Navigating the complexity of AI adoption in psychotherapy by identifying key facilitators and barriers

Abstract

Artificial intelligence (AI) technologies in mental healthcare offer promising opportunities to reduce therapists' burden and enhance healthcare delivery, yet adoption remains challenging. This study identified key facilitators and barriers to AI adoption in mental healthcare, precisely psychotherapy, by conducting six online focus groups with patients and therapists, using a semi-structured guide based on the NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Data from N = 32 participants were analyzed using a combined deductive and inductive thematic analysis. Across the seven NASSS domains, 36 categories emerged. Sixteen categories were identified as factors facilitating adoption, including useful technology elements, the customization to user needs, and cost coverage. Eleven categories were perceived as barriers to adoption, encompassing the lack of human contact, resource constraints, and AI dependency. Further nine, such as therapeutic approach and institutional differences, acted as both facilitators and barriers depending on the context. Our findings highlight the complexity of AI adoption in mental healthcare and emphasize the importance of addressing barriers early in the development of AI technologies.

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