Identifying Barriers and Enablers to the Adoption of AI-Based Triage Tools in Emergency Departments
Bibliographic record
Abstract
This study aimed to explore the perceived barriers and enablers influencing the adoption of artificial intelligence (AI)-based triage tools in emergency departments (EDs) from the perspective of frontline healthcare professionals. A qualitative research design was employed, utilizing semi-structured interviews with 19 participants—including emergency physicians, triage nurses, department managers, clinical administrators, and health informatics experts—working in emergency departments across Canada. Participants were selected using purposive sampling to ensure diversity in professional roles and institutional settings. Data collection continued until theoretical saturation was reached. Interviews were transcribed verbatim and analyzed using grounded theory methodology. Open, axial, and selective coding were conducted with the assistance of NVivo software to identify emerging themes and construct a conceptual model of AI adoption dynamics. The analysis revealed five core categories shaping AI-based triage adoption: (1) perceived risk and uncertainty, including lack of trust in AI outputs and concerns over legal liability; (2) institutional and organizational readiness, such as infrastructure limitations and workflow misalignment; (3) human capital and knowledge systems, including digital literacy gaps and lack of training; (4) system-level support and governance, highlighting the role of managerial commitment and national policy frameworks; and (5) value proposition and practical benefits, including efficiency gains, clinical decision support, and user-friendly integration. These categories reflected the interplay of technical, organizational, and human factors that either hindered or enabled AI integration in emergency care settings. Adopting AI-based triage tools in emergency departments requires addressing a complex ecosystem of trust, readiness, training, infrastructure, and systemic support. The findings underscore the importance of clinician engagement, targeted education, transparent design, and multi-level policy alignment to ensure effective and sustainable implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".