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. 2012 Mar;18(1):44-56.
doi: 10.4258/hir.2012.18.1.44. Epub 2012 Mar 31.

Application of social network analysis to health care sectors

Hae Lan Jang  1 , Young Sung Lee, Ji-Young An
Affiliations

Application of social network analysis to health care sectors

Hae Lan Jang et al. Healthc Inform Res. 2012 Mar.

Abstract

Objectives: This study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine.

Methods: Papers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals.

Results: The core keyword with the highest centrality value was "Risk Factor," followed by "Molecular Sequence Data," "Neoplasms," "Signal Transduction," "Brain," and "Amino Acid Sequence." Core keywords varied between time intervals, changing from "Molecular Sequence Data" to "Risk Factors" over time. "Risk Factors" was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: "Molecular Sequence Data," with a high centrality value, had a decreasing slope at certain intervals, whereas "SNP," with a low centrality value, had an increasing slope at certain intervals.

Conclusions: The social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

Keywords: Bibliometrics; Knowledge Bases; Medical Subject Headings; National Institutes of Health; Periodicals as Topic.

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Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Figure 1
Figure 1
Keywords network of 190 in National Institutes of Health after pruning off 100 degree below (1967-2010, n = 61).
Figure 2
Figure 2
Keywords network in National Institutes of Health After pruning off 25 degree below (before 2000 year, n = 57).
Figure 3
Figure 3
Keywords network in National Institutes of Health after pruning off 28 degree below (2000-2002, n = 60).
Figure 4
Figure 4
Keywords network in National Institutes of Health after pruning off 30 degree below (2003-2005, n = 60).
Figure 5
Figure 5
Keywords network in National Institutes of Health after pruning off 27 degree below (2006-2008, n = 60).
Figure 6
Figure 6
Keywords network in National Institutes of Health after pruning off 27 degree below (2009-2010, n = 53).
See this image and copyright information in PMC

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