Triage Nurses' Perceptions of Artificial Intelligence: A Cross-Sectional Study

Area of Science:

• Emergency Medicine
• Health Informatics
• Nursing Research

Background:

• Emergency department (ED) crowding and patient complexity strain traditional triage.
• Artificial intelligence (AI) offers potential support for ED triage decision-making.
• Nurse perceptions are critical for the successful adoption of AI in clinical practice.

Purpose of the Study:

• To assess triage nurses' perceptions of AI dimensions in emergency departments.
• To identify challenges and facilitators for AI integration in triage.
• To explore demographic associations with AI perceptions among nurses.

Main Methods:

• A cross-sectional online survey was administered to 84 triage nurses across 7 Italian EDs.
• The survey evaluated perceptions of AI reliability, applicability, training, relational impact, and safety.
• Statistical analyses included descriptive statistics, chi-square tests, Mann-Whitney U, and Welch ANOVA.

Main Results:

• Nurses reported positive perceptions regarding relational impact (63.1%) and reliability (48.8%).
• Concerns were noted for training (36.9%) and safety (36.9%), with a mean of 2.33 total challenges.
• Older nurses (≥55 years) reported lower positive perceptions for training and higher perceived barriers.

Conclusions:

• Triage nurses exhibit cautious optimism towards AI, valuing its potential benefits but concerned about training and safety.
• Older age is associated with increased perceived barriers to AI adoption.
• Competency-based education and clear governance frameworks are essential for human-centered AI integration in emergency triage.