Publikasi Scopus 2024 per tanggal 31 Maret 2024 (233 artikel)

Kekalih A.; Adi N.P.; Soemarko D.S.
Kekalih, Aria (55633562200); Adi, Nuri Purwito (57023786200); Soemarko, Dewi Sumaryani (57192889490)
55633562200; 57023786200; 57192889490
Preparation and Challenges in Developing a Big Data Analysis Framework in Occupational Medicine in Indonesia
2024
Journal of UOEH
46
1
113
118
5
0
Department of Community Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia; Department of Occupational Health Practice and Management, Institute of Industrial and Ecological Sciences, University of Occupational and Environmental Health, Japan
Kekalih A., Department of Community Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia; Adi N.P., Department of Community Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia, Department of Occupational Health Practice and Management, Institute of Industrial and Ecological Sciences, University of Occupational and Environmental Health, Japan; Soemarko D.S., Department of Community Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia
This mini review explores the transformative potential of big data analysis and artificial intelligence (AI) in reforming occupational medicine in Indonesia. Emphasizing the preconditions, case studies, and benefits, it underscores the role of big data in enhancing worker well-being. The review highlights the importance of informative health big data, especially in high-risk industries, with examples of case studies of AI implementation in occupational medicine during the COVID-19 pandemic and other relevant scenarios. While acknowledging the challenges of AI implementation, the essay identifies the role of academic and professional organizations as pioneers in big data utilization. Six potential benefits that are identified, including improved patient care and efficient resource allocation, demonstrate the transformative impact of big data analysis. The proposed pathway of preparation underscores the need for awareness, skill enhancement, and collaboration, addressing challenges in data management and stakeholder engagement. The conclusion emphasizes continuous assessment, feasibility studies, and commitment as essential steps in advancing occupational medicine through big data analysis.
big data analysis; indonesia; occupational medicine
Artificial Intelligence; Big Data; Humans; Indonesia; Occupational Medicine; Pandemics; artificial intelligence; big data; human; Indonesia; occupational medicine; pandemic
0387821X
38479865
Review
Q3
302
15069