How words discredit: A taxonomy of stigmatizing language in the electronic health record

Language in electronic health records (EHRs) can transmit stigma, discrediting patients in ways that undermine the clinician-patient relationship and compromise future care. We sought to develop a taxonomy of stigmatizing language in EHRs to understand what patients are being stigmatized for, how that stigma is conveyed linguistically, and why.

We conducted a two-stage qualitative analysis of EHR notes from multiple clinical contexts in a large U.S. academic health system. For both stages, we drew enriched samples using natural language processing (NLP) to identify notes with at least one stigmatizing keyword from prior studies. First, we open coded 296 notes to generate categories of stigmatizing language and linguistic mechanisms, and to develop a preliminary taxonomy. We then applied and refined this framework by coding 400 additional notes.

We identified six categories of stigmatizing sentiments characterizing patients as: (1) Socially Undesirable, (2) Difficult to Interact With, (3) Incompetent, (4) Manipulative, (5) Noncompliant, and (6) Not Credible. These were implied through negative descriptions of patient behavior portraying them as, e.g., Demanding, Adversarial, Deceptive, etc. Linguistic mechanisms extended beyond keywords, including practices for emphasizing the intensity of patient behavior (e.g., intensifiers), marking distance or divergence from the patient's perspective (e.g., skeptical evidentials), and casting the clinician as the neutral or rational party (e.g., euphemisms).

Stigmatizing language in EHRs is not limited to discrete terms but is embedded in broader linguistic practices that shape how patients are represented and understood, particularly those describing how they fail to align with clinical expectations. This language may serve to document professional challenges, but it nonetheless reinforces paternalistic norms and compromises care. Understanding these dynamics is critical for moving toward patient-centered documentation and reducing harm in the EHR.