AI could ‘revolutionise’ care for heart failure patients, say researchers

Up to a million people in the UK live with heart failure, a condition that reduces quality of life and increases the risk of hospital admission.

Researchers at the University of Dundee say AI that can quickly and accurately scan patient records could improve the welfare of people with heart failure (Jeff Moore/PA). (PA Wire)

Artificial intelligence (AI) capable of identifying medical treatments from scans of patient records could “revolutionise” care for people with heart failure, scientists have said.

In a pilot project led by the University of Dundee, researchers worked with an AI company to develop software that can quickly and accurately scan medical records of heart failure patients and determine what treatment would best benefit them.

With up to a million people in the UK living with heart failure, the researchers say that by removing the “difficult and often time-consuming” process of manually checking records this could help ensure patients receive personalised treatment at an early stage.

The researchers added that this had the potential to improve the welfare of people living with heart failure, a condition that reduces quality of life and increases the risk of being admitted to hospital with symptoms such as breathlessness and fluid build-up.

AI is already beginning to show its potential, but projects such as this are a demonstration of how we can harness this to revolutionise patient care

Dr Ify Mordi, University of Dundee

Project lead Dr Ify Mordi of the University of Dundee said, “AI is already beginning to show its potential, but projects such as this are a demonstration of how we can harness this to revolutionise patient care.

“Over the past few years, there have been major advances in heart failure care with new medications, however, many patients who were diagnosed before these advances may not yet be on up-to-date treatment.

“Unfortunately, due to service constraints, sometimes the first opportunity to identify patients who might benefit from more intensive treatment is after their condition has deteriorated and they have been admitted to hospital, which might be too late.

“If we could identify such patients at an earlier stage, we might be able to intervene before a deterioration, but at present doing this is often difficult and time-consuming, as it requires busy clinical teams to do a manual search of patient records over weeks and months.”

The pilot project involved the development of software capable of scanning heart echo reports (ultrasound scans) along with other medical data.

The study team then applied it to 1,200 patient records, and found that the personalised treatment plans they subsequently developed led to improvements in quality of life and markers of heart stress for those involved.

Andrew Conkie, chief executive of Red Star AI, the company that developed the software, said the technology had the potential to improve population health “at scale”.

This study allowed us to identify - across a large population - patients who were not on the correct treatment

Andrew Conkie, Red Star AI

“The NHS holds vast quantities of cradle-to-grave data, but this is far more than any single person can understand,” he said.

“Additionally, a lot of healthcare data is held in a free text format, which is difficult to analyse at scale.

“This study allowed us to identify – across a large population – patients who were not on the correct treatment.

“Importantly, we then presented these patients to the cardiologists allowing them to make the final decision on how best to treat the patient.

“The improvements in quality of life shown in this study demonstrate the potential for AI to improve population health at scale.”

The team hopes to develop a system that is fully integrated into the NHS clinical workflow, and to use it in a larger study potentially involving other regions.

The study was conducted with support from DataLab Scotland, the Small Business Research Initiative and the University of Dundee Health Informatics Centre.