A trial at Barts Health NHS Trust showed that AI technology made it possible to quickly identify patients at risk of complications from diabetes. In just weeks, the AI software was able to analyse a volume of data that clinicians say would have taken over 100 years to review manually.
The AI technology, supplied by Clinithink, was used by the trust to scan 14.2 million documents to identify patients with diabetic foot disease. The software, called CLiX unlock, trawled through medical records and notes to find 30% more patients with diabetes and 375% more patients with diabetic foot problems, making it easier for clinicians to schedule earlier treatments to spare feet and limbs from amputation.
Barts Health, which delivers healthcare to a diverse population of 2.5 million people in east London, was able to use the to analyse documents related to 775,217 patients written between 2018 and 2020. This identified 61,756 patients with diabetes and, of these, 3,119 patients with DFD.
Dr Charles Gutteridge, Chief Clinical Information Officer at Barts Health, said: “Attempting this scale of analysis manually would have been frankly impossible.
“Theoretically, it would have taken one clinician over a hundred years to review that volume of documents. So not only does AI technology help us find patients who we couldn’t otherwise find, it also saves precious clinical time.
“This is a first and most important step in being able to treat many patients earlier than would have been possible using a manual process to find them and preventing the seriou complications that may result in amputation.”
The Barts Health team now plans to use the characteristics extracted by the software in the cohort it identified, along with input from other sources, to determine whether the approach can predict which patients are most likely to develop the severe complications associated with diabetic foot disease (DFD).
Mr Sandip Sarkar, Consultant Vascular Surgeon at Barts Health and Lead Clinician for the project, added: “Using this advanced AI technology, we are very excited about the possibility of being able to predict which patients are most likely to experience the worst consequences of DFD.
“This will enable us to focus our precious clinical resource on those patients likely to benefit most from early intervention, which will also reduce the burden on hard-pressed acute services. This is how we need to manage chronic disease in the post-COVID era.”
Source : https://www.healtheuropa.eu/ai-technology-could-prevent-thousands-of-diabetes-related-amputations/110426
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