Electronic health record data quality variability across a multistate clinical research network

Yahia Mohamed, Xing Song, Tamara M. McMahon, Suman Sahil, Meredith Zozus, Zhan Wang, Lemuel R. Waitman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems. Methods: To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality. Results: The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs. Conclusion: Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.

Original languageEnglish (US)
Article numbere130
JournalJournal of Clinical and Translational Science
Volume7
Issue number1
DOIs
StatePublished - May 15 2023

Keywords

  • Electronic health records
  • Greater Plains Collaborative
  • PCORnet
  • common data model
  • data quality

ASJC Scopus subject areas

  • General Medicine

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