Data collected by primary care providers in electronic medical record (EMR) systems can be used to support a variety of actions including provision of day-to-day care, decision support, practice reflection, research and public health surveillance. The extent to which EMR data can be relied on to support such actions is dependent on the quality of the held data. While a body of literature describing data quality assessment techniques exists, few studies speak to the challenges of evaluating data quality in the Canadian primary care setting. In response, researchers at the University of Victoria's eHealth Observatory are refining data quality definitions and developing techniques for the evaluation of EMR data quality within the Canadian primary care context.
With electronic medical record (EMR) adoption programs under way in several Canadian jurisdictions, healthcare organizations, funders and the public are starting to look for evidence that the investments are having positive impact.
One measure that may be used to infer how meaningfully a given EMR system is being utilized in practice is that of data quality. Varying levels of data completeness, correctness and consistency can be indicative of the overall frequency and sophistication with which a given EMR is being used.
Moreover, a data quality evaluation can be used to determine the readiness of a given practice's EMR data set for reliable use with advanced EMR features. These can include chronic disease management reporting or drug-interaction decision support; submission to research and public health surveillance programs, such as the Canadian Institute for Health Information's (CIHI) Primary Care Voluntary Reporting System or the Canadian Primary Care Sentinel Surveillance Network; and safe and meaningful interaction within distributed, interoperable care networks, such as those expected to emerge with the Canadian maturation of Infoway-modelled jurisdictional electronic health record (EHR) systems.
Researchers at the University of Victoria's eHealth Observatory are currently devising EMR data quality evaluation methods and engaging in early studies to test those methods against EMR data sets of participating primary care practices. The intent of this article is to share our findings regarding methods that have been used to assess data quality in published studies, how well those methods apply in the Canadian e-health context, and a framework for how to design a context-appropriate data quality evaluation.
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