Abstract
This study examines how Large Language Models advise users during healthcare conflicts where power differences matter (e.g., patient vs. clinician, junior vs. senior staff). We introduce a Thomas–Kilmann–grounded benchmark of 150 short healthcare conflict vignettes constructed from author-defined role, hierarchy, and option-style rules, with LLM-assisted drafting followed by validation and revision. Each vignette is paired with five response options representing common conflict styles: accommodate, assert, compromise, collaborate, and avoid. We evaluate multiple leading LLM families in both single-turn (stateless) and history-conditioned settings, and compare their choices with a small human-judgment study. Across models, recommendations concentrate heavily on collaboration, with assertion a distant second and very little accommodation or avoidance. Models show hierarchy-sensitive shifts in assertiveness, while remaining strongly collaboration-dominant overall. Human choices are more varied, suggesting LLMs may over-default to one “best” conflict approach. These findings motivate conflict-aware evaluation and calibration to better support autonomy in healthcare advice.