| Resumo: |
This doctoral research aims to develop a governance model for digital health research data to enhance the quality of collected data and translate research outcomes into an integrated framework for planning, implementing, and evaluating public health initiatives. Employing a mixed-methods approach, the study combines qualitative and quantitative methods, qualifying as an exploratory and explanatory study. The specific objectives include proposing the adoption of frameworks and tools to promote a data quality model for scientific research in digital health, developing a tool with data collection, management, and evaluation, and proposing an implementation research model based on digital health principles and guidelines. The work is grounded in FAIR principles and the RE-AIM framework, addressing data quality and governance in health research, focusing on tuberculosis (TB) and rare diseases (RD) in the Brazilian context. The research significantly contributes to the understanding of digital data governance in health, highlighting the importance of standardized practices and collaborative efforts to improve the quality of health research data in Brazil. Challenges addressed include integrating diverse data sources, ensuring security and confidentiality of data, while enhancing public health outcomes through digital innovations. The presented model emphasizes data quality optimization, being fundamental for planning, executing, and evaluating health interventions. The research operates under robust computational research structures and health information systems, ensuring transparent data management. The interdisciplinary nature of the research highlights its significant strength, integrating insights from data science, public health, and health policy, offering a holistic view of the challenges and opportunities in digital health. By creating a unified implementation quality manual, the study provides mechanisms for network collaboration, strengthening robust data management, and promoting a culture of shared practices essential for elevating the overall quality of data in health research. |
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