Personas Shift Clinical Action Thresholds in Large Language Models

Background and aimsClinical LLM deployment is shifting from feasibility to liability, while current guidance largely treats model behavior as a control problem. We tested whether decision-style system prompts shift clinical action thresholds when clinical facts are held constant, and whether these shifts are consistent across settings and models. MethodsWe defined nine physician personas by crossing three ethical orientations (duty-, care-, utilitarian) with three cognitive styles (intuitive, integrative, analytic). Twenty open-weight LLMs were evaluated on 2,500 simulated ED vignettes and 2,500 MIMIC-IV-Note discharge summaries. For each text, models answered five binary decision items (safety, autonomy, treatment, resource use, follow-up). Each condition was repeated ten times, yielding 5,000,000 total decisions. ResultsUnder baseline prompting, models answered "Yes" to 42.8% of decisions. Persona prompts shifted affirmative rates from 36.9% to 46.4%, a 9.5-percentage-point swing under fixed clinical evidence. Effects were largest in autonomy and treatment and were consistent across corpora (85.7% directional agreement; r = 0.82 for effect sizes). Susceptibility varied by model (4.9-16.1 points), with no consistent protection from medical fine-tuning or model size. ConclusionsDecision-style system prompts reliably change clinical action thresholds in LLMs under fixed evidence. Prompting is a policy-setting layer, not just a communication layer, and should be treated as a first-class deployment configuration.