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Cited by 234Electronic Health Records SystemsMedical Coding and Health InformationSurvey Methodology and Nonresponse

Accuracy of Race, Ethnicity, and Language Preference in an Electronic Health Record

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Klinger, Elissa V., Carlini, Sara V., Gonzalez, Irina, Hubert, Stella St., Linder, Jeffrey A., Rigotti, Nancy A., Kontos, Emily Z., Park, Elyse R. +2 more·2015·Journal of General Internal Medicine
DOI 10.1007/s11606-014-3102-8PMC PMC4441665
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Abstract

Background Collection of data on race, ethnicity, and language preference is required as part of the "meaningful use" of electronic health records (EHRs). These data serve as a foundation for interventions to reduce health disparities.Objective Our aim was to compare the accuracy of EHR-recorded data on race, ethnicity, and language preference to that reported directly by patients.Design/subjects/main measures Data collected as part of a tobacco cessation intervention for minority and low-income smokers across a network of 13 primary care clinics (n = 569).Key results Patients were more likely to self-report Hispanic ethnicity (19.6 % vs. 16.6 %, p < 0.001) and African American race (27.0 % vs. 20.4 %, p < 0.001) than was reported in the EHR. Conversely, patients were less likely to complete the survey in Spanish than the language preference noted in the EHR suggested (5.1 % vs. 6.3 %, p < 0.001). Thirty percent of whites self-reported identification with at least one other racial or ethnic group, as did 37.0 % of Hispanics, and 41.0 % of African Americans. Over one-third of EHR-documented Spanish speakers elected to take the survey in English. One-fifth of individuals who took the survey in Spanish were recorded in the EHR as English-speaking.Conclusion We demonstrate important inaccuracies and the need for better processes to document race/ ethnicity and language preference in EHRs.

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  • Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduce bias.

    “Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduced bias.12,34,43–50”

    From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [34]

    The Discussion reports inaccuracies in EHR demographic data, including a possible undercount of patients identifying as African American or Hispanic, and says accurate data are needed to monitor health disparities. That supports a concern about validity and potential bias, but the paper does not establish that incomplete data were missing not at random. The count of ten studies (66%) is the citing authors’ synthesis, not a finding attributed to this individual paper. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Thirteen studies speculated that poor data quality for Hispanic patients may be due to the fluid, debatable, and problematic nature of the definition of race and ethnicity.

    “Thirteen studies speculated that this may be due to the “fluid, debatable, and problematic”34 nature of the definition of race and ethnicity.11,22,24–26,34,37,40,41,45,46,80,81”

    From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [34]

  • True prevalence among certain population groups may not be discovered.

    “Furthermore, less reliance on formal care for PLWD among some communities [69], inadequate collection of socio-economic and demographic information and historic under-representation in EHRs and research among some communities [70], means true prevalence among certain population groups may not be discovered [71].”

    From The Impact of demographic, socio-economic and geographic factors on mortality risk among people living with dementia in England (2002–2016) (2021) · cites this paper as [71]

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Authors
Klinger, E. V., Carlini, S. V., Gonzalez, I., Hubert, S. S., Linder, J. A., Rigotti, N. A., Kontos, E. Z., Park, E. R., Marinacci, L. X., & Haas, J. S.
Affiliations
  • Brigham and Women's Hospital (US)
  • Harvard University (US)
ORCID
Venue
Journal of General Internal Medicine
Publisher
Springer Science and Business Media LLC
Published
2014-12-20
Volume
30
Issue
6
Pages
719-723
ISSN
  • 0884-8734 (ISSN-L)
  • 1525-1497
DOI
10.1007/s11606-014-3102-8
PMC
PMC4441665
OpenAlex
W2088121266
Cited by
234
References
18
Citations by year
2026: 16 · 2025: 31 · 2024: 35 · 2023: 52 · 2022: 38 · 2021: 30 · 2020: 8 · 2019: 11 · 2018: 6 · 2017: 2 · 2016: 3 · 2015: 2
FWCI
4.26
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Journal of General Internal Medicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Harvard University
  • Harvard Catalyst (TR001102)
  • National Institutes of Health (UL1TR001102, 1UL1TR0011-02)
  • National Cancer Institute (P50CA148596)
  • National Center for Research Resources
  • National Center for Advancing Translational Sciences (TR001102)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
Keywords
language preference, electronic health records, health disparities, meaningful use, tobacco cessation interventions, primary health centers, low-income smokers
MeSH terms
Adult, Aged, Documentation, Ethnicity, Female, Humans, Language, Male, Middle Aged, Smoking Cessation, Racial Groups, Health Status Disparities, Young Adult, Electronic Health Records, Self Report

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