Preparation and Challenges in Developing a Big Data Analysis Framework in Occupational Medicine in Indonesia
Aria Kekalih1, Nuri Purwito Adi1,2, Dewi Sumaryani Soemarko1
1Department of Community Medicine, Faculty of Medicine, Universitas Indonesia.
Journal of UOEH
|March 13, 2024
Summary
Big data analysis and artificial intelligence (AI) can revolutionize occupational medicine in Indonesia by improving worker well-being and healthcare efficiency. This transformation requires preparation, collaboration, and addressing data challenges for successful implementation.
Area of Science:
- Occupational Medicine
- Health Informatics
- Artificial Intelligence
Background:
- Reforming occupational medicine in Indonesia necessitates leveraging advanced data analytics.
- Worker well-being is paramount, especially in high-risk industries, requiring innovative health data solutions.
Approach:
- This mini-review explores the preconditions, case studies, and benefits of big data and AI in Indonesian occupational medicine.
- Examines AI implementation in occupational health during the COVID-19 pandemic and other scenarios.
Key Points:
- Big data analysis and AI offer six key benefits, including enhanced patient care and optimized resource allocation.
- Academic and professional organizations are crucial pioneers in adopting big data for occupational health.
- Challenges in AI implementation include data management and stakeholder engagement.
Conclusions:
- Advancing occupational medicine in Indonesia requires continuous assessment, feasibility studies, and a commitment to big data utilization.
- A pathway for preparation involves enhancing awareness, skills, and fostering collaboration among stakeholders.
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