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Error Rates in Race and Ethnicity Designation Across Large Pediatric Health Systems.

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  • 1Michigan Child Health Equity Collaborative, Ann Arbor.

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|September 3, 2024
PubMed
Summary
This summary is machine-generated.

Electronic medical record (EMR) race and ethnicity data have high error rates, impacting health equity studies. Consolidating data options improved accuracy, but significant misattribution persists, potentially undermining care improvement efforts.

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Area of Science:

  • Health Informatics
  • Health Equity Research
  • Clinical Data Management

Background:

  • Accurate racial and ethnic designations are crucial for identifying health disparities and improving clinical care.
  • Misattribution of race and ethnicity in electronic medical records (EMRs) risks overlooking existing inequities or creating false ones.

Purpose of the Study:

  • To determine the error rate of racial and ethnic attribution in EMRs across three major Michigan pediatric health systems.
  • To assess the impact of data consolidation on the accuracy of race and ethnicity matching.

Main Methods:

  • A cross-sectional study involving 4,333 parent/guardian surveys collected from outpatient, emergency, and inpatient settings.
  • Parental report of child's race and ethnicity was used as the gold standard, compared against EMR designations.
  • Data matching involved exact comparison, prioritizing minoritized groups, and collapsing categories into fewer options.

Main Results:

  • Exact matching of parental race report with EMRs showed high error rates (41%-78%).
  • Consolidating race options improved matching rates, narrowing differences between health systems (79%-88%).
  • Ethnicity matching ranged from 65% to 95%, with missing data also contributing to non-matches.

Conclusions:

  • Significant errors in EMR race and ethnicity data can undermine efforts to address health disparities.
  • The complexity of data categories may contribute to misattribution, highlighting a need for standardized and validated data collection.
  • Further investigation is needed to understand the disconnect between data category design and disparity analysis.