Patient Privacy in the Era of Big Data.
Patient Privacy in the Era of Big Data.
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DOI：
10.4274/balkanmedj.2017.0966
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发表时间：
2018-01-20
影响因子：
3
通讯作者：
Kayaalp M
中科院分区：
文献类型：
--
作者：
Kayaalp M
1948年联合国大会通过的《世界人权宣言》将隐私权定义为一项基本人权。然而，对于什么是隐私仍然没有达成共识。在本文中，我们将回顾从希波克拉底时代到社交媒体和大数据时代，隐私作为一个概念的演变。为了了解现代患者隐私保护措施并正确解读美国当前的监管框架，我们需要分析和理解个人身份信息、个人身份健康信息、受保护的健康信息和去身份化的概念。《健康保险流通和责任法案》的隐私规则确定了监管框架，并在保护措施和获取二级（科学）用途的健康信息之间取得平衡。该规则规定了健康信息受法律保护的条件，以及如何将受保护的健康信息去标识化以供二次使用。随着人工智能和计算语言学的出现，计算文本去识别算法产生的去识别结果几乎与人类专家产生的结果一样，但速度更快，更一致，而且基本上是免费的。现代临床文本去识别系统现在为大数据铺平了道路，使科学家能够访问去识别的临床信息，同时坚定地保护患者隐私。然而，临床文本去识别并不是一个完美的过程。为了最大限度地保护患者隐私，并将临床和科学信息从电子医疗保健系统的限制中解放出来，所有利益相关者，包括患者、卫生机构和机构审查委员会、科学家和科学界，以及监管和执法机构必须密切合作。一方面，公共卫生法和隐私条例规定了规则和责任，例如仅要求和授予科学研究所需的健康信息。另一方面，去识别系统的开发人员提供了使用不同操作模式的指导方针，以最大限度地提高其工具的有效性和去识别的成功。拥有临床知识库的机构需要严格遵守这些规则和指导方针，以成功地保护患者隐私。为了向科学界打开大数据的大门，医疗机构需要得到公众、科学界以及地方、州和联邦立法者和政府机构的支持，以实现去识别和数据共享。
Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data. To appreciate the modern measures of patient privacy protection and correctly interpret the current regulatory framework in the United States, we need to analyze and understand the concepts of individually identifiable information, individually identifiable health information, protected health information, and de-identification. The Privacy Rule of the Health Insurance Portability and Accountability Act defines the regulatory framework and casts a balance between protective measures and access to health information for secondary (scientific) use. The rule defines the conditions when health information is protected by law and how protected health information can be de-identified for secondary use. With the advents of artificial intelligence and computational linguistics, computational text de-identification algorithms produce de-identified results nearly as well as those produced by human experts, but much faster, more consistently and basically for free. Modern clinical text de-identification systems now pave the road to big data and enable scientists to access de-identified clinical information while firmly protecting patient privacy. However, clinical text de-identification is not a perfect process. In order to maximize the protection of patient privacy and to free clinical and scientific information from the confines of electronic healthcare systems, all stakeholders, including patients, health institutions and institutional review boards, scientists and the scientific communities, as well as regulatory and law enforcement agencies must collaborate closely. On the one hand, public health laws and privacy regulations define rules and responsibilities such as requesting and granting only the amount of health information that is necessary for the scientific study. On the other hand, developers of de-identification systems provide guidelines to use different modes of operations to maximize the effectiveness of their tools and the success of de-identification. Institutions with clinical repositories need to follow these rules and guidelines closely to successfully protect patient privacy. To open the gates of big data to scientific communities, healthcare institutions need to be supported in their de-identification and data sharing efforts by the public, scientific communities, and local, state, and federal legislators and government agencies.
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影响因子：
4
作者：
Meystre SM;Friedlin FJ;South BR;Shen S;Samore MH
通讯作者：
Samore MH
DOI：
10.1197/jamia.m2131
发表时间：
2008-01-01
影响因子：
6.4
作者：
Johnson, Stephen B.;Bakken, Suzanne;Stetson, Peter
通讯作者：
Stetson, Peter
影响因子：
2.9
作者：
Feinberg, Wilfred
通讯作者：
Feinberg, Wilfred
影响因子：
3.5
作者：
Gupta, D;Saul, M;Gilbertson, J
通讯作者：
Gilbertson, J
影响因子：
3
作者：
Kushida, Clete A.;Nichols, Deborah A.;Griffin, Kara
通讯作者：
Griffin, Kara