Blog
Notes on FHIR data quality.
Essays from MedVertical on continuous validation, interoperability, and the evidence layer healthcare data needs.
The German FHIR Landscape: ISiK, MII, gematik, and Why It's More Complex Than It Looks
Germany has one of the densest and most overlapping FHIR landscapes in Europe. ISiK, MII, DiGA, KHZG — they're not the same thing, they don't share the same profiles, and hospitals may need to work with several of them simultaneously.
Terminology Drift: The Silent Killer of FHIR Data Quality
Your FHIR data was valid when it was created. It may not be valid today. Terminology drift is a common — and often under-monitored — source of FHIR data quality failures.
Three Questions Every FHIR Team Should Be Able to Answer
FHIR teams rarely fail because they lack a validator. They fail when they cannot answer whether production data is valid now, when errors first appeared, and what the conformance state was in the past.
Why We Built Records
Records didn't start as a data quality layer. It started while I was designing clinical research systems and ran into a problem many healthcare UX teams face: interfaces are only as reliable as the FHIR constraints behind them.
Records
Turn FHIR validation into operational evidence.
Records continuously validates FHIR data, tracks drift over time, and keeps reproducible evidence for audits, releases, and partner integrations.