OperationsOwn ModelsHigh complexity
Clinical Prediction Models
Predict readmission, deterioration and length of stay from the hospital's own history.
How we approach it
Models trained on local outcomes, which reflect the hospital's population and practice better than generic tools. Fairness is checked across patient groups, performance is monitored after go-live, and models are retrained on a schedule. Outputs show their main drivers so clinicians can judge them.
Business value
Earlier intervention based on the hospital's own data
Technology stack
- Python
- clinical data warehouse
- MLflow
- model monitoring
- EHR integration
Related service
Proprietary AI DevelopmentRelated use cases
- Healthcare · OperationsClinical Decision SupportBring guidelines and the patient's own record together at the point of care, without the tool becoming an unregulated medical device.
- Healthcare · OperationsPatient Triage OptimisationPrioritise patients in emergency departments and urgent care when demand outruns capacity.
- Healthcare · OperationsMedical Records IntegrationJoin patient records spread across hospital systems, laboratories and community care into one usable view.