Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governance surrounding its use and integration is often poorly understood. What the Health AI Partnership (HAIP) aims to do in this research is to better define the requirements for adequate organizational governance of AI systems in healthcare settings and support health system leaders to make more informed decisions around AI adoption. To work towards this understanding, we first identify how the standards for the AI adoption in healthcare may be designed to be used easily and efficiently. Then, we map out the precise decision points involved in the practical institutional adoption of AI technology within specific health systems. Practically, we achieve this through a multi-organizational collaboration with leaders from major health systems across the United States and key informants from related fields. Working with the consultancy IDEO [dot] org, we were able to conduct usability-testing sessions with healthcare and AI ethics professionals. Usability analysis revealed a prototype structured around mock key decision points that align with how organizational leaders approach technology adoption. Concurrently, we conducted semi-structured interviews with 89 professionals in healthcare and other relevant fields. Using a modified grounded theory approach, we were able to identify 8 key decision points and comprehensive procedures throughout the AI adoption lifecycle. This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States. We hope these findings can inform future efforts to build capabilities to promote the safe, effective, and responsible adoption of emerging technologies in healthcare.

翻译：私营与公共部门的结构及规范塑造着新兴技术在实际中的应用方式。在医疗健康领域，尽管人工智能应用日益普及，但围绕其使用与整合的组织治理机制却往往认知不足。健康人工智能伙伴关系（HAIP）旨在通过本研究更精准地界定医疗场景中AI系统所需的组织治理要求，并支持医疗系统领导者围绕AI采纳做出更明智的决策。为实现这一认知，我们首先探讨如何设计易于高效使用的医疗AI采纳标准。随后，我们绘制特定医疗系统内AI技术实际机构化采纳过程中涉及的具体决策节点。在实践层面，我们通过与全美主要医疗系统领导层及相关领域关键知情者开展多组织协作来达成目标。借助咨询机构IDEO[dot]org，我们得以组织面向医疗与AI伦理专业人士的可用性测试会议。可用性分析揭示了一个围绕模拟关键决策节点构建的原型，该原型契合组织领导层应对技术采纳的决策方式。与此同时，我们与89位医疗及相关领域专业人士进行了半结构化访谈。采用修正的扎根理论方法，我们识别出AI采纳生命周期中的8个关键决策节点及全面程序。这是迄今针对美国医疗系统AI采纳过程中现有治理结构与流程最详尽的质性分析之一。我们希望这些发现能为未来构建能力、推动医疗领域安全、有效且负责任地采纳新兴技术提供参考依据。