PatientAct: Theory-Grounded Mental Health Client Simulation

2026-08-12

AI Summary

This study addresses a critical limitation in existing large language model–driven client simulators for psychotherapy, which often produce overly compliant responses lacking the resistance and causal depth characteristic of real clinical interactions. To enhance ecological validity, the authors propose a novel client simulation framework grounded in clinical theory, integrating the 5Ps case formulation approach with a dynamic trust mechanism. This framework employs a dynamic memory layer to track the therapeutic alliance and introduces a trust threshold to modulate emotion–behavior modeling, thereby generating clinically coherent responses. Evaluated across 40 diverse clinical scenarios, the method produces highly plausible and varied client profiles, significantly outperforming baseline models in both the diversity of resistance expression and behavioral authenticity, thus advancing the clinical fidelity of simulated clients.

Abstract

LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as equally accessible. We present PatientAct, a framework for client simulation grounded in established clinical theories. Our profiles integrate the 5Ps clinical case formulation, providing causal depth without tying the design to any single therapeutic modality. During simulation, profiles include a dynamic memory layer in which items carry trust thresholds (e.g., symptoms are available early, whereas formative memories require a sustained therapeutic alliance). At each turn, the client's emotional reaction and behavior are modeled before generating a response. If the therapist approaches gated content, PatientAct expresses resistance in terms of quantity, content, and style rather than defaulting to cooperation or a single resistance pattern. We evaluate our framework on 40 clinical situations and demonstrate that it generates diverse profiles with high clinical plausibility. Moreover, PatientAct significantly outperforms the baselines, yielding substantial gains in resistance quality and behavioral realism. Our code and data will be publicly available via github.com/Sahandfer/PatientHub.

Problem

• mental health simulation
• client resistance
• therapeutic alliance
• clinical realism
• LLM-based simulation

Innovation

• client simulation
• clinical case formulation
• trust thresholds
• behavioral realism
• therapeutic resistance