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Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy

Hongbin Na, Tao Shen, Shumao Yu, Ling Chen

TL;DR

IPAEval introduces a client-centered, multi-session treatment outcome evaluation framework that leverages clinical interviews to automatically populate psychometric tests and track therapeutic progress across sessions. It combines cross-session client-contextual assessment with session-focused client-dynamics assessment, augmented by items-aware reasoning to link interview content to psychometric items. Evaluations on the TheraPhase dataset show IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions and outperforms single-session baselines, with ablations confirming the value of items-aware reasoning. The work advances mental health care by shifting from therapist-centered, single-session evaluation to a holistic, client-informed longitudinal approach, paving the way for more nuanced, actionable treatment adjustments.

Abstract

In psychotherapy, therapeutic outcome assessment, or treatment outcome evaluation, is essential for enhancing mental health care by systematically evaluating therapeutic processes and outcomes. Existing large language model approaches often focus on therapist-centered, single-session evaluations, neglecting the client's subjective experience and longitudinal progress across multiple sessions. To address these limitations, we propose IPAEval, a client-Informed Psychological Assessment-based Evaluation framework that automates treatment outcome evaluations from the client's perspective using clinical interviews. IPAEval integrates cross-session client-contextual assessment and session-focused client-dynamics assessment to provide a comprehensive understanding of therapeutic progress. Experiments on our newly developed TheraPhase dataset demonstrate that IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions, outperforming previous single-session models and validating the benefits of items-aware reasoning mechanisms.

Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy

TL;DR

IPAEval introduces a client-centered, multi-session treatment outcome evaluation framework that leverages clinical interviews to automatically populate psychometric tests and track therapeutic progress across sessions. It combines cross-session client-contextual assessment with session-focused client-dynamics assessment, augmented by items-aware reasoning to link interview content to psychometric items. Evaluations on the TheraPhase dataset show IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions and outperforms single-session baselines, with ablations confirming the value of items-aware reasoning. The work advances mental health care by shifting from therapist-centered, single-session evaluation to a holistic, client-informed longitudinal approach, paving the way for more nuanced, actionable treatment adjustments.

Abstract

In psychotherapy, therapeutic outcome assessment, or treatment outcome evaluation, is essential for enhancing mental health care by systematically evaluating therapeutic processes and outcomes. Existing large language model approaches often focus on therapist-centered, single-session evaluations, neglecting the client's subjective experience and longitudinal progress across multiple sessions. To address these limitations, we propose IPAEval, a client-Informed Psychological Assessment-based Evaluation framework that automates treatment outcome evaluations from the client's perspective using clinical interviews. IPAEval integrates cross-session client-contextual assessment and session-focused client-dynamics assessment to provide a comprehensive understanding of therapeutic progress. Experiments on our newly developed TheraPhase dataset demonstrate that IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions, outperforming previous single-session models and validating the benefits of items-aware reasoning mechanisms.
Paper Structure (32 sections, 9 equations, 5 figures, 8 tables)

This paper contains 32 sections, 9 equations, 5 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Therapist Assessment using LLMs.
  4. Client-centered Psychological Assessment.
  5. Methodology
  6. Task Definition of IPAEval Framework
  7. Cross-session Client-contextual Assessment
  8. Items-Aware Reasoning.
  9. Psychological Assessment.
  10. Remark: Avoiding Excessive Speculation.
  11. Session-focused Client-dynamics Assessment
  12. Remark: Advantages and Versatility of PSDI.
  13. TheraPhase Dataset
  14. Construction Process.
  15. Experiments
  16. ...and 17 more sections

Figures (5)

  • Figure 1: What is Treatment Outcome?
  • Figure 2: An illustration of client-informed psychological assessment-based evaluation (IPAEval).
  • Figure 3: The impact of model parameters on symptom detection, symptom severity evaluation, and treatment outcome prediction. Dashed lines represent the best-performing closed-source models.
  • Figure 4: The impact of items-aware reasoning on psychological assessment and treatment outcomes evaluation using human-annotated data across four OpenAI models.
  • Figure 5: Error distribution across different models.