Project Overview
An NHS Acute Trust with 420 consultants and 1,200 clinical staff wanted to deploy an AI clinical documentation...
Technology Stack
Compliance & Standards
The Challenge
An NHS Acute Trust with 420 consultants and 1,200 clinical staff wanted to deploy an AI clinical documentation assistant — transcribing clinical conversations (ward rounds, outpatient consultations, emergency assessments) into structured clinical notes and automatically populating the EPR (Electronic Patient Record). MHRA SaMD considerations (is the AI a medical device?), NHS DSP Toolkit standard 4 (AI governance), DCB0129 clinical safety, DTAC, UK GDPR Article 9 (health data processed by AI), NHSX AI Lab ethics guidance, and WCAG 2.1 AA were the requirements. Budget: £100,000.
Our Approach
MHRA SaMD Classification for Clinical AI
The AI transcription tool processes clinical audio and generates clinical note text — the notes are reviewed and edited by a clinician before saving to EPR.
MHRA SaMD classification
clinical documentation assistant with mandatory clinician review = Class I (software does not drive clinical decisions — note is a draft for clinician to complete).
ClickMasters produced the MHRA software classification justification documentation
intended purpose (draft note generation, not clinical decision), clinician in the loop (cannot save without clinician review/approval).
AI Speech Recognition and Clinical NLP
Whisper (OpenAI) speech-to-text: medical vocabulary fine-tuned (NHS clinical vocabulary — procedure codes, anatomy, drug names).
SNOMED CT entity extraction
clinically-relevant entities (symptoms, diagnoses, medications, procedures) extracted and mapped to SNOMED CT UK codes.
Structured note generation
Claude API (Anthropic) — clinical conversation transcript → structured SOAP note (Subjective, Objective, Assessment, Plan).
Hallucination mitigation
AI cannot add information not present in the transcript — output is constrained to transcript content only.
UK GDPR Article 9 AI Processing Governance
Clinical audio is Article 9 special category health data.
AI processing legal basis
- Article 9(2)(h) — provision of health care.
- NHS DSP Toolkit standard 4 (Data-Driven Technology Assurance): algorithmic impact assessment, model documentation (model card — training data, limitations, performance metrics), and bias testing (demographic parity across patient age, ethnicity, and accent groups).
Bias testing
Whisper speech recognition accuracy across UK regional accents — additional fine-tuning for accents with below-threshold accuracy.
Mandatory clinician review
AI-generated draft cannot be saved to EPR without clinician editing and explicit approval.
Review interface
side-by-side (transcript and draft note), diff highlighting (AI additions highlighted for clinician attention), and one-click entity correction (SNOMED CT suggestion corrections).
EPR integration
FHIR R4 Clinical Impression resource → SystmOne or Epic via HL7 v2 MDM (Medical Document Management) message.
Audit
every draft generated, every edit made, every approval — immutable audit log.
The Results
DTAC approved all 5 domains.
MHRA Class I classification confirmed.
Platform live at 16 weeks, £92,000 — under budget.
Clinical note documentation time: 8.2 minutes → 2.4 minutes (71% reduction).
Clinician edit rate: 64% of AI drafts required significant editing (expected — AI is a first draft, not a final note).
SNOMED CT coding accuracy: 91% entity match.
Transcript accuracy (Whisper fine-tuned): 97.3% word error rate below 5%.
NHS DSP Toolkit standard 4: assessment passed.
“71% reduction in documentation time. 420 consultants × 71% time saving = real capacity for patient care. SNOMED CT 91% accuracy was the clinical governance committee's threshold. MHRA Class I classification correct — the mandatory clinician review is what makes this a documentation tool, not a decision support tool. DTAC first submission." — Chief Medical Information Officer, NHS Acute Trust (name withheld)”
Project Details
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