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AI scribe evaluation by specialist clinicians.

A note that reads well can still be wrong. Specialist clinicians review AI-generated notes against the source consultation, and PhD data scientists turn their reviews into clear, measurable findings.

You find out where your scribe falls short, how often, and which errors matter clinically.

CONSULTATION VS AI NOTEILLUSTRATIVE EXAMPLE
CONSULTATION"Chest tightness when I walk uphill, for about two weeks… I take ramipril… I'm allergic to penicillin."
AI-GENERATED NOTE Two-week history of chest tightness. Medications: ramipril. No known drug allergies.
Symptom detail: omission. "On exertion" is missing, which changes the clinical picture.
Medication: accurate.
Allergy status: incorrect, potential safety concern. The patient reported a penicillin allergy.
Clinicians compare each note with its source and grade every finding by severity.
WHAT WE CHECK

The errors that matter in a clinical note.

Omissions
Clinically relevant information from the consultation that is missing from the note.
Unsupported statements
Content in the note that the consultation does not support, often called hallucinations.
Factual errors
Wrong medications, doses, values, laterality, timings or attribution of who said what.
Safety-relevant content
Whether allergies, red flags, safety-netting and follow-up plans are captured correctly.
Structure and usability
Whether the note follows the expected format and would work for the next clinician who reads it.
Consistency across settings
How performance varies across specialties, consultation types and the scenarios that matter to you.
HOW AN EVALUATION RUNS

Clinical judgement, measured properly.

  1. 01

    Agree scope and criteria

    We agree the consultations to sample, the review rubric and how errors are graded by severity, before any review starts.

  2. 02

    Clinician review

    Clinicians matched to the setting compare each note with its source consultation. Where appropriate, notes are reviewed independently by more than one clinician and disagreements are resolved.

  3. 03

    Analysis and reporting

    PhD data scientists turn the reviews into findings you can act on: errors by type and severity, agreement between reviewers and comparisons across versions where relevant.

WHAT YOU RECEIVE · DEPENDING ON SCOPE

  • Expert-reviewed notes
  • Error findings by type and severity
  • Safety-related observations
  • Comparisons across model versions where relevant
  • Structured report with recommendations
  • Documented review criteria

Our evaluations provide clinical insight to support product development. They do not certify clinical safety, confer regulatory approval or replace formal validation requirements.

Often combined with.

Clinical AI Evaluation Extend the evaluation to other outputs, models or versions. Learn more Clinical Training Data & Expert Feedback Turn the errors we find into clinician-corrected examples. Learn more Guide: how to evaluate a clinical LLM Rubrics, severity grading, reviewer agreement and red-teaming. Read the guide

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