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

研究和公共卫生目的对健康数据的需求从未如此之大，从临床试验透明度倡议的增长，电子医疗记录被用于建立学习型医疗保健系统，真实世界证据数据库的发展，整合了来自多个来源的健康数据并用于观察研究，以及与可穿戴设备和其他监测设备连接的愿望。随着学术机构、医疗服务提供者组织、医疗保健系统和生命科学公司之间的合作日益加强，数据的收集来自医疗服务提供者、付款人、雇主、健康计划，甚至患者自己。***我们在隐私研究方面取得了许多重要成就，这代表了对现有工作的改进。随着卫生数据用途的演变和正在共享的卫生数据的性质的演变，有一些重要领域需要进行更多的研究：*** -允许出于次要目的共享数据的最佳方法之一是去识别数据。去识别的一个关键部分是对再识别风险的估计。小队列对估计再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。这对于为罕见疾病和病症的临床试验、数据共享和研究开发成功的模式将是重要的。***-随着数据来源的增加，有更多的需求加入数据集，以建立真实世界的证据数据库。通常，在不考虑可能增加重新识别风险的情况下，将个别去识别数据链接起来。有必要围绕重新识别风险发展一种组合理论。组合理论将有助于估计使用源数据中的信息重新识别关联数据集的风险。***-数据复杂性快速增长；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来确保其适当扩展。有必要采用专门为卫生数据集设计的流化去识别方法。***-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。需要为自由格式文本的去识别性开发专门为去识别性上下文设计的更现实的评估框架，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出新的方法，促进二级目的的电子健康信息共享，同时保护患者的隐私和提供者的身份。********

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.********