Navigating electronic health record accuracy by examination of sex incongruent conditions.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 39254529.
- Also identified by DOI 10.1093/jamia/ocae236 and PMC identifier 11631140.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The increasing reliance on electronic health records (EHRs) for research and clinical care necessitates robust methods for assessing data quality and identifying inconsistencies. To address this need, we develop and apply the incongruence rate (IR) using sex-specific medical conditions. We also characterized participants with incongruent records to better understand the scope and nature of data discrepancies. In this cross-sectional study, we used the All of Us Research Program's latest version 7 (v7) EHR data to identify prevalent sex-specific conditions and evaluated the occurrence of incongruent cases, quantified as IR. Among the 92 597 males and 152 551 females with condition occurrence data available from All of Us and sex-conformed gender, we identified 167 prevalent sex-specific conditions. Among the 37 537 biological males and 95 499 biological females with these sex-specific conditions, we detected an overall IR of 0.86%. Attempt to include non-cisgender participants result in inflated overall IR. Additionally, a significant proportion of participants with incongruent conditions also presented with conditions congruent to their biological sex, indicating a mix of accurate and erroneous records. These incongruences were not geographically or temporally isolated, suggesting systematic issues in EHR data integrity. Our findings call attention to the existence of systemic data incongruences in sex-specific conditions and the need for robust validation checks. Extending IR evaluation to non-cisgender participants or non-sex-based conditions remain a challenge. The sex condition-specific IR, when applied to adult populations, provides a valuable metric for data quality assessment in EHRs.
Medical subject headings
- Electronic Health Records
- Data Accuracy