Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.
Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.
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DOI：
10.13063/2327-9214.1239
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Bailey C
中科院分区：
文献类型：
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作者：
Huser V;DeFalco FJ;Schuemie M;Ryan PB;Shang N;Velez M;Park RW;Boyce RD;Duke J;Khare R;Utidjian L;Bailey C
如果电子健康记录数据或行政索赔数据的分析结果应该得到公众和研究界的信任，数据质量和是否适合分析是至关重要的。我们描述了由观察卫生数据科学和信息学合作(OHDSI)开发的数据质量分析工具(称为阿喀琉斯脚跟)，并比较了该工具的输出，将其应用于七个不同组织的24个大型医疗保健数据集。我们重点介绍了12条数据质量规则，这些规则确定了24个数据集中至少10个数据集中的问题，并提供了至少一个数据集中确定的71条规则的全套。Achilles Heel是一个免费提供的软件，它提供了一组有用的数据质量规则，并能够添加额外的规则。我们还展示了对所有参与网站的结构化电子邮件采访的结果，这些网站收集了关于阿喀琉斯高跟鞋的价值的定性评论，用于数据质量评估。我们的分析首次对数据质量工具的输出进行了比较，该工具实现了一组固定(但可扩展)的数据质量规则。多亏了一个通用的数据模型，我们能够快速比较来自美国、欧洲和亚洲几个国家的多个数据集。
Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community. We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations. We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation. Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.