Feasibility and utility of applications of the common data model to multiple, disparate observational health databases
Feasibility and utility of applications of the common data model to multiple, disparate observational health databases
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DOI：
10.1093/jamia/ocu023
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发表时间：
2015-05-01
影响因子：
6.4
通讯作者：
Ryan, Patrick B.
中科院分区：
文献类型：
--
作者：
Voss, Erica A.;Makadia, Rupa;Ryan, Patrick B.
ObjectiveTo evaluate the utility of applying the Observational Medical Outcomes Partnership（OMOP）Common Data Model（CDM）across multiple observational databases within a organization and to apply standardized analytics tools for conducting observational research.Materials and Methods六个去识别的患者级数据集转化为OMOP CDM。我们评估了在标准化过程中发生的信息丢失的程度。我们开发了一个标准化的分析工具，复制队列建设过程中，从已发表的流行病学协议，并适用于所有6个数据库的分析，以评估时间执行和可比性的results.Results转换到CDM导致在所有6个数据库的信息丢失最小。排除的患者和观察结果是由于源系统中确定的数据质量问题，96%至99%的病情记录和90%至99%的药物记录使用标准词汇成功映射到CDM中。完整的队列复制和描述性的基线总结2队列6 databases.Discussion在不到1小时的标准化过程中提高了数据质量，提高了效率，并促进了跨数据库的比较，以支持更系统的方法来观察研究。数据源之间的比较显示，使用该方案，入选标准的影响具有一致性，并确定了数据库之间患者特征和编码实践的差异。（通过清洁发展机制），内容（通过带有源代码映射的标准词汇表），分析可以使机构能够将基于网络的方法应用于跨多个，不同的观察性健康数据库。
Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research.Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results.Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour.Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases.Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.