Research study shows the cost-effectiveness of AI-enhanced heart failure screening
Early research shows that primary care clinicians using artificial intelligence electrocardiogram tools identified more unknown cases of heart pump weakness, also known as low ejection fraction, than without artificial intelligence. The new findings were published in Mayo Clinic Proceedings: Digital Health This type of screening has also been shown to be cost-effective in the long term, particularly in outpatient settings.
Gradual decline in heart function can be treated with medication, but it is difficult to detect. When a patient’s heart is unable to pump blood effectively, patients may or may not experience symptoms, and doctors may not order an echocardiogram or other diagnostic test to check ejection fraction unless symptoms occur. Peter Noseworthy, MD, a cardiologist at the Mayo Clinic and co-author of the study, noted that using artificial intelligence to catch hidden signs of heart failure during routine medical visits could mean treating patients earlier, slowing or stopping disease progression, and reducing related Medical care costs over time.
According to the study, the cost-effectiveness ratio for using AI-ECG was $27,858 per quality-adjusted life year, a measure of quality and longevity of life. The program was particularly cost-effective in the outpatient setting, with a much lower cost-effectiveness ratio of $1,651 per adjusted life year.
Researchers studied the economic impact of using an AI-ECG tool by using real-world information from 22,000 participants in the established EAGLE trial, tracking which patients had weaker heart pumps and which did not. They model the long-term progression of the disease, assigning a value to the health burden on patients and its impact on economic value.
“We classify the patient as AI-ECG positive, which means we recommend further testing for low ejection fraction, or AI-ECG negative, which requires no further testing. We then treat the patient along the normal care pathway and understand how much that will cost. cost.
Dr. Yao, the study’s senior author, noted that cost-effectiveness is an important aspect in evaluating AI technologies when considering what to implement in clinical practice.
“We know that early diagnosis leads to better, more cost-effective treatment options. To achieve this, we have been building a framework for the evaluation and implementation of artificial intelligence. The next step is to find ways to streamline this process so that We can reduce the time and resources required to conduct such a rigorous assessment,” said Dr. Yao.
The research was funded by the Mayo Clinic’s Robert D. and Patricia E. Kern Center for Health Care Services Sciences. The Mayo Clinic and certain researchers have a financial interest in the technology cited in this release. Mayo Clinic will use any revenue it receives to support its nonprofit mission of patient care, education and research.
2024-12-04 19:51:47