Desenvolvimento de um framework para estruturação de dados voltado à transformação digital na saúde ocupacional

Abstract

The increasing complexity of industrial organizations and the need for faster and more evidence-based decision-making have made digital transformation a strategic directive, including in the fields of Occupational Health and Safety (OHS). This dissertation proposed the development of a framework for data structuring aimed at supporting digital transformation in occupational health. The research was conducted using a modeling and simulation approach, which enabled the identification of informational bottlenecks and the construction of customized solutions aligned with the company's operational reality. Three digital tools were developed: (i) a software application for occupational anamnesis, featuring a local database structure and automated report generation; (ii) a digital system for clinical and occupational records, with structured data export for managerial support; and (iii) a psychosocial risk assessment tool applying artificial intelligence algorithms (Random Forest, SVC, KNN, and neural networks), allowing sector-specific psychosocial risk analysis based on simulated and scalable data. All solutions were designed with a focus on usability, protection of sensitive data (in compliance with the Brazilian General Data Protection Law - LGPD), and future integration with corporate systems.The technical contribution of this research also includes the structured generation of 21 proactive indicators, organized into three decision-making levels: operational (daily monitoring), tactical (planning of corrective actions), and strategic (support for the formulation of organizational health policies). These indicators reflect the evolution of data maturity in the occupational environment and support an evidence-based management approach. As a methodological contribution, this study advocates for the customization of psychosocial risk assessment instruments, as opposed to widely recommended standardized models, which often lack adaptability to specific organizational contexts. The results demonstrate that the proposed framework promotes significant improvements in information organization, traceability, predictive analysis, and decision-making support in OHS. It represents a replicable, scalable, and technically feasible solution for industries seeking to align their occupational health processes with the principles of Industry 4.0. Thus, the research contributes to the field of Production Engineering by integrating digital transformation principles with occupational health management practices. [resumo fornecido pelo autor]