Navigating the value proposition of artificial intelligence in cardiovascular disease prevention

Abstract

Artificial intelligence (AI) is reshaping healthcare, influencing everything from administrative workflows to direct patient care. It holds promise in addressing the leading cause of death in the United States and a significant driver of costs - cardiovascular disease (CVD). Traditional reimbursement models are slow to incorporate AI. This article explores alternative financial incentives for AI in CVD prevention in fee-for-service (FFS) and value-based care models across four domains - risk prediction, diagnostics, imaging, clinical decision support, and administrative strategies - where AI may provide indirect revenue generation, cost savings, and efficiency gains. Under FFS, AI can enhance revenue by driving appropriate healthcare utilization, improving billing accuracy, and streamlining administrative workflows. In value-based models, AI aligns with incentives to prevent disease progression, reduce hospitalizations, and optimize shared savings. While AI-powered tools offer a compelling financial value proposition in cardiovascular prevention, their real-world adoption and impact will depend on successful clinical validation and seamless integration into existing workflows. The future of AI in cardiovascular care depends on a shift in reimbursement models, regulatory adaptation, and continued evidence generation demonstrating cost-effectiveness and improved outcomes. As healthcare transitions toward value-based care, AI has the potential to be a catalyst for better prevention and long-term cost savings, but only if its business case is strategically developed and implemented.

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