How one health tech CTO unblocked four staged clinical AI models in 90 days with three infrastructure changes, and the checklist every healthcare engineering team needs before the next model tries to ship.
The Data Was Fine. The Scaffolding Around It Was Not.
Three of six models failed because warehouse data formats were incompatible.
Two of six failed due to missing lineage documentation required for audits.
One of six failed because no production monitoring framework was in place.
Instrument pipelines for null rates, schema drift, and distribution shifts. Run in observation mode first; alerting goes live in week 4. Catches the format mismatches that were stalling three models.
End-to-end provenance from EHR source to model input feature. Machine-readable export for FDA, ONC, and CMS audit standards. Unblocks the two models waiting on audit-grade documentation.
Input distribution monitoring, output anomaly detection, accuracy tracking against labeled production cases. Routes alerts to engineering and clinical informatics. Satisfies the customer requirement that was blocking model six.
Null rates, schema drift, distribution shifts — monitored at every pipeline boundary. Data problems get caught and labeled before they look like model problems.
Every input feature traceable from EHR source through transformation to model input. FDA's AI/ML action plan, ONC FHIR rules, and CMS audit standards increasingly require it.
Live monitoring of input distribution, output anomalies, accuracy against labeled production, with routed alerts to engineering and clinical informatics. The contract requirement no clinical AI customer will sign without.
Data quality monitoring separates data problems from model problems engineering debugs the right thing the first time.
Infrastructure that lets a trained model deploy into a clinical environment, receive production data, produce validated outputs, and have its performance monitored — without engineering intervention for data quality, format inconsistency, or compliance gaps.
Input distribution versus training, output anomalies outside expected ranges, accuracy against labeled production samples, and routed alerts to both engineering and clinical informatics — so the team that needs to act sees the signal first.
FDA's AI/ML action plan, ONC FHIR R4 and USCDI requirements, and CMS audit standards increasingly require organizations to document data provenance, transformations, and ownership for AI inputs. Customers ask for it because their own auditors will.
