Perceptions and attitudes of emergency department nurses toward artificial intelligence applications in triage: a qualitative study

Object This study aims to explore the cognition and attitude of emergency department nurses toward the application of artificial intelligence (AI) in triage, reveal the challenges faced in the application process, and provide suggestions for promoting the application of AI triage in China. Methods This study adopted a qualitative research design and employed the Colaizzi phenomenological method. Purposive sampling was used to select emergency department nurses from September 2025 to December 2025 for semi-structured in-depth interviews. Result A total of 18 research subjects were included in this study, 2 themes and 6 sub-themes were identified: (1) Nurses’ cognition of the application of AI triage, including reducing work pressure, having concerns, and the boundary uncertainty of human-AI collaboration; (2) Nurses’ demands for the application of AI in triage include data security and information accuracy, clinical adaptation and data intercommunication, as well as ethical guarantees. Conclusion Nurses have a relatively rational understanding of the application of AI in triage, which can bring positive impacts to the triage process, but they also face multiple challenges. The development and application of future AI triage systems should focus on the demands of patient safety, data security, and information accuracy, to enhance the efficiency of emergency triage and alleviate the pressure on nurses.

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Publication Details

Journal
Frontiers in Public Health
Published
2026-03-16
DOI
https://doi.org/10.3389/fpubh.2026.1795233
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Perceptions and attitudes of emergency department nurses toward artificial intelligence applications in triage: a qualitative study

Xiao Ma, Na Liu, Qi Wang, Xinrui Mao et al.
Frontiers in Public Health
Artificial Intelligence in Healthcare and Education
article

Perceptions and attitudes of emergency department nurses toward artificial intelligence applications in triage: a qualitative study

Xiao Ma, Na Liu, Qi Wang, Xinrui Mao, Shuqin Xia, Lu Yin, Hang Wang
article en

Abstract

Object This study aims to explore the cognition and attitude of emergency department nurses toward the application of artificial intelligence (AI) in triage, reveal the challenges faced in the application process, and provide suggestions for promoting the application of AI triage in China. Methods This study adopted a qualitative research design and employed the Colaizzi phenomenological method. Purposive sampling was used to select emergency department nurses from September 2025 to December 2025 for semi-structured in-depth interviews. Result A total of 18 research subjects were included in this study, 2 themes and 6 sub-themes were identified: (1) Nurses’ cognition of the application of AI triage, including reducing work pressure, having concerns, and the boundary uncertainty of human-AI collaboration; (2) Nurses’ demands for the application of AI in triage include data security and information accuracy, clinical adaptation and data intercommunication, as well as ethical guarantees. Conclusion Nurses have a relatively rational understanding of the application of AI in triage, which can bring positive impacts to the triage process, but they also face multiple challenges. The development and application of future AI triage systems should focus on the demands of patient safety, data security, and information accuracy, to enhance the efficiency of emergency triage and alleviate the pressure on nurses.

Frontiers in Public HealthVol. 14
Tongji University (CN), Hubei University of Medicine (CN), Shanghai East Hospital (CN)
Tongji University
Openalex Percentile: Top 23%
Artificial Intelligence in Healthcare and Education
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Perceptions and attitudes of emergency department nurses toward artificial intelligence applications in triage: a qualitative study — Xiao Ma, Na Liu, et al. · Frontiers in Public Health (2026) | TGRS Research Map | TGRS