3 - (SYM 98) Combatting Therapist Drift: Ai-supported Supervision in a Clinical and Counseling Psychology Training Program

Abstract Body
Therapist drift, a phenomenon characterized by a gradual departure from the principles, techniques, and protocols of evidence-based therapeutic approaches towards less directive treatments, presents a significant challenge in the field, particularly among novice therapists. This phenomenon can stem from various factors including inadequate supervision, personal biases, and difficulties in maintaining high fidelity to established protocols. Enhanced clinical training standards have the potential to elevate the quality of  empirically-supported intervention in everyday treatment settings and improve client outcomes. Harnessing the potential of Artificial Intelligence (AI) in clinical supervision offers a promising solution to address therapist drift in university-based training clinics. This presentation will describe the implementation of AI technologies to enhance the skills of novice therapists, focusing on improving case conceptualization and tailoring treatment plans to individual client needs. By leveraging AI-supported supervision, training clinics can bolster therapist proficiency and reduce the likelihood of drift. Results from the implementation of digital tools, including AI, in one such program will be presented. Highlighting the role of training clinics as ideal environments for innovation, the discussion will offer practical insights gleaned from field experiences to guide future implementations of AI in clinical supervision. Key lessons will include strategies for reducing therapist drift and overcoming implementation barriers in higher education. The presentation will also discuss the necessary attention to ethical considerations before and during the implementation process including data privacy, algorithmic bias, and transparency.