Tools for standardizing clinical research metadata using HL7 FHIR

使用 HL7 FHIR 标准化临床研究元数据的工具

• 批准号: 9353446
• 负责人: Guoqian Jiang
• 金额: $ 47.7万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9353446
• 关键词: Area; Big Data; Big Data to Knowledge; Biology; Cells; Clinical; Clinical Informatics; Clinical Research; Collaborations; Communities; Complex; Computer software; Data; Data Analytics; Data Discovery; Data Element; Data Set; Databases; Development; Dictionary; Ecosystem; Face; Failure; Funding Opportunities; Goals; Healthcare; Informatics; International; Malignant Neoplasms; Metadata; Methods; Modeling; Ontology; Patients; Performance; Phenotype; Pilot Projects; Process; Records; Research Infrastructure; Research Personnel; Resources; Semantics; Services; Standardization; System; Technology; Testing; Theoretical model; Translational Research; United States National Institutes of Health; anticancer research; base; big biomedical data; clinical data warehouse; clinically relevant; computer based Semantic Analysis; computerized data processing; data exchange; data integration; data modeling; data resource; design; genomic profiles; health data; indexing; interoperability; method development; next generation; open source; outreach; repository; research study; response; software development; tool; tool development; usability; web portal

Project Summary The proposed project is in response to the U01 Funding Opportunity Announcement (FOA) for the Big Data to Knowledge (BD2K) Development of Software Tools and Methods for Biomedical Big Data in the topic area of applying metadata. The overall goal here is to design, develop and evaluate an integrated platform for clinical research metadata standardization leveraging both standards-based representation and scalable Semantic Web technologies. The ultimate goal is to advance clinical research data discovery and analytic capabilities for clinical and translational centers and investigators. Clinical and translational research studies increasingly involve the manipulation of large datasets (e.g., patient records and genomic profiles) and the application of complex methods. To derive clinically relevant conclusions from such large datasets, the clinical and translational research community faces significant data integration challenges related to scalability, interactivity, representation standards, sustainability, and robustness. Failure to deal with these challenges will have a significant negative impact on downstream data reuse, sharing and analysis in the broader scientific communities. Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous clinical systems. Amongst the emerging national and international initiatives on the standardization of DCM modeling are the Clinical Informatics Modeling Initiative (CIMI) and the HL7 Fast Healthcare Interoperability Resources (FHIR). FHIR is an emerging HL7 standard; it leverages existing logical and theoretical models to provide a consistent, easy to implement, and rigorous mechanism for exchanging data between healthcare applications. However, currently the toolbox that enables HL7 FHIR as a global data model to standardize clinical research metadata is very limited. Such metadata include data dictionaries associated with clinical research datasets and a variety of underlying data models in the existing integrated data repositories (IDRs) such as the Informatics for Integrating Biology and the Bedside (i2b2). The proposed project leverages emerging Semantic Web technologies to provide a scalable standards-based framework that enables effective and efficient big data integration and semantic sharing. The proposed project builds on semantic metadata software and infrastructure developed in our previous projects, including an NIH U24 bioCADDIE (biomedical and healthCAre Data Discovery Index Ecosystem) pilot project (PI: Jiang) that investigates the feasibility of indexing clinical research datasets using HL7 FHIR, and an NCI U01 supplement (PI: Jiang) that creates an open-source IDR (e.g., i2b2) with FHIR- based cancer data services for cancer research. The objective of the proposed project is to consolidate, develop, and evaluate methods and tools for standardizing clinical research metadata and data models using HL7 FHIR. Our specific aims are: 1) Consolidate our bioCADDIE tools for indexing clinical research metadata using HL7 FHIR; 2) Create methods and tools for integrating i2b2 clinical data repository with HL7 FHIR; 3) Deploy an integrated web-portal for community-based metadata harmonization and tool dissemination. The proposed project will produce a suite of methods and tools for clinical research metadata standardization using HL7 FHIR and effectively facilitate secondary use of clinical research data and applications, ultimately advancing clinical and translational data discovery and analytics.

项目摘要 拟议的项目是对U01资助机会公告（FOA）的回应 知识（BD2K）在主题领域的生物医学大数据的软件工具和方法开发 应用元数据。这里的总体目标是设计，开发和评估一个集成平台 临床研究元数据标准化利用基于标准的表示和可扩展 语义网络技术。最终目标是进步临床研究数据发现和分析 临床和转化中心和研究人员的功能。临床和翻译研究 越来越多地涉及大型数据集的操纵（例如，患者记录和基因组概况）和 复杂方法的应用。为了从如此大的数据集得出临床相关的结论，临床 转化研究社区面临着与可伸缩性相关的重要数据集成挑战， 互动性，表示标准，可持续性和鲁棒性。未能应对这些挑战将 在更广泛的科学方面对下游数据重用，共享和分析有重大负面影响 社区。详细的临床模型（DCM）被视为保留可计算的基础 含义在异质临床系统之间交换数据时。在新兴国家 DCM建模标准化的国际计划是临床信息学建模 倡议（CIMI）和HL7快速医疗保健互操作性资源（FHIR）。 FHIR是新兴的HL7 标准;它利用现有的逻辑和理论模型来提供一致，易于实施的 在医疗保健应用之间交换数据的严格机制。但是，当前是工具箱 使HL7 FHIR作为全球数据模型，以标准化临床研究元数据非常有限。这样的 元数据包括与临床研究数据集相关的数据词典和各种基础数据 现有的集成数据存储库（IDR）中的模型，例如用于整合生物学和的信息学 床边（I2B2）。拟议的项目利用了新兴的语义Web技术来提供 基于可扩展的标准框架，可以有效，有效的大数据集成和语义 分享。拟议的项目建立在我们的语义元数据软件和基础架构上 以前的项目，包括NIH U24 BiocAddie（生物医学和医疗保健数据发现指数 生态系统）试点项目（PI：Jiang），该项目研究了使用临床研究数据集索引的可行性 HL7 FHIR和NCI U01补充（PI：Jiang），它与FHIR-创建开源IDR（例如I2B2） 基于癌症研究的癌症数据服务。拟议项目的目的是合并， 开发和评估使用使用临床研究元数据和数据模型标准化的方法和工具 HL7 FHIR。我们的具体目的是：1）合并我们的生物添加工具来索引临床研究元数据 使用HL7 FHIR； 2）创建将I2B2临床数据存储库与HL7 FHIR集成的方法和工具； 3） 为基于社区的元数据协调和工具传播部署一个集成的Web门户。这 拟议的项目将生产一套用于临床研究元数据标准化的方法和工具 HL7 FHIR并有效地促进了临床研究数据和应用的次要使用，最终 推进临床和翻译数据发现和分析。