Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review

Widespread and sustained uptake of AI-based clinical decision support systems (CDSSs) in real-world health care settings is uncommon, despite their potential to improve patient care and reduce clinician burnout. Although previous studies have examined determinants of implementing AI-based CDSSs, limited evidence has synthesized barriers and facilitators identified during actual clinical implementation and use.

The objectives of this scoping review were to (1) map and synthesize barriers to and facilitators of implementing AI-based CDSSs in real-world health care settings and (2) draw on this knowledge to inform future implementation strategies.

Five electronic databases (MEDLINE, Embase, CINAHL, APA PsycInfo, and the Cochrane Library) were searched from inception to May 2022. Eligible studies included primary research describing real-world implementation processes or reporting determinants (barriers and facilitators) of implemented AI-based CDSSs in any health care setting. Studies focused on non-decision support tasks, non-AI CDSSs, patient-facing tools, or development or effectiveness without implementation were excluded. No study design restrictions were applied. Full texts were reviewed to extract explicit statements describing determinants influencing implementation. These determinants were classified as barriers or facilitators and mapped to the Consolidated Framework for Implementation Research (CFIR) by 2 independent reviewers. A qualitative synthesis was conducted.

After removing 4234 duplicate records, 10,875 articles were screened by title and abstract, which excluded 10,355 articles. After further exclusions based on full-text availability, 494 full-text articles were assessed for eligibility, of which 13 met the inclusion criteria. Nine of these studies reported explicit implementation determinants and were included in the CFIR-based synthesis. Studies were primarily conducted in the United States and involved multicenter implementation of machine learning-based CDSSs in critical care and emergency medicine settings. A total of 28 determinants (16 barriers and 12 facilitators) were identified. Barriers were most frequently mapped to the inner setting, innovation, and individuals domains, whereas facilitators were most frequently mapped to the implementation process and innovation domains. Common barriers included limited algorithm interpretability, data quality and management challenges, misalignment with clinical workflows, and insufficient user capability and motivation. Facilitators included early and ongoing assessment of end-user needs, stakeholder engagement, peer endorsement, and robust supporting evidence.

This review identified key determinants influencing the real-world implementation of AI-based CDSSs, highlighting the importance of system design, organizational context, and implementation strategies. However, the small number of studies reporting explicit implementation determinants underscores a critical gap in the literature, suggesting that many real-world implementations do not adequately evaluate or report factors influencing adoption and sustained use. Addressing this gap will be essential for advancing the translation of AI-based CDSSs into routine clinical practice. These findings provide a foundation for developing targeted implementation strategies and emphasize the need for more rigorous, implementation-focused research in real-world health care settings.