Artificial intelligence is increasingly integrated into healthcare workflows, yet most evaluations focus on model accuracy rather than economic viability in real clinical settings. To address this gap, the paper introduces FLARE—a systematic, uncertainty‑aware framework for assessing the financial and operational impact of AI adoption in healthcare.
FLARE combines fuzzy logic, time‑driven activity‑based costing (TDABC), and return on investment (ROI) analysis to estimate, under uncertainty, the cost of clinical service delivery, AI development and operation, and the economic consequences of workflow integration.
The framework is demonstrated through an early health technology assessment case study: AI‑assisted large vessel occlusion (LVO) detection in the CT stroke pathway for acute ischemic stroke. The case shows how a unified activity‑based model can quantify conventional pathway cost, AI‑related development and recurring costs, and AI‑enabled service savings.
Under expected assumptions, the analysis identifies a break‑even threshold of roughly 3,992 patients per year, with a positive first‑year ROI at typical annual stroke volumes of about 5,000 patients. The results further reveal that economic benefit depends not only on algorithmic performance but also on patient volume, verification time, infrastructure choices, and workflow design.
FLARE offers a transparent and practical decision‑support framework for early‑stage AI adoption evaluation. By making uncertainty, resource use, and implementation trade‑offs explicit, it helps clinicians, administrators, and policymakers determine when AI deployment is economically viable and where operational changes may improve value.
Blogger's Review: FLARE’s integration of cost accounting with uncertainty analysis provides a pragmatic tool for commercializing AI in healthcare, and its methodology could be extended to many other clinical domains.