Are automated documentation-error judges fit to measure ambient AI scribes? A pre-registered, blinded human-validation study
TL;DR
The judges behave as a consistent, near-non-differential, clinician-equivalent instrument, which licenses a directional AI-versus-clinician contrast under a non-differential misclassification argument, subject to its conditions.
Abstract
Objectives Safety claims for ambient artificial intelligence (AI) scribes rest on automated judges that detect documentation errors and grade clinical risk. Expert reviewers are under-sensitive and disagree with one another, so no gold standard exists and validation cannot mean accuracy. We tested whether such judges are a defensible instrument: reproducible, within the envelope of expert disagreement, and non-differential across arms. Methods Pre-registered, blinded validation study nested in a multi-country simulation of ambient AI documentation (English setting), reported per GRRAS. Ten external clinicians independently adjudicated a stratified sample of 434 pipeline flags, retained and screen-discarded, blinded to note authorship, identification source, the pipeline's verdict and severity tier. Agreement used Gwet's AC1; proportions carry Wilson intervals. Three propositions were pre-specified: envelope parity, non-differential behaviour across arms, and concordance on consensus cases. Results All ten reviewers completed: 565 adjudications across 434 items, 131 of them double-rated. Inter-clinician agreement on genuineness was fair (raw 59%, 95% CI 50 to 67; AC1 0.24), leaving no human consensus to serve as truth. Judge-clinician agreement was 64% (95% CI 60 to 68), overlapping that interval. Behaviour was near-symmetric on contrast-critical metrics: kept-precision 74% for AI against 81% for clinician notes, and severity signed gap +0.06 against -0.09 tiers. One sub-metric was asymmetric: removed-confirmed 56% against 42%, so the screen over-removes more on clinician notes, a direction conservative to the parent contrast. On 77 consensus items the pipeline concurred on 70% (95% CI 59 to 79). Latent-class triangulation placed the genuine-error rate among flagged candidates at 68% (94% credible interval 48 to 83). Conclusions The judges behave as a consistent, near-non-differential, clinician-equivalent instrument. This licenses a directional AI-versus-clinician contrast under a non-differential misclassification argument, subject to its conditions. It is not a claim of accuracy, which moderate consensus concordance and fair reliability preclude, and the genuine-error rate is best reported as an interval.