ArticleJournal of medical Internet research2026
Determinants of AI Scribe Adoption in Allied Health: Mixed Methods Study Guided by the Capability, Opportunity, and Motivation Model for Behavior Change.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
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Abstract
Background: AI scribes are emerging tools designed to reduce clinician documentation burden and improve clinical quality and efficiency by transcribing and documenting patient interactions in real-time. Despite growing evidence of their efficacy and impact across medical disciplines, there remains limited research on factors that influence successful implementation, particularly in allied health practice. Objective: This study explored the factors influencing the adoption of an AI scribe, including barriers and enablers among allied health clinicians, to inform ongoing implementation in public health settings. Methods: A mixed methods design was employed within a large Australian hospital and health service. Data were collected through surveys (n=97) and semistructured interviews (n=27) with allied health clinicians across multiple disciplines, including both adopters and nonadopters of an AI scribe. Survey data were analyzed descriptively. Interview transcripts were coded and thematically analyzed using a deductive approach guided by the COM-B (Capability, Opportunity, Motivation-Behavior) model to identify determinants of behavior. Results: Adoption was shaped by factors that could function as both barriers and enablers across the domains of capability, opportunity, and motivation. Enablers included tailored training, technology literacy, access to equipment and suitable recording spaces, leadership and peer support, and clear organizational communication. The absence of these factors, along with time and capacity constraints and unclear processes, hindered adoption. Responses to change, perceptions of the AI scribe's value, safety, reliability, and privacy, and prior experience with similar tools also influenced adoption, depending on individual perspectives. Conclusions: The successful implementation of an AI scribe in allied health settings requires multifaceted strategies that address COM-B factors. Key priorities include allocating sufficient time for uptake and adoption, providing tailored training, ensuring access to relevant resources, fostering leadership and peer support, and providing organizational direction. These findings offer actionable insights to guide future implementation efforts and advance digital transformation in allied health practice.
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