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新冠疫情期间台北地区紧急医疗系统的激增能力:一种系统动力学方法

Surge Capacity of Taipei's Regional Emergency Medical System during COVID-19: A System Dynamics Approach.

作者信息

Chen Chih Chang, Hung Su Ying

机构信息

Department of Marketing Management, Takming University of Science and Technology, Taipei 11451, Taiwan.

Administrative Center, Taipei Hospital, Ministry of Health and Welfare, New Taipei City 24213, Taiwan.

出版信息

Emerg Med Int. 2024 Mar 14;2024:5524382. doi: 10.1155/2024/5524382. eCollection 2024.

DOI:10.1155/2024/5524382
PMID:38516360
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10957250/
Abstract

BACKGROUND

The community transmission of COVID-19 has caused the breakdown of the regional emergency medical system (REMS), impacting the rights and care of regional patients with acute and severe conditions. This study proposes a model for the surge capacity of REMS to plan for readiness and preparedness during challenging events that overload capacity.

METHODS

The surge capacity of REMS during the COVID-19 pandemic was studied. The data collection included 26 hospitals that received the data. To simulate the dynamics of Taipei's REMS surge capacity, we observed its ability to treat COVID-19 patients with moderate to severe acute respiratory distress syndrome (ARDS). This will involve monitoring the stock of ventilators, physicians, and nurses within the subsystem loops.

RESULTS

Healthcare managers and administrators can use the overload model and hypothetical scenarios to develop new scenarios with different demands on surge capacity. The REMS system capacity model can be used as an aid to guide planning and cross-checking for address Prepare to plan.

CONCLUSIONS

We combined data regarding the availability of ventilators, physicians, nurses, specialized beds, and general acute care beds in our simulations. Thus, our simulations, with support from a well-established regional command and management structure, could help REMS achieve the optimal surge capacity.

摘要

背景

新型冠状病毒肺炎(COVID-19)的社区传播导致区域应急医疗系统(REMS)崩溃,影响了患有急重症的区域患者的权益和救治。本研究提出了一种区域应急医疗系统的激增能力模型,以便在能力过载的挑战性事件期间规划准备工作。

方法

研究了COVID-19大流行期间区域应急医疗系统的激增能力。数据收集包括26家接收数据的医院。为了模拟台北区域应急医疗系统激增能力的动态变化,我们观察了其治疗中度至重度急性呼吸窘迫综合征(ARDS)的COVID-19患者的能力。这将涉及监测子系统回路内呼吸机、医生和护士的储备情况。

结果

医疗保健管理人员和行政人员可以使用过载模型和假设情景来制定对激增能力有不同需求的新情景。区域应急医疗系统容量模型可作为一种辅助工具,用于指导规划和交叉核对,以进行预案规划。

结论

我们在模拟中综合了有关呼吸机、医生、护士、专科病床和普通急性护理病床可用性的数据。因此,在完善的区域指挥和管理结构的支持下,我们的模拟可以帮助区域应急医疗系统实现最佳激增能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/1ab98df0c410/EMI2024-5524382.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/df5772107e56/EMI2024-5524382.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/52cf020426fb/EMI2024-5524382.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/1ab98df0c410/EMI2024-5524382.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/df5772107e56/EMI2024-5524382.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/52cf020426fb/EMI2024-5524382.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315d/10957250/1ab98df0c410/EMI2024-5524382.003.jpg

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