A Voice-Enabled Virtual Patient System for Interactive Training in Standardized Clinical Assessment

TL;DR

This paper presents a voice-enabled virtual patient system powered by a large language model, designed to improve training for mental health clinicians through realistic, scalable clinical assessment simulations.

Contribution

It introduces a novel LLM-based virtual patient system that accurately simulates clinical profiles and dialogue, validated by expert raters for realism and adherence.

Findings

High adherence to clinical profiles (mean MADRS score difference 0.52)

Strong inter-rater reliability (0.90) across assessment items

Positive expert ratings on realism and cohesiveness

Abstract

Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities, which can impact data quality in clinical trials. To address this gap, we introduce a voice-enabled virtual patient simulation system powered by a large language model (LLM). This study describes the system's development and validates its ability to generate virtual patients who accurately adhere to pre-defined clinical profiles, maintain coherent narratives, and produce realistic dialogue. We implemented a system using a LLM to simulate patients with specified symptom profiles, demographics, and communication styles. The system was evaluated by 5 experienced clinical raters who conducted 20 simulated structured MADRS interviews across 4 virtual patient personas. The virtual patients demonstrated strong adherence to their clinical profiles, with a mean item difference between rater-assigned MADRS scores and configured scores of 0.52 (SD=0.75). Inter-rater reliability across items was 0.90 (95% CI=0.68-0.99). Expert raters consistently rated the qualitative realism and cohesiveness of the virtual patients favorably, giving average ratings between "Agree" and "Strongly Agree." Our findings suggest that LLM-powered virtual patient simulations are a viable and scalable tool for training clinicians, capable of producing high-fidelity, clinically relevant practice scenarios.