Barriers to Protocol Adherence in Emergency Departments and Evidence-Based Strategies for Successful Implementation: A Scoping Review.

Background: Emergency departments (EDs) operate under severe time pressure, diagnostic uncertainty, and resource constraints, making the consistent application of clinical protocols challenging. This scoping review aimed to map the barriers to protocol adherence and the strategies reported to support implementation in emergency care. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched for publications from January 2000 to June 2026. Following predefined eligibility criteria, 58 publications were included and charted according to clinician-related, guideline-related, patient-related, and organizational determinants, and we reported the implementation strategies. No formal design-specific risk-of-bias or certainty-of-evidence assessment was performed; therefore, the synthesis was intended to map the available evidence rather than establish the comparative effectiveness. Results: Commonly reported barriers included limited guideline knowledge and clinical experience, cognitive overload and occupational fatigue, poor guideline usability and workflow compatibility, patient communication difficulties and clinical complexity, overcrowding, staffing shortages, and limited organizational support. The reported strategies included education and simulation, audit and feedback, clinical decision support, workflow redesign, multidisciplinary collaboration, and leadership engagement. However, the heterogeneity in study designs, clinical settings, definitions of adherence, and reported outcomes precluded ranking these strategies or determining whether particular combinations were superior. Conclusions: Protocol adherence in EDs appears to be shaped by interacting clinician-related, guideline-related, patient-related, and organizational factors. The identified strategies may support implementation, but their relative and comparative effectiveness remains uncertain. Digital health and artificial intelligence should be considered priorities for prospective evaluation rather than established solutions.