Harmonization can improve the quality of data in electronic health records, which ultimately can boost use for secondary purposes, according to research published this month in eGEMs (Generating Evidence & Methods to Improve Patient Outcomes).
EHRs hold a bevy of data that can be used not only in a clinical setting but for a wide range of other uses, such as research, administrative decision making and quality improvement. However, the information often is varied, even within the same EHR system, which negatively affects its quality and could have an adverse impact on findings generated.
The researchers, from the University of Colorado and Kaiser Permanente Northwest, reviewed data quality publications, experts and other sources to identify potential standardized data quality terms, focusing on three data quality categories: conformance of data; completeness; and plausibility of the data values, as well as on the ability of the EHR data to be compared based on both internal (verification) and external (validation) characteristics.
They aligned a wide array of existing data quality concepts that have, in the past, had different and sometimes inconsistent terms. To that end, the researchers succeeded in creating an inclusive data quality framework for standardizing data quality assessments and reporting using shared vocabulary, which then could be used to determine if EHR data was “fit” for other uses.
The researchers noted that this was an “initial step” and that establishing standardized, validated methodologies for assessment and reporting data quality from EHRs is “crucial.”
“Future research on the DQ terminology should aim to verify its generalizability and utility, add fitness for use terms and concepts and extend the application of the terminology to emerging forms of digital health data poised to provide new insights into health and well-being,” they concluded.