A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research.
A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research.
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DOI：
10.1097/mlr.0b013e318257dd67
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发表时间：
2012-07
期刊：
影响因子：
3
通讯作者：
Steiner JF
中科院分区：
文献类型：
--
作者：
Kahn MG;Raebel MA;Glanz JM;Riedlinger K;Steiner JF
临床和公共卫生研究问题的答案越来越需要来自多个站点的汇总数据。来自电子健康记录和其他临床来源的数据对此类研究有用，但需要严格的质量评估。数据质量评估在多中心研究中尤为重要，以区分护理的真实变化和数据质量问题。我们提出了一个“适合使用”的数据质量评估的概念模型和规划和进行单站点和多站点数据质量评估的过程模型。这些方法说明使用以前的多站点研究的例子。多站点数据质量评估的关键组成部分包括：对变量和数据质量维度进行深思熟虑的优先排序，以便进行评估;制定和使用标准化的数据质量评估方法，随着时间的推移，这种方法可以提高数据的效用;站点内部和站点之间的评估迭代周期;针对已知容易出现质量问题的数据领域进行评估;以及详细记录数据质量评估的理由和结果，以告知数据用户。评估过程需要研究中心级数据提供者、数据协调中心和主要研究者之间的持续沟通。一个概念为基础的和系统执行的方法来评估数据质量是必不可少的，以实现电子革命在医疗保健的潜力。高质量的数据使“学习型医疗保健组织”能够分析自己的信息并采取行动，将其结果与同行进行比较，并从人口的角度解决关键的科学问题。
Answers to clinical and public health research questions increasingly require aggregated data from multiple sites. Data from electronic health records and other clinical sources are useful for such studies, but require stringent quality assessment. Data quality assessment is particularly important in multisite studies to distinguish true variations in care from data quality problems. We propose a “fit-for-use” conceptual model for data quality assessment and a process model for planning and conducting single-site and multisite data quality assessments. These approaches are illustrated using examples from prior multisite studies. Critical components of multisite data quality assessment include: thoughtful prioritization of variables and data quality dimensions for assessment; development and use of standardized approaches to data quality assessment that can improve data utility over time; iterative cycles of assessment within and between sites; targeting assessment toward data domains known to be vulnerable to quality problems; and detailed documentation of the rationale and outcomes of data quality assessments to inform data users. The assessment process requires constant communication between site-level data providers, data coordinating centers, and principal investigators. A conceptually based and systematically executed approach to data quality assessment is essential to achieve the potential of the electronic revolution in health care. High-quality data allow “learning health care organizations” to analyze and act on their own information, to compare their outcomes to peers, and to address critical scientific questions from the population perspective.