Efficient and privacy-enhancing consent management for health informatics data sharing

项目摘要： 为了降低成本和改善健康结果，患者数据系统化是至关重要的 收集、清理和分析，从而使我们能够为 诊断、管理和治疗疾病。一个示例应用领域是哮喘控制和预测。 仅在美国，约有4000万人患有终生哮喘(占美国人口的13%)和2600万人 人们(8%)患有目前的哮喘。开发更好的哮喘发作预测模型可能会导致 在加强预防策略、改善患者结局和显著降低医疗成本方面 减少了紧急护理需求。阻碍这种卫生保健管理模式的关键因素之一 是对分散在多个组织中的患者数据进行集成。这个根本性的挑战是 尤其是慢性疾病，如哮喘，患者通常在多个机构接受治疗 在一个区域内。此外，由于数据不准确，单站点研究可能提供不准确的图像。为 例如，由于某些选择偏见，来自特定种族群体的患者数量可能代表不足 在一个地方。此外，如果所有的急诊就诊，疾病的严重程度信息可能不完整 没有被记录下来。如果没有适当的记录联系和数据复制，许多疾病特有的情况 可能被过多地代表。例如，据报道，跨机构重复数据消除后，记录数量 与糖尿病相关的减少了24.0%，哮喘减少了28.0%，心肌梗死减少了10.9%。 因此，以减少重复的方式合并记录以及 个人信息的碎片化。尽管一直在努力实施健康信息 促进数据集成和交换的交换，跨多个医疗保健将患者记录链接起来 组织会带来巨大的安全和隐私挑战。与此同时，随着医疗保健的使用 分析和数据共享增加，必须通过以下方式确保患者对整体数据分析渠道的信任 要求患者做出“同意决定”。此同意决定涉及共享和访问 患者的健康数据，用于治疗、支付和医疗保健操作。因此，我们的医疗保健 当今的分析研究迫切需要一种产品，能够管理患者的同意，同时允许 跨多个机构的医疗保健数据的安全和隐私保护链接。 为了应对这些挑战，我们将开发一种隐私保护解决方案，该解决方案可以有效地捕获 同意，使用捕获的同意信息收集分布在特定资源中的患者数据 高效的卫生组织和2)跨不同卫生组织的不同用户托管的数据的链接 同时保护患者隐私并提供责任。尽管有一些解决方案可以管理 医疗同意和隐私保护记录联系，它们没有整合在一起。此外，现有的 技术要么不能轻松扩展以处理大量数据和/或在记录过程中泄漏敏感信息 联动过程。最后，我们不知道有任何现有的工具将私有记录链接与私有相结合 提供责任的区块链。

Project Abstract: To reduce costs and enhance health outcomes, it is of critical importance that patient data are systematically gathered, cleaned and analyzed, thereby allowing us to build more accurate, timely and reliable models for diagnosing, managing and treating diseases. One example application domain is asthma control and prediction. In USA alone, about 40 million people suffered from lifetime asthma (13% of the USA population) and 26 million people (8%) suffered from current asthma. Developing better predictive models for asthma attacks, can result in enhancing preventive strategies, improving patient outcomes, and significantly reducing healthcare costs due to reduced emergency care need. One of the key factors obstructing such models for health care management is the integration of patient data that are scattered across multiple organizations. This fundamental challenge is particularly acute for chronic diseases such as asthma where patients often receive care at multiple institutions within a region. Furthermore, single site studies may provide inaccurate picture due to data inaccuracies. For example, due to certain selection biases, number of patients from certain race group maybe underrepresented in one location. In addition, severity information of diseases may not be complete if all the emergency care visits are not recorded. Without proper record linkage and data duplication, many of the disease specific conditions may be over-represented. For instance, it is reported that after cross-institution deduplication, number of records related to diabetes reduced 24.0%, asthma reduced 28.0%, and myocardial infarction reduced 10.9%. Therefore, it is of paramount importance to merge records in a manner that mitigates duplication, as well as fragmentation, of an individual’s information. Although there have been efforts to implement health information exchanges to facilitate data integration and exchange, linking patient records across multiple health care organizations create significant security and privacy challenges. At the same time, as the usage of healthcare analytics and the data sharing increases, patient trust in the overall data analytics pipeline must be ensured by asking patients to make a “consent decision”. This consent decision concerns the sharing and accessing of the patient’s health data for treatment, payment, and health care operations purposes. As a result, our healthcare analytics research nowadays is at utmost need of a product that can manage patient consent while allowing secure and privacy-preserving linkage of health care data across multiple institutions. To address these challenges, we will develop a privacy-preserving solution that can 1) efficiently capture consent, use the captured consent information to gather patient data distributed across resources within a certain health organization efficiently and 2) link the data hosted by different users across disparate health organizations while protecting patient privacy and providing accountability. Although there exist some solutions for managing healthcare consent and privacy-preserving record linkage, they are not integrated. In addition, existing techniques either do not easily scale for large amounts of data and/or leak sensitive information during record linkage process. Finally, we are not aware of any existing tool that combines private-record linkage with private blockchains for providing accountability.