用患者视角评估心理治疗效果，跨阶段追踪症状变化
Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy
- 构建患者导向评估框架，融合多轮对话上下文与单次会话动态
- 在400对初末期记录上验证，显著提升症状追踪准确性
- 适合临床研究者、智能诊疗系统开发者使用
心理治疗中的疗效评估对心理健康服务至关重要，需系统性评价治疗过程与结果。现有大模型方法多聚焦于治疗师视角的单次评估，忽视患者主观体验及跨多次会话的纵向进展。为此，我们提出IPAEval——基于患者知情的心理评估框架，通过临床访谈自动化实现患者视角的疗效评估。该框架整合跨会话的患者情境评估与会话聚焦的患者动态评估，全面理解治疗进展。具体采用两阶段提示策略，将患者信息映射至量表题项，实现可解释、结构化的心理评估。在新构建的TheraPhase数据集（含400对初始与完成阶段患者记录）上的实验表明，IPAEval能有效追踪症状严重程度与治疗成效，优于闭源与开源基线模型，验证了题项感知推理机制的优势。
原文摘要 · Abstract (English)
In psychotherapy, therapeutic outcome assessment, or treatment outcome evaluation, is essential to 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, which automates treatment outcome evaluations from the client's perspective using clinical interviews. It integrates cross-session client-contextual assessment and session-focused client-dynamics assessment for a comprehensive understanding of therapeutic progress. Specifically, IPAEval employs a two-stage prompt scheme that maps client information onto psychometric test items, enabling interpretable and structured psychological assessments. Experiments on our new TheraPhase dataset, comprising 400 paired initial and completion stage client records, demonstrate that IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions, outperforming baseline approaches across both closed-source and open-source models, and validating the benefits of items-aware reasoning mechanisms.
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