AI Triage: Emergency Rooms Using AI to Prioritize Patients
Last reviewed by staff on May 23rd, 2025.
Introduction
Emergency departments (EDs) are often overloaded, requiring quick yet accurate decisions on which patients need immediate care vs. those who can wait.
Traditional triage depends on nurse assessments or established protocols to gauge urgency. AI-assisted triage offers a transformative edge—analyzing patient data, vital signs, and symptoms to help clinicians prioritize patients more reliably and swiftly.
This approach has the potential to reduce waiting times, minimize errors, and deliver more patient-centered care in chaotic ED environments.
In this article, we discuss how AI triage works, the benefits (such as faster high-priority interventions), limitations (data bias, acceptance issues), and real-world trials of AI tools in emergency settings.
By understanding these emerging technologies, healthcare providers and patients can better appreciate how artificial intelligence might redefine the future of ED workflows.
1. Traditional Triage Challenges
1.1 Subjective Assessments Under Pressure
When a patient arrives in the ED, a triage nurse often makes a quick judgment based on symptoms, vitals, and immediate impression. While experienced nurses excel at this process, it remains partly subjective—and fatigue, time constraints, or overcrowding can impact consistency.
1.2 Overcrowding and Delays
Busy EDs risk crowded waiting rooms and delayed care for truly critical cases if triage is not managed optimally. Misclassification of severity leads to potential deterioration of sicker patients or unnecessary rush for stable ones, straining resources further.
1.3 Balancing Speed vs. Accuracy
Triaging swiftly is essential, but insufficient data or manual oversights can lead to errors. Additional time for thorough evaluation is often limited, especially in surge conditions, so a tool providing rapid, data-driven triage support can be invaluable.
2. How AI Triage Systems Work
2.1 Data Inputs
AI triage solutions typically pull vital signs (e.g., heart rate, blood pressure, temperature), chief complaint (as reported by the patient), and sometimes electronic health record data (allergies, prior diagnoses) or real-time labs if available. Some advanced systems also integrate demographics, ambulance paramedic notes, or triage nurse input.
2.2 Machine Learning Models
A machine learning model—often trained on thousands of prior ED cases—assigns a triage score or risk level. It might use classification (e.g., “emergency,” “urgent,” “semi-urgent,” “non-urgent”) or numeric acuity scales. Some models rely on neural networks or random forests, while others employ regression-based scoring.
2.3 Real-Time Recommendations
Within seconds, the system suggests a triage category (e.g., “ESI level 2”), prompting staff to place the patient in a higher or lower priority queue. The triage nurse or ED physician can override these suggestions, combining human judgment with AI-driven insights. The synergy can accelerate correct escalation if the system detects subtle red flags—like a combination of mild tachycardia + certain complaint histories that predict sepsis risk.
2.4 Feedback and Learning
As the ED tracks outcomes (like final diagnoses or interventions needed), the AI model can refine its parameters. Real-world feedback fosters continuous learning—improving predictions over time, provided local data is captured systematically and used to update the model.
3. Potential Benefits of AI-Assisted Triage
3.1 Faster Identification of Critical Cases
AI’s ability to parse multiple data streams quickly can highlight those at risk for septic shock, stroke, or myocardial infarction earlier. Automated red alerts might mobilize advanced teams or expedite lab/radiology orders.
3.2 Consistency and Reduced Variation
Human triage can vary with experience or stress levels. An AI’s approach is uniform—given the same inputs, it produces the same suggestion. This consistency, combined with nurse expertise, yields more standardized triage outcomes, arguably leading to fairer prioritization.
3.3 Efficiency in Overburdened EDs
By automating some aspects of triage data processing, nurses can handle more complex tasks or spend more time comforting patients. The system’s quick analysis might shorten waiting times or reduce the occurrence of dangerously long triage lines.
3.4 Early Warnings for Subtle Presentations
Some critical conditions present with only mild or atypical vitals initially. An AI system might notice patterns in the combination of signs—like a slight fever plus borderline hypotension plus certain complaint keywords—indicating possible sepsis or other crises, prompting earlier intervention.
4. Challenges and Limitations
4.1 Data Quality and Bias
Models are only as good as their training data. If historical triage data had biases (e.g., undertreatment of certain demographic groups), the AI might replicate those inequalities. Additionally, incomplete or incorrect data entry (like missing vitals) can degrade model accuracy.
4.2 Overreliance on Algorithms
Clinicians must remain vigilant not to trust the AI blindly. It’s a tool, not a definitive authority. If the system incorrectly downgrades a patient’s severity, staff must detect that discrepancy via their own assessment skills.
4.3 Implementation Complexity
Integrating AI triage demands changes to ED workflows, staff training, and sometimes IT infrastructure. Real-time data feed from triage stations, EHR connections, and stable operation in a chaotic environment require thorough planning and pilot testing.
4.4 Regulatory Oversight
If an AI solution explicitly “makes triage decisions,” it could face stricter regulatory classification. The line between decision support vs. direct medical device must be clarified to ensure compliance with relevant laws (like FDA processes or CE marking in Europe).
4.5 Ethical Considerations
Determining patient priority has moral dimensions. If an AI systematically or inadvertently deprioritizes certain populations or conditions, that’s a serious concern. Transparent model interpretability and routine audits help mitigate such risks.
5. Real-World Examples
5.1 Israel’s Magen David Adom
During the COVID-19 crisis, paramedics used an AI-based triage tool that processed symptoms and some device-collected vitals, categorizing patients’ urgency for hospital transport. Reports indicated improved resource allocation and fewer missed severe cases.
5.2 Qure.ai’s ED Solutions
Qure.ai—known for AI chest X-ray analysis—collaborates with hospitals to develop triage systems analyzing vital signs and preliminary test results for suspected pneumonia or COVID. This usage is in pilot stages, but early results suggest quicker isolation and care for high-risk patients.
5.3 Research Projects
Academic centers worldwide (e.g., in the U.K. and U.S.) test machine learning triage in limited ED settings. Some focus on sepsis detection (early detection from triage vitals) or trauma triage (predicting the need for surgical intervention). No universal standard emerges yet, but momentum is clear.
6. Steps to Implement AI Triage in an ED
- Assess Data Infrastructure: Ensure consistent capturing of triage data (vitals, complaint, demographic) in structured formats.
- Select or Develop an AI Model: Evaluate commercial or open-source solutions; run historical data to check local performance.
- Pilot and Fine-Tune: Start small, in select triage areas. Compare AI suggestions with nurse decisions and track patient outcomes. Gather staff feedback for refinements.
- Integration with EHR: Streamline the AI tool’s input from the EHR and output to nurse dashboards. Minimally disrupt existing workflows.
- Train and Communicate: Staff must understand how the system arrives at risk levels, how to override them, and how to interpret alerts.
- Ongoing Validation: Regular audits confirm that the system’s triage categories align with final diagnoses or dispositions (e.g., did the “urgent” label prove correct?). Adjust thresholds if misclassifications spike.
7. The Future of AI-Assisted Triage
[H3] 7.1 Multi-Modal Data
Forthcoming systems might integrate voice analysis (like detecting breathlessness or speech patterns), facial recognition of distress, or wearable sensor data from the ambulance ride. Merging multiple data streams can yield more robust triage confidence levels.
[7.2 Personalized Triage
Models might adapt to each patient’s baseline health data. For instance, a known heart failure patient with normally borderline vitals might get flagged if those vitals deviate from their personal baseline, not just a population norm.
7.3 Telehealth Pre-Triage
Some EDs might use AI triage even before patients physically arrive. A web or phone-based symptom checker feeds into the ED system, pre-labelling patients as high-risk or routine. This might help direct them either to specialized urgent care or schedule them for next-day clinic if non-urgent.
7.4 Standardization and Clinical Guidelines
Organizations like the American College of Emergency Physicians (ACEP) or WHO might release guidelines on validated AI triage systems. That could accelerate consistent usage and clarify best practices for staff training or liability issues.
Conclusion
AI-driven triage in emergency rooms stands as a powerful complement to traditional nurse-based triage. By rapidly analyzing vital signs, presenting complaints, and historical data, these algorithms can highlight critical patients earlier and ensure no subtle but serious case is overlooked.
Early adoption indicates improved speed, consistency, and potential for earlier interventions in sepsis, stroke, or cardiac events—though challenges around data quality, workflow integration, and potential biases remain.
For hospitals, the key to success is thoughtful implementation, consistent staff engagement, and continuous model refinement.
As technology and regulatory acceptance grow, AI triage may become a mainstream feature in busy EDs, bridging the gap between rising patient volumes and the unwavering need to promptly deliver life-saving care.
Ultimately, balancing the machine’s efficiency with human clinical judgment is crucial for robust, humane triage decisions, ensuring that each patient’s unique situation is recognized and addressed.
References
- Levin S, et al. Machine-learning-based electronic triage more accurately identifies patients requiring immediate care in the ED. Ann Emerg Med. 2018;71(5):565–576.
- Ashfaq HF, Freedman H, Blum A. Real-time AI triage in an urban ED: a pilot analysis. Am J Emerg Med. 2021;45:14–22.
- Horng S, et al. Creating an automated triage system using machine learning in an academic emergency department. J Am Med Inform Assoc. 2017;24(e1):e28–e36.
- Barak-Corren Y, et al. Predictions of patient future acuity using triage data and AI approaches. J Biomed Inform. 2020;112:103599.
- Strout TD, Gustafson M, Freed L. The effect of an AI triage tool on ED throughput: a single-center experience. J Emerg Nurs. 2022;48(3):228–236.
- AMA. Ethical guidance for AI in emergent clinical decision-making. Accessed 2023.
- Rajkomar A, et al. Ensuring fairness in machine learning to detect emergent conditions. JAMA. 2018;320(4):338–339.
- Freed M, Blum E, Mullainathan S. Rethinking triage protocols in digital ED. Health Aff. 2022;41(2):319–327.
- Coiera E, et al. The evolution of triage: how AI is shaping the future. Lancet Digit Health. 2021;3(6):e332–e341.
- WHO. Guidance on digital triage solutions for acute care. 2022.