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Network Analysis of Academic Medical Center Websites in the United States

Healthcare resources are published annually in repositories such as the AHA Annual Survey Database(TM). However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital syst...

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Autores principales: He, Shuhan, Chen, David, Black, Kameron Collin, Chong, Paul, Marzouk, Sammer, Yoon, Byung-Jun, Davis, Kendrick, Lee, Jarone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147938/
https://www.ncbi.nlm.nih.gov/pubmed/37117246
http://dx.doi.org/10.1038/s41597-023-02104-3
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author He, Shuhan
Chen, David
Black, Kameron Collin
Chong, Paul
Marzouk, Sammer
Yoon, Byung-Jun
Davis, Kendrick
Lee, Jarone
author_facet He, Shuhan
Chen, David
Black, Kameron Collin
Chong, Paul
Marzouk, Sammer
Yoon, Byung-Jun
Davis, Kendrick
Lee, Jarone
author_sort He, Shuhan
collection PubMed
description Healthcare resources are published annually in repositories such as the AHA Annual Survey Database(TM). However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.
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spelling pubmed-101479382023-04-30 Network Analysis of Academic Medical Center Websites in the United States He, Shuhan Chen, David Black, Kameron Collin Chong, Paul Marzouk, Sammer Yoon, Byung-Jun Davis, Kendrick Lee, Jarone Sci Data Data Descriptor Healthcare resources are published annually in repositories such as the AHA Annual Survey Database(TM). However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system. Nature Publishing Group UK 2023-04-28 /pmc/articles/PMC10147938/ /pubmed/37117246 http://dx.doi.org/10.1038/s41597-023-02104-3 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
He, Shuhan
Chen, David
Black, Kameron Collin
Chong, Paul
Marzouk, Sammer
Yoon, Byung-Jun
Davis, Kendrick
Lee, Jarone
Network Analysis of Academic Medical Center Websites in the United States
title Network Analysis of Academic Medical Center Websites in the United States
title_full Network Analysis of Academic Medical Center Websites in the United States
title_fullStr Network Analysis of Academic Medical Center Websites in the United States
title_full_unstemmed Network Analysis of Academic Medical Center Websites in the United States
title_short Network Analysis of Academic Medical Center Websites in the United States
title_sort network analysis of academic medical center websites in the united states
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147938/
https://www.ncbi.nlm.nih.gov/pubmed/37117246
http://dx.doi.org/10.1038/s41597-023-02104-3
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