An NHS-style mid-size acute trust (engagement Q2 2024) where the prior vendor had shipped a triage classifier and ceased trading, model still in production, generating incorrect triage scores for ~8% of patients, with zero documentation, no codebase access, and no model cards. We reverse-engineered the existing model from its outputs (treating outputs as ground truth until we understood why it failed), identified a systematic under-triage bias against elderly patients with atypical presentations, and shipped a stabilisation patch within two weeks. Then rebuilt the full triage model on the trust's 5-year admission records with clinical-governance involvement at every feature-selection and threshold-setting decision. Triage accuracy 97.3% on held-out data, reviewed and approved by the clinical governance board. Unnecessary A&E admissions reduced ~40%. The trust now owns the full model, training pipeline, and monitoring stack.
Vendor lock-in on ML models is a governance risk, always require full model documentation and portability at contract stage.
Forensic diagnosis of a broken model requires treating its outputs as ground truth until you understand why it fails.
Clinical stakeholder involvement in feature selection is not optional, it's how you get clinical governance approval.
2 hours. No cost. We'll tell you honestly whether AI makes sense for your case.
Book a call