Exploring the Nurses’ Perceptions on the Artificial Intelligence-Powered Triage System in Improving the Patient Emergency Nursing Outcomes

Emergency departments frequently experience high patient volumes, leading to prolonged waiting times and subjective variation in traditional triage decisions. Artificial Intelligence (AI)-powered triage systems are emerging globally to optimize clinical resource allocation, yet their integration into low-resource healthcare settings such as those in the Philippines requires a clear understanding of frontline clinical perspectives. This study explored emergency room nurses' perceptions of the implementation, utility, and impact of AI-powered triage systems on patient emergency nursing outcomes. Utilizing a qualitative approach rooted in Grounded Theory and guided by the philosophical doctrine of subjectivism, face-to-face interviews were conducted with seven (7) randomly selected nurses at Dr. Rafael Tumbokon Memorial Hospital. Data analysis followed an ontological-interpretive approach, mapping emerging narratives onto Roy’s Adaptation Model to conceptualize how clinical staff navigate technological advancements in healthcare settings. Three core thematic clusters emerged from the data: Positive Outlook, Training and Proper System Utilization, and AI-Powered Triage as Support. While the hospital relies strictly on manual, traditional triage protocols, all respondents demonstrated baseline theoretical knowledge of AI algorithms. Nurses universally perceived AI triage as a valuable tool capable of standardizing workflows, accelerating patient prioritization, and reducing wait times—particularly for novice staff during peak hours. However, respondents unanimously rejected the notion of AI as a standalone assessment mechanism, emphasizing that algorithms lack clinical intuition, critical observation, and qualitative reasoning. Nurses are highly receptive to adopting AI innovations, provided the technology is introduced gradually with structured training.