ArticleHealth affairs (Project Hope)2022
Negative Patient Descriptors: Documenting Racial Bias In The Electronic Health Record.
Article in Health affairs (Project Hope), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
152 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Understanding Stigmatizing Language Lexicons: A Comparative Analysis in Clinical Contexts.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Pooled it
- Patient Experiences of Disrespectful or Judgmental Language in Medical Records.JAMA network open · 2026Article
- Algorithmic gaslighting in healthcare: mechanisms, harms, and epistemic injustice in context.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.JMIR medical informatics · 2026Article
- Medical Diagnosis, ED Visits, and Hospitalizations for Substance Use in U.S. Children's Hospitals: A Retrospective Multi-Center Cohort Study.Children (Basel, Switzerland) · 2026Article
- Social Determinants of Health Coding Among Hospital Visits Made by Adults With Cancer.JCO oncology practice · 2026Article
- Challenges in medical algorithmic fairness.JNCI cancer spectrum · 2026Review
- Stigmatizing Language in Gender-Expansive Patient Records: Corpus Development, Disparity Analysis, and Natural Language Processing-Based Detection Study.Journal of medical Internet research · 2026Article
- Surrogate Decision-Making in Carceral Healthcare.Journal of general internal medicine · 2026Article
- A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- "A lot of the times, patients really don't know what questions to ask:" Communication perspectives of Black patients with advanced lung cancer.Patient education and counseling · 2026Article
- Racial and Demographic Differences in Use of a Trauma Center Confidential Status Policy.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026Article
- Article
- The Use of Biased Language in the Care of Seriously Ill Children: A Pilot Study.The American journal of hospice & palliative care · 2026Article
- Identifying Patterns of Stigmatizing Language Use in the Safety Net.Journal of general internal medicine · 2026Article
- Examining Barriers to Cancer Care: A Needs Assessment of African American and Black African Immigrants in Iowa.Journal of racial and ethnic health disparities · 2026Article
- How words discredit: A taxonomy of stigmatizing language in the electronic health record.Patient education and counseling · 2026Article
- Article
92 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.