德国医疗保健数据的协调使用OMOP共同数据模型 - - 实践报告
Nicole Hechtel1, Johanna Apfel-Starke2, Sophia Köhler2
1Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover, Germany.
Studies in health technology and informatics
|June 30, 2023
概括
本研究详细介绍了汉诺威医学院首次实施观察医学结果伙伴关系 (OMOP) 共同数据模型. 它强调了协调德国医疗保健术语的挑战,以进行现实世界的数据分析.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据管理 临床数据管理
- 现实世界的数据分析.
背景情况:
- 数据协调对于大规模的医疗数据分析和现实世界的证据生成至关重要.
- 观察医学结果伙伴关系 (OMOP) 共同数据模型为数据协调提供了一个标准化的框架.
- 汉诺威医学院 (MHH) 建立了一个企业临床研究数据仓库 (ECRDW),以整合临床数据.
研究的目的:
- 在MHH的ECRDW数据源上实施OMOP共同数据模型.
- 识别和解决将德国医疗保健术语映射到OMOP标准中的挑战.
- 为了促进标准化的现实世界数据分析在MHH.
主要方法:
- 在MHH ECRDW基础架构中实施OMOP共同数据模型.
- 分析德国医疗保健术语,并将其映射到OMOP标准概念中.
- 开发战略,以克服术语绘制挑战.
主要成果:
- 首次成功地在MHH实施了OMOP共同数据模型.
- 确定与德国医疗保健术语相关的特定挑战 (例如,ICD-10-GM,OPS).
- 证明将本地数据与全球标准协调的可行性和复杂性.
结论:
- 在现有的临床数据仓库上可以实现OMOP共同数据模型.
- 将当地术语映射到OMOP是一个重大但可以管理的挑战.
- 通过OMOP进行标准化数据,促进了强大的真实世界数据研究和证据生成.
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