Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note

The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group.

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Publication Details

Journal
Demography
Published
2024-09-27
DOI
https://doi.org/10.1215/00703370-11582088
Citations
2
Primary Topic
Healthcare Policy and Management
Type
article
Field-Weighted Citation Impact
2.26

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article

Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note

Jordan Young, Barbara Entwisle, Paul R. Chelminski, Victoria Udalova et al.
2 citations
Demography
Healthcare Policy and Management
2.26
article

Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note

Jordan Young, Barbara Entwisle, Paul R. Chelminski, Victoria Udalova, Timothy S. Carey, Aubrey Limburg
article en
2 citations

Abstract

The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group.

DemographyVol. 61(5)
University of North Carolina at Chapel Hill (US), United States Census Bureau (US)
Georgia Clinical and Translational Science Alliance, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Carolina Population Center, University of North Carolina at Chapel Hill
Reduced inequalities
Openalex Percentile: Top 8%
Healthcare Policy and Management
2.26
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Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note — Jordan Young, Barbara Entwisle, et al. · Demography (2024) | TGRS Research Map | TGRS