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

dc.creatorOrtiz Barrios, Miguel Angel
dc.creatorArias Fonseca, Sebastian
dc.creatorIshizaka, Alessio
dc.creatorBarbati, Maria
dc.creatorAvendano-Collante, Betty
dc.creatorNavarro Jiménez, Eduardo
dc.date2023-09-06T14:09:32Z
dc.date2026
dc.date2023-09-06T14:09:32Z
dc.date2023
dc.date.accessioned2023-10-03T19:13:53Z
dc.date.available2023-10-03T19:13:53Z
dc.identifierMiguel Ortiz-Barrios, Sebastián Arias-Fonseca, Alessio Ishizaka, Maria Barbati, Betty Avendaño-Collante, Eduardo Navarro-Jiménez, Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study, Journal of Business Research, Volume 160, 2023, 113806, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2023.113806
dc.identifier0148-2963
dc.identifierhttps://hdl.handle.net/11323/10447
dc.identifier10.1016/j.jbusres.2023.113806
dc.identifier1873-7978
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9169083
dc.descriptionThe 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.
dc.format22 páginas
dc.formatapplication/pdf
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dc.languageeng
dc.publisherElsevier Inc.
dc.publisherUnited States
dc.relationJournal of Business Research
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dc.rights© 2023 Elsevier Inc. All rights reserved.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S0148296323001649
dc.subjectCovid-19
dc.subjectDiscrete-Event Simulation (DES)
dc.subjectArtificial Intelligence (AI)
dc.subjectRandom Forest (RF)
dc.subjectIntensive Care Unit (ICU)
dc.subjectHealthcare
dc.titleArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


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