Project Overview
An NHS Foundation Trust reporting 85,000 radiology examinations per year had a radiology reporting backlog — a...
Technology Stack
Compliance & Standards
The Challenge
An NHS Foundation Trust reporting 85,000 radiology examinations per year had a radiology reporting backlog — average 12 days wait for routine chest X-ray reports, 4.2 days for urgent. The trust wanted to build an AI-assisted reporting tool: triage AI for urgent finding prioritisation (flagging potential pneumothorax, PE, pneumonia for urgent review) and a structured reporting template engine to reduce radiologist typing time. DCB0129 clinical safety, DTAC all 5 domains, MHRA medical device regulations (AI software as medical device), and UK GDPR Article 9 were mandatory. Budget: £95,000.
Our Approach
MHRA Medical Device Classification
- AI-assisted radiology reporting software is a Class IIa medical device under UK MDR 2002.
- MHRA registration required before clinical use.
Clinical Evaluation Report
- literature review of chest X-ray AI performance characteristics, comparison with intended purpose.
- CE marking (legacy) or
UKCA marking
ClickMasters worked with the trust's MHRA-registered technical file under the CE transition period.
Triage AI Algorithm
Pre-trained chest X-ray AI model (using CheXNet architecture, fine-tuned on UK NHS Trust's anonymised historical imaging data — DICOM images with radiologist reports as labels).
Output
probability score for urgent findings (pneumothorax, pneumonia, pleural effusion, cardiomegaly).
Threshold calibration
95% sensitivity for pneumothorax (missing one is catastrophic).
Worklist prioritisation
high-probability urgent findings pushed to top of reporting queue.
CSO
Consultant Radiologist (clinical safety officer must be a clinical professional for medical device AI).
Hazard Log
false negative hazard (AI misses urgent finding) — mitigated by: AI as triage support only (radiologist reviews all exams), high sensitivity threshold, and mandatory clinical review.
False positive hazard
- unnecessary urgent recall — mitigated by positive predictive value monitoring with radiologist override audit trail.
- DTAC Domain 1–5
Evidence
- Domain 1 (Clinical Safety): DCB0129 Clinical Safety Case Report.
- Domain 2 (Data Protection): Article 9 DPIA, DSP Toolkit assessment.
- Domain 3 (Technical Security): Cyber Essentials Plus, CREST pen test.
- Domain 4 (Interoperability): FHIR R4 DiagnosticReport integration with EPR, DICOM standards compliance.
- Domain 5 (Usability): radiologist user research (12 participants), WCAG 2.1 AA.
The Results
DTAC approved all 5 domains.
MHRA registered.
Clinical deployment at 20 weeks, £88,000 — under budget.
Urgent finding detection: 12 pneumothorax cases flagged in first 3 months — all confirmed by radiologist as genuine (zero false positives requiring urgent recall in first 3 months).
Routine chest X-ray reporting wait: 12 days → 7.2 days (39% improvement from worklist optimisation alone).
Radiologist reporting time: structured template reduced average report time 4.8 → 3.1 minutes.
“Twelve pneumothorax cases in three months — every one a genuine urgent finding, zero false positives. The DCB0129 process with a radiologist as CSO was absolutely right for a medical device AI. DTAC first submission pass on a Class IIa medical device is something we will be proud of for a long time." — Chief Clinical Information Officer, NHS Foundation Trust (name withheld)”
Project Details
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