The clinical semantic layer. Legal and metadata compliance alone does not make data AI-ready. Bavel applies a multi-standard canonical model that combines FHIR R4 for exchange, OMOP CDM for analytics, openEHR for clinical archetypes, DICOM for imaging, and a full terminology stack including SNOMED CT, LOINC, ICD-10, ATC, RxNorm, and Orphanet. Governance logs follow W3C PROV-style provenance with full audit trail.
EHDS-readyData Modelling for EHDS
EHDS compliance is not only a legal question. It is a data quality and infrastructure question. Before any dataset can be discovered, accessed, or reused under EHDS, it must be structured, described, semantically normalised, and governed. Bavel Health addresses all four tasks.
What data must be modelled.
EHDS secondary use begins with the categories of electronic health data that institutions must make available under Article 33, including EHR data, claims, registries, genomics, public health data, clinical trials, biobanks, and research datasets. Bavel maps each category to a governed modeling object, from patient encounters, diagnoses, and medications through to imaging studies, genomic variants, registry cases, clinical outcomes, and cohort definitions.
Making data discoverable before it is accessed. EHDS requires datasets to be understandable before access is granted. Bavel structures every dataset with a complete metadata profile aligned to HealthDCAT-AP, the EU standard for health dataset catalogues. This includes dataset scope and population, data holder identity, distribution formats, access policies, opt-out logic, data quality indicators, provenance and transformation history, and the legal basis covering EHDS purpose, GDPR basis, ethics approval, and HDAB permit.
Five priority models Bavel builds for every institution.
How Bavel delivers this.
Bavel Health builds and operationalises all five models through FlexGrid, its clinical data harmonisation engine. FHIR is the interoperability entry point, not the research execution layer. FlexGrid transforms fragmented clinical, imaging, genomic, registry, and outcomes data into a canonical harmonised abstraction layer that is consistent, interoperable, queryable, and analysis-ready.
The strategic reality.
Healthcare data is inherently fragmented and will remain so for decades. The question is not which standard wins. The question is how all standards are harmonised into a unified, queryable research layer. That is the infrastructure challenge EHDS places on every data holder. It is the challenge Bavel Health can solve.
Ready to model your institution's data for EHDS? Contact Bavel Health to begin your dataset assessment