Emergency Nurses' Experiences With Artificial Intelligence-Driven Triage Clinical Decision Support Systems: A...

Emergency department overcrowding remains a critical global challenge, and artificial intelligence-driven clinical decision support systems for triage have emerged as promising solutions to improve patient prioritization and workflow efficiency. Although quantitative evidence demonstrates performance benefits, understanding emergency nurses' lived experiences with these systems is essential for successful implementation. This protocol describes a planned systematic review to synthesize qualitative evidence on emergency nurses' experiences with artificial intelligence-driven triage clinical decision support systems.

This review will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 and Enhancing Transparency in Reporting the Synthesis of Qualitative Research guidelines. Systematic searches will be conducted across MEDLINE, CINAHL, PsycINFO, Web of Science, Scopus, and ACM Digital Library from 2015 to December 2025. Qualitative and mixed-methods studies exploring emergency nurses' experiences, perceptions, and attitudes toward artificial intelligence-driven triage decision support will be included. Two reviewers will independently screen studies using Covidence and assess quality using the Critical Appraisal Skills Program Qualitative Checklist. Data will be synthesized using thematic synthesis.

The completed review will identify themes characterizing emergency nurses' experiences with artificial intelligence-driven triage clinical decision support systems, including perceived benefits and concerns, impacts on professional autonomy and clinical judgment, barriers and facilitators to adoption, and educational and support needs.

This protocol establishes a rigorous methodological framework for synthesizing qualitative evidence essential to informing nurse-centered implementation of artificial intelligence triage tools. Understanding emergency nurses' perspectives will guide technology design, implementation strategies, and workforce development to optimize artificial intelligence integration in emergency care.