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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=649988
• 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.***We have made many critical achievements in our privacy research which represent improvements over the existing body of work. As health data uses evolve and the nature of health data that is being shared also evolves, there are important areas that require more study:*** - One of the best ways to allow the sharing of data for secondary purposes is to de-identify it. A key part of de-identification is the estimation of re-identification risk. Small cohorts pose a particular challenge to estimate re-identification risk. We will develop suitable estimators for small data sets. This will be important in developing successful models for clinical trials data sharing and studies on rare diseases and conditions.***- With the growing number of sources of data, there is more demand to join data sets for building real world evidence databases. Often the individual de-identified data are being linked without consideration of the potential increase in re-identification risk. There is a need to develop a composition theory around re-identification risk. A composition theory would facilitate the estimation of the risk of re-identification of a linked data set using information from the source data.***- Data complexity is growing rapidly; the data is constantly being updated and growing. The resulting complex data sets will require big data de-identification methods to ensure they scale appropriately. There is a need for streaming de-identification methods that are designed specifically for health data sets.***- With the increasing availability of free-form medical text in EMRs, the analysis of this information is adding detail and context to structured data. More realistic evaluation frameworks for the de-identification of free-form text need to be developed that are designed specifically for the de-identification context, and then tools evaluated using that framework.***Our lab has been effective in transitioning its research results into practice through standards and software that have been adopted globally. We will continue this trend as we develop new methods from this research, facilitating the sharing of electronic health information for secondary purposes while protecting the privacy of patients and the identity of providers.********

对用于研究和公共卫生目的的健康数据的需求从未如此之大，从临床试验透明度计划的增长，电子病历被用于构建学习型医疗保健系统，真实的世界证据数据库的开发，该数据库集成了来自多个来源的健康数据并用于观察性研究，以及希望链接到可穿戴设备和其他监测设备。随着学术机构、提供者组织、医疗保健系统和生命科学公司之间的合作不断增加，数据收集来自提供者、付款人、雇主、健康计划甚至患者本身。*我们在隐私研究方面取得了许多重要成就，这些成就代表了对现有工作的改进。随着卫生数据用途的演变和共享的卫生数据性质的演变，有一些重要领域需要更多的研究：* -允许为次要目的共享数据的最佳方法之一是消除数据的身份识别，消除身份识别的一个关键部分是估计重新识别的风险。小群体对估计重新识别风险提出了特别的挑战。我们将为小数据集开发合适的估计器。这对于开发临床试验数据共享和罕见疾病和病症研究的成功模型至关重要。随着数据源数量的不断增加，人们对连接数据集以构建真实的世界证据数据库的需求越来越大。通常情况下，个人去识别数据的链接没有考虑重新识别风险的潜在增加。有必要围绕重新识别风险发展一种组合理论。组合理论将有助于使用源数据中的信息估计重新识别链接数据集的风险。数据复杂性正在迅速增长;数据不断更新和增长。由此产生的复杂数据集将需要大数据去识别方法，以确保它们适当地扩展。需要专门为健康数据集设计的流式去识别方法。随着电子病历中自由格式医学文本的可用性越来越高，对这些信息的分析正在为结构化数据添加细节和上下文。需要为自由形式文本的去识别化制定更现实的评估框架，专门针对去识别化背景设计，然后使用该框架评估工具。我们的实验室通过全球采用的标准和软件，有效地将其研究成果转化为实践。我们将继续这一趋势，因为我们从这项研究中开发新的方法，促进电子健康信息的共享，同时保护患者的隐私和提供者的身份。