Data-Driven Occupational Health: Analytical Approaches to Risk Management

Abstract

This systematic review aims to examine how the application of data analysis methods, particularly artificial intelligence algorithms, contributes to identifying patterns related to occupational health disorders and supporting strategic decision making in people management. The search was conducted in the BVS and PubMed databases, covering publications from 2020 to 2025, following the PRISMA 2020 guidelines, and resulting in 12 studies included in the final sample. The findings show that machine learning forms the core methodological framework of the analyzed studies, reinforcing its relevance in the field of occupational health. The primary studies demonstrated that techniques such as neural networks, fuzzy logic, and evolutionary algorithms make it possible to anticipate occupational risks and diagnose work-related diseases. These approaches are complemented by data-collection technologies, including wearable and fixed sensors used to monitor postural, biomechanical, and movement patterns in real time, which enhances the quality and diversity of data feeding the models. The analysis also identified methodological advances, with emphasis on predictive models and hybrid architectures, while highlighting gaps related to model standardization, multicenter validation, and integration into organizational systems. The use of artificial intelligence strengthens predictive capacity, supports people management, and informs preventive policies, promoting safer and more sustainable work environments. However, the consolidation of this approach requires system interoperability, organizational integration, and the training of managers in data analysis and digital ethics to ensure reproducibility, transparency, and practical applicability of these models in occupational settings.