# Artificial intelligence in emergency department triage: perspective of human professionals

## TL;DR

This study explores how healthcare and IT professionals view AI in emergency department triage, finding varied attitudes and highlighting the need for education and alignment.

## Contribution

The study identifies distinct stakeholder clusters and emphasizes the role of hedonic motivation and education in AI adoption for triage.

## Key findings

- Three clusters of professionals were identified: cautious/critical, enthusiastic/optimistic, and balanced/pragmatic.
- Hedonic motivation was a key driver of enthusiasm for AI in triage.
- Educational strategies are needed for both developers and healthcare professionals to support AI adoption.

## Abstract

The triage process in emergency departments (EDs) is complex, and AI-based solutions have begun to target it. At this pivotal stage, the challenge lies less in designing smarter algorithms than in fostering trust and alignment among medical and technical stakeholders. We explored professional attitudes towards AI in ED triage, focusing on alignments and misalignments across backgrounds.

An anonymous online cross-sectional survey was distributed through professional networks of healthcare providers and IT professionals, between May 2024 and February 2025. The questionnaire covered four areas: (a) the General Attitudes towards Artificial Intelligence Scale (GAAIS); (b) professional background and career level; (c) challenges and priorities for AI applications in triage; and (d) the AI Attitude Scale (AIAS-4). Constructs from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) were also applied. Cluster analysis (KMeans) was conducted based on GAAIS-positive, GAAIS-negative, and AIAS-4 scores.

From a total of 151 professionals, Kmeans identified three clusters: K0 (cautious/critical, n = 39), K1 (enthusiastic/optimistic, n = 35), and K2 (balanced/pragmatic, n = 77). Approximately two-thirds of K2 (47/77; 61%) were healthcare providers. Six out of 20 (30%) medical professionals in K0 reported that AI could play no role in ED triage, but only 1/15 (7%) and 1/47 (2%) of healthcare providers gave this response in K1 and K2, respectively. Lack of knowledge of AI tools was also most frequent in K0 (14/39; 36%). Recognition of necessity of constraints showed marked contrasts in their mean ± SD scores: (a) for data availability/quality, 2.95 ± 1.98 (K0), 4.27 ± 1.1 (K1), and 4.20 ± 0.94 (K2); (b) for the integration of AI-based applications into existing workflows, 2.95 ± 1.05, 4.20 ± 0.94, and 3.47 ± 1.02 in K0, K1, and K2, respectively. Among the UTAUT2 constructs, hedonic motivation differed most significantly, with mean ± SD values of 3.41 ± 1.0 (K0), 6.86 ± 0.97 (K1), and 5.07 ± 1.08 (K2).

Stakeholders' perspectives on AI in ED triage are heterogeneous and not solely determined by professional background or role. Hedonic motivation emerged as a key driver of enthusiasm. Educational strategies should follow two directions: (a) structured AI programs for enthusiastic developers from diverse fields, and (b) AI literacy for all healthcare professionals to support competent use as consumers.