VBC AI Scorecard: 10 Criteria for Investor-Ready Health Platforms

The promise of artificial intelligence in healthcare is undeniable, yet for health plans and investors navigating the complex landscape of value-based care (VBC), the critical question isn’t merely “does it work?” but “does it demonstrably deliver value that aligns with VBC contracts?” As VBC models mature, the need for AI health platforms to provide robust, peer-reviewed outcomes data and clear financial performance metrics becomes paramount. Without this verifiable evidence, even the most innovative AI solution risks being sidelined from meaningful participation in arrangements designed to reward demonstrable improvements in health and cost efficiency.

The VBC-Ready AI Health Scorecard: 10 Criteria for Participation

The transition to value-based care demands a fundamental shift in how health plans and providers evaluate technology. For AI health platforms, this means moving beyond aspirational claims to concrete, auditable results. Our VBC-Ready AI Health Scorecard outlines 10 essential criteria that determine which platforms can truly participate in value-based arrangements: peer-reviewed evidence, auditable ROI, risk adjustment, performance guarantee, multi-year data, population outcomes, data sharing, scalability, regulatory compliance, and clinical oversight. These criteria serve as a crucial filter for discerning genuine VBC partners from those offering unproven solutions. Consider the diverse landscape of AI health companies. Omada Health, Hinge Health, and Spring Health operate in chronic disease management and behavioral health, areas ripe for VBC innovation. iRhythm Technologies and HeartFlow offer diagnostic and prognostic tools that impact clinical pathways. Noom focuses on weight management, while BetterHelp provides mental health services. Commure, on the other hand, is building infrastructure. While each plays a vital role, their readiness for VBC contracts hinges on their ability to meet these rigorous standards. For instance, a platform like iRhythm Technologies, with its extensive real-world evidence and recent FDA 510(k) clearances for design modifications to its Zio AT device and an expected launch of its next-generation Zio MCT device in 2026, presents a different profile than a newer entrant still building its outcomes portfolio. HeartFlow received FDA 510(k) clearance for its Next Gen HeartFlow Plaque Analysis algorithm in September 2025, which has shown a 21% improvement in plaque detection and is now covered by Cigna. Hinge Health, which went public in May 2025 and had a market capitalization of approximately $4.3 billion as of May 2026, has expanded into new markets and conditions, including launching a migraine care program in Q1 2026 after receiving FDA 510(k) clearance for its Enso wearable. BetterHelp, acquired by Teladoc in 2015, faced a $7.8 million FTC settlement in 2023 regarding user data handling but has since moved to accept insurance coverage. As Hemant Taneja, a prominent voice in healthcare investment, has often emphasized, the path to scale in healthcare is inextricably linked to demonstrating clear value and clinical efficacy. The ability to provide peer-reviewed evidence is non-negotiable. This isn’t just about showing an AI model works in a lab; it’s about demonstrating its effectiveness in diverse patient populations under real-world conditions, published in reputable journals. An auditable ROI, demonstrating clear cost savings or improved health outcomes per dollar spent, is equally critical for health plan executives. This requires robust methodologies for calculating financial impact, often involving risk adjustment to account for baseline differences in patient populations. A performance guarantee, where the AI vendor shares risk based on achieving predefined outcomes, is increasingly expected in VBC contracts. Furthermore, multi-year data is essential to prove sustained impact, not just short-term gains. Platforms must demonstrate population outcomes, showing measurable improvements across cohorts, not just individual cases. Effective data sharing capabilities, compliant with stringent privacy regulations, are paramount for integration into existing health systems and for transparent reporting. Scalability ensures that the solution can be deployed across large patient populations without compromising quality or efficacy. Finally, strict regulatory compliance, adhering to frameworks like CMS VBC Rules and HIPAA, and robust clinical oversight are fundamental to trust and adoption.

Regulatory Foundations and Payer Expectations

The framework for value-based care is not merely aspirational; it is codified by significant regulatory bodies and industry standards. CMS VBC Rules, particularly those established by the Center for Medicare and Medicaid Innovation (CMMI), dictate the terms under which many VBC arrangements operate. These rules emphasize accountability for outcomes and cost containment, directly influencing payer requirements for AI health platforms. HIPAA, of course, remains the bedrock of patient data privacy and security, and any AI solution must demonstrate ironclad compliance. Significant updates to HIPAA are taking effect in 2026, including a major Security Rule update introducing new cybersecurity requirements and stricter compliance expectations, as well as new compliance requirements for substance use disorder (SUD) records that became mandatory in February 2026. While a final rule for the proposed Security Rule changes is still uncertain and facing pushback, organizations are advised to prepare. Certifications like HITRUST or SOC 2 Type II continue to be highly relevant, with HITRUST often providing stronger credibility in healthcare due to its rigor and regulatory alignment. HIPAA compliance requirements for health tech Beyond federal regulations, organizations like the National Committee for Quality Assurance (NCQA) set standards for quality measurement and improvement, which health plans frequently incorporate into their VBC contracts. NCQA updated its credentialing standards effective July 1, 2025, introducing shorter primary source verification windows and monthly ongoing monitoring, and has proposed updates for 2026 Health Equity Accreditation. AHIP (America’s Health Insurance Plans) and employer coalitions are increasingly vocal about the need for demonstrable ROI and clinical effectiveness from digital health solutions. Even specialty organizations like the American College of Cardiology (ACC) contribute to clinical guidelines that AI tools must align with to be considered credible. The convergence of these regulatory and organizational demands creates a high bar for AI health platforms seeking to engage in value-based care. As Dr. Eric Topol has consistently highlighted, the integration of AI into clinical practice must be underpinned by rigorous validation and a clear understanding of its impact on patient care and system efficiency, noting a “paradox” where proven AI tools are underutilized while unproven large language models are widely adopted.

The Imperative for Evidenced-Based AI in VBC

The message to AI health platforms, health plan executives, and investors is clear: the era of speculative AI in healthcare is drawing to a close, particularly within value-based care. The VBC-Ready AI Health Scorecard provides a concrete benchmark for evaluating solutions. Platforms that prioritize peer-reviewed evidence, demonstrate auditable ROI, offer performance guarantees, and show multi-year, population-level outcomes will be the ones that thrive. Those that cannot meet these rigorous standards will find themselves increasingly marginalized from the most significant and financially rewarding opportunities in healthcare. The investment thesis in health AI must now explicitly include a robust plan for outcomes data generation and transparent financial performance reporting. This shift is not just about compliance; it’s about building a sustainable future where AI truly delivers on its promise of better health outcomes at a lower cost. NCQA standards for digital health solutions