FLARE Framework Calculates 3992-Patient Annual Break-Even for AI LVO Detection

FLARE integrates fuzzy logic with time-driven activity-based costing and ROI analysis to model conventional CT stroke pathway costs, AI development and recurring operational costs, and workflow integration effects under parameter uncertainty. The arXiv 2608.23643 case study quantifies verification time, infrastructure choices, and patient volume as primary drivers of net economic value rather than isolated algorithmic metrics.

Data output shows conventional pathway cost per case, AI-added fixed and variable costs, and net savings once volume exceeds the calculated threshold. Sensitivity runs confirm that verification time reductions and infrastructure reuse shift the break-even point by hundreds of patients, while performance alone produces smaller variance.

Existing health technology assessment methods treat economic viability as a post-deployment exercise; FLARE embeds it at the design stage. This addresses a documented gap where accuracy-focused evaluations overlook operational trade-offs that determine whether deployment occurs at all.

Hospitals adopting FLARE-style models will require activity-level data collection protocols and explicit uncertainty ranges before procurement decisions. Regulators may reference similar thresholds when updating AI reimbursement criteria.