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 拟议的项目是为了响应U 01大数据资助机会公告（FOA）， 知识（BD 2K）开发生物医学大数据的软件工具和方法，主题领域为 应用元数据。总体目标是设计、开发和评估一个综合平台， 临床研究元数据标准化利用基于标准的表示和可扩展 语义网技术。最终目标是推进临床研究数据发现和分析 为临床和翻译中心及研究者提供的能力。临床和转化研究 越来越多地涉及对大数据集的操作（例如，患者记录和基因组图谱）， 复杂方法的应用。为了从如此大的数据集中得出临床相关的结论， 和转化研究社区面临着与可扩展性相关的重大数据集成挑战， 交互性、表示标准、可持续性和鲁棒性。如果不能应对这些挑战， 对下游数据的再利用、共享和更广泛的科学分析产生重大的负面影响。 社区.详细的临床模型（DCM）被认为是保留可计算的基础。 这意味着当数据在异构临床系统之间交换时。在新兴国家中， 临床信息学建模（Clinical Informatics Modeling） 倡议（CIMI）和HL 7快速医疗保健互操作性资源（FHIR）。FHIR是一种新兴的HL 7 标准;它利用现有的逻辑和理论模型提供一致的、易于实现的 在医疗保健应用程序之间交换数据的严格机制。然而，目前， 使HL 7 FHIR作为全球数据模型标准化临床研究元数据的能力非常有限。等 元数据包括与临床研究数据集和各种基础数据相关的数据字典 现有的综合数据库（IDR）中的模型，如整合生物学的信息学， 床边（i2 b2）。拟议的项目利用新兴的语义网技术， 可扩展的基于标准的框架，可实现有效和高效的大数据集成和语义 共享拟议的项目建立在语义元数据软件和基础设施上， 以前的项目，包括NIH U24 bioCADDIE（生物医学和健康CAre数据发现索引 生态系统）试点项目（PI：Jiang），该项目研究使用 HL 7 FHIR，以及创建开源IDR的NCI U 01补充（PI：Jiang）（例如，i2 b2）与FHIR- 基于癌症研究的癌症数据服务。拟议项目的目标是巩固， 开发和评估标准化临床研究元数据和数据模型的方法和工具， HL7 FHIR。我们的具体目标是：1）整合我们的bioCADDIE工具，用于索引临床研究元数据 使用HL 7 FHIR; 2）创建用于将i2 b2临床数据存储库与HL 7 FHIR集成的方法和工具; 3） 部署一个综合门户网站，用于基于社区的元数据统一和工具传播。的 拟议的项目将使用 HL 7 FHIR并有效促进临床研究数据和应用程序的二次使用，最终 推进临床和转化数据发现和分析。