建立观察医疗结果伙伴关系的T-MSIS分析文件的共同数据模型
1National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, MD United States of America.
Informatics in medicine unlocked
|June 12, 2023
概括
这项研究将转换的MSIS分析文件 (TAF) 医疗补助记录转化为观察医疗结果伙伴关系 (OMOP) 共同数据模型 (CDM). 这种新的CDM支持为公共保险患者提供证据生成,这些患者在研究中通常代表性不足.
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
- 公共卫生 公共卫生
背景情况:
- 医疗补助数据,特别是转换的MSIS分析文件 (TAF),包含有关公共保险受益人的有价值信息.
- 现有的数据结构可能不适合大规模,多地点的研究,特别是对于代表性不足的患者群体.
研究的目的:
- 将TAF医疗补助记录转化为观察医疗结果伙伴关系 (OMOP) 共同数据模型 (CDM).
- 创建一个标准化的数据集,适合生成现实世界的证据,重点关注公共保险的低收入患者.
主要方法:
- 使用数据块进行数据转换.
- 对TAF进行的数据量和内容评估.
- 从TAF概念开发到OMOP概念的翻译映射.
- 创建了用于数据转换的提取,转换和加载 (ETL) 代码.
主要成果:
- 从TAF记录成功创建了一个OMOP CDM.
- 最终的CDM包括119,048,562个个人和24,806,828,121个临床观察 (2014-2018).
结论:
- 在OMOP CDM格式中转换的TAF数据为OMOP网络研究提供证据.
- 这一努力增强了研究能力,以了解公共保险覆盖的低收入人口的健康结果.
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