发表机构
University of Calgary（卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出FLARE框架，结合模糊逻辑等方法评估医疗AI采用的财务与运营影响，经案例研究验证其可量化成本与节约额，确定盈亏平衡点，为医疗AI部署决策提供支持。
AI 中文摘要
人工智能正越来越多地被引入医疗流程，但大多数评估强调模型准确率，而非在真实临床环境中采用的经济价值。本研究提出FLARE，一种用于评估医疗AI采用的财务与运营影响的系统性不确定性感知框架。FLARE结合模糊逻辑、时间驱动作业成本法与投资回报率分析，以估算临床服务交付成本、AI开发与运营成本，以及不确定性下流程整合的经济后果。该框架通过一项针对急性缺血性卒中CT卒中通路中AI辅助大血管闭塞检测的早期卫生技术评估案例研究得到验证。案例研究显示FLARE可在统一的作业基础模型中量化传统通路成本、AI相关开发与经常性成本，以及AI赋能的服务节约额。在预期假设下，分析确定年约3992例患者的盈亏平衡点，在年卒中典型规模约5000例患者时实现第一年正投资回报率。结果进一步表明，经济效益不仅取决于算法性能，还取决于患者数量、验证时间、基础设施选择及流程设计。FLARE为医疗AI采用的早期评估提供了透明且实用的决策支持框架，通过明确不确定性、资源使用与实施权衡，帮助临床医生、管理者及政策制定者确定AI部署何时具备经济可行性，以及运营调整可在何处提升价值。
英文摘要
Artificial intelligence is increasingly being introduced into healthcare workflows, yet most evaluations emphasize model accuracy rather than whether adoption is economically worthwhile in real clinical settings. This study proposes FLARE, a systematic and uncertainty-aware framework for evaluating the financial and operational implications of adopting AI in healthcare. FLARE combines fuzzy logic, time-driven activity-based costing, and return on investment analysis to estimate the cost of clinical service delivery, the cost of AI development and operation, and the economic consequences of workflow integration under uncertainty. The framework was demonstrated through an early health technology assessment case study of AI-assisted large vessel occlusion detection in the CT stroke pathway for acute ischemic stroke. The case study shows how FLARE can quantify conventional pathway cost, AI-related development and recurring costs, and AI-enabled service savings within a unified activity-based model. Under expected assumptions, the analysis identified a break-even threshold of approximately 3,992 patients per year, with positive first-year return on investment at typical annual stroke volumes of about 5,000 patients. The results further show that economic benefit depends not only on algorithmic performance, but also on patient volume, verification time, infrastructure choices, and workflow design. FLARE provides a transparent and practical decision-support framework for early-stage evaluation of AI adoption in healthcare. 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.
