Analysis of financial models for AI adoption in healthcare


The real-world business case for the use of AI in clinical practice continues to be a compelling topic. In this paper, scientists examined different potential financial models. The researchers argue that reimbursement is critical to the widespread adoption of AI, but achieving this requires alignment between different stakeholders. The authors analysed fee-for-service (FFS) and value-based care (VBC) as potential reimbursement mechanisms. FFS is familiar but resource-intensive, while VBC can be complex but financially rewarding. The researchers also propose a new model based on the average market price plus an add-on. The researchers state that ideally, the model should be transparent, sustainable and incentivise the appropriate use of AI. The paper concludes that existing FFS and VBC pathways can be leveraged for specific AI applications, while advocating for improvements to existing reimbursement frameworks.

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Scaling Adoption of Medical AI — Reimbursement from Value-Based Care and Fee-for-Service Perspectives

New England Journal of Medicine AI, 2024


Sustainable reimbursement is key for medical artificial intelligence (AI) to benefit patients and populations at scale; however, achieving reimbursement is complex and requires the support of various stakeholders. We explain the roles of the different stakeholders and the extent to which reimbursement mechanisms, including fee-for-service and value-based care, align stakeholder interests and facilitate the scaling of medical AI adoption.