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病房和重症监护病房中新冠病毒疾病（COVID-19）床位占用情况的实时预测

Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units.

作者信息

Baas Stef, Dijkstra Sander, Braaksma Aleida, van Rooij Plom, Snijders Fieke J, Tiemessen Lars, Boucherie Richard J

机构信息

Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.

Elisabeth-TweeSteden Ziekenhuis, Tilburg, The Netherlands.

出版信息

Health Care Manag Sci. 2021 Jun;24(2):402-419. doi: 10.1007/s10729-021-09553-5. Epub 2021 Mar 25.

DOI:10.1007/s10729-021-09553-5
PMID:33768389
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7993447/
Abstract

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital's data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital's control centre and is used in several Dutch hospitals during the second COVID-19 peak.

摘要

本文提出了一个数学模型，该模型基于预测的患者流入量、他们在病房和重症监护病房（ICU）的住院时间（LoS）以及病房和ICU之间的患者转移情况，对一家医院病房和ICU收治的COVID-19患者数量进行实时预测。该预测所需的数据直接从医院的数据仓库获取。所得算法在荷兰首个COVID-19高峰期的数据上进行了测试，结果表明预测非常准确。该预测可以在医院的控制中心进行实时可视化，并且在第二个COVID-19高峰期被多家荷兰医院使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/9f70085f0b08/10729_2021_9553_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/72d5b9e38dd1/10729_2021_9553_Fig5_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/9f70085f0b08/10729_2021_9553_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/2ef17544f18a/10729_2021_9553_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/5cdae588ad9c/10729_2021_9553_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/02a406a1ebcb/10729_2021_9553_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/cc3e0a59f178/10729_2021_9553_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/72d5b9e38dd1/10729_2021_9553_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/add45604a551/10729_2021_9553_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/1f6607561163/10729_2021_9553_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d59a/8238716/9f70085f0b08/10729_2021_9553_Fig8_HTML.jpg

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PLoS One. 2021 Jan 13;16(1):e0245272. doi: 10.1371/journal.pone.0245272. eCollection 2021.
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