MULTI-SITE EVALUATION OF A DATA QUALITY TOOL FOR BIG DATA IN HEALTHCARE

Evaluation of data quality in large healthcare datasets. abstract: Data quality and fitness for analysis are crucial if outputs of big data analyses should be trusted by the public and the research community. Here we analyze the output from a data quality tool called Achilles Heel as it was applied to 24 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 developed by Observational Health Data Sciences and Informatics (OHDSI) community and is a freely available software that provides a useful starter set of data quality rules. Our analysis represents the first data quality comparison of multiple datasets across several countries in America, Europe and Asia.大型医疗数据集的数据质量评估。摘要：若要使大数据分析的结果获得公众与科研界的信赖，数据质量与分析适配性至关重要。本文针对一款名为阿喀琉斯之踵（Achilles Heel）的数据质量工具的运行结果展开分析，该工具已被应用于7家不同机构的24个数据集。我们筛选出12条数据质量规则，这些规则在24个数据集的至少10个中均检出了问题，并整理了在至少1个数据集中被检出问题的全部71条规则。阿喀琉斯之踵由观察性医疗数据科学与信息学（Observational Health Data Sciences and Informatics, OHDSI）社区开发，是一款免费可用的软件，附带一套实用的基础数据质量规则集。本研究是首次针对美洲、欧洲与亚洲多个国家的多数据集开展的数据质量对比分析。