Massachusetts General Hospital, Boston, USA.
Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
Sci Data. 2023 Apr 28;10(1):245. doi: 10.1038/s41597-023-02104-3.
Healthcare resources are published annually in repositories such as the AHA Annual Survey Database. 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.
医疗资源每年在 AHA 年度调查数据库等资源库中发布。然而，这些数据资源库是通过繁琐的手动调查技术创建的，并且不如所代表的医院系统网站信息更新频繁。此外，对于患者来说，这种资源也不容易以易于使用的格式获得。网络分析技术有可能创建拓扑图，以帮助患者在寻找医疗服务时找到路径。本研究探讨了四十家美国学术医疗中心网站的拓扑结构。网络分析用于分析和可视化 48686 个网页。检查了网络结构的几个要素，包括基本网络属性和中心性度量分布。使用 Louvain 社区检测算法来检查这些技术在多大程度上允许在网络内识别医疗资源。结果表明，具有相关医疗服务的网站往往会形成可观察到的集群，这些集群对于绘制医院系统内关键资源的地图非常有用。
