CLEARvalidate
Your assurance engine for ambient voice technology
Evidence-based AI scribe validation for the NHS. CLEARvalidate provides independent, vendor-neutral validation of ambient voice technology — giving trusts the transparency, assurance and evidence they need to adopt AI safely.
AI scribes are new “employees” without rigorous performance checks
Black box AI technology makes every generated note unpredictable and potentially dangerous, creating significant liability for the organisations adopting it.
The problem today
The result
Our solution: validated safety
To overcome these challenges and mitigate risks, CLEARvalidate provides independent, evidence-based validation, providing transparency — potentially detecting 80% of critical errors before they enter patient records.
Why monitor AI scribes now?
AI scribes are now classified as AI as a Medical Device (AIaMD), requiring objective evidence of safety and effectiveness, continuous risk management, post-market surveillance and adverse event reporting. The black box nature of AI makes it hard to prove performance, manage ongoing risks and monitor real-world impact — creating major compliance challenges.
Clinical risk
The unpredictable outputs of black box AI increase the risk of medication errors, dosage mistakes and missed negations entering patient records.
Financial impact
Unvalidated AI outputs, driven by the black box problem, amplify the risk of clinical negligence claims — adding to the £5.1bn the NHS spends annually.
Regulatory risk
Black box AI makes demonstrating adherence to DCB0129/0160 standards extremely difficult, leading to potential regulatory breaches.
The most rigorous AVT validation available to the NHS
More than just feedback. Tested on over 1,000 real-world consultations, with early predictive models achieving AUC > 0.7.
Audio quality check
Analyses the sound quality. Noisy backgrounds generate poor notes — we check the recording is clear enough for accurate processing.
Speech clarity assessment
Analyses the speech quality. A quiet background but poor speech quality impacts overall transcription even more.
Language and meaning analysis
Analyses both meaning and lexical overlap with doctor-verified versions, ensuring the AI has understood the medical context correctly — not just the words.
Expert review and AI learning
AI and medical experts work together to identify why errors occur and the significance of errors — understanding root causes so corrective measures are focused in the right areas.
Predictive error detection
Builds a smart system that predicts errors in real time, instantly flagging high-risk notes for review and preventing errors before they impact patient care.
Navigating compliance in the AI black box era
For your trust
Meet MHRA AIaMD requirements today. Catch errors before they reach patients. Maintain AI benefits whilst ensuring quality and compliance.
For your clinicians
Build confidence in AI-generated documentation. Focus review time only where needed. Receive continuous improvement through targeted feedback.
For your patients
Ensure validated, accurate medical documentation. Maintain quality care records. Build trust through transparent AI governance and oversight.
Fully integrated solution — cost benefit
Based on published literature and CLEARnotes data, demonstrating significant efficiency and quality gains for a service handling 1,000 notes per month.
Two ways to deploy CLEARvalidate
Retrospective validation
Integrated validation
Data inputs required
What clinicians see
Integration architecture
A 24-week proof of value
A structured pilot designed to generate the evidence your trust needs to make a confident scale-up decision.
Scope
Two specialties (high-volume outpatient suggested), with weekly QA reports and a view to developing real-time validation.
Success metrics
Governance
Weekly steering group (CCIO, CSO, IG) with stop/go gates at weeks 4, 8 and 12. Full data export on completion for your own evaluation.
Implementation timeline
Built for NHS assurance requirements
Regulatory compliance
Security standards
Information governance
*Certification statuses marked with an asterisk require current status confirmation.
East Lancashire Hospitals NHS Trust
Pre-operative assessments, September to November 2024 — part of a 1,007 consultation analysis.
Hot-spot identification
Specific high-risk areas identified in medications and plan sections.
Training interventions
Targeted improvements delivered on the basis of the findings.
Peer-to-peer discussion
A named clinical sponsor is available for peer-to-peer discussion upon request.
Ready to validate your AI scribe?
Contact us to arrange a detailed technical and clinical discussion tailored to your trust’s specific implementation needs and AI governance requirements.