The four infrastructure failure modes that determine whether a promising clinical AI pilot becomes a production system or a canceled project, with a case study of each.
Live EHR data has missing fields, free-text-in-structured-fields, and code version mismatches that curated training data suppressed. The model performs differently on data it actually receives.
2025 surveys show integration proves 89% more complex than originally estimated. Without certification and integration work, outputs cannot reach clinical workflow.
Input distribution shifts, accuracy drift, and hallucinations go undetected until a clinician catches them. By then, the contract is at risk.
Data quality, code-set versioning, and EHR data fidelity get instrumented first. The model trains on data shaped like production from the start.
Certification, write-back, and authentication work runs in parallel with model development, not after pilot success.
Monitor accuracy drift, input distribution shift, output anomalies, and hallucination rates from day one of clinical exposure.
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