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Semantic Harmonization Workflow of Health Data: Application in the Post-COVID Network Netherlands
Hajar Hasannejadasl1, Shuxin Zhang2,3, Bianca M Boxma-de Klerk4
1Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
We developed a workflow to create metadata-rich codebooks, enhancing semantic interoperability for diverse health datasets. This improves data integration and reuse in biomedical research.
Area of Science:
- Biomedical Informatics
- Data Science
- Health Informatics
Background:
- Semantic interoperability is crucial for integrating and reusing multi-source health data in research.
- Existing challenges persist in applying FAIR principles to heterogeneous clinical and patient-reported data.
Purpose of the Study:
- To introduce a structured workflow for generating metadata-rich codebooks to enhance semantic interoperability.
- To address the operationalization of FAIR principles for complex health datasets.
Main Methods:
- A structured workflow combining manual curation and expert-driven semantic annotation using biomedical ontologies.
- Integration of contextual suffixes to capture measurement nuances.
- Application of the workflow to the Post-COVID Network Netherlands (PCNN) dataset.
Main Results:
- Successful harmonization of variables across demographics, COVID-19 history, and questionnaires in a PCNN subset.
- Generation of metadata-rich codebooks supporting semantic interoperability.
- Development of mappings designed for expert review to ensure accuracy.
Conclusions:
- The proposed workflow effectively supports semantic interoperability for heterogeneous health data.
- Metadata-rich codebooks are essential for data integration and reuse in biomedical research.
- Expert review ensures the accuracy and reproducibility of data harmonization efforts.
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