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在新冠疫情期间使用离散事件模拟平衡稀缺的医院资源

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.

作者信息

Melman G J, Parlikad A K, Cameron E A B

机构信息

Institute for Manufacturing, Department of Engineering, University of Cambridge, 17 Charles Babbage Rd, Cambridge, CB3 0FS, UK.

Modelling Support, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

出版信息

Health Care Manag Sci. 2021 Jun;24(2):356-374. doi: 10.1007/s10729-021-09548-2. Epub 2021 Apr 9.

Abstract

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

摘要

新冠疫情扰乱了医疗保健业务,并导致大量择期手术被取消。世界各地的医院都做出了改变生活的资源分配决策,并将新冠患者的护理置于优先地位。由于缺乏有效的模型来评估涵盖新冠护理和非新冠护理的资源分配策略,医院面临做出次优的本地资源分配决策的风险。本文提出了一种离散事件模拟模型,以描述新冠、择期手术和急诊手术患者的流程。基于英国阿登布鲁克医院475例新冠患者和28831例非新冠患者的数据,构建了特定于新冠的患者流程和手术患者流程网络。该模型能够评估三种资源分配策略,适用于两种新冠疫情波情景:主动取消择期手术、被动取消择期手术以及围堵手术室容量。结果表明,无论新冠情景如何,就总直接死亡人数和手术执行数量而言,围堵策略优于其他策略。然而,这确实是以重症监护拒收率增加50%为代价的。就医院总体表现而言,如果超过7.3%的择期手术可被视为挽救生命的手术,那么优先考虑新冠的被动取消策略就不再有利。此外,该模型展示了及时的医院准备和人员可用性对大流行期间治疗患者能力的影响。该模型可以在大流行和灾难期间帮助世界各地的医院评估其资源分配策略,并确定重新定义患者优先级的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/8238715/45447dd0acc3/10729_2021_9548_Fig1_HTML.jpg

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