Beyond Algorithm: Emergency Department Professionals' Perspectives on Machine Learning-Based Triage Integration—A Qualitative Study.
Emergency department (ED) overcrowding has necessitated more efficient triage processes. Traditional methods can struggle to keep up with increasing patient volumes, and interest in machine learning-based triage systems is increasing. However, the perspectives of emergency department professionals,...
| Publicado en: | Inquiry (00469580) Vol. 62; pp. 1 - 14 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
10/14/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188898017&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188898017 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 10/14/2025 vid: 62 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 188898017 188898017 188898017 10.1177/00469580251376921 188898017 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Beyond Algorithm: Emergency Department Professionals' Perspectives on Machine Learning-Based Triage Integration—A Qualitative Study. aug: au: Emre M., Güvey Fevzi M., Esen Efe, Onganer affil: Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkiye sug: subj: Physicians, Emergency Psychosocial Factors Emergency Nurses Psychosocial Factors Physician Attitudes Nurse Attitudes Machine Learning Algorithms Decision Support Systems, Clinical Triage Emergency Service Human Male Female Adult Middle Age Turkiye Multicenter Studies Qualitative Studies Grounded Theory Semi-Structured Interview Convenience Sample Audiorecording Thematic Analysis Artificial Intelligence Patient Education Patient Satisfaction Professional-Patient Relations Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Emergency department (ED) overcrowding has necessitated more efficient triage processes. Traditional methods can struggle to keep up with increasing patient volumes, and interest in machine learning-based triage systems is increasing. However, the perspectives of emergency department professionals, who play a critical role in triage decision-making, are often overlooked in ML development. This qualitative study explores emergency department professionals' perspectives on the potential for machine learning-based triage to enhance triage processes in emergency departments. Semi-structured interviews were conducted with 13 ED professionals (9 physicians, 4 nurses) from 6 hospitals in Istanbul. A grounded theory approach was used to analyze the data, identifying themes related to current triage challenges, attitudes toward machine learning-based triage, and suggestions for ML algorithm development. Three main themes emerged: (i) patient and public interaction with triage, (ii) technology and ML in triage, and (iii) triage processes and challenges. Healthcare professionals expressed optimism about the potential of machine learning-based triage but raised concerns about the accuracy of current technology and the need for ML models to integrate complex clinical judgments, particularly regarding pain assessment and patient behavior. While machine learning-based triage has the potential to significantly enhance ED triage, emergency department professionals' experiential insights are crucial for the development of more accurate and usable ML models which focus on incorporating human expertise. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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