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

基本信息

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
项目类别：
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

项目摘要

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：为了降低成本并提高健康状况，重要的是患者数据是至关重要的收集，清洁和分析，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。一个示例应用程序域是哮喘控制和预测。仅在美国，约有4000万人患有终身哮喘（占美国人口的13％）和2600万人们（8％）患有目前的哮喘。为哮喘攻击开发更好的预测模型，可能会导致在增强预防策略，改善患者预后并大大降低应有的医疗费用减少紧急护理需求。阻碍此类模型的医疗保健管理的关键因素之一是散布在多个组织中的患者数据的集成。这个基本挑战是特别是患者经常在多个机构接受护理的哮喘等慢性疾病特别急性在一个区域内。此外，由于数据不准确，单个站点研究可能会提供不准确的情况。为了例如，由于某些选择偏见，某些种族组的患者人数可能不足在一个位置。此外，如果所有急诊就诊，疾病的严重性信息可能还不完整未记录。没有适当的记录连锁和数据重复，许多特定疾病的疾病可能会过多代表。例如，据报道，跨机构重复数据删除后，记录数量与糖尿病有关，降低了24.0％，哮喘降低了28.0％，心肌梗塞降低了10.9％。因此，以减轻重复的方式合并记录至关重要分裂，个人信息。尽管已经努力实施健康信息交流以促进数据集成和交换，将患者记录链接在多个医疗保健中组织提出了重大的安全性和隐私挑战。同时，作为医疗保健的使用分析和数据共享增加，必须通过确保对整体数据分析管道的患者信任要求患者做出“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，付款和医疗保健操作。结果，我们的医疗保健如今，分析研究最多需要可以管理患者同意的产品，同时允许确保多个机构的医疗保健数据的安全和隐私链接。为了应对这些挑战，我们将开发一个保护隐私的解决方案，可以有效捕获同意，使用捕获的同意信息收集在某个资源中分发的患者数据有效的健康组织和2）将不同用户在不同卫生组织中托管的数据链接同时保护患者隐私并提供问责制。尽管有一些解决方案医疗保健的同意和保留隐私的记录链接，它们没有集成。另外，存在技术要么不容易扩展大量数据和/或记录中泄漏敏感信息链接过程。最后，我们不知道将私人唱片链接与私人链接结合的任何现有工具可提供问责制的区块链。