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.
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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 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]
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“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]
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“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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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
Collection of Data on Patients' Race and Ethnic Group by Physician Practices
Wynia, M. K., Ivey, S. L., & Hasnain‐Wynia, R. (2010) · New England Journal of Medicine · doi:10.1056/nejmsb0910799 · Cited by 56
McAlpine, D., Beebe, T., Davern, M., & Call, K. (2007) · Health Services Research · doi:10.1111/j.1475-6773.2007.00771.x · Cited by 55
Clark, C. R., Soukup, J. R., Govindarajulu, U. S., Riden, H. E., Tovar, D. A., & Johnson, P. A. (2011) · Health Affairs · doi:10.1377/hlthaff.2010.0319 · Cited by 43
Hamilton, N. S., Edelman, D., Weinberger, M., & Jackson, G. L. (2009) · North Carolina Medical Journal · doi:10.18043/ncm.70.4.296 · Cited by 43
Measuring Racial And Ethnic Health Care Disparities In Massachusetts
Weinick, R. M., Caglia, J. M., Friedman, E., & Flaherty, K. (2007) · Health Affairs · doi:10.1377/hlthaff.26.5.1293 · Cited by 34
Hasnain‐Wynia, R., Taylor-Clark, K. A., & Anise, A. (2010) · Medical Care Research and Review · doi:10.1177/1077558710382549 · Cited by 21
Looking Forward: Cross‐cutting Issues in the Collection and Use of Racial/Ethnic Data
Lurie, N., & Fremont, A. M. (2006) · Health Services Research · doi:10.1111/j.1475-6773.2006.00553.x · Cited by 20
Use of Practice-Based Research Network Data to Measure Neighborhood Smoking Prevalence
Linder, J. A., Rigotti, N. A., Brawarsky, P., Kontos, E. Z., Park, E. R., Klinger, E. V., Marinacci, L., Li, W., & Haas, J. S. (2013) · Preventing Chronic Disease · doi:10.5888/pcd10.120132 · Cited by 16
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Ethical Machine Learning in Healthcare
Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645
Examination of Stigmatizing Language in the Electronic Health Record
Himmelstein, G., Bates, D. W., & Zhou, L. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.44967 · Cited by 210
The quality of social determinants data in the electronic health record: a systematic review
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Toward a fine-scale population health monitoring system
Belbin, G. M., Cullina, S., Wenric, S., Soper, E. R., Glicksberg, B. S., Torre, D., Moscati, A., Wojcik, G. L., Shemirani, R., Beckmann, N. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.034 · Cited by 136
Coleman, K. J., Stewart, C., Waitzfelder, B. E., Zeber, J. E., Morales, L. S., Ahmed, A. T., Ahmedani, B. K., Beck, A., Copeland, L. A., Cummings, J. R., et al. (2016) · Psychiatric Services · doi:10.1176/appi.ps.201500217 · Cited by 129
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
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