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Preparation and Challenges in Developing a Big Data Analysis Framework in Occupational Medicine in Indonesia.
Kekalih, Aria; Adi, Nuri Purwito; Soemarko, Dewi Sumaryani.
Afiliação
  • Kekalih A; Department of Community Medicine, Faculty of Medicine, Universitas Indonesia.
  • Adi NP; Department of Community Medicine, Faculty of Medicine, Universitas Indonesia.
  • Soemarko DS; Department of Occupational Health Practice and Management, Institute of Industrial and Ecological Sciences, University of Occupational and Environmental Health, Japan.
J UOEH ; 46(1): 113-118, 2024.
Article em En | MEDLINE | ID: mdl-38479865
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina do Trabalho Limite: Humans País/Região como assunto: Asia Idioma: En Revista: J UOEH Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina do Trabalho Limite: Humans País/Região como assunto: Asia Idioma: En Revista: J UOEH Ano de publicação: 2024 Tipo de documento: Article