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患者感染率对急诊科患者流量的影响:挪威案例的混合模拟研究

The Impact of Patient Infection Rate on Emergency Department Patient Flow: Hybrid Simulation Study in a Norwegian Case.

作者信息

Terning Gaute, El-Thalji Idriss, Brun Eric Christian

机构信息

Department of Safety, Economics, and Planning, University of Stavanger, 4036 Stavanger, Norway.

Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4036 Stavanger, Norway.

出版信息

Healthcare (Basel). 2023 Jun 30;11(13):1904. doi: 10.3390/healthcare11131904.

Abstract

The COVID-19 pandemic put emergency departments all over the world under severe and unprecedented distress. Previous methods of evaluating patient flow impact, such as in-situ simulation, tabletop studies, etc., in a rapidly evolving pandemic are prohibitively impractical, time-consuming, costly, and inflexible. For instance, it is challenging to study the patient flow in the emergency department under different infection rates and get insights using in-situ simulation and tabletop studies. Despite circumventing many of these challenges, the simulation modeling approach and hybrid agent-based modeling stand underutilized. This study investigates the impact of increased patient infection rate on the emergency department patient flow by using a developed hybrid agent-based simulation model. This study reports findings on the patient infection rate in different emergency department patient flow configurations. This study's results quantify and demonstrate that an increase in patient infection rate will lead to an incremental deterioration of the patient flow metrics average length of stay and crowding within the emergency department, especially if the waiting functions are introduced. Along with other findings, it is concluded that waiting functions, including the waiting zone, make the single average length of stay an ineffective measure as it creates a multinomial distribution of several tendencies.

摘要

新冠疫情使全球各地的急诊科陷入了严重且前所未有的困境。在快速演变的疫情中,以往评估患者流量影响的方法,如现场模拟、桌面研究等,极其不切实际、耗时、成本高昂且缺乏灵活性。例如,要在不同感染率下研究急诊科的患者流量,并通过现场模拟和桌面研究得出见解是具有挑战性的。尽管规避了许多此类挑战,但模拟建模方法和基于混合智能体的建模仍未得到充分利用。本研究通过使用已开发的基于混合智能体的模拟模型,调查了患者感染率上升对急诊科患者流量的影响。本研究报告了不同急诊科患者流量配置下的患者感染率调查结果。本研究结果量化并证明,患者感染率的上升将导致患者流量指标(平均住院时间和急诊科拥挤程度)逐渐恶化,尤其是在引入等待功能的情况下。连同其他研究结果一起得出的结论是,包括等候区在内的等待功能使单一平均住院时间成为一项无效指标,因为它产生了多种趋势的多项分布。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbca/10340372/d6aed87a445a/healthcare-11-01904-g001.jpg

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