Evidence map›Paper›PMID 41919289›Full record

ArticleFrontiers in public health2026

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

Xinrui Mao, Na Liu, Hang Wang, Qi Wang, Shuqin Xia, Xiaoya Ma, Lu Yin

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xinrui MaoEmergency Department, Shanghai East Hospital, Shanghai, China.
Na LiuEmergency Department, Shanghai East Hospital, Shanghai, China.
Hang WangSchool of Medicine, Tongji University, Shanghai, China.
Qi WangShanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Shuqin XiaEmergency Department, Shanghai East Hospital, Shanghai, China.
Xiaoya MaSchool of Medicine, Tongji University, Shanghai, China.
Lu YinEmergency Department, Shanghai East Hospital, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelEmergency NursingNursing Staff, HospitalTriageAdultChinaEmergency Service, HospitalFemaleHumansInterviews as TopicMaleQualitative Researchartificial intelligenceemergency department nursesnurseperceptionsqualitative study

Identifiers

PMID41919289
PMCPMC13033648

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.