New study shows AI alerts help more heart patients get life-saving valve disease care

A major new national study led by Inova cardiologist Wayne Batchelor, MD, MHS, MBA, shows that artificial intelligence (AI) can help doctors find and treat serious heart valve disease sooner – especially among patients who are often diagnosed too late.

Dr. Batchelor, an interventional cardiologist and President of Inova’s Medicine Service Line, and Schar Chair of Inova Schar Heart and Vascular, served as Principal Investigator and Chair of the Steering Committee for the ALERT study. The findings were presented as a late-breaking clinical trial at the American College of Cardiology’s Annual Scientific Sessions and simultaneously published in the Journal of the American College of Cardiology.

Using AI to spot serious heart valve disease earlier

Heart valve conditions such as aortic stenosis and mitral regurgitation can be life-threatening if left untreated. Yet many patients – particularly women and people from racial and ethnic minority groups – are not diagnosed or referred for treatment in time.

The ALERT study explored whether AI-powered alerts built into electronic health records could help close that gap.

“Our goal as physicians is straightforward: make sure patients who need treatment don’t fall through the cracks,” said Dr. Batchelor. “Doctors today are faced with an overwhelming amount of information. This study shows that well-designed, real-time AI alerts can help us recognize serious disease sooner and take action before patients get sicker.”

Dr. Batchelor also noted that earlier treatment benefits both patients and the healthcare system. “Severe valve disease can lead to repeated hospitalizations and, in some cases, death. Helping patients get timely care improves quality of life and reduces unnecessary healthcare costs.”

How the ALERT study worked

The ALERT trial included 765 clinicians across 35 hospitals in five U.S. health systems, reviewing more than 2,000 heart ultrasound tests (echocardiograms).

Using an AI-powered platform, clinicians received electronic alerts when a patient’s test results showed signs of severe valve disease – but no follow-up plan was documented. These alerts prompted providers to consider referral to a heart valve specialist or heart team.

Compared with usual care, AI alerts helped move patients faster toward appropriate evaluation and treatment.

Key findings

• Earlier identification of patients eligible for guideline directed valve procedures
• 40% relative increase in valve procedures within 90 days
• 27% increase in evaluations by a multidisciplinary heart team

Many patients went on to receive transcatheter aortic valve replacement (TAVR) – a less invasive procedure that replaces a damaged valve without open-heart surgery. TAVR often allows for shorter hospital stays and quicker recovery.

Addressing gaps in heart care

The study also highlighted persistent disparities in heart valve treatment. Today, White patients account for about 90% of TAVR procedures, while women and patients from Black, Hispanic, Asian, and other racial and ethnic groups are less likely to receive timely referrals.

“These tools don’t replace doctors – they support them,” said Matthew Sherwood, MD, System Director of Interventional Cardiology and Co-Director of Inova Structural Heart Disease Program. “By flagging patients earlier and based on objective data, we can make sure treatment decisions are driven by medical need and not delayed by system barriers or unconscious bias.”

Building the future of heart care

“Using technology to better support patients and clinicians is a top priority at Inova,” said Christopher O’Connor, MD, President of Inova Schar Heart and Vascular and Schar Distinguished Chair. “AI-driven insights like these help us improve access, streamline care, and ensure patients receive the most appropriate treatment as early as possible.”

“This research shows what’s possible when clinical leadership and technology work together,” said Dr. Batchelor. “It’s about delivering the right care to the right patient at the right time – and making sure no one is left behind.”