PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions

Doctor-patient consultations require multi-turn, context-aware communication
tailored to diverse patient personas. Training or evaluating doctor LLMs in
such settings requires realistic patient interaction systems. However,
existing simulators often fail to reflect the full range of personas seen in
clinical practice. To address this, we introduce PATIENTSIM, a patient
simulator that generates realistic and diverse patient personas for clinical
scenarios, grounded in medical expertise. PATIENTSIM operates using: 1)
clinical profiles, including symptoms and medical history, derived from real-
world data in the MIMIC-ED and MIMIC-IV datasets, and 2) personas defined by
four axes: personality, language proficiency, medical history recall level,
and cognitive confusion level, resulting in 37 unique combinations. We
evaluated eight LLMs for factual accuracy and persona consistency. The top-
performing open-source model, Llama 3.3 70B, was validated by four clinicians
to confirm the robustness of our framework. As an open-source, customizable
platform, PATIENTSIM provides a reproducible and scalable solution that can be
customized for specific training needs. Offering a privacy-compliant
environment, it serves as a robust testbed for evaluating medical dialogue
systems across diverse patient presentations and shows promise as an
educational tool for healthcare.