Beyond Algorithm: Emergency Department Professionals' Perspectives on Machine Learning-Based Triage Integration-A Qualitative Study

Abstract

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.