Clinical Questions and Psychological Change: How Can Artificial Intelligence Support Mental Health Practitioners?

Area of Science:

• Psychology
• Artificial Intelligence
• Computational Linguistics

Background:

• Clinical psychology approaches aim for mental health promotion but face challenges in monitoring psychological change.
• Clinical questions are key to activating and managing psychological change, yet their differential impact is not fully understood.

Purpose of the Study:

• To investigate how different types of clinical questions foster psychological change in therapy.
• To determine if a machine learning (ML) model can differentiate patients' responses based on the type of clinical question asked.

Main Methods:

• Analysis of a dataset comprising 14,567 therapist-patient interaction texts.
• Categorization of patient answers based on whether questions prompted description of their clinical situation or evaluation of their mental health.
• Application of a machine learning model for text analysis to discern patterns in patient responses.

Main Results:

• The ML model successfully distinguished between patient answers elicited by different question types, confirming their varied impact.
• Results corroborated the hypothesis that question valence influences patient responses.
• The study demonstrated the potential for ML to recognize subtle differences in therapeutic dialogue.

Conclusions:

• Different types of clinical questions have distinct effects on patient responses and psychological change.
• Machine learning and AI models can be trained to identify these differences, potentially assisting therapists.
• Future applications include AI-driven suggestions for clinical questions to enhance therapist skills and patient care.