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

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.

The role of resource orchestration in humanitarian operations: a COVID-19 case in the US healthcare

“…This may mean that the learning gained in the previous two waves shifted the focus into the treatment rather than being influenced by the change in Covid deaths. Our paper compliments recent research on dynamic resource allocation of resources during the COVID-19 pandemic (Ma et al, 2021). Our aim was not to offer a dynamic programming model to study the allocation of isolation and ordinary beds for patients, COVID-19, emergency, and elective care, but to illustrate how resources can be 'structured', 'bundled' and 'leveraged' to ensure resource allocation decisions and fair deployment of resources during the pandemic.…”

“…Scholars (Behl and Dutta, 2018;Chiapetta Jabbour et al, 2019;Craighead et al, 2020) suggest that there is limited literature in terms of (i) how public operations and supply chains could be involved and organised so as to support the preparation and prevention of disasters; (ii) what kind of resources need to be (re-) configured to deal with different kinds and pace of disasters; and (iii) the utilisation of organisation theories in the area of humanitarian logistics and supply chain management. In a recent study, Ma et al (2021) proposed a dynamic programming model that allocates hospital beds in three types of patients, that is, COVID-19, emergency, and elective-care. Still, limited studies focus in healthcare context as mathematical modelling or researchers' opinions were the main method of investigation (Chowdhury et al, 2021).…”

A Multiobjective Variable Neighborhood Strategy Adaptive Search to Optimize the Dynamic EMS Location–Allocation Problem

“…The critical issue mentioned during the COVID-19 pandemic [10] was healthcare system management, particularly the resource allocation problem. Ma et al [11] proposed a dynamic programming model to study bed allocation in hospitals for different patient types during the pandemic. Yuk-Chiu Yip [12] presented guidelines for healthcare resource allocation based on an ethical analysis.…”

“…As shown in Table 1, D was set to 5. The initial WP used in this paper was a real number, which was uniformly randomly generated between 0 and 1 using Equation (11).…”

Decision analysis of international joint prevention and control of public health emergencies

“…Kabir et.al., ( 2021 ) proposed an epidemic model related to behavioral dynamics, and found that using bonuses for emergency rescue package at the individual level can reduce infection and improve the success of quarantine policy. Ma et al ( 2022 ) found that the overall utility of patients can be greatly improved by dynamically implementing the bed subsidy plan at different stages of the COVID-19 pandemic. Shen et al ( 2021 ) believed that government subsidies to consumers can promote the development of mask companies and improve social welfare.…”