TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments (2504.21851v1)

Abstract: Objectives: While LLMs have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. Materials and Methods: We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for Post-Traumatic Stress Disorder (PTSD). To guide the generation of appropriate clinical responses, we propose a Dialogue Acts schema specifically designed for clinical interviews. Additionally, we develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians. Results: A comprehensive set of evaluation metrics is designed to assess the dialogue system from both the agent and patient simulation perspectives. Expert evaluations by conversation and clinical specialists show that TRUST performs comparably to real-life clinical interviews. Discussion: Our system performs at the level of average clinicians, with room for future enhancements in communication styles and response appropriateness. Conclusions: Our TRUST framework shows its potential to facilitate mental healthcare availability.

An Expert Overview of the TRUST Dialogue System for PTSD Diagnostic Assessment

The academic paper titled "TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments" describes a sophisticated dialogue system framework termed TRUST, which is designed to leverage LLMs for conducting formal diagnostic interviews specifically for the evaluation of Post-Traumatic Stress Disorder (PTSD). The paper addresses a critical gap in the existing applications of LLMs in mental healthcare, highlighting the need for structured diagnostic dialogue systems capable of replicating clinician behavior during standard psychiatric assessments.

Objectives and Innovations

TRUST aims to bridge challenges in mental healthcare accessibility due to the shortage of qualified mental health providers and the barriers posed by high medical costs. By facilitating clinician-level dialogue through an automated system, it endeavors to alleviate these bottlenecks and expand access to formal PTSD diagnoses. Key innovations include the introduction of a Dialogue Acts schema tailored to clinical interviews, which deconstructs the complex decision-making processes involved in generating clinician-like responses and enhances system controllability across different mental health conditions beyond PTSD.

Methodology and System Architecture

The TRUST framework comprises two primary modules: Database and Framework. The Database module stores metadata for each diagnostic variable, historical interaction data, and assessments scores, while the Framework orchestrates dialogue flow through LLM-powered submodules: Conversation and Assessment.

The Conversation module operates by generating Dialogue Acts (DA) tags to guide responses, ensuring adherence to structured interview protocols. Moreover, the integration of patient simulation, using dataset transcripts from real clinical encounters, offers a scalable method for robust system evaluation without the continual need for human clinician input.

Evaluation and Results

The system was rigorously evaluated by both conversation specialists and domain experts in PTSD, yielding performance indicators that suggest TRUST performs comparably to real-life clinical interviews routinely conducted by average clinicians. In terms of agent dialogue quality, three metrics—Comprehensiveness, Appropriateness, and Communication Style—were explored. The system demonstrated reliable adherence to clinical protocols with minimal divergence from interview standards while acknowledging the potential for future improvements in conversational nuances and dynamic patient interactions.

Discussion and Implications

The authors recognize certain limitations, including instances of inference misalignment and verbosity in dialogue generation, as well as challenges in maintaining the authenticity of real-world conversational cues. The evaluation highlighted areas for improvement in ensuring question relevance and effective simulation faithfulness, avoiding hallucination risks from LLM outputs.

The implications of TRUST, and systems like it, extend far into the domain of mental healthcare by potentially reducing the time and resources spent on standard diagnostic interviews, thus allowing for broader scalability in serving a more significant number of patients. By providing a template adaptable to other structured diagnostic protocols, TRUST creates avenues for facilitating automated assessments in various psychiatric conditions, potentially transforming the landscape of patient care delivery.

Future Directions

The paper paves the way for continued exploration into integrating LLM capabilities with clinical practices, emphasizing the need for refining models to provide consistent, safe, and relevant patient interactions within sensitive healthcare environments. As advancements are made in AI capabilities, further work can expand on adapting such systems for a wider range of mental health disorders, ensuring ethical compliance and reliability in deploying AI systems within healthcare protocols.

In conclusion, this paper delivers scholarly insights into the application of LLMs within structured psychiatric assessments, marking an essential milestone in developing AI systems that promise expanded access to mental healthcare diagnostics, thereby addressing the pressing issue of care accessibility and efficiency.