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Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center

BACKGROUND: Collection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Ther...

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Autores principales: Proumen, Rachael, Connolly, Hannah, Debick, Nadia Alexandra, Hopkins, Rachel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463428/
https://www.ncbi.nlm.nih.gov/pubmed/37608282
http://dx.doi.org/10.1186/s12913-023-09825-6
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author Proumen, Rachael
Connolly, Hannah
Debick, Nadia Alexandra
Hopkins, Rachel
author_facet Proumen, Rachael
Connolly, Hannah
Debick, Nadia Alexandra
Hopkins, Rachel
author_sort Proumen, Rachael
collection PubMed
description BACKGROUND: Collection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution. METHODS: A survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR. RESULTS: Race was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient’s self-identity. Preferred language was 100% concordant with the EHR. CONCLUSIONS: At an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients’ self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09825-6.
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spelling pubmed-104634282023-08-30 Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center Proumen, Rachael Connolly, Hannah Debick, Nadia Alexandra Hopkins, Rachel BMC Health Serv Res Research BACKGROUND: Collection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution. METHODS: A survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR. RESULTS: Race was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient’s self-identity. Preferred language was 100% concordant with the EHR. CONCLUSIONS: At an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients’ self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09825-6. BioMed Central 2023-08-22 /pmc/articles/PMC10463428/ /pubmed/37608282 http://dx.doi.org/10.1186/s12913-023-09825-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Proumen, Rachael
Connolly, Hannah
Debick, Nadia Alexandra
Hopkins, Rachel
Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center
title Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center
title_full Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center
title_fullStr Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center
title_full_unstemmed Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center
title_short Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center
title_sort assessing the accuracy of electronic health record gender identity and real data at an academic medical center
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463428/
https://www.ncbi.nlm.nih.gov/pubmed/37608282
http://dx.doi.org/10.1186/s12913-023-09825-6
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