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[1]
Research study shows the cost-effectiveness of AI-enhanced heart failure screening
Earlier research showed that primary care clinicians using AI-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, M.D., a Mayo Clinic cardiologist and co-author of the study, notes 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. 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, Ph.D., a professor of Health Services Research at Mayo Clinic. Dr. 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 Dr. Yao. 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 in this news release. Mayo Clinic will use any revenue it receives to support its not-for-profit mission in patient care, education and research.
[2]
AI can boost cost-effective heart failure screening, study shows
Earlier research showed that primary care clinicians using AI-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, M.D., a Mayo Clinic cardiologist and co-author of the study, notes 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. 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, Ph.D., a professor of Health Services Research at Mayo Clinic. Dr. 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 Dr. Yao.
[3]
AI tool for heart failure screening demonstrates long-term cost savings
Mayo ClinicDec 5 2024 Earlier research showed that primary care clinicians using AI-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, M.D., a Mayo Clinic cardiologist and co-author of the study, notes 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. 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." Xiaoxi Yao, Ph.D., Professor of Health Services Research at Mayo Clinic Dr. 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 Dr. Yao. 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 in this news release. Mayo Clinic will use any revenue it receives to support its not-for-profit mission in patient care, education and research. Mayo Clinic Journal reference: Thao, V., et al. (2024). Cost-Effectiveness of AI-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the EAGLE Trial. Mayo Clinic Proceedings Digital Health. doi.org/10.1016/j.mcpdig.2024.10.001
[4]
Research study shows the cost-effectiveness of AI- | Newswise
ROCHESTER, Minn. -- Earlier research showed that primary care clinicians using AI-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, M.D., a Mayo Clinic cardiologist and co-author of the study, notes 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. 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, Ph.D., a professor of Health Services Research at Mayo Clinic. Dr. 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 Dr. Yao. 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 in this news release. Mayo Clinic will use any revenue it receives to support its not-for-profit mission in patient care, education and research. ### About Mayo Clinic Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.
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A new study by Mayo Clinic researchers demonstrates that AI-enhanced electrocardiogram (AI-ECG) tools for detecting weak heart pumps are not only effective but also cost-efficient, especially in outpatient settings.
A recent study published in Mayo Clinic Proceedings: Digital Health has revealed that artificial intelligence-enhanced electrocardiogram (AI-ECG) tools are not only effective in identifying unknown cases of weak heart pumps but are also cost-effective in the long term, particularly in outpatient settings 1234.
Incremental drops in heart function can be challenging to detect, as patients may not always exhibit symptoms when their heart is not pumping effectively. Traditionally, doctors may not order diagnostic tests like echocardiograms unless symptoms are present. However, the AI-ECG tool has shown promise in catching hidden signals of heart failure during routine visits 12.
The study, which analyzed data from 22,000 participants in the EAGLE trial, found that the cost-effectiveness ratio of using AI-ECG was $27,858 per quality-adjusted life year (QALY) 1234. This measure takes into account both the quality of life and years lived. Notably, the program proved to be even more cost-effective in outpatient settings, with a significantly lower cost-effectiveness ratio of $1,651 per QALY 1234.
Researchers simulated the long-term progression of heart disease using real-world information from the EAGLE trial. They categorized patients as either AI-ECG positive (requiring further testing for low ejection fraction) or AI-ECG negative (no further tests needed) 1234.
Dr. Xiaoxi Yao, a professor of Health Services Research at Mayo Clinic and senior author of the study, explained:
"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" 1234.
Dr. Peter Noseworthy, a Mayo Clinic cardiologist and co-author of the study, emphasized that using AI to detect hidden signals of heart failure during routine visits could lead to earlier treatment for patients. This early intervention has the potential to delay or stop disease progression and reduce related medical costs over time 1234.
The researchers stress the importance of cost-effectiveness in evaluating AI technologies for clinical practice implementation. Dr. Yao noted:
"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" 1234.
This study, funded by the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, represents a significant step forward in the integration of AI technologies in healthcare, potentially improving patient outcomes while optimizing resource allocation.
Reference
[2]
Medical Xpress - Medical and Health News
|AI can boost cost-effective heart failure screening, study shows[3]
A large international study reveals that AI is 14 times more accurate than human technicians in analyzing long-term ECG recordings, potentially revolutionizing cardiac diagnostics and addressing global healthcare worker shortages.
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A new AI program called PanEcho has shown remarkable accuracy in interpreting echocardiograms, potentially reducing wait times for results and speeding up medical care for heart patients.
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Recent studies showcase the power of AI in improving cardiovascular disease risk prediction through enhanced analysis of ECG and CT scan data, offering more precise and actionable insights for clinicians.
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Researchers from MIT and Harvard Medical School have developed CHAIS, an AI model that analyzes ECG data to predict heart failure risk, potentially replacing invasive procedures with comparable accuracy.
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A new AI model developed by researchers at Imperial College London can identify female patients at higher risk of heart disease by analyzing electrocardiograms (ECGs), potentially improving early detection and treatment for women.
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