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首页> 外文期刊>BMC Medical Informatics and Decision Making >Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading
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Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading

机译：Dynamic-ETL：健康数据提取，转换和加载的混合方法

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Background Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks. Methods We designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health data (i.e., “health data domain experts”) and target common data model. Results D-ETL was implemented to perform ETL operations that load data from various sources with different database schema structures into the Observational Medical Outcome Partnership (OMOP) common data model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets. Conclusions D-ETL supports a flexible and transparent process to transform and load health data into a target data model. This approach offers a solution that lowers technical barriers that prevent data partners from participating in research data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health data.
机译：背景电子健康记录（EHR）包含以专有格式存储的详细临床数据，以及非标准的代码和结构。参与多站点临床研究网络要求将EHR数据重组并转换为通用格式和标准术语，并以最佳方式链接到其他数据源。转换数据以符合网络要求所需的专业知识和可扩展解决方案超出了许多医疗保健组织的范围，因此需要实用工具来降低数据对临床研究网络的贡献。方法我们设计并实现了一种健康数据转换和加载方法，我们将其称为动态ETL（提取，转换和加载）（D-ETL），该方法通过使用可伸缩，可重用和可自定义的代码来自动化过程的一部分，而保留需要复杂编码语法知识的过程的手动方面。这种方法提供了异构数据ETL所需的灵活性，语义专业知识的变化以及转换逻辑的透明性，这对于跨临床研究共享网络实施ETL约定至关重要。处理工作流由ETL规范指南指导，该指南由ETL设计人员开发，他们对健康数据（即“健康数据领域专家”）的结构和语义有广泛的了解，并针对通用数据模型。结果实现D-ETL以执行ETL操作，该操作将来自具有不同数据库架构结构的各种来源的数据加载到观察性医疗成果合作伙伴关系（OMOP）通用数据模型中。结果表明，ETL规则组合方法和D-ETL引擎通过自动查询生成来协调源数据集，为健康数据转换提供了可扩展的解决方案。结论D-ETL支持灵活，透明的过程，以将健康数据转换并加载到目标数据模型中。这种方法提供了一种解决方案，该解决方案降低了技术障碍，阻止了数据合作伙伴参与研究数据网络，从而促进了使用二次电子健康数据进行比较有效性研究的进展。

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