One note in three: a verified census of three deployed AI scribes, and the instrument that counted it
2026-08-31 • Computation and Language
Computation and LanguageArtificial IntelligenceComputers and Society
AI summaryⓘ
The authors studied three AI tools that write doctor notes during patient visits to see how often they make mistakes. They found that about 31% of the notes contained errors, especially with allergies, medications, and invented patient details. Some mistakes were not caught by existing error categories, like recording treatments that doctors later took back. Different methods for checking errors gave very different results, showing how tricky it is to measure AI note accuracy. The authors shared all their findings and tools to help others understand and improve AI scribes.
Ambient AI scribeClinical notesPrimary careAdversarial auditError taxonomyPatient identityMedication errorsModel review instructionVerification processAI in healthcare
Authors
Sebastian Fox, Luke Markham, Ryan Lail, Michael Karotsieris
Abstract
Ambient AI scribes draft clinical notes under the reassurance that a clinician signs every note. We audited three commercial AI scribes on the same 142 consultations: 565 notes from recorded UK primary-care and US ambulatory encounters plus authored scenarios. Twelve discovery passes proposed 13,678 candidate errors; the 5,898 clearing an importance filter went to an adversarial panel of two models from different families, each told to refute what it could, and 618 survived. One note in three (31.3% [27.0, 35.6]) carries a verified failure, concentrated in allergy and medication information, invented patient identity, and history written up as examination on telephone consultations that can contain none. No product was given a patient record; setting aside the two classes a record would have prefilled, invented identity and dates, the rate is 24.8% [20.8, 29.0]. One failure mode did not fit our scheme, drawn from published scribe-error taxonomies: a treatment the clinician retracts, recorded as delivered care. Two clinicians adjudicated blind, disjoint samples: a physician author upheld 20 of 21 findings (95.2% [77.3, 99.2]) and an independent clinician, not an author, 12 of 12 ([75.8, 100]); both judged every sampled refusal genuine. A failure rate depends on the instrument as much as the scribes. With model, evidence and settings fixed, the review instruction alone moves the share of candidates verified from 9.3% to 79.0%, and the reviewing family moves it too: alone at that instruction the gentler flags 54.8% of notes against 27.8%. Between 28% and 97% of sampled notes carry a failure depending on the standard. Published audits disagree among themselves by a margin instrument differences alone can produce: omission is 54-86% of their errors against our 23.1%. We release all 618 findings with transcript-side evidence, every prompt and model version, and the re-runnable pipeline.