The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge the chasm between AI model development and achievable benefit. To accomplish this, the Data Science team at Stanford Health Care has developed a Testing and Evaluation (T&E) mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan. We report on FURM assessments done to evaluate six AI guided solutions for potential adoption, spanning clinical and operational settings, each with the potential to impact from several dozen to tens of thousands of patients each year. We describe the assessment process, summarize the six assessments, and share our framework to enable others to conduct similar assessments. Of the six solutions we assessed, two have moved into a planning and implementation phase. Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.

翻译：使用人工智能（AI）指导患者护理或运营流程的影响，是AI模型输出、基于该输出的决策协议以及相关利益方采取必要后续行动的能力三者之间的相互作用。在部署前评估这种相互作用的影响，并在部署后实时研究，对于弥合AI模型开发与实际效益之间的鸿沟至关重要。为此，斯坦福医疗保健数据科学团队开发了一套测试与评估（T&E）机制，通过伦理审查识别潜在价值冲突、模拟评估有用性、财务预测评估可持续性，以及分析确定IT可行性、设计部署策略、推荐前瞻性监控与评估计划，来识别公平、有用且可靠的AI模型（FURM）。我们报告了针对六种AI引导解决方案（涵盖临床和运营场景）进行的FURM评估结果，每种方案每年可能影响数十至数万名患者。我们描述了评估过程，总结了六项评估，并分享了我们的框架以帮助其他机构开展类似评估。在评估的六种解决方案中，有两种已进入规划与实施阶段。我们的创新贡献——通过模拟进行有用性估算、量化可持续性的财务预测、以及进行伦理评估的流程——及其底层方法和开源工具，可供其他医疗系统对候选AI解决方案进行可操作的评估。