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Andrew MacLean

Using AI to Flag Coronary Artery Calcification: A Leap Towards Value-Based Care

Coronary Artery Calcification, Patient Management, Workflow
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In the ever evolving landscape of healthcare, AI is proving to be a game changer, especially in the realm of cardiovascular health. One notable application for AI is to flag patients with coronary artery calcification (CAC). This innovation is not only enhancing value-based care, but is empowering care teams with the best tools to promote population health. By combining imaging AI algorithms with natural language processing (NLP) that scours health records, systems can effectively catch patients at risk and provide preventive care much earlier than they could have otherwise.

The Radiologist’s Dilemma: CAC Measurement

Measuring CAC using non-gated, non-contrast CT scans is a validated technique for estimating CAC levels, which helps clinicians advise patients on necessary lifestyle changes to help prevent a heart attack in the future. Radiologists face significant challenges in consistently identifying and reporting CAC due to the time-consuming nature of the task. Incidental findings of CAC are common, but often go unreported due to the visual effort to quantify the calcification across multiple CT slices. Statistics suggest that approximately 50-75% of CAC burdens are not documented in radiology reports, meaning many patients are unaware of the dangers present in their coronary arteries.

Financial Incentives and Quality Metrics

As it stands, there are incentives for radiologists to automatically include and calculate CAC in their reports, driven by programs like the Merit-based Incentive Payment System (MIPS). Despite these incentives, the actual documentation of CAC remains somewhat sporadic. The key to overcoming this barrier lies in the automation of CAC identification and reporting. AI can seamlessly integrate into the radiology workflow, identifying and quantifying CAC without creating an additional burden on the radiologist. This not only enhances the efficiency of radiology departments, but contributes to the overall quality metrics of the health system.

AI and Population Health Management

The real value of AI in CAC identification extends beyond the reading room. By integrating imaging AI algorithms that identify CAC in CT scans and natural language processing (NLP) that can retroactively flag CAC cases in health records, providers can significantly improve population health management. When coming across suspected high levels of CAC, patients who are not already under the care of cardiology can be flagged for follow-up, ensuring they receive the necessary attention before a major cardiovascular event occurs. This proactive approach aligns perfectly with the principles of value-based care, which emphasize preventive measures and efficient management of chronic conditions.

Bridging the Gap Between Radiology and Cardiology

One of the challenges in managing CAC patients is the coordination between radiology and cardiology departments. AI helps to bridge this gap by ensuring that incidental findings of CAC are not overlooked. When AI detects CAC in a CT scan, it can be automatically inserted into the radiologist’s preliminary report, which is then picked up by NLP systems. These systems can cross-reference patient data to identify those who need a cardiology referral, ensuring seamless communication and follow-up. This multidisciplinary approach is crucial for effective patient management and optimizing outcomes.

Enhancing Collaboration with AI

AI is revolutionizing the way health systems identify and manage CAC. By automating the measurement process and integrating with NLP, AI enables radiologists to efficiently report CAC while ensuring that patients receive timely cardiology referrals and follow-up treatment. This not only improves individual patient outcomes, but also enhances population health management, aligning with the principles of value-based care. As healthcare continues to evolve, the integration of AI in CAC stands out as a prime example of how technology can be leveraged to provide better care that ultimately benefits patients, providers and health systems alike.

Click here to learn more about the transformative impact of Artificial Intelligence in cardiovascular treatment.

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Andrew MacLean

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