Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Med Care Res Rev. 2010 Oct;67(5):503-27. doi: 10.1177/1077558709359007. Epub 2010 Feb 11.
Previous reviews of research on electronic health record (EHR) data quality have not focused on the needs of quality measurement. The authors reviewed empirical studies of EHR data quality, published from January 2004, with an emphasis on data attributes relevant to quality measurement. Many of the 35 studies reviewed examined multiple aspects of data quality. Sixty-six percent evaluated data accuracy, 57% data completeness, and 23% data comparability. The diversity in data element, study setting, population, health condition, and EHR system studied within this body of literature made drawing specific conclusions regarding EHR data quality challenging. Future research should focus on the quality of data from specific EHR components and important data attributes for quality measurement such as granularity, timeliness, and comparability. Finally, factors associated with poor or variability in data quality need to be better understood and effective interventions developed.
先前对电子健康记录 (EHR) 数据质量的研究综述并未关注质量测量的需求。作者对 2004 年 1 月以来发表的关于 EHR 数据质量的实证研究进行了回顾,重点关注与质量测量相关的数据属性。所审查的 35 项研究中有许多项都检查了数据质量的多个方面。66%评估了数据准确性,57%评估了数据完整性,23%评估了数据可比性。由于文献中研究的数据集元素、研究环境、人群、健康状况和 EHR 系统各不相同,因此很难就 EHR 数据质量得出具体结论。未来的研究应集中于特定 EHR 组件的数据质量以及对质量测量重要的数据属性,例如粒度、及时性和可比性。最后,需要更好地了解与数据质量差或数据质量变化相关的因素,并开发有效的干预措施。