Economic Evaluation of AI-Driven Decision Support in Coronary Revascularization
Overview
This simulation study evaluated an AI-enabled clinical decision support tool for coronary revascularization using real-world data from 25,942 patients. The AI tool optimized treatment decisions, resulting in significant cost savings and quality-adjusted life year (QALY) gains compared to actual clinical decisions.
Background
Coronary artery disease treatment decisions often involve choosing among medical therapy, percutaneous coronary intervention (PCI), and coronary artery bypass grafting (CABG). While AI models have been developed to assist in these decisions, their health economic impact has been underexplored. This study used retrospective data from Alberta, Canada, to simulate the economic value of AI-guided treatment decisions based on predicted risks of major adverse cardiovascular events and mortality. The analysis focused on cost-effectiveness at a willingness-to-pay threshold of $50,000 per QALY.
Data Highlights
Metric
Value
Number of patients analyzed
25,942
Percentage of treatment decisions shifted (full AI adoption)
72.4%
Average cost saving per patient
$22,960
QALY gain equivalent per patient
Up to $22,439
Percentage of treatment decisions shifted (limited AI adoption)
53.2%
QALY gain equivalent per patient (limited adoption)
Up to $32,214
Key Findings
AI-driven decision support led to a shift in 72.4% of actual treatment decisions when fully adopted.
These shifts resulted in an average cost saving of $22,960 per patient.
Quality-adjusted life year gains were equivalent to up to $22,439 per patient with full AI adoption.
Even with limited AI adoption, 53.2% of decisions shifted, yielding QALY gains equivalent to up to $32,214 per patient.
AI predictions incorporated 3- and 5-year risks of major adverse cardiovascular events and all-cause mortality to guide treatment choices.
Use of AI potentially reduces future complications and improves patient outcomes, optimizing health system economic value.
Clinical Implications
Incorporating AI-driven clinical decision support in coronary revascularization can enhance treatment personalization, leading to improved patient outcomes and substantial cost savings. Clinicians may consider integrating such AI tools to optimize decision-making, especially given the significant proportion of treatment plans that could be economically improved. Even partial adoption of AI guidance can yield meaningful health and economic benefits.
Conclusion
AI-enabled decision support for coronary revascularization has the potential to significantly optimize treatment decisions, reduce healthcare costs, and improve patient quality of life. These findings support further integration and evaluation of AI tools in clinical practice for coronary artery disease management.
References
Mohr FW et al. Lancet 2013 -- Coronary artery bypass graft surgery versus percutaneous coronary intervention in patients with three-vessel disease and left main coronary disease
Magnuson EA et al. Circulation Cardiovasc Interventions 2022 -- Cost-effectiveness of percutaneous coronary intervention versus bypass surgery for patients with left main disease
Farkouh ME et al. Am Heart J 2008 -- Design of the FREEDOM trial for optimal management of multivessel disease in diabetes mellitus
Boden WE et al. N Engl J Med 2007 -- Optimal medical therapy with or without PCI for stable coronary disease
Gaudino M et al. Lancet 2023 -- Current concepts in coronary artery revascularisation
