Using the CER Hub to ensure data quality in a multi-institution smoking cessation study.
Using the CER Hub to ensure data quality in a multi-institution smoking cessation study.
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使用 CER Hub 确保多机构戒烟研究的数据质量。
DOI：
10.1136/amiajnl-2013-002629
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Hazlehurst,BrianL
中科院分区：
文献类型：
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作者：
Walker,KariL;Kirillova,Olga;Gillespie,SuzanneE;Hsiao,David;Pishchalenko,Valentyna;Pai,AkshathaKalsanka;Puro,JonE;Plumley,Robert;Kudyakov,Rustam;Hu,Weiming;Allisany,Art;McBurnie,MaryAnn;Kurtz,StephenE;Hazlehurst,BrianL
涉及具有不同电子健康记录(EHR)的多个机构的比较有效性研究(CER)依赖于高质量的数据。为确保来自不同电子健康记录系统和实施的数据统一，CER中心信息平台利用CER中心提供的工具和数据格式制定了质量保证(QA)程序。在一项关于初级保健戒烟服务的研究中实施的QA过程，使用了带有一组质量检查的“emrAdapter”工具来查询根据CER Hub公共数据框架提取的初级保健遭遇记录的大样本。该工具部署到每个研究站点，生成错误报告，指出要在当地修复的数据问题，并汇总可与中央站点共享的数据，以便进行质量审查。在由六个卫生系统组成的CER Hub网络中，数据完整性和正确性问题在第一次迭代中非常普遍，在QA过程的三次迭代之后，这些问题都得到了显著改善。遇到的一个常见问题是，当地电子健康记录数据值与共同数据框架定义的数据值之间的映射不完整。高度自动化和分布式的质量保证过程有助于确保从EHR提取的患者护理数据的正确性和完整性，用于多机构CER戒烟研究。
Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the ‘emrAdapter’ tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.