Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

Miguel Ortiz-Barrios, Sebastián Arias-Fonseca, Alessio Ishizaka, Maria Barbati, Betty Avendaño-Collante and Eduardo Navarro-Jiménez

Journal of Business Research, 2023, vol. 160, issue C

Abstract: The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Keywords: Covid-19; Discrete-Event Simulation (DES); Artificial Intelligence (AI); Random Forest (RF); Intensive Care Unit (ICU); Healthcare (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:160:y:2023:i:c:s0148296323001649

DOI: 10.1016/j.jbusres.2023.113806

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