Innovation in healthcare is forever changing how we see and experience the medical industry. The environment is offering HKU’s Faculty of Business and Economics (the Faculty) a unique opportunity to be at the forefront of utilising rich data, creating better health outcomes for everyone.
Dr. Huiyin OUYANG
Huiyin Ouyang received her Ph.D. degree in Statistics and Operations Research from the University of North Carolina Chapel Hill. She obtained her master and bachelor degree from Tsinghua University. Before joining the Faculty of Business and Economics (now HKU Business School), The University of Hong Kong, she was a postdoctoral fellow in the Department of Industrial Engineering and Management Sciences, Northwestern University.
- IIMT3636 Decision and Risk Analysis I/ BUSI0036 Quantitative Analysis for Business Decisions I
- MSBA6014 Business Simulation
- Simulation analytics
- Modeling and analysis of stochastic systems
- Design and control of queueing systems
- Health care management and service operations
- Data-driven decision making
- “Allocation of Intensive Care Unit Beds in Periods of High Demand,” (with N.T. Argon and S. Ziya), Operations Research, 2020, 68(2), 591-608.
The objective of this paper is to use mathematical modeling and analysis to develop insights into and policies for making bed allocation decisions in an intensive care unit (ICU) of a hospital during periods when patient demand is high. We first develop a stylized mathematical model in which patients’ health conditions change over time according to a Markov chain. In this model, each patient is in one of two possible health stages, one representing the critical and the other representing the highly critical health stage. The ICU has limited bed availability and therefore when a patient arrives and no beds are available, a decision needs to be made as to whether the patient should be admitted to the ICU and if so, which patient in the ICU should be transferred to the general ward. With the objective of minimizing the long-run average mortality rate, we provide analytical characterizations of the optimal policy under certain conditions. Then, based on these analytical results, we propose heuristic methods, which can be used under assumptions that are more general than what is assumed for the mathematical model. Finally, we demonstrate that the proposed heuristic methods work well by a simulation study, which relaxes some of the restrictive assumptions of the mathematical model by considering a more complex transition structure for patient health and allowing for patients to be possibly queued for admission to the ICU and readmitted from the general ward after they are discharged.