Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 28;10(1):245.
doi: 10.1038/s41597-023-02104-3.

Network Analysis of Academic Medical Center Websites in the United States

Shuhan He #  1 , David Chen #  2 , Kameron Collin Black  3 , Paul Chong  4 , Sammer Marzouk  5 , Byung-Jun Yoon  6   7 , Kendrick Davis  8 , Jarone Lee  1
Affiliations

Network Analysis of Academic Medical Center Websites in the United States

Shuhan He et al. Sci Data. .

Abstract

Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. 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.

PubMed Disclaimer

Conflict of interest statement

SH has the following interests to declare (below); otherwise, the authors have no competing interests to declare. SH: Unrelated: Advisory Board: Covid Act Now, Safeter.App. Co-Founder: Co-Founder, Executive Board ConductScience Inc. Committees: American College of Emergency Physician Supply Chain Task Force. Research Funding: Foundation for Opioid Response Efforts (FORE). Personal Fees: Withings Inc, Boston Globe, American College of Emergency Physicians, Maze Eng Inc, ConductScience Inc, Curative Medical Associates, VIO Med Spa New England. No other disclosures are reported. SH is a volunteer at Emojination.

Figures

Fig. 1
Fig. 1
Methodology for data crawling via Screaming Frog web crawler and subsequent visualization and analysis of data via Gephi and NetworkX. The final step of data visualization/analysis included a cleaning step ridding data of URLs containing “.css” and “.js” addresses.
Fig. 2
Fig. 2
Bar graph of network-wide metric values for 40 academic medical center website network graphs. Eleven metrics are shown, including total number of nodes, total number of edges, average degree, density, diameter, average shortest path length, transitivity, average clustering, assortativity, modularity, and number of communities.
Fig. 3
Fig. 3
Two-dimensional uniform manifold approximation and projection plot of the eleven network-wide metrics for 40 academic medical center websites. Website networks with a more similar set of metrics values will cluster more closely.
Fig. 4
Fig. 4
Comparison of the network graph visualization of the a) number 1-ranked academic medical center website (University of Utah Health Sciences Center; 0.495) and b) number 40-ranked academic medical center website (The University of Iowa; 0.153) based on the Importance Index. Nodes in the network are colored based on their degree, where red-colored nodes have a greater number of edges that connect to other nodes and tend to be centrally located relative to black-colored smaller sized nodes with a fewer number of connected edges.
Fig. 5
Fig. 5
Methodology for identifying academic medical centers, compiling data for their respective websites via Screaming Frog web crawler, then visualization/analysis of data via Gephi and NetworkX. The data set consists of any website that could be crawled by a crawl depth of at least 3 or more.
Fig. 6
Fig. 6
Network graph visualization of the University of Louisville academic medical center website. Community detection using Louvain clustering identified eight communities based on modularity.
See this image and copyright information in PMC

References

    1. Chiesi, A. M. Network analysis. International Encyclopedia of the Social & Behavioral Sciences. 10499–10502. 10.1016/b0-08-043076-7/04211-x (2001).
    1. Barabási AL, Albert R. Emergence of scaling in random networks. Science. 2001;286:509–512. doi: 10.1126/science.286.5439.509. - DOI - PubMed
    1. Kit Delgado, M. et al. National survey of preventive health services in US emergency departments. Annals of Emergency Medicine, American College of Emergency Physicians. 10.1016/j.annemergmed.2010.07.015 (2010). - PMC - PubMed
    1. Baggio, R., Antonioli Corigliano, M. On the importance of hyperlinks: A network science approach. Höpken, W., Gretzel, U., Law, R. Information and Communication Technologies in Tourism 2009. 309–318. 10.1007/978-3-211-93971-0_26 (2009).
    1. Raisi H, Baggio R, Barratt-Pugh L, Willson G. Hyperlink network analysis of a tourism destination. Journal of Travel Research. 2018;57:671–686. doi: 10.1177/0047287517708256. - DOI