Map ED Evidence: Qualitative Analysis of AI Triage

The PLOS ONE scoping review mapped 19 ED triage studies (2013–2025) and shows that ML/DL models often outperform traditional triage on predictive metrics, while lacking external validation and explainability.

Evidano is an AI-powered qualitative data analysis platform that ingests papers, transcripts, and EHR exports to help teams extract themes, surface failure modes, and run reproducible audits.

• The review included 19 studies from 2013–2025, with many single-center results and several models reporting AUCs above 0.90 for domain-specific tasks.
• Primary gaps in the literature are explainability, multicenter external validation, and limited nurse-led research, which together constrain safe deployment.
• Run a 2–4 week qualitative-first pilot using a 7-step workflow to reproduce insights from the scoping review and produce stakeholder-ready codebooks and visuals.

The PLOS ONE scoping review (published 25 Jun 2026) mapped 19 studies from 2013–2025 and reports ML/DL gains over traditional triage while flagging gaps in external validation and explainability.

A June 25, 2026 scoping review in PLOS ONE mapped AI use in ED triage and included 19 studies spanning 2013–2025.

• Audience payoff: learn how to extract themes and failure modes from that heterogeneous literature and from your own triage notes.
• Why it matters: the review finds high AUC in domain-specific models (some >0.90) but warns against deploying opaque models without multicenter validation.
• Quick win: use AI-assisted qualitative workflows to turn diverse papers, transcripts, and EHR triage notes into reproducible insights, no code required on Evidano.

The scoping review's key metrics, geographic distribution, methods, performance headlines, and primary gaps are summarized in the table below.

The review found that machine learning and deep learning models can outperform traditional triage protocols on predictive metrics, particularly when models include physiological vitals and unstructured text.

The 19 studies consistently show that machine learning and deep learning models can outperform traditional triage protocols on predictive metrics, especially when models incorporate physiological vitals and unstructured text (nursing notes, speech).

However, the review shows most work is single-center and exploratory: external validation and explainability are sparse, and recent LLMs (for example, ChatGPT) performed with low-to-moderate agreement compared with experienced clinicians.

• Why accuracy can be misleading: ED data are class-imbalanced, so sensitivity and AUC matter more for catching high-acuity patients.
• Value of unstructured data: NLP on triage notes or speech increases signal for symptom identification and acuity prediction.
• Deployment friction: interoperability, trust (black-box models), and automation bias are recurring implementation blockers.

This 7-step pilot runs in 2–4 weeks and reproduces insights from the scoping review on your own corpus.

• 1) Collect: assemble the 19 papers, local triage notes (n examples), and any relevant audio; import into Evidano.
• 2) Normalize: transcribe audio (with custom dictionary), translate non-English notes, and redact PII.
• 3) Auto-code: run AI-assisted coding to surface initial themes and frequent terms (vitals, symptoms, delays).
• 4) Validate: review and refine the codebook with clinicians; lock codes for reproducibility.
• 5) Cross-segment analysis: compare themes by site, triage level, and outcome (admission, ICU).
• 6) Visualize & explain: generate co-occurrence networks and hierarchical code trees to present to stakeholders.
• 7) Pilot & monitor: deploy decision-support prototypes in shadow mode and collect qualitative feedback for model and UI iteration.

The PLOS scoping review shows promise for AI in ED triage but also clear limits: lack of external validation and explainability are barriers that require qualitative-first audits before deployment.

The PLOS scoping review shows promise for AI in ED triage but also clear limits: lack of external validation and explainability are barriers. If you are evaluating these models, start with a qualitative-first audit to identify failure modes and clinician concerns before engineering or procurement.

• Research ethics note: these analyses are non-diagnostic and should be used for evaluation and workflow design; patient-level deployment requires clinical governance and regulatory compliance.
• Ready to convert papers and triage notes into an evidence-backed decision brief? Start a reproducible pilot on Evidano and export stakeholder-ready visualizations and codebooks, or Try Evidano for free.