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医学科学数据共享与使用的伦理要求和管理规范(二)隐私变迁与挑战 被引量:9

Ethical Requirements and Management Standards for the Sharing and Re-use of Scientific Data in Health Care and Medicine(Ⅱ)Privacy Changes and Challenges
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摘要 个人健康隐私的保护是医学科学数据共享与再使用面临的挑战之一。受保护的个人健康信息随着生命科学的进展,特别是新一代测序技术在生物医学研究中的广泛应用而发生变化。随着临床基因测序的应用,基于电子健康记录的研究也带来遗传隐私风险。此外,数字化储存和电子传送极大地增加了隐私泄露的风险。共享数据的价值被认为与数据的完整性成正比,数据共享中必须处理好隐私保护与数据价值之间的矛盾和平衡。FAIR原则和去识别数据分别是再利用的数据标准和隐私保护的国际共识。通常这两个原则并不矛盾,但是不能解决所有问题。因此,建议包括分级共享等多种策略相结合。我们将在数据共享的伦理要求和管理规范中作为焦点内容之一进一步讨论。 Protection of individual health privacy is one of the challenges faced during sharing and re-use of scientific data in healthcare.The protected individual health information has changed with the progress of life science,especially the next-generation sequencing(NGS)has been widely used in biomedical research.Electric health record(EHR)-based secondary research also poses genetic privacy risks with the advent of clinical gene sequencing.In addition,the risk of privacy disclosure increases greatly through digital storage and release.It is believed that the value of the shared data is directly proportional to the integrity of data;and the contradiction between privacy protection and data value must be balanced in practice.Nowadays,FAIR principles and de-identification are suggested as international consensus for second research data standard and privacy protection respectively.They cannot solve all the problems in practice,although there is no contradiction generally.Thus,a combination of strategies is suggested,including hierarchical sharing.Then we will further discuss privacy protection as one of the focuses in the ethical requirements and management norms of data sharing.
作者 关健 Guan Jian(The National Population and Health Scientific Data Centre,Peking Union Medical College Hospital,Chinese Academyof Medical Sciences&Peking Union Medical College,Beijing 100730,China)
出处 《中国医学伦理学》 2020年第3期288-293,共6页 Chinese Medical Ethics
关键词 数据共享 个人健康隐私 受保护的健康信息 隐私保护 遗传隐私 去识别数据 FAIR原则 Data Sharing Individual Health Privacy The Protected Health Information Privacy Protection Genetic Privacy De-identification Data FAIR principles
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