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The Analytic Information Warehouse (AIW): A platform for analytics using electronic health record data

机译:分析信息仓库(AIW):使用电子病历数据进行分析的平台

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摘要

Objective: To create an analytics platform for specifying and detecting clinical phenotypes and other derived variables in electronic health record (EHR) data for quality improvement investigations. Materials and methods: We have developed an architecture for an Analytic Information Warehouse (AIW). It supports transforming data represented in different physical schemas into a common data model, specifying derived variables in terms of the common model to enable their reuse, computing derived variables while enforcing invariants and ensuring correctness and consistency of data transformations, long-term curation of derived data, and export of derived data into standard analysis tools. It includes software that implements these features and a computing environment that enables secure high-performance access to and processing of large datasets extracted from EHRs. Results: We have implemented and deployed the architecture in production locally. The software is available as open source. We have used it as part of hospital operations in a project to reduce rates of hospital readmission within 30. days. The project examined the association of over 100 derived variables representing disease and co-morbidity phenotypes with readmissions in 5. years of data from our institution's clinical data warehouse and the UHC Clinical Database (CDB). The CDB contains administrative data from over 200 hospitals that are in academic medical centers or affiliated with such centers. Discussion and conclusion: A widely available platform for managing and detecting phenotypes in EHR data could accelerate the use of such data in quality improvement and comparative effectiveness studies.
机译:目的:创建一个分析平台,用于指定和检测电子病历(EHR)数据中的临床表型和其他派生变量,以进行质量改进调查。材料和方法:我们已经开发了用于分析信息仓库(AIW)的体系结构。它支持将以不同物理模式表示的数据转换为通用数据模型,根据通用模型指定派生变量以实现重用,在强制执行不变式的同时计算派生变量,并确保数据转换的正确性和一致性,派生对象的长期管理数据,并将导出的数据导出到标准分析工具中。它包括实现这些功能的软件和一个计算环境,该计算环境可以安全地高性能访问和处理从EHR中提取的大型数据集。结果:我们已经在本地生产中实施并部署了该架构。该软件可作为开源软件使用。我们在一个项目中将它用作医院运营的一部分,以在30天内降低住院率。该项目检查了来自机构临床数据仓库和UHC临床数据库(CDB)的5年数据中100多个代表疾病和合并症表型的衍生变量与再入院的关联。 CDB包含来自学术医学中心或附属医疗中心的200多家医院的管理数据。讨论和结论:广泛使用的用于管理和检测EHR数据表型的平台可以加速此类数据在质量改善和比较有效性研究中的使用。

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