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AI Case Study

UCLH plans to use machine learning to triage patients in A&E and better predict demand for the service

UCL Hospitals in partnership with Alan Turing Institute is testing using AI to address its resource shortage and to serve patients better. As a first step AI will be used in Accident and Emergency (A&E) units to prioritise patients and to predict demand for better resource allocation.



Healthcare Providers And Services

Project Overview

"Using data taken from thousands of presentations, a machine learning algorithm might indicate, for instance, whether a patient with abdomen pain was likely to be suffering from a severe problem, like intestinal perforation or a systemic infection, and fast-track those patients preventing their condition from becoming critical. Machine learning algorithms can provide new ways of diagnosing disease, identifying people at risk of illness and directing resources. In theory, doctors and nurses could be responsively deployed on wards, gravitating to locations with the highest demand at certain times of day."

Reported Results

Planned; results not yet available




General Operations


"UCLH failed to meet the 4 hour wait at Emergency services while overall only 76% of patients were seen within 4 hours across England. To improve patient care and manage resources better UCLH is partnering with the Alan Turing Institute."



Historic patient case data

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