Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure

2026-05-13

arXiv.org

AI Summary

Existing patient simulation methods often suffer from insufficient realism and limited controllability, frequently leading to excessive information disclosure and inadequate behavioral diversity. To address these limitations, this work proposes the PatientsWithPersonality (PWP) framework, which introduces the HEXACO six-dimensional personality model into virtual patient modeling for the first time. By explicitly parameterizing latent personality traits, PWP enables fine-grained control over dialogue style, cooperativeness, and information disclosure. Integrated with large language model generation and validated through automated scoring, PWP significantly enhances simulation realism. Clinical evaluations demonstrate that the generated dialogues closely resemble those produced by human actors, substantially outperforming current approaches while markedly reducing instances of information overdisclosure.

Abstract

Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies. However, existing approaches often lack realism and controllability, often oversharing information unprompted, and failing to capture the wide variability of patient behavior. Here, we introduce PatientsWithPersonality (PWP), a patient simulation framework that generates realistic yet diverse virtual patient responses through explicit personality parametrization over a latent patient state. Grounded in HEXACO, a six-dimensional personality space used to quantify and parameterize human behavioral traits, our approach enables fine-grained control over conversational style, cooperativeness, and information disclosure within a unified framework. In a clinician evaluation, PWP is judged nearly as realistic as recorded human actors and clearly ahead of prior simulators, while being flagged as"too informative"far less often. Conditioning on HEXACO axes yields personas whose configured traits are recoverable by both clinicians and an autorater, span a substantially wider behavioral footprint than the closest baseline, and prevent oversharing. Altogether, our framework paves the way for more accurate and informative LLM benchmarking through our realistic and steerable patient simulator.

Problem

• patient simulation
• realism
• controllability
• behavioral diversity
• information oversharing

Innovation

• patient simulation
• personality modeling
• HEXACO
• controlled diversity
• selective disclosure