Summary
Standards such as FHIR and OMOP provide flexible data models designed to support diverse use cases.
However, flexibility without constraint leads to divergence.
Organisations implement optional fields differently, define local extensions, and apply inconsistent coding systems. This produces datasets that are structurally valid but semantically incompatible.
As a result, integration requires additional mapping, transformation, and validation layers, increasing complexity downstream.
Effective standard adoption requires:
- constrained implementation profiles
- enforced validation rules
- shared semantic definitions
Standards define structure.
Constraints ensure consistency across systems.
Interested in collaborating?
If this perspective resonates and you are exploring collaboration across research, governance, or secure data environments, I welcome the conversation.