New findings show AI-ECG tools provide early diagnosis of low ejection fraction, improving quality of life while reducing related medical costs.
Summary: A new study published in Mayo Clinic Proceedings: Digital Health demonstrates that using artificial intelligence (AI)-enhanced electrocardiogram (ECG) tools to detect low ejection fraction—a sign of heart failure—is cost-effective, particularly in outpatient settings. The AI-ECG screening, which identifies patients with a weak heart pump during routine visits, can lead to earlier treatment, delay disease progression, and reduce long-term healthcare costs. Researchers found a cost-effectiveness ratio of $27,858 per quality-adjusted life year, with outpatient settings showing significantly greater cost-effectiveness at $1,651 per quality-adjusted life year. The study underscores the potential of AI in improving patient outcomes and its economic value in clinical practice.
Key Takeaways:
- AI-ECG Tools Facilitate Early Detection of Heart Failure: AI-enhanced ECG tools can identify low ejection fraction during routine visits, enabling earlier treatment and potentially preventing disease progression.
- Cost-Effectiveness in Outpatient Settings: The study found that AI-ECG screening is highly cost-effective in outpatient care, with a cost-effectiveness ratio of $1,651 per quality-adjusted life year.
- Framework for AI Implementation in Healthcare: Researchers emphasize the importance of establishing a rigorous evaluation framework for AI technologies, paving the way for streamlined integration into clinical practice to improve both care and cost efficiency.
Earlier research showed that primary care clinicians using artificial intelligence (AI)-electrocardiogram (ECG) tools identified more unknown cases of a weak heart pump, also called low ejection fraction, than without AI.
New study findings published in Mayo Clinic Proceedings: Digital Health suggest that this type of screening is also cost-effective in the long term, especially in outpatient settings.
Incremental drops in heart function are treatable with medication but can be hard to spot. Patients may or may not have symptoms when their heart is not pumping effectively, and doctors may not order an echocardiogram or other diagnostic test to check ejection fraction unless there are symptoms. Peter Noseworthy, MD, a Mayo Clinic cardiologist and co-author of the study, notes in a release that using AI to catch the hidden signals of heart failure during a routine visit can mean earlier treatment for patients, delaying or stopping disease progression, and fewer related medical costs over time.
AI-ECG Screening: Cost-Effectiveness Results
According to the study, the cost-effectiveness ratio of using AI-ECG was $27,858 per quality-adjusted life year—a measure of the quality of life and years lived. The program was especially cost-effective in outpatient settings, with a much lower cost-effectiveness ratio of $1,651 per quality-adjusted life year.
The researchers studied the economic impact of using the AI-ECG tool by using real-world information from 22,000 participants in the established EAGLE trial and following which patients had weak heart pumps and which did not. They simulated the progression of disease in the longer term, assigning values for the health burden on patients and the resulting effect on economic value.
“We categorized patients as either AI-ECG positive, meaning we would recommend further testing for low ejection fraction, or AI-ECG negative with no further tests needed. Then we followed the normal path of care and looked at what that would cost. Did they have an echocardiogram? Did they stay healthy or develop heart failure later and need hospitalization? We considered different scenarios, costs, and patient outcomes,” says Xiaoxi Yao, PhD, a professor of Health Services Research at Mayo Clinic, in a release.
Implications for AI in Clinical Practice
Yao, who is the senior author of the study, notes that cost-effectiveness is an important aspect of the evaluation of AI technologies when considering what to implement in clinical practice.
“We know that earlier diagnosis can lead to better and more cost-effective treatment options. To get there, we have been establishing a framework for AI evaluation and implementation. The next step is finding ways to streamline this process so we can reduce the time and resources required for such rigorous evaluation,” says Yao in a release.
This study was funded by Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. Mayo Clinic and some of the researchers have a financial interest in the technology referenced. Mayo Clinic notes in a release that it will use any revenue it receives to support its not-for-profit mission in patient care, education, and research.
