Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project.
Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project.
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DOI：
10.1016/j.jbi.2012.01.009
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发表时间：
2012-08
影响因子：
4.5
通讯作者：
Chute CG
中科院分区：
文献类型：
--
作者：
Rea S;Pathak J;Savova G;Oniki TA;Westberg L;Beebe CE;Tao C;Parker CG;Haug PJ;Huff SM;Chute CG
战略卫生 IT 高级研究项目 (SHARP) 计划由国家卫生信息技术协调员办公室于 2010 年设立，支持消除更多采用卫生 IT 障碍的研究成果。 SHARP Area 4 联盟 (SHARPn) 设想的改进将使电子健康记录 (EHR) 能够用于次要目的，例如护理过程和结果改进、生物医学研究和国家健康的流行病学监测。这项工作的主要信息学问题领域之一是对来自全国许多医疗保健组织和提供者的不同健康数据进行标准化。 SHARPn 团队正在开发开源服务和组件，以支持电子健康记录中存储的操作临床数据的普遍交换、共享和重用或“流动性”。 SHARPn 框架设计和开发一年后，我们使用来自两个大型医疗机构：Mayo Clinic 和 Intermountain Healthcare 的数千份患者电子记录，展示了端到端数据流和原型 SHARPn 平台。该平台的部署目的是 (1) 接收多种格式的源 EHR 数据，(2) 从 EHR 叙述文本生成结构化数据，以及 (3) 使用常见的详细临床模型和统一健康信息学标准术语对 EHR 数据进行标准化，(4) 由表型分析服务使用标准化数据规范进行访问。介绍了该原型 SHARPn 平台的架构。 EHR 数据吞吐量演示成功地规范了来自两个独立组织和 EHR 系统的结构化和叙述性本地 EHR 数据。基于演示，讨论了可互操作二次使用的 EHR 数据标准化所面临的挑战。
The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation’s health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation’s many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or ‘liquidity’ of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.