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Intensive care unit (ICU) beds are valuable resources that are typically in short supply and therefore their effective and efficient use is essential particularly during periods when patient demand is high. In "Allocation of Intensive Care Unit Beds in Periods of High Demand," H. Ouyang, N.T. Argon, and S. Ziya provide insights into what kind of patient prioritization decisions are likely to improve patient health outcomes by analyzing stylized mathematical formulations that capture the fundamental trade-off involved in ICU bed management. They also propose simple policies that are likely to perform well in practice and test them with a simulation study. Findings suggest that even simple policies are likely to bring significant benefits, although more work is needed to investigate whether there could be benefits to using methods that aim to capture patient health condition in a manner that is more precise than assumed in the paper. 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. [ABSTRACT FROM AUTHOR]