A team from the University of Dundee, UK, demonstrated the potential for Artificial Intelligence to find better care for patients with heart conditions, according to a study presented at the European Society of Cardiology Conference in London. Using Red Star AI, the authors discuss how the system is able to identify alternative treatments for patients who may be receiving outdated or less than optimal treatments.
Up to a million patients in the UK live with heart failure. This condition reduces quality of life and is associated with an elevated risk of hospitalisation due to fluid build and problems breathing. According to the team, AI could tailor treatment for each patient to improve their quality of life.
“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,” said Dr Ify Mordi, Senior Lecturer at the University of Dundee.
“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 researchers relied on Red Star AI to develop software to scan patient’s exams, including echo reports, heart ultrasounds, and other medical data. The aim was to determine what treatment would be the best for each patient. Based on these assessments, the team was then able to create personalised plans for patients that lead to improvements in quality of life and markers of heart stress. Doctors have already try to do this, but the sheer number of health records to analyse makes this process very inefficient. The Scottish team emphasises how AI was able to scan the same records quickly and accurately.
“The NHS holds vast quantities of cradle to grave data, but this is far more than any single person can understand. Additionally, a lot of healthcare data is held in a Free Text format, which is difficult to analyse at scale,” said Andrew Conkie, CEO of Red Star AI. “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.”