ESOC 2023: AI tool outperforms human emergency call handlers in identifying stroke, new study shows
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The results, evaluated on calls from 2021, revealed that the AI framework performed more effectively than emergency call handlers in identifying stroke cases. The AI framework achieved a recall (sensitivity) of 63.0% and a precision (positive predictive value) of 24.9%, which resulted in an F1 score of 35.7. In contrast, emergency call handlers had a recall of 52.7% and precision of 17.1%, resulting in an F1 score of 25.8.
Dr Jonathan Wenstrup, one of the lead authors of the study from Copenhagen University Hospital, commented, "As one of the first points of contact for patients seeking medical assistance, emergency call handlers play a critical role in facilitating early and accurate stroke recognition. Many stroke cases can go undetected at this stage, leading to delays in treatment that can have potentially life-threatening consequences for patients."
Across Europe, stroke is the second leading cause of death and a major cause of adult disability, affecting over 1 million people each year. As populations continue to grow and age, the number of people living with stroke is projected to increase by 27% between 2017 and 2047 in the EU. Despite this, many strokes can be prevented, and if treated early, the likelihood of a positive outcome can be greatly improved.
"With the implementation of this new, cost-effective supporting tool, we can enhance stroke identification and ensure more patients receive appropriate and timely care, ultimately improving patient outcomes," added Dr Wenstrup.
View original content:https://www.prnewswire.co.uk/news-releases/esoc-2023-ai-tool-outperforms-human-emergency-call-handlers-in-identifying-stroke-new-study-shows-301830617.html
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