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迈向在德国大学医院联盟中实施观察医疗结果合作组织(OMOP)。

Towards Implementation of OMOP in a German University Hospital Consortium.

作者信息

Maier C, Lang L, Storf H, Vormstein P, Bieber R, Bernarding J, Herrmann T, Haverkamp C, Horki P, Laufer J, Berger F, Höning G, Fritsch H W, Schüttler J, Ganslandt T, Prokosch H U, Sedlmayr M

出版信息

Appl Clin Inform. 2018 Jan;9(1):54-61. doi: 10.1055/s-0037-1617452. Epub 2018 Jan 24.

Abstract

BACKGROUND

In 2015, the German Federal Ministry of Education and Research initiated a large data integration and data sharing research initiative to improve the reuse of data from patient care and translational research. The Observational Medical Outcomes Partnership (OMOP) common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools could be used as a core element in this initiative for harmonizing the terminologies used as well as facilitating the federation of research analyses across institutions.

OBJECTIVE

To realize an OMOP/OHDSI-based pilot implementation within a consortium of eight German university hospitals, evaluate the applicability to support data harmonization and sharing among them, and identify potential enhancement requirements.

METHODS

The vocabularies and terminological mapping required for importing the fact data were prepared, and the process for importing the data from the source files was designed. For eight German university hospitals, a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis was provided. Last, a federated/distributed query to test the approach was executed.

RESULTS

While the mapping of ICD-10 German Modification succeeded with a rate of 98.8% of all terms for diagnoses, the procedures could not be mapped and hence an extension to the OMOP standard terminologies had to be made.Overall, the data of 3 million inpatients with approximately 26 million conditions, 21 million procedures, and 23 million observations have been imported.A federated query to identify a cohort of colorectal cancer patients was successfully executed and yielded 16,701 patient cases visualized in a Sunburst plot.

CONCLUSION

OMOP/OHDSI is a viable open source solution for data integration in a German research consortium. Once the terminology problems can be solved, researchers can build on an active community for further development.

摘要

背景

2015年,德国联邦教育与研究部发起了一项大型数据集成与数据共享研究计划,以提高患者护理和转化研究数据的再利用。观察性医疗结局合作组织(OMOP)通用数据模型和观察性健康数据科学与信息学(OHDSI)工具可作为该计划的核心要素,用于统一所使用的术语,并促进跨机构的研究分析联合。

目的

在由八家德国大学医院组成的联盟内实现基于OMOP/OHDSI的试点实施,评估其对支持它们之间的数据协调与共享的适用性,并确定潜在的增强需求。

方法

准备了导入事实数据所需的词汇表和术语映射,并设计了从源文件导入数据的流程。为八家德国大学医院提供了一个预先配置了OMOP数据库、OHDSI工具以及导入数据和进行分析的作业的虚拟机。最后,执行了一个联合/分布式查询来测试该方法。

结果

虽然ICD-10德国修订版的映射在所有诊断术语中成功率达到了98.8%,但手术操作术语无法映射,因此必须对OMOP标准术语进行扩展。总体而言,已导入了300万住院患者的数据,包括约2600万个病情、2100万个手术操作和2300万个观察结果。一个用于识别一组结直肠癌患者的联合查询成功执行,并在旭日图中显示了16701例患者病例。

结论

OMOP/OHDSI是德国研究联盟中数据集成的一个可行的开源解决方案。一旦术语问题能够得到解决,研究人员可以依托一个活跃的社区进行进一步开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac2e/5801887/363a18e55023/10-1055-s-0037-1617452-i170129ra-1.jpg

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