Decision Model for Allocation of Intensive Care Unit Beds for Suspected COVID-19 Patients under Scarce Resources

Abstract

This paper puts forward a decision model for allocation of intensive care unit (ICU) beds under scarce resources in healthcare systems during the COVID-19 pandemic. The model is built upon a portfolio selection approach under the concepts of the Utility Theory. A binary integer optimization model is developed in order to find the best allocation for ICU beds, considering candidate patients with suspected/confirmed COVID-19. Experts’ subjective knowledge and prior probabilities are considered to estimate the input data for the proposed model, considering the particular aspects of the decision problem. Since the chances of survival of patients in several scenarios may not be precisely defined due to the inherent subjectivity of such kinds of information, the proposed model works based on imprecise information provided by users. A Monte-Carlo simulation is performed to build a recommendation, and a robustness index is computed for each alternative according to its performance as evidenced by the results of the simulation.

Linking digital surveillance and in-depth virology to study clinical patterns of viral respiratory infections in vulnerable patient populations

“…In the acute care setting, use of the ViVI ScoreApp can help to grade disease severity in real time if implemented into the medical documentation workflow, thereby aiding physicians to allocate scarce intensive care beds based on evidence and in a transparent way, which has become a key concern during the COVID-19 pandemic ( Frej et al., 2021 ).…”

Solving Patient Allocation Problem during an Epidemic Dengue Fever Outbreak by Mathematical Modelling

“…Recently, the rapid outbreak of the coronavirus has attracted increasing attentions from researchers to apply mathematical techniques for solving resource allocation problems in healthcare system. Different mathematical models are constructed by [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ] to optimally allocate critical hospital supplies to treat COVID-19 patients. For instances, [ 22 , 23 ] considered an optimal allocation of ICU beds (intensive care unit beds) and personal protective equipment, respectively, under the scarcity of available resources.…”

“…Different mathematical models are constructed by [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ] to optimally allocate critical hospital supplies to treat COVID-19 patients. For instances, [ 22 , 23 ] considered an optimal allocation of ICU beds (intensive care unit beds) and personal protective equipment, respectively, under the scarcity of available resources. A binary integer optimization model is developed to find the best allocation for ICU beds, considering candidate patients with suspected/confirmed COVID-19 [ 22 ].…”

“…For instances, [ 22 , 23 ] considered an optimal allocation of ICU beds (intensive care unit beds) and personal protective equipment, respectively, under the scarcity of available resources. A binary integer optimization model is developed to find the best allocation for ICU beds, considering candidate patients with suspected/confirmed COVID-19 [ 22 ]. Santini [ 24 ] proposed an Integer Programming formulation to maximize the number of tests a country can perform and validated their approach on both real-life data from Italy and synthetic instances.…”

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

“…These challenges required quick solutions and responses and presented many opportunities for optimization. Examples include optimizing prediction models to quantify future COVID-19 cases [6] – [24] , optimizing the allocation of critical hospital supplies to treat COVID-19 patients [25] – [32] , and developing decision support tools to guide data-driven mitigation strategies to combat the spread of the virus [33] – [37] . While numerous studies have tried to tackle these challenges, the COVID-19 pandemic continues to expose the flaws and shortcomings of current systemic policies, many of which were unanticipated and unprepared for.…”