■对于在美国推进学习卫生系统(LHS)至关重要,为去识别的数据提供监管安全港,减少了从大规模护理中学习的障碍,同时将隐私风险降至最低。我们将去识别的数据政策作为一种机制,用于综合LHS的临床护理和人类受试者研究的道德义务,在概念上和实践上整合了护理和研究,模糊患者和受试者的角色。
■首先,我们讨论了在临床护理的受信背景下,针对系统二次使用收集的数据和组织对患者的尊重.我们认为,没有传统的知情同意或有利于个人的义务,取消身份可能允许二次使用取代护理的主要目的。接下来,我们考虑了通过隐私保护来最大限度地减少危害和通过促进学习和转化护理来最大限度地提高收益的有效性。我们发现,鉴于庞大的健康数据和当前的技术,去识别无法完全保护隐私。然而,它给学习带来了限制,也给有效的翻译带来了障碍。之后,我们在LHS伦理框架内评估去认同对分配正义的影响,在该伦理框架中,患者有义务为学习做出贡献,系统有义务将知识转化为更好的护理.这样的系统可能会加剧健康差距,因为它加速学习,而没有机制来确保个人的贡献和利益是公平和平衡的。
■我们发现,尽管其既有优势,在全系统范围内使用去识别对于信令方面可能是次优的,保护隐私或促进学习,满足患者和受试者的正义要求。
■最后,我们强调道德,社会经济,技术和法律挑战以及下一步措施,包括对实现LHS最大化效率的新方法的批判性评价,有效的学习和公正的翻译,而没有去识别的妥协。
UNASSIGNED: Critical for advancing a Learning Health System (LHS) in the U.S., a regulatory safe harbor for deidentified data reduces barriers to learning from care at scale while minimizing privacy risks. We examine deidentified data policy as a mechanism for synthesizing the ethical obligations underlying clinical care and human subjects research for an LHS which conceptually and practically integrates care and research, blurring the roles of patient and subject.
UNASSIGNED: First, we discuss respect for persons vis-a-vis the systemic secondary use of data and tissue collected in the fiduciary context of clinical care. We argue that, without traditional informed consent or duty to benefit the individual, deidentification may allow secondary use to supersede the primary purpose of care. Next, we consider the effectiveness of deidentification for minimizing harms via privacy protection and maximizing benefits via promoting learning and translational care. We find that deidentification is unable to fully protect privacy given the vastness of health data and current technology, yet it imposes limitations to learning and barriers for efficient translation. After that, we evaluate the impact of deidentification on distributive justice within an LHS ethical framework in which patients are obligated to contribute to learning and the system has a duty to translate knowledge into better care. Such a system may permit exacerbation of health disparities as it accelerates learning without mechanisms to ensure that individuals\' contributions and benefits are fair and balanced.
UNASSIGNED: We find that, despite its established advantages, system-wide use of deidentification may be suboptimal for signaling respect, protecting privacy or promoting learning, and satisfying requirements of justice for patients and subjects.
UNASSIGNED: Finally, we highlight ethical, socioeconomic, technological and legal challenges and next steps, including a critical appreciation for novel approaches to realize an LHS that maximizes efficient, effective learning and just translation without the compromises of deidentification.