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INGENIO-DQ: Data Quality Assessment for a Precison Medicine Data Model Using an International Common Framework
Paula Rubio-Mayo1,2, Jens Declerck3, Julia Hernández-Sánchez1
1Digital Research Group, Research Institute Hospital 12 de Octubre, Madrid, Spain.
This study assessed data quality for the INGENIO project, which advances precision medicine through harmonized data. The implemented methodology ensured high data completeness and consistency for cancer research.
Area of Science:
- Oncology
- Biomedical Informatics
- Public Health
Background:
- Cancer poses a significant global health challenge.
- Advancing precision medicine requires robust, harmonized data collection.
- The INGENIO project aims to enhance cancer research through integrated data analysis.
Purpose of the Study:
- To implement and evaluate a data quality assessment methodology for the INGENIO project.
- To ensure the validity and reliability of collected cancer data.
- To establish homogeneous and evaluable data collection methods.
Main Methods:
- Utilized the iHD framework for data quality assessment.
- Implemented a common Electronic Data Capture (EDC) system using REDCap.
- Validated the EDC data model according to international guidelines.
Main Results:
- Assessed data quality for 317 manually loaded cases at Hospital Universitario 12 de Octubre.
- Achieved approximately 95% completeness in the dataset.
- Reached 100% consistency in the collected data.
Conclusions:
- The implemented data quality assessment methodology is effective in a real-world research environment.
- High data quality, including completeness and consistency, was achieved for the INGENIO project.
- The validated EDC data model supports reliable precision medicine research in oncology.
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