How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues
This addresses the need for scalable, privacy-preserving data in healthcare, but is incremental in providing a framework for assessing clinical fidelity.
The paper tackled the problem of evaluating synthetic Prolonged Exposure therapy conversations for PTSD, finding that while synthetic data replicates key linguistic features (e.g., Readability Score 89.2 vs. 88.1), it struggles with subtle therapeutic dynamics like distress monitoring.
Synthetic data adoption in healthcare is driven by privacy concerns, data access limitations, and high annotation costs. We explore synthetic Prolonged Exposure (PE) therapy conversations for PTSD as a scalable alternative for training clinical models. We systematically compare real and synthetic dialogues using linguistic, structural, and protocol-specific metrics like turn-taking and treatment fidelity. We introduce and evaluate PE-specific metrics, offering a novel framework for assessing clinical fidelity beyond surface fluency. Our findings show that while synthetic data successfully mitigates data scarcity and protects privacy, capturing the most subtle therapeutic dynamics remains a complex challenge. Synthetic dialogues successfully replicate key linguistic features of real conversations, for instance, achieving a similar Readability Score (89.2 vs. 88.1), while showing differences in some key fidelity markers like distress monitoring. This comparison highlights the need for fidelity-aware metrics that go beyond surface fluency to identify clinically significant nuances. Our model-agnostic framework is a critical tool for developers and clinicians to benchmark generative model fidelity before deployment in sensitive applications. Our findings help clarify where synthetic data can effectively complement real-world datasets, while also identifying areas for future refinement.