Outcome-grounded effect of clinically stigmatizing information on large language model emergency triage prioritization

Whether stigmatizing input language biases large language model (LLM) triage prioritization against acutely ill patients is unknown. In a controlled, outcome-grounded experiment at two academic emergency departments, Emergency Severity Index-matched pairs (one deteriorating within 6 h, one not) were evaluated by three open-weight models (Gemma, Qwen, DeepSeek) before and after inserting one demographic, social, or stigma-related attribute. Eighteen conditions included neutral and stigmatizing formulations of the same concepts.

Across 221,556 comparisons, the stigmatizing frequent-emergency-department-use formulation produced significant harmful reprioritization in all six model-dataset cells (up to 9.4%), exceeding its neutral counterpart within pairs in every cell (+1.4 to +7.1 points). Psychiatric history also produced significant shifts (up to 9.5%), though its stigmatizing-versus-neutral difference reached significance for only one model. Race, language, and insurance showed no consistent harmful shifts.

Stigmatizing formulations of clinical information can bias LLM triage prioritization against deteriorating patients, making input selection and formulation safety-critical design choices. This work was supported by institutional funds provided by the University of Texas Southwestern Medical Center Office of the President to the Andrew Jamieson laboratory in the Lyda Hill Department of Bioinformatics. The funder had no role in study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit.

Claude Opus 4.6 (Anthropic) assisted with data-analysis scripting and manuscript drafting; all content was reviewed and revised by the authors, who take responsibility for its integrity. Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA Philip Jarrett, Peter Yun, Emmanuel Ohuabunwa, Doreen Agboh & D. Mark Courtney Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA Dr.

Philip Jarrett reports participation on a medical advisory board for GE Healthcare and research support from the National Foundation of Emergency Medicine beyond the scope of this work. The other authors have no competing interests to report. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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