Barriers and Facilitators to AI Adoption in Allied Health Clinical Practice

Background and Purpose: Artificial intelligence (AI) is increasingly integrated into healthcare, offering opportunities to enhance clinical decision-making, efficiency, and patient care. However, adoption in allied health clinical practice remains limited due to complex individual, organizational, technological, ethical, and contextual factors. This study aimed to explore the barriers and facilitators influencing AI adoption among allied health professionals to inform strategies for effective and sustainable implementation.

Methods: A qualitative research design was employed, involving semi-structured interviews, focus groups, and document analysis with allied health professionals across multiple disciplines, including physiotherapy, occupational therapy, speech pathology, and dietetics. Data were analyzed using thematic analysis, identifying key patterns, themes, and interrelationships among adoption determinants.

Key Findings: Four overarching domains emerged: individual factors, organizational factors, technological and ethical factors, and external/professional context. Trust, confidence, organizational support, and professional collaboration were central mediators influencing adoption intention and routine clinical use. Barriers included limited AI literacy, workflow incompatibility, lack of explainability, and unclear regulatory guidance, whereas facilitators encompassed training, leadership engagement, clinical champions, ethical safeguards, and supportive policies. The study highlighted the complex, multi-level interactions that shape adoption in allied health settings.

Conclusion: AI adoption in allied health practice is influenced by intertwined personal, organizational, technological, and contextual factors. Addressing barriers through education, workflow integration, ethical safeguards, and supportive policies can facilitate adoption and enhance clinical outcomes. These findings provide actionable insights for healthcare organizations, policymakers, and educators seeking to implement AI responsibly and effectively in allied health practice.