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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 20, 2025
Date Accepted: Aug 10, 2026

The final, peer-reviewed published version of this preprint can be found here:

Determinants of AI Scribe Adoption in Allied Health: Mixed Methods Study Guided by the Capability, Opportunity, and Motivation Model for Behavior Change

Ryan L, Hattingh L, Wall D, Stanich H, Ross N, da Cal J, Milne E, Wenke R

Determinants of AI Scribe Adoption in Allied Health: Mixed Methods Study Guided by the Capability, Opportunity, and Motivation Model for Behavior Change

J Med Internet Res 2026;28:e88088

DOI: 10.2196/88088

Determinants of AI Scribe Adoption in Allied Health: A Mixed Methods Study Guided by COM-B

  • Laura Ryan; 
  • Laetitia Hattingh; 
  • Dominic Wall; 
  • Hayley Stanich; 
  • Nicole Ross; 
  • Juan da Cal; 
  • EJ Milne; 
  • Rachel Wenke

ABSTRACT

Background:

Artificial intelligence (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 semi-structured interviews (n = 27) with allied health clinicians across multiple disciplines, including both adopters and non-adopters of an AI scribe. Survey data were analysed descriptively. Interview transcripts were coded and thematically analysed using a deductive approach guided by COM-B to identify determinants of behaviour.

Results:

Adoption was shaped by factors that could function as both barriers and enablers across the domains of Capability, Opportunity, and Motivation. Enablers to adoption included tailored training, technology literacy, access to equipment and suitable recording spaces, leadership and peer support, and clear organisational 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 Capability, Opportunity, and Motivational 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 organisational direction. These findings offer actionable insights to guide future implementation efforts and advance digital transformation in allied health practice. Clinical Trial: N/A


 Citation

Please cite as:

Ryan L, Hattingh L, Wall D, Stanich H, Ross N, da Cal J, Milne E, Wenke R

Determinants of AI Scribe Adoption in Allied Health: Mixed Methods Study Guided by the Capability, Opportunity, and Motivation Model for Behavior Change

J Med Internet Res 2026;28:e88088

DOI: 10.2196/88088

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