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Assessing the Harmonization of Structured Electronic Health Record Data to Reference Terminologies and Data Completeness Through Data Provenance

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    Description

    This study assesses the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues and identifies issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance. Queries were distributed to three Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at four conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs.

    Author(s)

    Keith Marsolo, Lesley Curtis, Laura Qualls, Jennifer Xu, Yinghong Zhang, Thomas Phillips, C. Larry Hill, Gretchen Sanders, Judith C. Maro, Daniel Kiernan, Christine Draper, Kevin Coughlin, Sarah K. Dutcher, José J. Hernández-Muñoz, Monique Falconer

    Corresponding Author

    Keith Marsolo; Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina.

    Email: keith.marsolo@duke.edu