[Time] 16:00-17:00, March 28 (Saturday), 2020 

[Location] Online

Lotus Pond Rain Classroom: W141WB

ZOOM ID: 624 417 188

Tecent ID：205 645 630

[Speaker]  Dr. Huiyin Ouyang

[Host] Dr. Fang He

[Title] Allocation of Intensive Care Unit Beds in Periods of High Demand

Note: We will register the seminar for the attendees by the record in Lotus Pond Rain Classroom.

[Abstract] We used mathematical modelling and computer simulations to investigate how ICU beds should be prioritized in cases of high-patient demand such as an influenza epidemic to achieve the greatest good for the greatest number. 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.

[Bio] Dr. Huiyin Ouyang is an assistant professor in the Faculty of Business and Economics at the University of Hong Kong. She received her Ph.D. degree in Statistics and Operations Research from the University of North Carolina Chapel Hill, and her master and bachelor degree from Tsinghua University. Before joining the University of Hong Kong, she was a postdoctoral fellow in the Department of Industrial Engineering and Management Sciences, Northwestern University.Dr. Ouyang is interested in the stochastic modeling, data-driven decision making and simulation analytics with applications in service and health care operations. 

All interested are welcome!