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

Abstract

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.