[论文解读] Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Jee Young Kim, William Boag+14 more|arXiv (Cornell University)|Apr 25, 2023

Electronic Health Records Systems被引用 5

一句话总结

该论文通过分析89名专业人士的定性研究及对医疗体系领导者的可用性测试，识别出美国医疗体系中人工智能组织治理的8个关键决策点。研究提出了一套结构化框架，以促进安全、公平且有效的AI采纳，使机构流程与临床、伦理及运营现实相一致。

ABSTRACT

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.

研究动机与目标

• 解决医疗领域AI缺乏明确组织治理结构的问题，尤其是在缺乏标准化监管框架的背景下。
• 理解医疗体系当前在临床环境中采纳和整合AI的决策方式。
• 识别阻碍负责任AI部署的机构能力、专业知识和决策流程方面的缺口。
• 为医疗体系领导者、AI开发者和政策制定者提供可操作的治理资源和共享标准。
• 通过聚焦临床医生自主权、跨学科协作和系统性问责，促进公平且可信的AI整合。

提出的方法

• 采用修改后的基础理论方法，对来自医疗、AI伦理及相关领域的89名专业人士进行半结构化访谈。
• 通过围绕AI采纳关键决策点设计的原型，对医疗和AI伦理专业人士进行可用性测试。
• 通过与主要美国医疗体系及外部专家的多组织协作，绘制机构决策流程。
• 识别出反复出现的治理挑战以及机构角色，如算法策展人、中介人和表述者。
• 分析组织工作流程、政策环境和临床整合需求，以界定实际的治理程序。
• 将研究发现综合为8个决策点的框架，反映现实世界中的机构采纳流程。

实验结果

研究问题

• RQ1 医疗体系在临床护理中采纳AI时，所面临的的核心决策点是什么？
• RQ2 当前的组织治理结构在多大程度上支持或阻碍AI在医疗中的安全与公平整合？
• RQ3 在技术评估之外，有效治理AI采纳所需的机构角色和能力是什么？
• RQ4 跨学科协作如何提升临床环境中AI整合的质量与可信度？
• RQ5 医疗体系中存在哪些系统性障碍，阻碍AI治理的持续性、公平性和问责性？

主要发现

• 在各医疗体系中识别出8个关键决策点，这些决策点构成了AI采纳组织治理的结构，形成可重复的机构决策框架。
• 临床医生对AI的信任取决于透明度、可解释性以及对模型行为和数据来源的持续记录。
• 新机构角色（如算法策展人、中介人和表述者）至关重要，但在当前医疗体系中往往缺失或资源不足。
• 由临床医生、数据科学家和技术专家组成的跨学科团队，对于评估可行性、减轻偏见和确保临床安全至关重要。
• AI治理必须是机构性的，而非仅算法性的，应聚焦于社会技术系统和结果，而非孤立的模型表现。
• 专业知识和资源在医疗体系内部及之间的不均衡分布，可能加剧AI采纳和结果中的不平等。