Identifying potential concurrent treatment areas and services that would better support patients with multiple, complex referral to treatment (RTT) pathways.


30 Second Summary

The project uses machine learning to support analysis of patients who are on multiple concurrent referral to treatment (RTT) pathways, focusing particularly on healthcare inequalities. It aims to build a model that can suggest services suitable for co-location, and identify the points at which patients start having concurrent pathways.

Machine Learning
Streamlit
Inequalities
Patient Pathways
Authors
Affiliation

Amaia Imaz Blanco

NHS England

Sean Aller

NHS England

The aim of this project is to use machine learning approaches to support analysis of patients with multiple concurrent RTT pathways, focusing particularly on healthcare inequalities.

The intention is to build a model that could identify/predict/suggest colocated services as well as points at which patients start having concurrent pathways.