EXPLORING THE RELATIONSHIP BETWEEN MEDICAL

ORGANIZATION INNOVATION AND OTHER MEDICAL

SUBJECTS USING SOCIAL NETWORK ANALYSIS

Tsai­Lin Huang

Department of Resources Engineering,National Cheng Kung University, Taiwan

Chia Nan University of Pharmacy and Science Department,

Hospital and Health Care Administration, Taiwan

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Jung­Hua Wu*

Department of Resources Engineering,National Cheng Kung University, Taiwan

*Corresponding Author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Tsair­Wei Chien

Chia Nan University of Pharmacy and Science Department,Hospital and Health Care Administration, Taiwan

Research Department of Chi­Mei Medical Center, Taiwan

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Abstract

The study aimed to investigate the current trend of research on organizational innovation to uncover topics and citations among MeSH clusters. We selected 3,751 abstracts, author names, countries, and MeSH terms on January 2, 2019, from Pubmed Central (PMC) based on the topic of organizational innovation from 2013 to 2017. We proposed a novel h-plus index that can effectively complement and efficiently improve the h-index for calculating the MeSH-type bibliometric indices in PMC. Four topics were addressed: (1) which dominant nations were in the field; (2) which MeSH terms were cited the most by papers in PMC; (3) is there any difference in indices among MeSH clusters; (4) which article was cited the most in the past. We programmed Microsoft Excel VBA routines to extract data. Google Maps and Pajek software were used for graphical representations. Social network analysis (SNA) has been widely used for visualizing author collaboration characteristics in academics. However, there is no such result of the subject research (i.e., medical subject headings, MeSH terms) in the literature on SNA. This study used the SNA and the results reveal that (1) the dominant nations that conducted research on organizational innovation were the USA, the UK, and Canada; (2) the MeSH terms of caregivers gained the highest in h-plus index (6.15) and impact factor (IF) (3.19), respectively; (3) significant differences are found in indices (p<0.05) among MeSH clusters; (4) the article (PMID= 23414420) published in 2013 was the most cited one (149 times). The MeSH weighted scheme and h-plus index can be applied to academics for computing MeSH citations or even authors in the future.

Keywords: H-plus index, Medical subject headings, Social network analysis, Anesthesiology, Bibliometric analysis 

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