Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA.
Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA; Department of Medicine, Weill Cornell Medicine, New York, NY, USA; Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, NY, USA.
J Biomed Inform. 2018 Aug;84:179-183. doi: 10.1016/j.jbi.2018.07.010. Epub 2018 Jul 19.
Although i2b2, a popular platform for patient cohort discovery using electronic health record (EHR) data, can support multiple projects specific to individual disease areas or research interests, the standard approach for doing so duplicates data across projects, requiring additional disk space and processing time, which limits scalability. To address this deficiency, we developed a novel approach that stored data in a single i2b2 fact table and used structured query language (SQL) views to access data for specific projects. Compared to the standard approach, the view-based approach reduced required disk space by 59% and extract-transfer-load (ETL) time by 46%, without substantially impacting query performance. The view-based approach has enabled scalability of multiple i2b2 projects and generalized to another data model at our institution. Other institutions may benefit from this approach, code of which is available on GitHub (https://github.com/wcmc-research-informatics/super-i2b2).
虽然 i2b2 是一个使用电子健康记录 (EHR) 数据发现患者队列的流行平台，它可以支持多个特定于单个疾病领域或研究兴趣的项目，但标准方法在项目之间复制数据，需要额外的磁盘空间和处理时间，从而限制了可扩展性。为了解决这个缺陷，我们开发了一种新的方法，将数据存储在单个 i2b2 事实表中，并使用结构化查询语言 (SQL) 视图来访问特定项目的数据。与标准方法相比，基于视图的方法将所需的磁盘空间减少了 59%，将提取-传输-加载 (ETL) 时间减少了 46%，而对查询性能没有显著影响。基于视图的方法实现了多个 i2b2 项目的可扩展性，并推广到我们机构的另一个数据模型。其他机构可能会受益于这种方法，其代码可在 GitHub 上获得 (https://github.com/wcmc-research-informatics/super-i2b2)。
