AI Case Study

UCLH is estimated to save the NHS £2-3 per appointment by predicting 90% of no-shows using AI

University College London hospital has developed a tool, based on data from 22,000 appointments for MRI scans, that can identify 90% of no-show patients. Although at an early stage, the technology could provide lower cost and waiting times as it is estimated that it can save £2-3 per appointment.

Industry

Healthcare

Healthcare Providers And Services

Project Overview

"A leading hospital has developed artificial intelligence to predict which patients are most likely to miss appointments.

University College Hospital in London created an algorithm using records from 22,000 appointments for MRI scans, allowing it to identify 90% of those patients who would turn out to be no-shows. The machine intelligence is not perfect – it also incorrectly flags about half of patients attending appointments as being at risk of not showing.

However, even an imprecise indication of which patients will attend could save hospitals vast sums of money and help cut waiting times.

“On average we estimate this could save £2-3 per appointment,” said Parashkev Nachev, a consultant neurologist at UCLH, who helped develop the tool. “Given that a big hospital could have nearly a million scheduled events per year, that could potentially be a lot of resource.”

UCLH plans to roll out a new system, based on the AI, to target patients most likely to be unreliable with phone calls by the end of the year.

In the latest project, Nachev and colleagues at the hospital’s National Institute for Health Research Biomedical Research Centre took data from 22,000 MRI scan appointments, which included the time of day, number of previous scans and how far from the hospital the person lived. The data did not include gender, age or ethnicity, details which are kept in a patient’s medical records, but are not as easy to extract from the hospital’s appointments database. These factors could help improve predictions, Nachev said, but they chose not to include them because they wanted to come up with a tool that could be readily rolled out under existing IT infrastructure.

Reported Results

The tool is reported to be able to predict 90% of no-shows, saving an estimated £2-3 per appointment.

Technology

Function

Background

"The project is part of a broader programme at UCLH that aims to bring the benefits of the machine-learning revolution to the NHS. As part of a partnership with the Turing Institute, in London, the hospital is exploring the potential for using artificial intelligence to take over a range of tasks traditionally performed by doctors, nurses and administrative staff, including diagnosing cancer on CT scans and deciding which A&E patients are seen first."

Benefits

Data

"Data from 22,000 MRI scan appointments, which included the time of day, number of previous scans and how far from the hospital the person lived."