Health economic simulation modeling of an AI-enabled clinical decision support system for coronary revascularization
By
Tom Mullie
Arjun Puri
Emma Bogner
Bryan Har
Colm J. Murphy
Robert C. Welsh
Benjamin Tyrrell
Christopher L. F. Sun
Joon Lee
February 16, 2026
Clinical Scorecard: Economic Evaluation of an AI-Driven Clinical Decision Support Tool for Coronary Revascularization: A Simulation Study
At a Glance
Category Detail
Condition Obstructive coronary artery disease requiring revascularization decisions Key Mechanisms AI-enabled prediction of 3- and 5-year major adverse cardiovascular events and all-cause mortality to guide treatment choice Target Population Adult patients with obstructive coronary artery disease Care Setting Cardiovascular care settings involving decision-making for medical therapy, percutaneous coronary intervention, or coronary artery bypass grafting
Key Highlights
AI decision support shifted 72.4% of actual treatment decisions to economically optimized options at $50,000/QALY willingness-to-pay. Average cost savings of $22,960 and QALY gains equivalent to $22,439 per patient were observed with AI-guided decisions. Even with limited clinician AI adoption, 53.2% decision shifts yielded QALY gains equivalent to $32,214 per patient. Guideline-Based Recommendations
Diagnosis
Use AI predictions of major adverse cardiovascular events and mortality to inform revascularization decisions. Management
Incorporate AI-enabled clinical decision support to optimize treatment selection among medical therapy, PCI, and CABG. Consider health economic outcomes alongside clinical factors when choosing revascularization strategies. Monitoring & Follow-up
Monitor patient outcomes and costs longitudinally to assess real-world impact of AI-guided treatment decisions. Risks
Potential challenges include clinician adoption rates and integration of AI tools into clinical workflows. Patient & Prescribing Data
Adults with obstructive coronary artery disease undergoing revascularization decision-making
AI-driven recommendations can substantially alter treatment choices, improving cost-effectiveness and patient quality-adjusted life years.
Clinical Best Practices
Integrate AI decision support tools to complement clinician judgment in coronary revascularization. Educate clinicians on interpreting AI predictions to enhance shared decision-making. Evaluate economic and clinical outcomes continuously to refine AI tool utility and adoption. References
