为观察医学结果开发综合数据质量管理系统 合作伙伴关系 共同数据模型
Seol Whan Oh1,2, Soo Jeong Ko1,2, Yun Seon Im1,2
1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Studies in health technology and informatics
|January 25, 2024
概括
为支持使用OMOP共同数据模型 (CDM) 的多中心研究,开发了一种新的医疗保健数据数据质量系统. 该系统证实了六家医院的整体错误率为0.197%,使得数据生成和评估更好.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 临床研究 临床研究
背景情况:
- 越来越多的研究需要强大的医疗数据处理.
- 开发用于多中心研究的共同数据模型 (CDM) 面临着重大数据质量挑战.
- 现有的OMOP CDM质量评估系统需要全面整合.
研究的目的:
- 开发和验证OMOP共同数据模型 (CDM) 版本5.3.1.1.的数据质量评估系统.
- 为解决阻碍多中心研究的持续数据质量问题.
- 提出一个高质量的数据生成和多中心CDM评估的框架.
主要方法:
- 创建了一个基于OMOP CDM v5.3.1.1.的数据质量评估系统.
- 通过映射现有的OMOP CDM质量评估系统,整合了2,433个先进的评估规则.
- 从六家医院的数据中验证了系统的有效性.
主要成果:
- 开发的系统证实了六家参与医院的整体数据错误率为0.197%.
- 该系统有效地在OMOP CDM框架内识别和量化数据质量问题.
- 在多中心研究环境中建立了数据质量的基准.
结论:
- 开发的数据质量评估系统对于评估OMOP CDM v5.3.1数据是有效的.
- 总体较低的错误率表明,使用适当的系统,有可能产生高质量的医疗数据.
- 拟议的计划为多中心研究和CDM评估提供了标准化,高质量的数据.
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