Valuing diagnostic AI: a structured reimbursement model for learning healthcare systems

Area of Science:

• Health Informatics
• Medical Economics
• Artificial Intelligence in Healthcare

Background:

• AI-based diagnostic decision support systems (DDSS) are increasingly vital in healthcare, aiding interpretation and improving outcomes.
• Current reimbursement structures struggle to evaluate and integrate DDSS, hindering adoption due to insufficient traditional KPIs.
• This disconnect limits the sustainable implementation of valuable AI diagnostic tools.

Purpose of the Study:

• To address the economic and regulatory gap for AI-assisted diagnostics.
• To introduce a structured, point-based reimbursement model for DDSS integration.
• To align reimbursement with the clinical value and complexity of AI in diagnostics.

Main Methods:

• Developed a structured, point-based reimbursement model referencing German and American coding systems.
• Calibrated points using a multi-criteria approach anchored to existing medical codes.
• Designed the model to link reimbursement levels with diagnostic complexity and AI contribution.

Main Results:

• The proposed model facilitates fair compensation for DDSS, encouraging meaningful use and responsible deployment.
• It establishes an auditable, feedback-driven structure supporting adaptive payments in learning healthcare systems.
• The framework acts as a governance mechanism aligning economic incentives with clinical and operational priorities.

Conclusions:

• The point-based model enables sustainable integration and transparent valuation of AI-driven diagnostics.
• It supports the development of learning healthcare systems by fostering continuous refinement of AI tools.
• This approach promotes responsible clinical adoption and aligns economic drivers with ethical considerations.