HealthcareProduction AI for Clinical Decision Support
Deploying production machine learning for clinical pathway optimisation across 12 NHS hospital sites — with full AI governance, MHRA-aligned documentation, and 94% model accuracy.
Client
NHS Foundation Trust
Expertise
Programme OutcomesResults at a glance
94%
Model accuracy
On live clinical data
12
Hospital sites
Full Trust estate rollout
78%
Clinician adoption
Within 3 months of go-live
0
Safety incidents
In 12 months of operation
The ChallengeDeploying AI safely in a clinical environment
The client — an NHS Foundation Trust operating across 12 hospital sites — had a clinical decision support proof of concept that had demonstrated strong accuracy in a research environment. The challenge was to move it from a research prototype to a production system trusted by clinicians and compliant with MHRA Software as a Medical Device guidance.
NHS organisations operate under exceptional governance scrutiny. Clinical staff trust in AI-assisted decision support is low by default, requiring demonstrable safety controls, explainability, and a clear governance framework backed by senior clinical leadership. Data access was governed by Data Security and Protection Toolkit (DSPT) requirements, adding further complexity to model training and validation.
BrezQ was engaged to design the AI governance framework, re-architect the model for production deployment, and manage the rollout across all 12 sites — working alongside the client's CCIO, data science team, and clinical champions.
The BrezQ ApproachGovernance-first AI deployment at NHS scale
BrezQ began with a six-week AI governance design phase — producing the Model Risk Framework, Algorithmic Impact Assessment, and Explainability Documentation required by MHRA's Software as a Medical Device framework. The governance artefacts were validated by a clinical governance committee and the Trust's legal and information governance teams before any model retraining began.
The model architecture was refactored for production — replacing the research-oriented Jupyter notebook pipeline with a production ML pipeline on Azure ML, with full model versioning, data lineage tracking, and automated drift detection. Training data was processed entirely within the Trust's Azure tenancy, with no patient data leaving the clinical environment.
A clinical champion programme was run across all 12 sites ahead of go-live — embedding BrezQ consultants alongside clinical staff for three weeks to build trust, gather feedback, and tune the model on site-specific patient pathway data. Model outputs were designed to be advisory, not directive, with full clinician override capability logged for audit.
Technologies Deployed
The OutcomeProduction AI trusted by clinicians — 94% accuracy, zero safety incidents
The model achieved 94% accuracy on live patient data across all 12 hospital sites — exceeding the 90% threshold set by the clinical governance committee as the minimum acceptable standard. Zero patient safety incidents were recorded in the first 12 months of live operation.
Clinician adoption reached 78% within 3 months of go-live — significantly above the 50% target — driven by the clinical champion programme and the design decision to make AI outputs genuinely useful rather than merely compliant. Pathway decision times reduced by 23% on average across participating wards.
The AI governance framework produced by BrezQ was adopted by NHS England as a reference framework for other Trusts deploying similar clinical decision support systems.
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