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.
Healthcare AI fails at 78.9% the highest rate across industries. 64% of those failures are infrastructure failures, not model failures. The model that worked in the pilot was never going to fail.
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.
Infrastructure-first planning catches production data mismatches and integration realities before they become 13.7-month failure cycles.
Pilots run on curated data, controlled environments, team-defined metrics, and no real EHR write-back. Production has live EHR data, customer-specific config, externally-defined metrics, and full workflow integration. The model that succeeded in the pilot was solving a different problem.
Accuracy drift against the live data, input distribution shift versus training, output anomalies outside expected clinical ranges, and hallucination rates. With routing to engineering and clinical informatics when thresholds are crossed.
No. 64% of scaling failures are infrastructure failures. About 80% of healthcare AI failures cite data quality as a contributing factor. Model quality is rarely the binding constraint.
