title: PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient
    Interactions
summary: 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, 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.
paper_authors._id: 6837b52b1233747046cfa5df
paper_authors.name: Daeun Kyung
paper_authors._id: 6837b52b1233747046cfa5e0
paper_authors.name: Hyunseung Chung
paper_authors._id: 6837b52b1233747046cfa5e1
paper_authors.name: Seongsu Bae
paper_authors._id: 6837b52b1233747046cfa5e2
paper_authors.name: Jiho Kim
paper_authors._id: 6837b52b1233747046cfa5e3
paper_authors.name: Jae Ho Sohn
paper_authors._id: 6837b52b1233747046cfa5e4
paper_authors.name: Taerim Kim
paper_authors._id: 6837b52b1233747046cfa5e5
paper_authors.name: Soo Kyung Kim
paper_authors._id: 6837b52b1233747046cfa5e6
paper_authors.name: Edward Choi
paper_title: PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient
    Interactions
paper_summary: 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, 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.
paper_ai_summary: PatientSim generates diverse and realistic patient personas using clinical data to evaluate LLMs in medical dialogue settings.
paper_ai_keywords: clinical profiles personas personality language proficiency medical history recall level cognitive confusion level patient simulator factual accuracy persona consistency privacy-compliant