L&M Consulting GroupWhat to gate
- Schema/profile validation (fail fast).
- Terminology normalization and code system checks.
- Unit normalization and time zone handling.
- Duplicate detection and idempotency rules.
Insights
AI-ready data quality workflows
Inconsistent clinical data can make dashboards and models fail quietly. Quality gates expose errors and assign each one to an owner.
By Joseph (Joey) Bartlett · Updated August 31, 2026
Define “AI-ready” for your use case
An Observation can satisfy a schema and still carry the wrong unit, an outdated code, a dangling reference, or a timestamp the downstream use case cannot interpret. Define quality against the decision the data must support.
- Completeness: required fields present (and not defaulted to meaningless values).
- Conformance: shape matches schema/profile expectations (HL7/FHIR/warehouse).
- Plausibility: values are clinically and operationally reasonable (units, ranges, timestamps).
- Consistency: codes and identities are stable across sources and time.
- Traceability: you can explain where a value came from and what transformed it.
Install quality gates in the pipeline
Run checks before publishing data downstream so problems are caught before they reach dashboards or models.
Put the checks into routine use
- Report each failure with a clear reason and named owner.
- Track and resolve an error backlog with named owners.
- Write runbooks: what to check, how to reproduce, how to fix.
- Use fixtures and regression tests so later changes do not reopen known failures.
Want validation built into your pipeline?
We can define quality gates, implement validation checks, and document ownership and response procedures.