Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).
Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).
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DOI：
10.5334/egems.199
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发表时间：
2018-04-13
期刊：
影响因子：
--
通讯作者：
Marsolo K
中科院分区：
文献类型：
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作者：
Qualls LG;Phillips TA;Hammill BG;Topping J;Louzao DM;Brown JS;Curtis LH;Marsolo K
分布式研究网络（drn）是美国国立卫生研究院（nih）和美国食品和药物管理局（fda）战略路线图的关键组成部分，因为它们正在努力向大规模证据生成系统迈进。国家以患者为中心的临床研究网络（PCORnet®）是首批在全国范围内整合来自多个领域的电子健康记录数据的drn之一。在DRN中进行分析之前，重要的是评估数据的质量和特征。PCORnet的协调中心负责评估基础数据质量，或者通过一个称为数据管理的过程评估广泛研究组合的适用性。数据管理涉及一组分析和查询活动，以评估数据质量，同时维护详细的文档和与网络合作伙伴的持续通信。PCORnet数据管理的第一个周期集中在PCORnet公共数据模型中的六个领域：人口统计、诊断、就诊、登记、程序和生命体征。数据管理过程提高了基础数据质量。值得注意的改进包括消除数据模型一致性错误；降：令人难以置信的身高、体重和血压值的下降；诊断和手术数量的增加；关键分析变量的数据更完整。基于第一个周期的发现，我们对管理流程进行了修改，以提高效率并进一步减少数据合作伙伴之间的差异。数据管理过程的迭代特性使PCORnet能够逐步提高数据质量的基础水平，并减少整个网络的可变性。这些活动有助于提高PCORnet内分析的透明度和可重复性，并可作为其他drn的模型。
Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data. PCORnet’s Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals. The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners. The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.