Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration.
Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration.
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DOI：
10.1016/j.jbi.2022.104002
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发表时间：
2022-03
影响因子：
4.5
通讯作者：
Jiang G
中科院分区：
文献类型：
--
作者：
Yu Y;Zong N;Wen A;Liu S;Stone DJ;Knaack D;Chamberlain AM;Pfaff E;Gabriel D;Chute CG;Shah N;Jiang G
大规模收集观测数据和数字技术有助于遏制COVID-19大流行。然而，多个公共数据模型（cdm）的共存以及不同cdm之间缺乏数据提取、转换和加载（ETL）工具导致了不同数据系统之间潜在的互操作性问题。本研究的目的是设计、开发和评估一个ETL工具，该工具将PCORnet CDM格式的数据转换为OMOP CDM。我们开发了一个开源的ETL工具来促进PCORnet CDM和OMOP CDM之间的数据转换。ETL工具使用从梅奥诊所PCORnet CDM随机选择的1000例患者的数据集进行评估。采用信息丢失、数据映射准确性和差距分析方法来评估ETL工具的性能。我们设计了一项实验，开展现实世界的COVID-19监测任务，以评估ETL工具的可行性。我们还根据MN EHR联盟COVID-19项目的数据收集标准评估了ETL工具用于COVID-19数据监测的能力。经过ETL处理，将18张PCORnet CDM表中1000例患者的全部记录成功转化为12张OMOP CDM表。所有概念映射的信息损失均小于0.61%。单元概念的字符串映射过程丢失了2.84%的记录。在手工映射过程中，除专业概念映射外，几乎所有字段的信息丢失率均为0%。所有字段的映射精度均为100%。COVID-19监测任务分别从原始PCORnet CDM和目标OMOP CDM收集了几乎相同的病例集（99.3%重叠）。最后，MN EHR联盟COVID-19项目的所有数据元素都可以从PCORnet CDM和OMOP CDM中获取。我们证明了我们的ETL工具能够满足PCORnet CDM和OMOP CDM之间的数据转换需求。这项工作的成果将促进不同机构之间的数据检索、交流、共享和分析，不仅适用于COVID-19相关项目，也适用于其他现实世界的循证观察性研究。
The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM. We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project. After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM. We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.
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