{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusions: 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.\n\nKey messages: • Healthcare professionals trust AI’s technical efficiency but remain sceptical of the risk of becoming over-reliant on this technology.\n\n• Patients’ expectations regarding AI use in emergency department settings highlight the potential benefits for equity and accessibility.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "L Tesei"
    },
    {
      "name": "M Zerbinati"
    },
    {
      "name": "D Devetti"
    },
    {
      "name": "G Trillò"
    },
    {
      "name": "L Brunelli"
    },
    {
      "name": "S Brusaferro"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d2e9p2tw2v907.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.1209",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13698358/",
  "id": "d2e9p2tw2v907",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.1209",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "6. Poster Displays"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "heading",
      "DE. Digital health and Artificial Intelligence"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223658270",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13698358/pdf/?tool=EBI",
  "pmcid": "PMC13698358",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/d2e9p2tw2v907.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:15Z",
      "native_id": "PMC13698358",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.1209"
    },
    {
      "first_seen": "2026-10-30T12:21:34Z",
      "native_id": "PMC:PMC13698358",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13698358"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Trust and impact of AI in Emergency Triage: a mixed-methods study",
  "title_text": "Trust and impact of AI in Emergency Triage: a mixed-methods study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:35:14Z",
  "url": "https://research.draconitic.com/paper/d2e9p2tw2v907",
  "venue": "The European Journal of Public Health",
  "versions": []
}