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.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：私营和公共部门的结构与规范在塑造新兴技术的实际应用中发挥着重要作用。在医疗领域，尽管人工智能的采用日益普遍，但围绕其使用与整合的组织治理往往理解不足。卫生人工智能伙伴关系（HAIP）在本研究中的目标是更清晰地界定医疗场景下人工智能系统所需充分组织治理的要求，并支持卫生系统领导者围绕人工智能采用做出更明智的决策。为实现这一理解，我们首先确定了如何设计易于高效使用的医疗领域人工智能采用标准。随后，我们绘制了特定卫生系统内人工智能技术实际操作中涉及的具体决策节点。实践中，我们通过与全美主要卫生系统领导者及相关领域关键信息提供者的多组织协作实现了这一目标。与咨询公司IDEO.org合作，我们得以与医疗和人工智能伦理专业人士开展可用性测试会议。可用性分析揭示了一个以模拟关键决策节点为核心的原型，这些节点与组织领导者处理技术采用的方法相吻合。同时，我们对89名医疗及其他相关领域的专业人士进行了半结构化访谈。采用修正的扎根理论方法，我们识别出人工智能采用全生命周期中的8个关键决策节点及全面流程。这是迄今对全美卫生系统在人工智能采用中现有治理结构与流程进行的最详尽的定性分析之一。我们希望这些发现能够为未来提升能力建设、促进医疗领域新兴技术安全、有效及负责任的应用提供借鉴。