GHUCCTS N3C COVID data mapping

GHUCCTS N3C COVID 数据映射

• 批准号: 10299876
• 负责人: Nawar Shara
• 金额: $ 9.99万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-28 至 2022-01-25
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10299876
• 关键词: Ambulatory Care; Architecture; Big Data; Big Data to Knowledge; COVID-19; Clinical; Clinical Data; Collaborations; Complex; Computerized Medical Record; Data; Data Analyses; Data Commons; Data Element; Data Science; Data Set; Data Sources; Data Store; Decision Making; Development; Electronic Health Record; Environment; Foundations; Health; Hospitals; Informatics; Institutes; Institution; Intervention; Lead; Logical Observation Identifiers Names and Codes; Maps; Medical; Methods; Nature; Observational Study; Occupational Health; Operations Research; Outcome; Patient-Focused Outcomes; Patients; Primary Health Care; Process; Protocols documentation; Reporting; Reproducibility; Research; Research Personnel; Resources; Secure; Semantics; Services; Source; Standardization; System; Systematized Nomenclature of Medicine; Technology; Translational Research; United States National Institutes of Health; Update; Visualization; cloud based; cohort; coronavirus disease; data ecosystem; data enclave; data modeling; data resource; data sharing; data standards; design; evidence base; graphical user interface; health data; health record; heterogenous data; improved; innovation; inpatient service; insight; interoperability; natural language; novel; predictive modeling; research study; syntax; tool

Abstract A major challenge to full utilization the available data and resources has been the complex nature of health data, and heterogeneity of data sources (including unstructured clinical notes) combined with a lack of standards. The lack of standards precludes semantic interoperability across platforms and between institutions. Instead, current approaches utilize resource intensive natural language processes to extract, transform, and correlate data from different sources for analysis. To improve translational science and accelerate research to improve patient outcomes, many new and innovative studies are leveraging large volumes of available data through standardized and shared data initiatives. With current advances in computing and health data analysis tools, methods and access, and to make data more meaningful, open, and accessible, research studies have moved beyond traditional retroactive reporting to pragmatic interventions and predictive capabilities. Ongoing efforts focus on exploiting common data standards and models such as the Observational Medical Outcomes Partnership (OMOP) standard—defined by the Observational Health Data Sciences and Informatics (OHDSI) consortium, and accepted as canon by both the NIH and PCORI— will lead the way to discover insights in textual narrative, enforce data standardization, and promote scalability and sharing. The OHDSI Common Data Models (CDM) makes data more meaningful, open, and accessible, which drives translational science and allows for consistent development of predictive models across different data sources. The National COVID Cohort Collaborative (N3C), ACT, BD2K-NIH Data Commons, the National Center for Data to Health (CD2H), and others are among the efforts that will lead to new discoveries and informed decision making, driven by data science and undergirded by mature Big Data technologies. We propose to design and establish novel, scalable, and standardized big data processes to massively abstract the raw electronic medical record datasets for observational studies. This project will develop a secure cloud-based environment to host these data, as well as the application programming and graphical user interfaces to support observational research studies leveraging these resources. By these means we will reduce the barriers to data standardization, annotation and sharing for reproducible analytics and begin to enforce complete semantic and syntactic interoperability between the resources in the data ecosystem. This effort will enable our investigators to study the effects of medical interventions and predict patients' health outcomes and generate the empirical evidence base necessary to establish best practices in observational analysis.

摘要 充分利用现有数据和资源的一个主要挑战是卫生问题的复杂性 数据和数据源的异质性（包括非结构化临床记录），加上缺乏 标准标准的缺乏阻碍了跨平台和机构之间的语义互操作性。 相反，当前的方法利用资源密集型自然语言过程来提取、转换和表达。 将不同来源的数据关联起来进行分析。为了提高转化科学和加速研究， 为了改善患者的治疗效果，许多新的和创新的研究正在利用大量的可用数据 通过标准化和共享数据举措。随着计算机和健康数据分析的发展， 工具，方法和访问，并使数据更有意义，开放和可访问，研究 从传统的追溯性报告转向务实的干预和预测能力。正在进行 工作重点是利用共同的数据标准和模型，如观察性医学结果 OMOP标准-由观察健康数据科学和信息学（OHDSI）定义 财团，并接受为佳能由美国国立卫生研究院和PCRI-将引领方式，发现见解， 文本叙述、加强数据标准化并促进可伸缩性和共享。OHDSI通用数据 模型（CDM）使数据更有意义，更开放，更可访问，这推动了转化科学和 允许跨不同数据源一致地开发预测模型。全国COVID 队列协作（N3 C），ACT，BD 2K-NIH数据共享，国家健康数据中心（CD 2 H）， 和其他的努力将导致新的发现和明智的决策， 数据科学和成熟的大数据技术支撑。我们建议设计和建立一个新的， 可扩展和标准化的大数据流程，以大量提取原始电子病历 观察性研究的数据集。该项目将开发一个安全的基于云的环境来托管这些 数据，以及支持观察研究的应用程序编程和图形用户界面 研究利用这些资源。通过这些手段，我们将减少数据标准化的障碍， 注释和共享，以实现可重复的分析，并开始强制执行完整的语义和语法 数据生态系统中资源之间的互操作性。这项工作将使我们的研究人员能够研究 医疗干预的效果和预测患者的健康结果，并产生经验证据 建立观测分析最佳实践所需的基础。