What to measure in a medical document AI pilot
A good pilot does not ask whether a demo looks intelligent. It compares one bounded workflow with an agreed baseline, makes failure costs visible and preserves qualified human responsibility.
Start with the operational baseline
Record the current process before measuring a new one. Without a baseline, a faster-looking interface can hide more rework, weaker coverage or a new escalation burden.
Documents and exceptions per month.
Median and high-percentile review time, not only an average.
Rework, disagreement and escalation frequency.
Where reviewers search for supporting or conflicting evidence.
Which failure types carry the highest financial, compliance or patient-impact risk.
Measure evidence, not only output agreement
CMS and NCHS official ICD-10-CM guidance emphasizes complete documentation and review of the full record. For a support workflow, code agreement alone is therefore too narrow: the evaluation should also ask whether the material evidence is present, correctly located and sufficient for the qualified reviewer to judge the finding.
Field-level agreement with an approved reference.
Evidence coverage: material findings with a usable source location.
Unsupported-finding rate and conflicting-evidence rate.
Exception quality: whether uncertainty is routed rather than hidden.
Reviewer action time and disagreement reasons.
Predefine expensive failures
A false positive, missed exception and wrong source citation do not have the same operating cost. Define severity tiers before reviewing results. NIST AI RMF materials similarly emphasize documenting intended use, limits, benchmarks, human oversight and the costs of errors in context.
Use a go-or-no-go decision, not a vanity score
Proceed when evidence coverage, failure handling, review time and operating fit meet the agreed threshold.
Redesign when one high-cost failure mode dominates an otherwise strong average.
Stop when data, integration or responsibility boundaries are unacceptable.
Do not convert a pilot result into a broad accuracy or compliance claim beyond its tested scope.