ResearchPod Summary
As artificial intelligence (AI) becomes more prevalent in clinical settings, most evaluations focus on algorithmic accuracy rather than the financial reality of adoption. This paper introduces FLARE (Fuzzy-Logic, Time-Driven Activity-Based Return on Investment Evaluation), a framework designed to quantify the economic and operational implications of AI in healthcare. The authors combine Time-Driven Activity-Based Costing (TDABC) with fuzzy logic to account for the inherent uncertainty in clinical workflows, such as variable staff time and patient complexity. They demonstrate the framework through a case study of AI-assisted large vessel occlusion (LVO) detection in the CT stroke pathway for acute ischemic stroke.
FLARE operates by mapping the entire lifecycle of an AI solution, from initial development and validation to ongoing operational costs. The framework uses triangular fuzzy numbers to represent activity durations, allowing for the calculation of pessimistic, expected, and optimistic cost scenarios. By integrating these estimates with TDABC, the authors calculate the cost of service delivery both with and without AI. This allows for a granular analysis of return on investment (ROI), break-even points, and the impact of patient volume on financial sustainability.
In the stroke pathway case study, the authors found that the AI solution reduces the cost per patient by approximately $54.65. While this per-patient saving is modest, it scales significantly with volume. The analysis identified a break-even threshold of 3,992 patients per year. At a typical annual volume of 5,000 patients, the AI solution yields a positive ROI within the first year of deployment. The results emphasize that economic success is not merely a function of model performance, but is deeply tied to workflow integration, infrastructure choices, and the ability to amortize fixed development costs over a sufficient patient base.
FLARE provides a transparent, evidence-based tool for hospital administrators and policymakers to evaluate AI investments before they are made. By making resource use, implementation trade-offs, and uncertainty explicit, the framework helps stakeholders move beyond hype-driven adoption to data-informed decision-making, identifying where operational changes or scaling strategies can improve the value of AI in clinical practice.
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