PATIENT-\Psi: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
<|reference_start|>PATIENT-\Psi: Using Large Language Models to Simulate Patients for Training Mental Health Professionals: Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-{\Psi}, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PATIENT-{\Psi}, we construct diverse patient cognitive models based on CBT principles and use large language models (LLMs) programmed with these cognitive models to act as a simulated therapy patient. We propose an interactive training scheme, PATIENT-{\Psi}-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-{\Psi}. To evaluate PATIENT-{\Psi}, we conducted a comprehensive user study of 13 mental health trainees and 20 experts. The results demonstrate that practice using PATIENT-{\Psi}-TRAINER enhances the perceived skill acquisition and confidence of the trainees beyond existing forms of training such as textbooks, videos, and role-play with non-patients. Based on the experts' perceptions, PATIENT-{\Psi} is perceived to be closer to real patient interactions than GPT-4, and PATIENT-{\Psi}-TRAINER holds strong promise to improve trainee competencies. Our code and data are released at \url{https://github.com/ruiyiw/patient-psi}.<|reference_end|>
@article{wang2024patient-{\psi}:,
    title={PATIENT-{\Psi}: Using Large Language Models to Simulate Patients for
  Training Mental Health Professionals},
    author={Ruiyi Wang, Stephanie Milani, Jamie C. Chiu, Jiayin Zhi, Shaun M.
  Eack, Travis Labrum, Samuel M. Murphy, Nev Jones, Kate Hardy, Hong Shen, Fei
  Fang, Zhiyu Zoey Chen},
    journal={arXiv preprint arXiv:2405.19660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19660},
    primaryClass={cs.CL}
}