The Challenge
EliteCare's clinics relied on manual, rules-based risk scoring that missed early warning signs and varied widely between sites, making it hard to prioritise care consistently.
Our Approach
We built end-to-end ML pipelines on their historical clinical data, trained risk models validated against clinician review, and delivered the scores inside the tools their teams already use, with monitoring and scheduled retraining to keep accuracy high.
Results
- 42% improvement in patient risk-scoring accuracy
- Consistent scoring standardised across 15 clinics
- Earlier identification of high-risk patients
- Transparent, auditable model outputs for clinical review