Health Equity

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 race or ethnicity.
Health Affairs
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Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record

How patients are described in their medical records matter

In this Health Affairs article, you'll learn about:
  • How racism and bias were studied in medical records using machine learning

" 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. "

  • The prevalence of negative descriptors for Black patients versus White

" 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."

  • How negative stigmatizing language can adversely effect health disparities

" Goddu and colleagues observed in their study of hypothetical chart notes that explicitly stigmatizing language (that is, language that conjured up negative stereotypes) negatively affected respondents’ attitudes toward the patient and resulted in less aggressive pain management plans.15 "

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