Trust and impact of AI in Emergency Triage: a mixed-methods study
Abstract
Background: The integration of Artificial Intelligence (AI) into Emergency Department (ED) triage is an emerging response to systemic healthcare challenges. While existing literature largely focuses on algorithmic performance, there is a significant gap regarding the perceptions of healthcare professionals and service users. This study evaluates the acceptability, trust, and potential of AI implementation from the perspectives of both providers and organised patient representatives.
Methods: A cross-sectional mixed-methods study was conducted during December 2025 in a northern region of Italy. The quantitative component involved a structured 16-item questionnaire administered to healthcare professionals working exclusively within the region’s emergency departments. The qualitative component consisted of semi-structured interviews with 4 patient advocacy groups. Data were analysed using thematic analysis alongside descriptive and inferential statistics, including Chi-square, Spearman, and Cramér tests.
Results: The 136 surveyed healthcare professionals (55% women, mean age 43) were evenly split regarding the acceptability of AI use in ED triage (50%, n = 68); however, physicians’ favourability was higher than that of nurses (70.0% vs 46.6%, p = 0.053). Concerning trust in AI, they showed high confidence in technical and clinical performance, such as reducing errors (47.8%), even though highlighting the risk of over-reliance on technology (69.1%). Patient associations recognized the potential for increased equity, specifically through multilingual and multiethnic access, as the most significant benefit of AI integration, but pointed to low confidence in the emotional and relational dimensions.
Conclusions: Professionals and patients appeared open to some AI implementation in ED settings, but both hope it will be used as a tool for professional and service enhancement rather than a replacement for humans in the loop.
Key messages: • Healthcare professionals trust AI’s technical efficiency but remain sceptical of the risk of becoming over-reliant on this technology.
• Patients’ expectations regarding AI use in emergency department settings highlight the potential benefits for equity and accessibility.
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BibTeX
@article{tesei2026trust,
title = {{Trust and impact of AI in Emergency Triage: a mixed-methods study}},
author = {L Tesei and M Zerbinati and D Devetti and G Trillò and L Brunelli and S Brusaferro},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1209},
url = {https://doi.org/10.1093/eurpub/ckag169.1209},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Trust and impact of AI in Emergency Triage: a mixed-methods study
AU - L Tesei
AU - M Zerbinati
AU - D Devetti
AU - G Trillò
AU - L Brunelli
AU - S Brusaferro
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1209
UR - https://doi.org/10.1093/eurpub/ckag169.1209
AB - Background: The integration of Artificial Intelligence (AI) into Emergency Department (ED) triage is an emerging response to systemic healthcare challenges. While existing literature largely focuses on algorithmic performance, there is a significant gap regarding the perceptions of healthcare professionals and service users. This study evaluates the acceptability, trust, and potential of AI implementation from the perspectives of both providers and organised patient representatives. Methods: A cross-sectional mixed-methods study was conducted during December 2025 in a northern region of Italy. The quantitative component involved a structured 16-item questionnaire administered to healthcare professionals working exclusively within the region’s emergency departments. The qualitative component consisted of semi-structured interviews with 4 patient advocacy groups. Data were analysed using thematic analysis alongside descriptive and inferential statistics, including Chi-square, Spearman, and Cramér tests. Results: The 136 surveyed healthcare professionals (55% women, mean age 43) were evenly split regarding the acceptability of AI use in ED triage (50%, n = 68); however, physicians’ favourability was higher than that of nurses (70.0% vs 46.6%, p = 0.053). Concerning trust in AI, they showed high confidence in technical and clinical performance, such as reducing errors (47.8%), even though highlighting the risk of over-reliance on technology (69.1%). Patient associations recognized the potential for increased equity, specifically through multilingual and multiethnic access, as the most significant benefit of AI integration, but pointed to low confidence in the emotional and relational dimensions. Conclusions: Professionals and patients appeared open to some AI implementation in ED settings, but both hope it will be used as a tool for professional and service enhancement rather than a replacement for humans in the loop. Key messages: • Healthcare professionals trust AI’s technical efficiency but remain sceptical of the risk of becoming over-reliant on this technology. • Patients’ expectations regarding AI use in emergency department settings highlight the potential benefits for equity and accessibility.
SN - 1101-1262
SN - 1464-360X
ER -