The aim of this project was to rank dispatch codes by typical clinical acuity of the patients in those codes using Machine Learning approaches. The project incorporated the views of a PPI group who helped to highlight the importance of patient medical history and co-morbidities in the model.
Phase 1 of the model used call-taker info (dispatch code and other items) plotted against conveyance. A series of Logistic Regression models was trained to test out optimal sample sizes and model regularisation, to boost accuracy. This led to Algorithm generation for phase 2, to test whether the scoring methodology in the Risk Stratification model could locate codes ‘not requiring an ambulance’.
The risk stratification model is in active use, increasing HART team utilisation, and leading to a trial of Enhanced Hear and Treat. The project allowed for a balanced pragmatic approach by senior decision makers.
BIG BET - AI to drive patient power and productivity: using machine learning and risk stratification on dispatch codes to identify when an ambulance is not clinically required, supporting smarter resource allocation and Enhanced Hear and Treat as an alternative to conveyance.