Analyzing and Visualizing Knowledge Structures of Health Informatics from 1974 to 2018: A Bibliometric and Social Network Analysis
- Author:
Tahereh SAHEB
1
;
Mohammad SAHEB
Author Information
- Publication Type:Review
- Keywords: Medical Informatics; Data Mining; Algorithms; Machine Learning; Publications
- MeSH: Data Mining; Delivery of Health Care; Humans; Informatics; Internet; Learning; Machine Learning; Medical Informatics; Patient Safety; Quality of Health Care
- From:Healthcare Informatics Research 2019;25(2):61-72
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: This paper aims to provide a theoretical clarification of the health informatics field by conducting a quantitative review analysis of the health informatics literature. And this paper aims to map scientific networks; to uncover the explicit and hidden patterns, knowledge structures, and sub-structures in scientific networks; to track the flow and burst of scientific topics; and to discover what effects they have on the scientific growth of health informatics. METHODS: This study was a quantitative literature review of the health informatics field, employing text mining and bibliometric research methods. This paper reviews 30,115 articles with health informatics as their topic, which are indexed in the Web of Science Core Collection Database from 1974 to 2018. This study analyzed and mapped four networks: author co-citation network, co-occurring author keywords and keywords plus, co-occurring subject categories, and country co-citation network. We used CiteSpace 5.3 and VOSviewer to analyze data, and we used Gephi 0.9.2 and VOSviewer to visualize the networks. RESULTS: This study found that the three major themes of the literature from 1974 to 2018 were the utilization of computer science in healthcare, the impact of health informatics on patient safety and the quality of healthcare, and decision support systems. The study found that, since 2016, health informatics has entered a new era to provide predictive, preventative, personalized, and participatory healthcare systems. CONCLUSIONS: This study found that the future strands of research may be patient-generated health data, deep learning algorithms, quantified self and self-tracking tools, and Internet of Things based decision support systems.
