Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology
Article
Sibley, MH, Bickman, L, Atkins, D et al. (2023). Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology
. COGNITIVE AND BEHAVIORAL PRACTICE, 10.1016/j.cbpra.2023.02.001
Sibley, MH, Bickman, L, Atkins, D et al. (2023). Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology
. COGNITIVE AND BEHAVIORAL PRACTICE, 10.1016/j.cbpra.2023.02.001
Implementation of behavior therapy for ADHD faces challenges in community settings. We describe development of a community-based implementation model for adolescent ADHD behavior therapy (Supporting Teens’ Autonomy Daily; STAND) blended with Motivational Interviewing (MI). A stakeholder-engaged development approach is used based on the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Resulting model features include: (a) task-shifting supervision from experts to agency supervisors, (b) holding bi-weekly technical assistance drop-ins to provide training and implementation supports, (c) MI integrity monitoring and feedback by artificial intelligence (AI), (d) AI-generated metrics for STAND content fidelity, (e) digitizing resources (manual, worksheets, tips, videos) on a clinician dashboard, (f) creating visual displays of feedback using badges and graphs, and (g) adding a rapport-building session prior to manualized content. We conducted stakeholder focus groups (N = 32) and two pilot studies to evaluate the new STAND AI measurement tool and revised service-delivery model (N = 6 therapists, 7 youth and parents, 3 agency supervisors). Results revealed advantages and disadvantages of the model, supported the promise of a STAND AI fidelity measurement tool, and indicated initial feasibility, acceptability, and agency engagement in STAND's community-based implementation model. We discuss future directions for continued iterative development and testing. Video examples are included as supplementary material.
