Advanced theory and methods for the de-identification of small cohorts, complex and composed health data

小群体、复杂组合健康数据去识别化的先进理论和方法

• 批准号: RGPIN-2016-06781
• 负责人: ElEmam, Khaled
• 金额: $ 2.26万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634404
• 关键词: Advanced; theory; methods; de; identification

The demand for health data for research and public health purposes has never been so great, from the growth in clinical trials transparency initiatives, Electronic Medical Records being used to build learning healthcare systems, the development of real world evidence databases that integrate health data from multiple sources and used for observational research, and a desire to link to wearables and other monitoring devices. The collection of data is coming from providers, payers, employers, wellness programs, and even patients themselves, with increasing collaboration between academic institutions, provider organizations, health care systems and life sciences companies.

研究和公共卫生目的对健康数据的需求从未如此之大，包括临床试验透明度倡议的增长、电子病历用于构建学习型医疗保健系统、集成来自多个领域的健康数据的真实世界证据数据库的开发。源并用于观察研究，并希望连接到可穿戴设备和其他监控设备。数据收集来自提供者、付款人、雇主、健康计划，甚至患者本身，学术机构、提供者组织、医疗保健系统和生命科学公司之间的合作日益加强。