Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.
Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.
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DOI：
10.13063/2327-9214.1201
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Welton J
中科院分区：
文献类型：
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作者：
Dziadkowiec O;Callahan T;Ozkaynak M;Reeder B;Welton J
我们检查以下内容：（1）使用为关系数据库开发的数据质量（DQ）框架作为从两个EPIC数据库中提取的数据集的数据清理工具的适当性，以及（2）使用DQ框架清理的数据集和未使用DQ框架清理的数据集的统计参数估计的差异。使用电子健康记录（EHR）中包含的数据有可能为新一波创新研究打开大门。如果没有充分准备如此大的数据集进行分析，结果可能是错误的，这可能会影响临床决策或比较连续性研究的结果。两个急诊科（艾德）数据集提取EPIC数据库（成人艾德和儿童艾德）作为例子，检查DQ的五个概念的基础上设计的EHR数据库DQ评估框架。第一组数据包含70 061次访问;第二组数据包含2 815 550次访问。SPSS提供了一些实例，以及如何应用这些EHR数据库提取的五个关键DQ概念的分步说明。开发了用于解决Kahn等人（2012年）提出的每个DQ概念的SPSS软件。使用Kahn的框架清理的数据集比没有这个框架清理的数据集产生了更准确的结果。未来的计划包括用R语言创建用于清理从EHR提取的数据的函数，以及将DQ检查与缺失数据分析功能相结合的R包。
We examine the following: (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a data set extracted from two EPIC databases, and (2) the differences in statistical parameter estimates on a data set cleaned with the DQ framework and data set not cleaned with the DQ framework. The use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large data sets for analysis, the results might be erroneous, which might affect clinical decision-making or the results of Comparative Effectives Research studies. Two emergency department (ED) data sets extracted from EPIC databases (adult ED and children ED) were used as examples for examining the five concepts of DQ based on a DQ assessment framework designed for EHR databases. The first data set contained 70,061 visits; and the second data set contained 2,815,550 visits. SPSS Syntax examples as well as step-by-step instructions of how to apply the five key DQ concepts these EHR database extracts are provided. SPSS Syntax to address each of the DQ concepts proposed by Kahn et al. (2012) was developed. The data set cleaned using Kahn’s framework yielded more accurate results than the data set cleaned without this framework. Future plans involve creating functions in R language for cleaning data extracted from the EHR as well as an R package that combines DQ checks with missing data analysis functions.