Inside 847 Production Clinical AI Notes

Official Schedule Context

A Series B clinical AI company had an ambient scribe in production for six months. Internal evals

passed every release. A clinical team spot-checked a sample weekly and saw nothing alarming. The

system had healthy NPS, expanding deployments, and the company was preparing for European market

expansion. We ran a structured audit on 847 production notes. Found 127 failures across six

categories. 23 were severity-critical - the kind that could directly alter a clinical decision. The

team's existing LLM-as-judge had reported zero failures across the same notes. This talk is the

engineering forensics of that audit. The audit setup: which production traces we sampled, how the

structured failure-mode coding worked, and the reviewer protocol. The results: three dominant

failure clusters - decision-status corruption (19 cases), structured omissions (34 cases), and

dosage substitution (12 cases) - and the underlying generation pattern behind each. For each cluster

I will show: a real anonymised trace, the eval rule that should have caught it but did not, an

explanation of why the eval missed it, and the criterion that does catch it. The pattern that

emerged in the data is engineering-actionable. The team had built a 20-criterion content-

faithfulness eval layer. The failures lived underneath it, in a missing intent layer. We replaced

the broad content layer with a five-criterion intent layer (decision status, omission impact, dosage

integrity, diagnostic chain, laterality consistency). Detection rate went from 0% to 96% on the

failure set. Compute cost dropped because the intent layer is cheaper to run than the content layer

it replaced. You will leave with a forensics protocol for auditing your own production AI, the five

intent criteria that generalise to any high-stakes domain, and the architectural pattern: build a

thin intent layer, not a thick content layer.

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