Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record

Little is known about how racism and bias may be communicated in the medical record. This study used machine learning to analyze electronic health records (EHRs) from an urban academic medical center and to investigate whether providers' use of negative patient descriptors varied by patient rac...

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Bibliographic Details
Published in:Health affairs (Millwood, Va.) Va.), 2022-02, Vol.41 (2), p.203-8
Main Authors: Sun, Michael, Oliwa, Tomasz, Peek, Monica E, Tung, Elizabeth L
Format: Article
Language:English
Subjects:
Bias
Computerized medical records
Coronaviruses
COVID-19
COVID-19 diagnostic tests
Electronic health records
Electronic medical records
Ethnic differences
Ethnicity
Health
Health care
Health care delivery
Health care facilities
Health disparities
Health records
Health services
Inclusion
Language
Learning
Learning algorithms
Machine learning
Medical records
Medical screening
Medicine
Minority & ethnic groups
Natural language processing
Patients
Race
Racial bias
Racial differences
Racial discrimination
Racism
Sentences
Sociodemographics
Stigma
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Summary:Little is known about how racism and bias may be communicated in the medical record. This study used machine learning to analyze electronic health records (EHRs) from an urban academic medical center and to investigate whether providers' use of negative patient descriptors varied by patient race or ethnicity. We analyzed a sample of 40,113 history and physical notes (January 2019-October 2020) from 18,459 patients for sentences containing a negative descriptor (for example, resistant or noncompliant) of the patient or the patient's behavior. We used mixed effects logistic regression to determine the odds of finding at least one negative descriptor as a function of the patient's race or ethnicity, controlling for sociodemographic and health characteristics. compared with White patients, Black patients had 2.54 times the odds of having at least one negative descriptor in the history and physical notes. Our findings raise concerns about stigmatizing language in the EHR and its potential to exacerbate racial and ethnic health care disparities.
ISSN:0278-2715
1544-5208
DOI:10.1377/hlthaff.2021.01423