Enterprise AI Analysis
Large Language Models (LLM) for Emergency Department Triage Based on Vital Signs
This analysis explores the groundbreaking potential of Large Language Models (LLMs) to revolutionize emergency department (ED) triage. By leveraging vital sign data, LLMs offer a path to improved efficiency and accuracy in patient prioritization, ensuring timely care for the sickest.
Executive Impact: Transforming ED Triage with AI
Integrating LLMs into ED triage presents a significant opportunity for healthcare systems to enhance operational efficiency and patient safety. This study demonstrates a moderate but promising concordance between AI-assigned and human-assigned triage scores, highlighting AI's potential as a powerful clinical decision-support tool. Early adoption could lead to more consistent triage practices and optimized resource allocation, although further validation is essential.
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Highest LLM Concordance
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Average Deviation (AAVD)
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Patients Correctly Triaged
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53.79%
Average LLM Triage Concordance
| LLM | AAVD | Concordance (%) |
|---|---|---|
| Claude Sonnet 4.5 | 0.37 | 62.37 |
| ChatGPT-5 Instant | 0.39 | 62.89 |
| Claude Opus 4.1 | 0.40 | 62.37 |
| Gemini 2.5 Flash | 0.42 | 43.81 |
| ChatGPT-40 Mini | 0.49 | 45.36 |
| ChatGPT-03 | 0.48 | 48.45 |
Potential for Consistent Patient Prioritization
In a busy emergency department, variability in human triage scores can lead to inconsistent patient flow and potential delays for critical cases. LLMs, as demonstrated by their performance in this study, offer a standardized approach to triage. By providing objective recommendations based on vital signs, AI can reduce inter-rater variability and ensure a more consistent application of triage protocols. For instance, an LLM consistently flagging specific vital sign patterns could assist less experienced staff in recognizing high-acuity patients, minimizing human error and improving overall ED efficiency.
Enterprise Process Flow
Gather Real-World Patient Data
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Feed Data to 12 LLMs
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LLMs Assign Triage Score
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Calculate Deviation & Concordance
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Compare LLM Performance
12
Number of LLMs Evaluated
Data-Driven Triage Decision Support
The study utilized real-world patient data including chief complaint, vital signs (temperature, heart rate, respiratory rate, oxygen saturation, blood pressure), and pain level from an academic trauma center. Each of the 12 LLMs received this data in a structured, tabular format and was prompted to assign an ESI score from 1–5. This direct, data-driven input, without additional training or prior knowledge of human-assigned scores, allowed for a pure assessment of the LLMs' ability to synthesize objective physiological parameters into a triage recommendation. This mimics a scenario where an AI could offer immediate, unbiased decision support at the point of care.
| Limitation | Future Work / Solution |
|---|---|
| LLMs lacked human-aspect & full patient info | Clinical validation against patient outcomes, access to EHR data. |
| Single clinician's triage as "ground truth" | Independent validation, multicenter studies. |
| Single prompt design, fixed LLM versions | Prompt engineering optimization, version drift assessment. |
| Small sample size (194 patients) | Larger, multicenter datasets. |
| Cannot determine impact on clinical outcomes | Prospective implementation studies. |
Bridging the Gap to Clinical Utility
While this study establishes the feasibility of using LLMs for ED triage, the path to real-world clinical utility requires addressing several key limitations. A critical next step involves prospective implementation studies that rigorously evaluate patient safety and workflow impact. For example, a pilot program deploying LLM-assisted triage in a real ED setting could measure clinical outcomes, error rates, and staff feedback. This would move beyond concordance with human scores to assess actual improvements in patient care and operational efficiency, thereby validating the true value of AI in a complex clinical environment.
Clinical Validation
Critical Need for
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Annual Cost Savings
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Annual Hours Reclaimed
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Strategic AI Implementation Roadmap
A phased approach ensures seamless integration, maximum ROI, and minimal disruption.
Phase 1: AI Needs Assessment & Strategy Development
Identify critical pain points in your ED triage process, evaluate current infrastructure, and define clear objectives for AI integration. This phase involves a thorough analysis of vital sign data collection, existing triage protocols, and resource allocation challenges.
Phase 2: Data Integration & LLM Pilot Deployment
Integrate LLM capabilities with existing data sources (e.g., EHRs for vital signs). Deploy a pilot program with selected LLMs to evaluate their real-time triage recommendations against human decisions in a controlled environment. Focus on validating concordance and identifying tendencies for over- or under-triage.
Phase 3: Performance Validation & Workflow Refinement
Conduct prospective studies to assess the LLM's impact on patient outcomes, safety, and operational efficiency. Refine prompts, integrate feedback from ED staff, and adapt workflows to leverage AI recommendations effectively while maintaining human oversight.
Phase 4: Full-Scale Integration & Continuous Optimization
Roll out LLM-assisted triage across all relevant ED units. Establish continuous monitoring systems to track performance, identify version drift, and optimize the model based on evolving clinical guidelines and new data. Explore expanding to multicenter validation for generalizability.
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