clinic Health System.
  • Real-time scheduling decision tool. Dr. Huang's work in real-time schedule decision-making focuses on developing a patient call back control system that minimizes treatment resource conflict to improve service quality. Additionally, he develops search algorithms to find the most appropriate providers based on their availability to minimize patient procedure lead time. Example projects include proton beam therapy gatekeeper logics development with simulation validation and a cardiovascular transcatheter aortic valve replacement (TAVR) procedure optimal search algorithm for pairing interventionalist and surgeon.
  • Predictive modeling. Dr. Huang's work focuses on using predictive analytics such as statistical and machine learning methods to inform decisions on staffing, schedule planning, and triage appropriateness for scheduling. This work is often performed as an element of scheduling and staffing optimization. Examples of Dr. Huang's predictive modeling work include cardiovascular ambulatory care nursing staffing needs for clinic and nonclinic work time, bone marrow transplant stem cells collection days prediction for level loading apheresis capacity, Echo lab patient scheduling urgency classification for better scheduling, and pain medication triage decisions for consultation and procedure.
  • Provider time allotment and patient assignment. Dr. Huang's work focuses on developing a provider assignment time allotment tracking algorithm to ensure appropriate fulfillment of employee commitment, and determining optimal provider panel size, patient assignment and care team structure to balance provider workload, reduce provider burnout and improve care continuity. Example projects in this area include cardiovascular medicine providers' time allotment tracker development and implementation, and family medicine panel size and care team redesign.

Significance to patient care

Dr. Huang's work provides evidence-based operation solutions to medical decision-making. His studies transform medical practice to be more efficient and patient-centric while being cost-effective.

Professional highlights

  • Robert D. and Patricia E. Kern Scientific Director for Applied Operations Research, Mayo Clinic, 2023-present.


Administrative Appointment

  1. Senior Associate Consultant II-Research, Health Care Delivery Research, Kern Center for the Science of Health Care Delivery

Academic Rank

  1. Associate Professor of Health Care Systems Engineering


  1. PhD Industrial and Operations Engineering, University of Michigan
  2. MSE Industrial and Operations Engineering, University of Michigan
  3. BSE Industrial and Operations Engineering, University of Michigan

Clinical Studies

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