ArticleJAMA network open2021
Physician Use of Stigmatizing Language in Patient Medical Records.
Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07602725 (Patient Experience and Trust When Viewing Original Medical Notes Versus Large Language Model), which is not on this map. Cited by 103 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Patient Experience and Trust When Viewing Original Medical Notes Versus Large Language Model (LLM)-Generated Plain Language Summaries
Who cites it
103 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.Journal of racial and ethnic health disparities · 2026Pooled it
- Understanding Stigmatizing Language Lexicons: A Comparative Analysis in Clinical Contexts.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Pooled it
- Hospital-Based Clinicians' Knowledge and Practices Regarding Stigmatizing Language in Clinical Documentation: A Multicenter Survey.Journal of general internal medicine · 2026Article
- Beyond the sum of its parts: a scoping review on complex mental health care needs.Social psychiatry and psychiatric epidemiology · 2026Review
- Patient Experiences of Disrespectful or Judgmental Language in Medical Records.JAMA network open · 2026Article
- Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.JMIR medical informatics · 2026Article
- An Environmental Scan of Alternate Level of Care (ALC) Policies in Ontario, Canada.Canadian geriatrics journal : CGJ · 2026Article
- Stigmatizing Language in Gender-Expansive Patient Records: Corpus Development, Disparity Analysis, and Natural Language Processing-Based Detection Study.Journal of medical Internet research · 2026Article
- It's Not Really the Words; It's How They Land: Lived Experience Perspectives on the Use of Language and Labels in Mental Health.International journal of environmental research and public health · 2026Article
- An Evaluation of AI-Generated Clinical Notes in the OpenNotes Era: A Thematic Analysis of Clinician Discourse.Journal of evaluation in clinical practice · 2026Article
- The Use of Biased Language in the Care of Seriously Ill Children: A Pilot Study.The American journal of hospice & palliative care · 2026Article
- Re-Centering Clinical Documentation in the Age of AI Scribes: Four Aims of the Patient Chart Note.JMIR medical education · 2026Article
- How words discredit: A taxonomy of stigmatizing language in the electronic health record.Patient education and counseling · 2026Article
- Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities.ArXiv · 2026Article
- Negative Descriptors of Patients With Sickle Cell Disease in the Electronic Health Record.JAMA network open · 2026Article
- Article
- Physician Attitudes Embedded Within Electronic Medical Records of Persons With and Without Serious Mental Illness.Schizophrenia bulletin · 2026Article
- Factors associated with healthcare professionals' use of stigmatizing language in individuals with diabetes in pregnancy.BMC pregnancy and childbirth · 2026Article
- "He said he could take his own advice": Stigmatizing language in notes documenting discharges against medical advice.Journal of hospital medicine · 2026Article
- Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician.Journal of the American College of Emergency Physicians open · 2026Article
43 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Negative attitudes toward patients can adversely impact health care quality and contribute to health disparities. Stigmatizing language written in a patient's medical record can perpetuate negative attitudes and influence decision-making of clinicians subsequently caring for that patient. Objective: To identify and describe physician language in patient health records that may reflect, or engender in others, negative and positive attitudes toward the patient. Design, Setting, and Participants: This qualitative study analyzed randomly selected encounter notes from electronic medical records in the ambulatory internal medicine setting at an urban academic medical center. The 600 encounter notes were written by 138 physicians in 2017. Data were analyzed in 2019. Main Outcomes and Measures: Common linguistic characteristics reflecting an overall positive or negative attitude toward the patient. Results: A total of 138 clinicians wrote encounter notes about 507 patients. Of these patients, 350 (69%) were identified as female, 406 (80%) were identified as Black/African American, and 76 (15%) were identified as White. Of 600 encounter notes included in this study, there were 5 major themes representing negative language and 6 themes representing positive language. The majority of negative language was not explicit and generally fell into one or more of the following categories: (1) questioning patient credibility, (2) expressing disapproval of patient reasoning or self-care, (3) stereotyping by race or social class, (4) portraying the patient as difficult, and (5) emphasizing physician authority over the patient. Positive language was more often more explicit and included (1) direct compliments, (2) expressions of approval, (3) self-disclosure of the physician's own positive feelings toward the patient, (4) minimization of blame, (5) personalization, and (6) highlighting patient authority for their own decisions. Conclusions and Relevance: This qualitative study found that physicians express negative and positive attitudes toward patients when documenting in the medical record. Although often not explicit, this language could potentially transmit bias and affect the quality of care that patients subsequently receive. These findings suggest that increased physician awareness when writing and reading medical records is needed to prevent the perpetuation of negative bias in medical care.
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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.