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Social network analysis of nationwide interhospital emergency department transfers in Taiwan
Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key networ...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909649/ https://www.ncbi.nlm.nih.gov/pubmed/36759680 http://dx.doi.org/10.1038/s41598-023-29554-4 |
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author | Tsai, Chu-Lin Cheng, Ming-Tai Hsu, Shu-Hsien Lu, Tsung-Chien Huang, Chien-Hua Liu, Yueh-Ping Shih, Chung-Liang Fang, Cheng-Chung |
author_facet | Tsai, Chu-Lin Cheng, Ming-Tai Hsu, Shu-Hsien Lu, Tsung-Chien Huang, Chien-Hua Liu, Yueh-Ping Shih, Chung-Liang Fang, Cheng-Chung |
author_sort | Tsai, Chu-Lin |
collection | PubMed |
description | Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key network metrics for monitoring network functions. This was a retrospective cohort study using the National Electronic Referral System (NERS) database in Taiwan. All interhospital ED transfers from 2014 to 2016 were included and transfer characteristics were retrieved. Descriptive statistics and social network analysis were used to analyze the data. There were a total of 218,760 ED transfers during the 3-year study period. In the network analysis, there were a total of 199 EDs with 9516 transfer ties between EDs. The network demonstrated a multiple hub-and-spoke, regionalized pattern, with low global density (0.24), moderate centralization (0.57), and moderately high clustering of EDs (0.63). At the ED level, most transfers were one-way, with low reciprocity (0.21). Sending hospitals had a median of 5 transfer-out partners [interquartile range (IQR) 3–7), while receiving hospitals a median of 2 (IQR 1–6) transfer-in partners. A total of 16 receiving hospitals, all of which were designated base or co-base hospitals, had 15 or more transfer-in partners. Social network analysis of transfer patterns between hospitals confirmed that the network structure largely aligned with the planned regionalized transfer network in Taiwan. Understanding the network metrics helps track the structure and process aspects of regionalized care. |
format | Online Article Text |
id | pubmed-9909649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99096492023-02-09 Social network analysis of nationwide interhospital emergency department transfers in Taiwan Tsai, Chu-Lin Cheng, Ming-Tai Hsu, Shu-Hsien Lu, Tsung-Chien Huang, Chien-Hua Liu, Yueh-Ping Shih, Chung-Liang Fang, Cheng-Chung Sci Rep Article Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key network metrics for monitoring network functions. This was a retrospective cohort study using the National Electronic Referral System (NERS) database in Taiwan. All interhospital ED transfers from 2014 to 2016 were included and transfer characteristics were retrieved. Descriptive statistics and social network analysis were used to analyze the data. There were a total of 218,760 ED transfers during the 3-year study period. In the network analysis, there were a total of 199 EDs with 9516 transfer ties between EDs. The network demonstrated a multiple hub-and-spoke, regionalized pattern, with low global density (0.24), moderate centralization (0.57), and moderately high clustering of EDs (0.63). At the ED level, most transfers were one-way, with low reciprocity (0.21). Sending hospitals had a median of 5 transfer-out partners [interquartile range (IQR) 3–7), while receiving hospitals a median of 2 (IQR 1–6) transfer-in partners. A total of 16 receiving hospitals, all of which were designated base or co-base hospitals, had 15 or more transfer-in partners. Social network analysis of transfer patterns between hospitals confirmed that the network structure largely aligned with the planned regionalized transfer network in Taiwan. Understanding the network metrics helps track the structure and process aspects of regionalized care. Nature Publishing Group UK 2023-02-09 /pmc/articles/PMC9909649/ /pubmed/36759680 http://dx.doi.org/10.1038/s41598-023-29554-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tsai, Chu-Lin Cheng, Ming-Tai Hsu, Shu-Hsien Lu, Tsung-Chien Huang, Chien-Hua Liu, Yueh-Ping Shih, Chung-Liang Fang, Cheng-Chung Social network analysis of nationwide interhospital emergency department transfers in Taiwan |
title | Social network analysis of nationwide interhospital emergency department transfers in Taiwan |
title_full | Social network analysis of nationwide interhospital emergency department transfers in Taiwan |
title_fullStr | Social network analysis of nationwide interhospital emergency department transfers in Taiwan |
title_full_unstemmed | Social network analysis of nationwide interhospital emergency department transfers in Taiwan |
title_short | Social network analysis of nationwide interhospital emergency department transfers in Taiwan |
title_sort | social network analysis of nationwide interhospital emergency department transfers in taiwan |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909649/ https://www.ncbi.nlm.nih.gov/pubmed/36759680 http://dx.doi.org/10.1038/s41598-023-29554-4 |
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