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From Raw Data to FAIR Data: The FAIRification Workflow for Brazilian Tuberculosis Research
Filipe Bernardi1, Vinicius Lima1, Gabriel Sartoretto1
1Ribeirão Preto Medical School, University of São Paulo, Brazil.
Studies in Health Technology and Informatics
|June 30, 2023
Summary
Low quality of tuberculosis data in Brazil hinders research and decision-making. Implementing FAIR data principles could improve data quality and strengthen health information systems, but compliance is currently low.
Area of Science:
- Public Health
- Infectious Disease Epidemiology
- Health Informatics
Background:
- Poor quality, availability, and integration of population health data impede effective decision-making.
- Research using Brazilian tuberculosis data faces significant challenges due to data limitations.
Purpose of the Study:
- To highlight the difficulties in conducting research with tuberculosis data in Brazil.
- To evaluate the compliance of Brazilian tuberculosis information systems with FAIR data principles.
Main Methods:
- The study assessed Brazilian tuberculosis information systems.
- Evaluation was conducted against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
Main Results:
- Brazilian tuberculosis information systems demonstrate a low compliance rate of only 37.75% with FAIR principles.
- Data quality management within these systems is not systematically implemented.
Conclusions:
- There is a critical need for organized data quality management in Brazilian tuberculosis information systems.
- Adoption of FAIR methodology is crucial for standardizing data and improving information sharing on tuberculosis.
- Strengthening national health information systems requires addressing data quality and overcoming system limitations.
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