AI features that clinical stakeholders will not trust
An LLM that summarises notes or flags anomalies is valuable until a clinician asks how it decided. Production healthcare AI needs explainability, human-in-the-loop controls, and audit trails from day one.
EHR integration that takes months instead of weeks
HL7 FHIR, Epic, Cerner - every integration is different and slower than expected. We have done these before and know where the edge cases hide.
Data pipelines that are not safe for clinical use
PHI leaking across environment boundaries. Logging that captures data it should not. Training pipelines that mix de-identified data with production records. These are not theoretical.
Products built for a single site that need to scale to ten
Hardcoded workflows. Configuration managed in spreadsheets. Multi-tenancy bolted on after the fact. The architecture worked for the pilot site - it does not work for enterprise rollout.
Inference latency that makes clinical AI impractical
Models that score accurately in evaluation but are too slow to be useful in a clinical workflow. Optimization for clinical latency requires different approaches than standard production AI.
Systems that cannot survive an audit
Incident response that is undocumented. Access controls that are technically present but not enforced. Change logs that exist but are not searchable. We have seen what auditors ask for.
