To evaluate the economic value of an AI-enabled coronary revascularization decision support system using real-world data, focusing on cost-effectiveness and patient outcomes.
Key Findings:
72.4% of treatment decisions shifted to economically optimized options at a willingness-to-pay of $50,000 per QALY, indicating significant potential for cost savings.
Average cost saving of $22,960 and QALY gain of $22,439 per patient, demonstrating the economic benefits of AI integration.
In a conservative scenario, 53.2% of decisions shifted, with a QALY gain of $32,214 per patient, highlighting the robustness of AI recommendations even under limited adoption.
Interpretation:
AI can enhance the economic value of treatment decisions by reducing costs and improving patient outcomes through optimized decision-making, leading to better resource allocation in healthcare.
Limitations:
Real patient data cannot be shared without permission from data custodians, which limits external validation of the findings.
AI adoption was assumed to be limited in conservative scenarios, which may underestimate the potential impact of AI in real-world settings.
Conclusion:
AI-driven decision support tools have the potential to significantly improve economic outcomes in coronary revascularization by optimizing treatment pathways and reducing costs.
