Department of Pediatrics, Anschutz Medical Center, Colorado Clinical and Translational Sciences Institute, University of Colorado, Aurora 80045, USA.
Med Care. 2012 Jul;50 Suppl(0):S21-9. doi: 10.1097/MLR.0b013e318257dd67.
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.
回答临床和公共卫生研究问题越来越需要从多个地点汇总数据。电子健康记录和其他临床来源的数据可用于此类研究，但需要严格的质量评估。在多站点研究中，数据质量评估尤为重要，可区分护理中的真实变化和数据质量问题。
我们提出了一个“适用”的数据质量评估概念模型和一个用于规划和进行单站点和多站点数据质量评估的过程模型。这些方法通过来自先前多站点研究的示例进行说明。
多站点数据质量评估的关键组成部分包括：仔细确定要评估的变量和数据质量维度；制定和使用标准化的数据质量评估方法，随着时间的推移提高数据的实用性；在站点内和站点之间进行评估的迭代周期；针对已知易出现质量问题的数据域进行评估；以及详细记录数据质量评估的基本原理和结果，为数据用户提供信息。评估过程需要站点级数据提供者、数据协调中心和主要研究人员之间的持续沟通。
基于概念的数据质量评估方法和系统执行对于实现医疗保健领域的电子革命潜力至关重要。高质量的数据允许“学习型医疗保健组织”分析和利用自己的信息，将自己的结果与同行进行比较，并从人群角度解决关键的科学问题。
