ROCHESTER, Minn. — Early research shows primary care clinicians using an AI-ECG tool identify more unknown cases of heart pump weakness, also known as low ejection fraction, than without AI It was shown that it did. The results of a new study published in Mayo Clinic Proceedings: Digital Health suggest that this type of screening is cost-effective in the long term, especially in outpatient settings.
Gradual decline in heart function can be treated with drugs, but it can be difficult to detect. If the heart is not beating effectively, the patient may or may not have symptoms, and unless there are symptoms, the doctor may order an echocardiogram or other diagnostic tests to check the ejection fraction. You can’t. Peter Noseworthy, M.D., a Mayo Clinic cardiologist and co-author of the study, says that using AI to catch hidden signals of heart failure during routine office visits could help patients treat patients earlier and slow the progression of the disease. or suspension, and associated reductions in medical care. It costs more over time.
According to this study, the cost-effectiveness ratio for using AI-ECG was $27,858 per quality-adjusted life year, a measure of quality of life and years lived. The program was particularly cost-effective in the outpatient setting, with a much lower cost-effectiveness ratio of $1,651 per quality-adjusted life year.
Researchers used real-world information from 22,000 participants in the established EAGLE trial to determine which patients’ heart pumps were weaker and which patients were weaker, and using an AI-ECG tool to We studied the economic impact. They simulated disease progression over time and assigned values
“We categorized patients as either AI-ECG positive (meaning we recommend further testing if the ejection fraction is low) or AI-ECG negative (no further testing required). Then , followed the usual course of treatment and considered the costs involved. Did they have an echocardiogram? Did they stay healthy, or would they later develop heart failure and require hospitalization? “We looked at different scenarios, costs and patient outcomes,” says Dr. Xiaoxi Yao, professor of health services research at the Mayo Clinic.
Dr. Yao, senior author of the study, said cost-effectiveness is an important aspect of evaluating AI technologies when considering what to implement in clinical practice.
“We know that early diagnosis can lead to better and more cost-effective treatment options. To get there, we are building a framework for AI evaluation and implementation. “The next step is to find ways to streamline this process so that it takes less time and the resources needed for such a rigorous evaluation,” Dr. Yao said. Masu.
This study was funded by Mayo Clinic’s Robert D. and Patricia E. Kahn Center for Health Care Delivery Sciences. Mayo Clinic and certain researchers have financial interests in the technology mentioned in this news release. Mayo Clinic uses proceeds received to support its nonprofit mission in patient care, education, and research.
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