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

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.