Cope with the COVID-19 pandemic: dynamic bed allocation and patient subsidization in a public healthcare system

Xin Ma, Xue Zhao, Pengfei Guo

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In many countries and territories, public hospitals play a major role in coping with the COVID-19 pandemic. For public hospital managers, on the one hand, they must best utilize their hospital beds to serve the COVID-19 patients immediately. On the other hand, they need to consider the need of bed resources from non-COVID-19 patients, including emergency and elective patients. In this work, we consider two control mechanisms for public hospital managers to maximize the overall utility of patients. One is the dynamic allocation of bed resources according to the evolution process of the COVID-19 pandemic. The other is the usage of a subsidy scheme to move elective patients from the public to private hospitals. We develop a dynamic programming model to study the allocation of isolation and ordinary beds and the effect of the subsidy policy in serving three types of patients, COVID-19, emergency, and elective-care. We first show that the dynamic allocation between isolation and ordinary beds can provide a better utilization of bed resources, by cutting down at least 33.5% of the total cost compared with the static policy (i.e., keeping a fixed number of isolation beds) when facing a medium pandemic alert. Our results further show that subsidizing elective patients and referring them to private hospitals is an efficient way to ease the overcrowded situation in public hospitals. Our results demonstrate that, by dynamically conducting bed allocation and subsidy scheme in different phases of the COVID-19 pandemic, patient overall utility can be greatly improved.

Original languageEnglish
Article number108320
Number of pages14
JournalInternational Journal of Production Economics
Volume243
Issue number108320
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Capacity planning
  • Dynamic allocation
  • Health care
  • Hospital beds
  • Pandemic

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