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本文引用的文献

1
Big Data and Public-Private Partnerships in Healthcare and Research: The Application of an Ethics Framework for Big Data in Health and Research.医疗保健与研究中的大数据及公私合作伙伴关系:健康与研究领域大数据伦理框架的应用
Asian Bioeth Rev. 2019 Sep 30;11(3):315-326. doi: 10.1007/s41649-019-00100-7. eCollection 2019 Sep.
2
Real-world Data to Generate Evidence About Healthcare Interventions: The Application of an Ethics Framework for Big Data in Health and Research.生成医疗保健干预证据的真实世界数据:大数据伦理框架在健康与研究中的应用。
Asian Bioeth Rev. 2019 Sep 4;11(3):289-298. doi: 10.1007/s41649-019-00095-1. eCollection 2019 Sep.
3
Openness in Big Data and Data Repositories: The Application of an Ethics Framework for Big Data in Health and Research.大数据与数据存储库中的开放性:大数据伦理框架在健康与研究领域的应用
Asian Bioeth Rev. 2019 Oct 1;11(3):255-273. doi: 10.1007/s41649-019-00097-z. eCollection 2019 Sep.
4
An Ethics Framework for Big Data in Health and Research.健康与研究领域大数据的伦理框架
Asian Bioeth Rev. 2019 Oct 1;11(3):227-254. doi: 10.1007/s41649-019-00099-x. eCollection 2019 Sep.
5
Genetic discrimination by Australian insurance companies: a survey of consumer experiences.澳大利亚保险公司的基因歧视:消费者体验调查。
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Identity inference of genomic data using long-range familial searches.利用远程家族搜索推断基因组数据的身份信息。
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JAMA Oncol. 2016 Mar;2(3):293-4. doi: 10.1001/jamaoncol.2015.5511.

精准医学与大数据:大数据伦理框架在健康与研究中的应用

Precision Medicine and Big Data: The Application of an Ethics Framework for Big Data in Health and Research.

作者信息

Schaefer G Owen, Tai E Shyong, Sun Shirley

机构信息

Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

Saw Swee Hock School of Public Health, National University of Singapore, Singapore.

出版信息

Asian Bioeth Rev. 2019 Sep 30;11(3):275-288. doi: 10.1007/s41649-019-00094-2. eCollection 2019 Sep.

DOI:10.1007/s41649-019-00094-2
PMID:33717316
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7747352/
Abstract

As opposed to a 'one size fits all' approach, precision medicine uses relevant biological (including genetic), medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone's disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in the (Xafis et al. 2019) to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner.

摘要

与“一刀切”的方法不同,精准医学利用有关个人的相关生物(包括基因)、医学、行为和环境信息,进一步实现医疗保健的个性化。这可能意味着能更好地预测某人的疾病风险,以及在其患病时进行更有效的诊断和治疗。通过将各种不同的数据集链接在一起,以揭示迄今未知的相关性和因果途径,大数据能够实现比以往更高的精准度和个性化定制。但这也引发了一些伦理问题,涉及利益平衡、匿名化的可行性、对家庭和群体的影响以及基因歧视。本文根据公共利益、正义、伤害最小化、透明度、参与度和反思性等价值观来分析这些问题,并将(Xafis等人,2019年)中提出的审议平衡方法应用于一个临床基因组数据共享的案例研究。有关如何使用该框架的解释,请参考那篇文章,包括对所涉及关键价值观的全面解释以及案例研究结尾处使用的平衡方法。我们的讨论旨在为参与精准医学实践以及治理和监督的人员提供帮助,以便以连贯和系统的方式解决出现的伦理问题。