Professor Argon Quoted in Scientific AmericanMarch 23, 2020
Research by Professors Nilay and Ziya, in collaboration with Dr. Huiyin Argon, a 2016 graduate of STOR, now an Assistant Professor at Hong Kong University's Business School, published in Informs under the title "Allocation of Intensive Care Unit Beds in Periods of High Demand," was quoted by Scientific American. The journal interviewed Professor Argon for an article on triaging intensive care patients.
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.
The abstract to the original paper by Ouyang, Argon, and Ziya, reads as follows:
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.
You can find the full text to the paper here