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Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China

INTRODUCTION: In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smo...

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Autores principales: Chen, Minjie, Fan, Yiling, Xu, Qingrong, Huang, Hua, Zheng, Xinyi, Xiao, Dongdong, Fang, Weilin, Qin, Jun, Zheng, Junhua, Dong, Enhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853970/
https://www.ncbi.nlm.nih.gov/pubmed/36684897
http://dx.doi.org/10.3389/fpubh.2022.1019073
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author Chen, Minjie
Fan, Yiling
Xu, Qingrong
Huang, Hua
Zheng, Xinyi
Xiao, Dongdong
Fang, Weilin
Qin, Jun
Zheng, Junhua
Dong, Enhong
author_facet Chen, Minjie
Fan, Yiling
Xu, Qingrong
Huang, Hua
Zheng, Xinyi
Xiao, Dongdong
Fang, Weilin
Qin, Jun
Zheng, Junhua
Dong, Enhong
author_sort Chen, Minjie
collection PubMed
description INTRODUCTION: In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams. METHODS: To solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used. RESULTS: We identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957. CONCLUSION: The medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.
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spelling pubmed-98539702023-01-21 Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China Chen, Minjie Fan, Yiling Xu, Qingrong Huang, Hua Zheng, Xinyi Xiao, Dongdong Fang, Weilin Qin, Jun Zheng, Junhua Dong, Enhong Front Public Health Public Health INTRODUCTION: In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams. METHODS: To solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used. RESULTS: We identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957. CONCLUSION: The medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries. Frontiers Media S.A. 2023-01-06 /pmc/articles/PMC9853970/ /pubmed/36684897 http://dx.doi.org/10.3389/fpubh.2022.1019073 Text en Copyright © 2023 Chen, Fan, Xu, Huang, Zheng, Xiao, Fang, Qin, Zheng and Dong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Chen, Minjie
Fan, Yiling
Xu, Qingrong
Huang, Hua
Zheng, Xinyi
Xiao, Dongdong
Fang, Weilin
Qin, Jun
Zheng, Junhua
Dong, Enhong
Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
title Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
title_full Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
title_fullStr Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
title_full_unstemmed Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
title_short Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China
title_sort medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the covid-19 pandemic: an innovative model response to a public health emergency in shanghai, china
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853970/
https://www.ncbi.nlm.nih.gov/pubmed/36684897
http://dx.doi.org/10.3389/fpubh.2022.1019073
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