Use Case

Drift & Regression Detection

Catch data quality regressions from server upgrades, profile changes, and terminology updates.

For: FHIR platform leads, data quality analysts

The problem

FHIR data drifts. Silently.

Your FHIR server passed validation on day one. Then a terminology server update changed 200 codings. A profile was updated with stricter cardinality. A vendor pushed a mapping fix that introduced new reference patterns. Nobody noticed for three weeks.

Drift is the gap between your validated baseline and the current reality. Without continuous comparison, you don't know what you don't know — until an audit or a downstream consumer surfaces the problem.

How Records solves it

Delta comparison against your known-good baseline.

Validation comparison showing drift between two runs

TriggerRoutine data ingestion over 3 months

EvidenceSpike in WARN ratios across 50,000 resources

DecisionUpdate mapping ETL logic

What you get

Side-by-side comparison of any two validation runs

New issues highlighted — see exactly what broke and when

Resolved issues tracked — confirm your fixes landed

Per-resource drill-down from delta to specific validation errors

See this on your own data.

We'll run a baseline, then show you the delta after a simulated change.

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