Independent Validation
AI scribe quality varies widely. Hospitals need a trusted way to measure it.
The Clinical Documentation Quality Initiative provides independent, physician-led validation of AI-generated clinical documentation across realistic clinical scenarios, measuring accuracy, completeness, hallucinations, clinical risk, and physician editing burden.
Validated AI-Scribe benchmark
Benchmark Report
Vendor Assessment
88%
Overall Clinical Usability
3%
Hallucination / Fabrication
92%
Medication & Allergy Safety
Rubric Dimension Scores
AI-generated notes are not all created equal.
Ambient scribe tools can reduce documentation burden, but health systems need more than vendor claims. Differences in transcription accuracy, medical reasoning, note structure, hallucination control, and clinically significant omissions can directly affect physician trust, patient safety, and procurement decisions.
Variable note quality across vendors
AI scribe tools vary significantly in transcription accuracy, medical reasoning, and note structure.
Risk of hallucinated clinical details
Without independent validation, unsupported or fabricated clinical information may go undetected.
Lack of specialty-specific validation
Generic testing fails to capture the nuances required for different medical specialties.
Independent benchmarking for clinical documentation AI.
We evaluate AI-generated clinical notes against physician-created reference standards using standardized encounters across multiple specialties and complexity levels.
Standardized clinical scenarios
Physician-designed encounters covering multiple specialties and complexity levels.
AI-generated note submission
Vendors submit documentation outputs for each standardized scenario.
Independent physician review
Board-certified physicians evaluate outputs against reference standards.
Benchmark report and validation
Comprehensive scorecard with detailed metrics and specialty breakdowns.
What the benchmark measures
Our validated AI-Scribe rubric evaluates every note across nine critical dimensions of clinical documentation quality.
Accuracy
Does the note accurately reflect the encounter and source material?
Completeness
Does the note include the clinically relevant history, examination, assessment, plan, follow-up, and safety-netting present in the source material?
Hallucination / Fabrication
Does the note contain any diagnosis, finding, medication, result, or plan not supported by the source material?
Clinical Reasoning Fidelity
Is the assessment and plan consistent with the actual clinical information from the encounter?
Medication & Allergy Safety
Are medications, doses, allergies, anticoagulation status, contraindications, and instructions documented accurately?
Follow-Up & Safety-Netting
Are follow-up plans, investigations, referrals, return precautions, and urgent warnings accurately documented?
Attribution & Speaker Accuracy
Are symptoms, statements, decisions, and recommendations correctly attributed to the patient, clinician, or caregiver?
Structure & Readability
Is the note organized, concise, clinically readable, and suitable for physician review?
Overall Clinical Usability
Is the note clinically usable after routine physician review and editing?
Tested against realistic clinical complexity.
The benchmark uses physician-designed encounters that include real-world documentation challenges: complex medication histories, changing timelines, comorbidities, ambiguous symptoms, incidental findings, patient concerns, and specialty-specific assessment and plan requirements.
Built for procurement, governance, and clinical trust.
Hospitals and health authorities need objective evidence before deploying AI scribes at scale. Our benchmark helps organizations compare vendors, understand clinical risk, support procurement decisions, and establish ongoing quality assurance.
Vendor comparison
Compare AI scribe vendors using standardized metrics and objective performance data.
Procurement support
Evidence-based documentation for RFPs, vendor selection, and clinical governance.
Ongoing quality monitoring
Track documentation quality over time with periodic re-validation assessments.
Physician-led. Structured. Transparent.
Each benchmark review is based on standardized scoring rubrics, independent clinical reviewers, reference-standard documentation, and structured assessment of hallucinations, omissions, and required physician edits.
Standardized clinical scenarios
Medical specialties covered
Physician-validated assessments
Development and Pilot Validation of the SCRIBE-AI Rubric for Evaluating AI-Generated Medical Documentation: A Modified Delphi Study
The SCRIBE-AI Rubric is currently being developed and evaluated through an ongoing modified Delphi research study. The study aims to establish expert consensus on the key characteristics of safe, accurate, complete, and clinically useful AI-generated medical documentation.
Evaluate AI documentation quality before deployment.
For health systems, procurement teams, and AI scribe vendors seeking independent evidence of clinical documentation performance.