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

针对健康信息学数据共享的高效且增强隐私的同意管理

• 批准号: 10385293
• 负责人: Fahad Shaon
• 金额: $ 25.29万
• 依托单位: DATA SECURITY TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10385293
• 关键词: Accountability; Acute; Address; Age; Asthma; Awareness; Big Data; Caring; Chronic Disease; Cloud Computing; Cohort Studies; Collection; Complex; Computer software; Consent; Data; Data Analytics; Data Linkages; Data Management Resources; Data Store; Databases; Development; Diabetes Mellitus; Diagnosis; Disease; Emergency Care; Emergency department visit; Ensure; Environment; Health; Health Care Costs; Health Care Research; Health Personnel; Healthcare; Hospitals; Individual; Informed Consent; Institution; Link; Location; Machine Learning; Medical; Medical Research; Modeling; Modernization; Myocardial Infarction; Nature; Online Systems; Ontology; Outcome; Patient-Focused Outcomes; Patients; Population; Prevention strategy; Privacy; Privatization; Process; Public Health Informatics; Race; Records; Reporting; Research; Resources; Running; Secure; Security; Selection Bias; Severities; Site; Specific qualifier value; Techniques; Theft; Time; Trust; Visit; asthma exacerbation; asthmatic patient; base; blockchain; care outcomes; cloud based; cohort; cost; data exchange; data integration; data sharing; distributed data; diverse data; encryption; flexibility; health care model; health care service organization; health data; health disparity; health management; health organization; improved; indexing; new technology; operation; patient oriented; patient privacy; payment; predictive modeling; prevent; privacy preservation; tool

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.

项目摘要： 为了降低成本和提高健康结果，系统地收集患者数据至关重要。 收集，清理和分析，从而使我们能够建立更准确，及时和可靠的模型， 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%），2600万人患有哮喘。 8%的人目前患有哮喘。开发更好的哮喘发作预测模型， 在加强预防策略，改善病人的结果，并显着降低医疗成本， 减少紧急护理需求。阻碍这种卫生保健管理模式的关键因素之一是 是分散在多个组织中的患者数据的集成。这一根本性挑战是 对于哮喘等慢性病尤其严重，患者经常在多个机构接受护理 在一个区域内。此外，由于数据不准确，单中心研究可能提供不准确的图片。为 例如，由于某些选择偏倚，来自某些种族组的患者数量可能代表性不足 在一个地方。此外，如果所有的紧急护理访问都是在 没有记录。如果没有适当的记录链接和数据复制，许多疾病的具体条件， 可能会被夸大。例如，据报告，在跨机构重复数据消除后， 与糖尿病相关的减少24.0%，哮喘减少28.0%，心肌梗死减少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。尽管已经做出努力， 交换，以促进数据集成和交换，将多个医疗保健系统中的患者记录联系起来 组织会带来重大的安全和隐私挑战。同时，随着医疗保健的使用， 随着分析和数据共享的增加，必须通过以下措施确保患者对整个数据分析管道的信任 要求患者做出“同意决定”。该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目的。因此，我们的医疗保健 如今的分析研究迫切需要一种产品，可以管理患者的同意，同时允许 在多个机构之间建立安全和隐私保护的医疗保健数据链接。 为了应对这些挑战，我们将开发一种隐私保护解决方案，它可以1）有效地捕获 同意，使用捕获的同意信息来收集在一定范围内跨资源分布的患者数据。 2）将不同用户托管的数据跨不同的卫生组织链接起来 同时保护患者隐私并提供问责制。尽管存在一些管理的解决方案， 医疗保健同意和隐私保护记录联系，它们没有整合。此外，现有 这些技术或者不容易针对大量数据进行缩放和/或在记录期间泄漏敏感信息 链接过程最后，我们不知道有任何现有的工具将私有记录链接与私有 区块链提供问责制。