Understanding the productive author who published papers in medicine using National Health Insurance Database

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Abstract

Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear.

By searching the PubMed database ( Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms.

Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40).

SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.

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Most cited references 19

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Co-Authorship Networks in the Digital Library Research Community

Johan Bollen, Herbert Van de Sompel, Michael Nelson … (2005)

The field of digital libraries (DLs) coalesced in 1994: the first digital library conferences were held that year, awareness of the World Wide Web was accelerating, and the National Science Foundation awarded $24 Million (U.S.) for the Digital Library Initiative (DLI). In this paper we examine the state of the DL domain after a decade of activity by applying social network analysis to the co-authorship network of the past ACM, IEEE, and joint ACM/IEEE digital library conferences. We base our analysis on a common binary undirectional network model to represent the co-authorship network, and from it we extract several established network measures. We also introduce a weighted directional network model to represent the co-authorship network, for which we define $AuthorRank$ as an indicator of the impact of an individual author in the network. The results are validated against conference program committee members in the same period. The results show clear advantages of PageRank and AuthorRank over degree, closeness and betweenness centrality metrics. We also investigate the amount and nature of international participation in Joint Conference on Digital Libraries (JCDL).

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Gephi: an open source software for exploring and manipulating networks

M. BASTIAN, S. HEYMANN, M. JACOMY … (2009)

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Applying Social Network Analysis to Understand the Knowledge Sharing Behaviour of Practitioners in a Clinical Online Discussion Forum

Samuel Alan Stewart, Syed Sibte Raza Abidi (2012)

Background Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment—an online discussion forum—for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. Objective The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Methods Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. Results The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional communication, but a dearth of non-nurse participants has been identified as a shortcoming. Conclusions The results of the analysis suggest that the discussion forum is active and healthy, and that, though few, the interprofessional and interinstitutional ties are strong.

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Author and article information

Journal

Journal ID (nlm-ta): Medicine (Baltimore)

Journal ID (iso-abbrev): Medicine (Baltimore)

Journal ID (publisher-id): MEDI

Title: Medicine

Publisher: Wolters Kluwer Health

ISSN (Print): 0025-7974

ISSN (Electronic): 1536-5964

Publication date Collection: February 2018

Publication date (Electronic): 23 February 2018

Volume: 97

Issue: 8

Electronic Location Identifier: e9967

Affiliations

[a ]Medical Research Department, Chi-Mei Medical Center

[b ]Department of Sport Management, College of Leisure and Recreation Management, Chia Nan University of Pharmacy and Science

[c ]National Taiwan University School of Medicine

[d ]Nephrology Department, Chi-Mei Medical Center, Tainan, Taiwan.

Author notes

[∗ ]Correspondence: Hsien-Yi Wang, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kung District, Tainan 710, Taiwan (e-mail: hsienyiwang@ 123456gmail.com ).

Article

Publisher ID: MD-D-17-08172 Accession ID: 09967

DOI: 10.1097/MD.0000000000009967

PMC ID: 5841958

PubMed ID: 29465594

SO-VID: 709e17a3-0712-4ce2-84ad-143ca5ff0164

License:

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

History

Date received : 27 December 2017

Date revision received : 30 January 2018

Date accepted : 31 January 2018

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Keywords: authorship collaboration,google maps,medline library,national health insurance database,social network analysis

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Keywords: authorship collaboration, google maps, medline library, national health insurance database, social network analysis

Understanding the productive author who published papers in medicine using National Health Insurance Database : A systematic review and meta-analysis

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Artificial Intelligence in Medicine

Most cited references 19

Co-Authorship Networks in the Digital Library Research Community

Gephi: an open source software for exploring and manipulating networks

Applying Social Network Analysis to Understand the Knowledge Sharing Behaviour of Practitioners in a Clinical Online Discussion Forum

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