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Is Research Data Trustworthy? A Quality Comparison Between FHIR, Trinetx and Clinical Data Sources
Sude E Kocman1, Helene Köster2, Christian Gulden1
1Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Erlangen, Germany.
Studies in Health Technology and Informatics
|May 17, 2025
Summary
Electronic health records (EHR) enable large-scale real-world data research. This study found good consistency but also discrepancies in EHR data quality across repositories due to differing code systems and mapping processes.
Area of Science:
- Health Informatics
- Clinical Data Management
- Real-World Data Research
Background:
- Electronic health records (EHR) provide valuable real-world data for clinical research.
- Accurate data transformation across clinical data repositories is crucial for reliable research.
- Variability in data quality can impact the validity of research findings.
Purpose of the Study:
- To evaluate the data quality and completeness of three clinical data repositories: Data Warehouse (DWH), Fast Healthcare Interoperability Resources (FHIR), and TriNetX.
- To identify discrepancies in key data elements (diagnosis, procedure, laboratory codes) within these repositories.
- To assess the impact of data transformation processes on research data.
Main Methods:
- Analysis of key data elements including diagnosis, procedure, and laboratory codes.
- Comparative evaluation of data quality and completeness across DWH, FHIR, and TriNetX repositories.
- Assessment of data using a specific clinical research question.
Main Results:
- Overall good consistency was observed across the analyzed data repositories.
- Discrepancies were identified, primarily attributed to variations in code systems, data filtering methods, and mapping processes.
- The study highlighted specific challenges in data harmonization for multicenter research.
Conclusions:
- Critical assessment of data provenance and transformation processes is essential for multicenter research using EHR data.
- Understanding the unique strengths and limitations of each data repository is vital for ensuring high-quality research outcomes.
- Standardization of data handling and mapping is recommended to improve data quality in clinical research.
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