Understanding the productive author who published papers in medicine using National Health Insurance Database: A systematic review and meta-analysis. Issue 8 (February 2018)

• Record Type: Journal Article
• Authors: Chien, Tsair-Wei; Chang, Yu; Wang, Hsien-Yi
• Other Names: Sayil. Cigdem section editor.
• Is Part Of: Medicine. Volume 97:Issue 8(2018)
• Publication Date: 2018-02
• Subjects: authorship collaboration -- Google Maps -- Medline library -- National Health Insurance Database -- social network analysis; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000009967
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : 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.