Emergency department (ED) triage evidence is fragmented: the PLOS ONE scoping review (published 25 Jun 2026) mapped 19 heterogeneous studies (2013–2025) and found strong predictive signals but limited external validation. Evidano is an AI-powered qualitative data analysis platform that ingests papers, transcripts, and EHR exports to surface themes, failure modes, and reproducible codebooks. This post shows researchers, UX teams, and policy analysts how to run a reproducible qualitative analysis of AI triage literature and triage-note corpora to surface patterns, risks, and operational recommendations, including a 7-step pilot you can run on your own datasets using Evidano.
Key Takeaways
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.
Fast take: What this PLOS review means for researchers
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.
Findings snapshot
The scoping review's key metrics, geographic distribution, methods, performance headlines, and primary gaps are summarized in the table below.
Findings snapshot
| Metric | Value | Note / Source |
|---|---|---|
| Included studies | 19 | Scoping review (published 25 Jun 2026) |
| Years covered | 2013–2025 | Concentration in 2021 (n=4) and 2023 (n=3) |
| Geography | Asia 47%, Europe 37%, N. America 11%, Latin America 5% | Reported by authors |
| Main methods | ML / DL; NLP; limited LLM evaluations (n=2) | ML dominant; some federated learning examples |
| Performance headline | Several models AUC > 0.90 (specific conditions) | High performance often single-center; external validation limited |
| Primary gaps | Explainability, external validation, nurse-led studies | Safety & deployment concerns |
What the review found (plain English)
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.
Implications for researchers, UX teams, and policy analysts
For qualitative researchers
Qualitative researchers should use thematic analysis to map model failure modes and the qualitative reasons behind misclassifications.
Use thematic analysis to map model failure modes (overtriage, undertriage) and the qualitative reasons behind misclassifications (missing context, ambiguous notes).
Triangulate study reports, EHR notes, and stakeholder interviews to create a robust codebook for further quantitative checks.
For clinical and UX teams
Clinical and UX teams should prioritize explainability and simulate workflows to measure automation bias and design overrides.
Prioritize explainability: require models to provide feature-level rationales tied back to clinical signs (for example, SpO2 drop).
Run simulated workflows with clinicians to measure automation bias and design overrides into the UI.
For policy & validation leads
Policy and validation leads should insist on multicenter external validation and lifecycle monitoring before deployment.
Insist on multicenter external validation and lifecycle monitoring before deployment.
Consider federated learning as a privacy-preserving path to broader validation, but still demand local performance checks.
Do more, faster with Evidano: operationalizing qualitative analysis of AI triage
Problem: Heterogeneous inputs (papers, notes, audio)
Evidano ingests PDFs, transcripts, and EHR exports and provides built-in transcription (custom dictionary) and translation to normalize multilingual triage notes.
Solution in Evidano: ingest PDFs, transcripts, and EHR exports; use built-in transcription (custom dictionary) and translation to normalize multilingual triage notes.
Problem: Hard-to-find themes across studies
Evidano provides automated thematic extraction and hierarchical coding to surface common failure modes across heterogeneous sources.
Solution in Evidano: automated thematic extraction + hierarchical codes → subcodes, frequency counts, and co-occurrence networks to surface common failure modes and high-risk patterns.
Problem: Comparing segments (sites, acuity levels)
Evidano enables cross-segment analyses that compare themes and language use by site, year, or triage level and exports charts for stakeholders.
Solution in Evidano: cross-segment analyses that compare themes and language use by site, year, or triage level, exportable charts for stakeholders.
Problem: Trust & auditability
Evidano records clickable source quotes, transparent analytic logs, and enterprise-grade encryption to support trust and auditability.
Solution in Evidano: clickable source quotes, transparent analytic logs, and enterprise-grade encryption. Data are never used to train third-party models.
Checklist: Qualitative analysis workflow for AI triage
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.
FAQ: qualitative analysis of AI triage
Q: When should I run a qualitative analysis versus a meta-analysis?
A: Run a qualitative analysis when sources are heterogeneous in methods, outcomes, or notes, or when you need to surface contextual failure modes and human factors.
Use qualitative analysis when sources are heterogeneous (methods, outcomes, notes) or when you need to surface contextual failure modes and human factors that numbers alone miss.
Q: How do I compare segments reliably?
A: Compare segments reliably by building comparable codebooks, normalizing terminology, and running cross-segment frequency and co-occurrence comparisons.
Build comparable codebooks, normalize terminology via transcription/translation, then run cross-segment frequency and co-occurrence comparisons with Evidano’s segmenting tools.
Q: What are the main gaps I should audit in current AI triage research?
A: Audit explainability, external multicenter validation, and the lack of nurse-led studies as primary gaps before considering deployment.
The primary gaps are explainability, external validation, and limited nurse-led research, which together present safety and implementation risks.
Wrapping up: next steps and a short ethics note
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.