Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slowto adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (theend-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested.Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicableto a wide variety of AI-tool implementations in clinical practice.Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and anationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included.We performed deductive direct content analysis, using the Consolidated Framework for ImplementationResearch (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations forImplementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data wasdescriptively analyzed.Results: We performed ten individual interviews, and one focus group with five physicians. The mostprominent constructs identified during the qualitative interim analyses were incorporated in the nationwidesurvey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation:evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change,compatibility, access to knowledge and information, and knowledge and beliefs about the intervention.Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conducteducational meetings, promote adaptability, develop educational materials, and distribute educationalmaterials.

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