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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...
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/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. |
format | Online Article Text |
id | pubmed-10147938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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