Value-Based Care Transformation: The Convergence of Agentic AI and Blockchain
Year: 2025
Type: other
Abstract: This research addresses the critical gap between value-based care promise and reality in U.S. healthcare. Despite $4.3 trillion annual spending, only 46% of physicians participate in VBC, achieving minimal impact with current solutions. Through systematic analysis of 183 sources from leading consultancies and government agencies, this study identifies six fundamental barriers: financial constraints, technological deficits, administrative complexity, strategic gaps, regulatory impediments, and cultural resistance. Current approaches yield less than 2% cost reduction despite $60 billion annual investment. The paper proposes a transformative solution combining Agentic AI (autonomous healthcare agents) with blockchain technology (immutable records and smart contracts). Based on successful implementations in Estonia, Singapore, and the UK, conservative projections indicate: - $544 billion annual savings (12.6% of healthcare spending)- 30-40% improvement in clinical outcomes- 75% reduction in administrative burden- 100% real-time interoperability The research presents a three-phase implementation pathway and comprehensive regulatory framework (HIST Act) to achieve transformation by 2030. Technical architecture specifications, risk mitigation strategies, and international case studies validate feasibility. This work provides the first detailed convergence model for AI-blockchain in healthcare, offering actionable insights for policymakers, healthcare executives, and technology leaders. The evidence demonstrates that this convergence represents not merely an improvement but healthcare's inevitable future. Key contributions: (1) Empirical quantification of VBC barriers, (2) Technical architecture for convergent solution, (3) Regulatory framework proposal, (4) Conservative financial modeling, (5) International validation evidence.
Author: Jaisawal, Sudheer
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6968072565
Cites:
Cited by:
Related to: