AI Case Study

"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.