Ortiz-Barrios Miguel, Arias-Fonseca Sebastián, Ishizaka Alessio, Barbati Maria, Avendaño-Collante Betty, Navarro-Jiménez Eduardo
Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia.
NEOMA Business School, 1 rue du Maréchal Juin, Mont-Saint-Aignan 76130, France.
J Bus Res. 2023 May;160:113806. doi: 10.1016/j.jbusres.2023.113806. Epub 2023 Mar 3.
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.
新冠疫情使重症监护病房（ICU）的运营陷入严重混乱。这种疾病的快速演变、床位容量限制、患者类型的广泛多样以及卫生供应链内部的不平衡，对政策制定者来说仍是一项挑战。本文旨在利用人工智能（AI）和离散事件模拟（DES）来支持新冠疫情期间的ICU床位容量管理。我们在一家西班牙医院连锁机构中对所提出的方法进行了验证，在那里我们首先确定了新冠患者入住ICU的预测因素。其次，我们应用随机森林（RF），利用在急诊科（ED）收集的患者数据来预测入住ICU的可能性。最后，我们将随机森林的结果纳入一个离散事件模拟模型，以协助决策者评估新的ICU床位配置，以应对下游服务预期的患者转移情况。结果表明，干预后床位等待时间中位数减少了32.42至48.03分钟。
