Cited by 43Medical Coding and Health InformationElectronic Health Records SystemsRace, Genetics, and Society

Concordance between self-reported race ethnicity and that recorded in a veteran affairs electronic medical record

Hamilton, Natia S., Edelman, David, Weinberger, Morris, Jackson, George L.·2009·North Carolina Medical Journal

Abstract

Background Using information from electronic health records (EHRs) to examine racial/ethnic health disparities is increasingly common. This study examines the degree of concordance between administratively recorded race/ethnicity and the criterion standard of self-reported race/ethnicity at a tertiary care Veterans Affairs Medical Center (VAMC) in North Carolina. Methods We compared self-reported race among 204 respondents to a cross-sectional mailed survey of patients with diabetes conducted in 2006-2007 to the race/ethnicity recorded in the EHR. Concordance was defined as the percent agreement between self-reported and administratively-reported race. Results The overall response rate to the survey was 68.9% (204 of 296). Of the 204 respondents, 32 (15.7%) reported a different race/ethnicity from the race/ethnicity reported in the EHR. Misclassification resulted from either the patient reporting a race/ethnicity and having the information missing in the EHR (9.3% of respondents) or the EHR having a different race/ethnicity listed than reported by the patient (6.3% of respondents). Limitations This study was conducted at one VAMC. Conclusions While we found misclassification of race/ethnicity in the EHR, the level of discordance is smaller than previously reported in the Veterans Health Administration. Despite this, efforts still need to be made to ensure correct information is included in the EHR.

Integrity signals

Registry

Crossref · 10.18043/ncm.70.4.296

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Several studies found that the plausibility of patient race/ethnicity information in their dataset was questionable because the data were incomplete; plausibility was low because a patient's race was reported correctly in one area but as 'other' in another.

    “For example, several studies found that the plausibility of the patient race/ethnicity information in their dataset was questionable because the data were incomplete; in other words, plausibility was low because a patient’s race was reported correctly in one area, but as “other” in another.21,86,87”

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 23 · Cited by 43

Authors
Hamilton, N. S., Edelman, D., Weinberger, M., & Jackson, G. L.
Affiliations
Durham VA Medical Center (US)
Venue
North Carolina Medical Journal
Published
2009-07
Volume
70
Issue
4
Pages
296-300
ISSN
0029-2559 (ISSN-L)
OpenAlex
W2342539798
Cited by
43
References
23
Citations by year
2026: 2 · 2025: 6 · 2024: 2 · 2023: 7 · 2022: 10 · 2021: 4 · 2019: 4 · 2018: 2 · 2017: 1 · 2014: 3 · 2013: 1
FWCI
0.70
Type
journal article
Language
en
Open access
closed
Locations
  • North Carolina Medical Journal (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
Keywords
self-reported race, electronic health records, racial and ethnic health disparities, veterans, diabetes mellitus, Veterans Affairs hospitals
MeSH terms
Aged, Cross-Sectional Studies, Diabetes Mellitus, Ethnicity, Female, Health Surveys, Humans, Male, Social Identification, United States, United States Department of Veterans Affairs, Racial Groups, Health Status Disparities, Electronic Health Records

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review