The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Accuracy of Race, Ethnicity, and Language Preference in an Electronic Health Record
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Unequal treatment: confronting racial and ethnic disparities in health care
(2003) · Choice Reviews Online · doi:10.5860/choice.40-5843 · Cited by 7,043
Unequal treatment: confronting racial and ethnic disparities in health care.
Smedley, B. D., Stith, A. Y., & Nelson, A. (2003) · University Libraries (University of Maryland) · doi:10.13016/1bbm-d7bq · Cited by 6,567
The “Meaningful Use” Regulation for Electronic Health Records
Blumenthal, D., & Tavenner, M. (2010) · New England Journal of Medicine · doi:10.1056/nejmp1006114 · Cited by 2,059
National Healthcare Disparities Report
Quality (2014) · Cited by 969
Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement
Ulmer, C. C., McFadden, B., & Nerenz, D. R. (2009) · University Libraries (University of Maryland) · doi:10.13016/jjuy-tsdb · Cited by 322
Hasnain-Wynia, R., & Baker, D. W. (2006) · Health Services Research · doi:10.1111/j.1475-6773.2006.00552.x · Cited by 257
National Healthcare Disparities Report, 2011
UNSPECIFIED (2011) · PsycEXTRA Dataset · doi:10.1037/e579292012-001 · Cited by 141
Staroselsky, M., Volk, L. A., Tsurikova, R., Pizziferri, L., Lippincott, M. F., Wald, J. S., & Bates, D. W. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.10.004 · Cited by 76
Massachusetts Health Reform and Disparities in Coverage, Access and Health Status
Zhu, J. M., Brawarsky, P., Lipsitz, S. R., Huskamp, H. A., & Haas, J. S. (2010) · Journal of General Internal Medicine · doi:10.1007/s11606-010-1482-y · Cited by 72
Self-citationRace and ethnicity: comparing medical records to self-reports
West, C. N. (2005) · Journal of the National Cancer Institute Monographs · doi:10.1093/jncimonographs/lgi041 · Cited by 64
Cited by
Sorted by citations
Items 201–234 of 234
State-offered ethnically targeted reproductive genetic testing
Clayton, E. W., & Brothers, K. B. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.74 · Cited by 1
Crisamore, K. R., Empey, P. E., Pelletier, J. H., Clark, R. S. B., & Horvat, C. M. (2022) · Journal of Pediatric Intensive Care · doi:10.1055/s-0042-1753537 · Cited by 1
Sivanushanthan, S., Wu, T., Wahl, A. D., Li, T., Luta, G., Song, J. H., O’Neill, S. C., & Conley, C. C. (2023) · Journal of Breast Imaging · doi:10.1093/jbi/wbad006 · Cited by 1
Overt and occult hypoxemia in patients hospitalized with novel coronavirus disease 2019Preprint
Gadrey, S. M., Mohanty, P., Haughey, S. P., Jacobsen, B. A., Dubester, K. J., Webb, K. M., Kowalski, R. L., Dreicer, J. J., Andris, R. T., Clark, M., et al. (2022) · medRxiv · doi:10.1101/2022.06.14.22276166 · Cited by 1
Come for Broadway, Stay for Bypass: Who Is New York’s Medical Tourist?
Thornton, M. (2019) · Springer proceedings in business and economics · doi:10.1007/978-3-030-12453-3_112
Hester, G. Z., Thornton, A., & Workman, J. K. (2023) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2023.113558
Campbell, J. I., Tabatneck, M., Wilt, G. E., Sun, M., He, W. A., Musinguzi, N., Hedt‐Gauthier, B., Lamb, G. S., Goldmann, D. A., Sabharwal, V., et al. (2023) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.22-0788
Yoder, L. R., Williams, D., Tian, Z., Shen, C., Zhou, S., Thomas, N. J., & Krawiec, C. (2023) · Journal of Pediatric Intensive Care · doi:10.1055/s-0043-1774719
Idnay, B., Liu, J., Fang, Y., Hernández, A., Kaw, S., Etwaru, A., Padilla, J. J., Ramírez, S., Marder, K. S., Weng, C., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae032
Vulnerabilities During and After Critical Illness
Danesh, V. C., & Ettlinger, J. A. (2024) · CHEST Critical Care · doi:10.1016/j.chstcc.2024.100078
Akgün, K. M., & Feder, S. L. (2025) · The AMA Journal of Ethic · doi:10.1001/amajethics.2025.6
Learning Health Systems Research
Tisdale, R. L., & Sarkar, U. (2025) · Medical Care · doi:10.1097/mlr.0000000000002121
Yiu, A. J., Stephenson, G. S., Chow, E., & O’Connell, R. (2025) · Applied Clinical Informatics · doi:10.1055/a-2441-3677 · Cited by 3
Cowden, J. D., Drake, R. G., Johnson, J., Kelty, K., & Ahmed, M. (2025) · Health Equity · doi:10.1089/heq.2024.0188
Descriptive and risk factor analysis of infantile cataracts
Nalbandyan, M. (2019) · doi:10.54014/2fdy-wf8j
Accuracy and Adequacy of Preferred Language Data in a Pediatric Electronic Health Record
Cowden, J. D., Drake, R. G., Johnson, J., Kelty, K., & Ahmed, M. (2025) · Hospital Pediatrics · doi:10.1542/hpeds.2025-008366
Gold, J. I., Elkaim, Y., Asher, S. B., Bogus, Z., Chai, T., Cohen, S., Condit, C., Elysee, I., Gehringer, B. N., Gray, S. M., et al. (2025) · medRxiv · doi:10.1101/2025.10.09.25337579
Regalado, R. C., Bulik‐Sullivan, E. C., Blank, A. G., Raff, L., Johnston, A., Beauchamp, M., Lee, Y. U., Cobos, C., Alpizar, M., Cifuentes, V., et al. (2025) · Digital Health · doi:10.1177/20552076251386884
Linnander, E. L., Ayedun, A. A., Boatright, D., Ackerman-Barger, K. W., Morgenthaler, T. I., Ray, N., Roy, B., Simpson, S., & Curry, L. A. (2022) · Figshare · doi:10.6084/m9.figshare.20408538
Linnander, E. L., Ayedun, A. A., Boatright, D., Ackerman-Barger, K. W., Morgenthaler, T. I., Ray, N., Roy, B., Simpson, S., & Curry, L. A. (2022) · Figshare · doi:10.6084/m9.figshare.20408547
Proumen, R., Connolly, H., Debick, N. A., & Hopkins, R. (2024) · Figshare · doi:10.6084/m9.figshare.26612144
Linnander, E. L., Ayedun, A. A., Boatright, D., Ackerman-Barger, K. W., Morgenthaler, T. I., Ray, N., Roy, B., Simpson, S., & Curry, L. A. (2022) · Figshare · doi:10.6084/m9.figshare.20408544
Linnander, E. L., Ayedun, A. A., Boatright, D., Ackerman-Barger, K. W., Morgenthaler, T. I., Ray, N., Roy, B., Simpson, S., & Curry, L. A. (2022) · Figshare · doi:10.6084/m9.figshare.20408550
Linnander, E. L., Ayedun, A. A., Boatright, D., Ackerman-Barger, K. W., Morgenthaler, T. I., Ray, N., Roy, B., Simpson, S., & Curry, L. A. (2022) · Figshare · doi:10.6084/m9.figshare.20408541
Batista, R., Hsu, A. T., Bouchard, L., Reaume, M., Rhodes, E., Sucha, E., Guérin, E., Prud’homme, D., Manuel, D. G., & Tanuseputro, P. (2024) · Figshare · doi:10.6084/m9.figshare.25708975
Kohn, R. A., Courtright, K. R., Grau‐Sepulveda, M. V., Olsen, M. K., Madden, V. L., Sewell, B. D., Sheu, D., Ahmad, Y. S., Auriemma, C. L., Nimetz, A., et al. (2026) · medRxiv · doi:10.64898/2026.01.02.26343350
Dhinsa, J., Mahajan, V., & Ngai, K. M. (2026) · Journal of Patient Experience · doi:10.1177/23743735261418003
Carroll, R. M., Braswell, A. A., Roberto, A., Estrem, H., & Prentice, C. R. (2026) · Journal of Pediatric Health Care · doi:10.1016/j.pedhc.2026.01.010
Miller, D. M., Li, Y., Husak, S. S., Abbatemarco, J., Ontaneda, D., Bermel, R. A., O’Mahony, J., & Lapin, B. (2026) · Multiple Sclerosis Journal · doi:10.1177/13524585261423408
Cardenas, A., Berardinetti, K., Mahant, S., & Do, V. (2026) · Discover Health Systems · doi:10.1007/s44250-026-00375-2
Barcroft, M., Davis, B. A., Bapat, R., Cosgrove, T., & Napolitano, S. K. (2026) · Journal of Perinatology · doi:10.1038/s41372-026-02676-z
Caregiver-Reported Quality of Pediatric Primary Care Language Services
Yu, L., Migliori, O., Schweiberger, K. A., Kurs‐Lasky, M., Schoemer, P. L., Heidenerich, K., Lion, K. C., DeCamp, L. R., Ray, K., Chaves‐Gnecco, D., et al. (2026) · PEDIATRICS · doi:10.1542/peds.2025-075312
Racial disparities in multiple sclerosis quality of care metrics
Harrison, K. L., Levasseur, V. A., Conway, A. P., Bhattiprolu, K., Naismith, R. T., Fitzgerald, K. C., Beier, M. L., Wang, Y., Budhathoki, C. B., Jones, M. R., et al. (2026) · Multiple Sclerosis Journal · doi:10.1177/13524585261478826
Holt, H. K., Ma, W., Gordon, H. S., Sun, J., GELLER, S. E., Lomahan, S., Bright, D., Cejtin, H. E., Tiro, J. A., & Peterson, C. E. (2026) · Gynecologic Oncology · doi:10.1016/j.ygyno.2026.10.016
Metadata
- 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
- Published
- 2014-12-20
- Volume
- 30
- Issue
- 6
- Pages
- 719-723
- ISSN
- 0884-8734 (ISSN-L)
- 1525-1497
- 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
- bronze
- doi.org
- 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
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
