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Airway management is a key skill in emergency and critical care settings, as maintaining a clear airway is essential for adequate oxygenation and ventilation.Head Tilt-Chin Lift TechniqueThe head tilt-chin lift maneuver is an essential technique primarily used in patients without suspected cervical spine injuries. To perform this maneuver, one hand is placed on the patient’s forehead, and gentle pressure is applied backward to tilt the head. The fingertips of the other hand are positioned...
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Nursing Assessment:Nursing management of acute coronary syndrome (ACS) involves taking the patient's history, focusing on primary complaints such as chest pain, dyspnea, and excessive sweating (diaphoresis), as well as other symptoms like back or jaw pain, nausea, vomiting, palpitations, dizziness, and fatigue. The nurse also reviews the patient's history of cardiac events, risk factors such as hypertension, diabetes, smoking, family history, and current medications.In the objective assessment,...
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Updated: Nov 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language

Oleksandr Ivanov, Lisa Wolf, Deena Brecher

    Journal of Emergency Nursing
    |December 28, 2020
    PubMed
    Summary
    This summary is machine-generated.

    A new machine learning model, KATE, accurately predicts patient acuity using electronic health records, outperforming nurses in triage accuracy. This AI tool can improve emergency department efficiency and reduce bias.

    Keywords:
    AcuityEmergency Severity IndexMachine learningTriage

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    Area of Science:

    • Emergency Medicine
    • Artificial Intelligence in Healthcare
    • Clinical Informatics

    Background:

    • Emergency department (ED) triage is essential for managing patient flow and resource allocation.
    • Increased patient volumes necessitate efficient and accurate methods for determining acuity and prioritization.

    Purpose of the Study:

    • To evaluate the efficacy of a machine learning algorithm (KATE) in predicting Emergency Severity Index (ESI) acuity using historical electronic health record (EHR) data.
    • To compare the accuracy of the KATE model against human triage by nurses and clinicians.

    Main Methods:

    • Development of the KATE triage model using a large dataset of 166,175 patient encounters from two hospitals.
    • Testing the KATE model against a random sample of patient encounters with clinician-assigned ESI acuity.
    • Utilizing the Emergency Severity Index (ESI) as the standard for acuity assignment.

    Main Results:

    • The KATE model achieved an accurate ESI acuity assignment rate of 75.7%, surpassing nurses (59.8%) and individual clinicians (75.3%).
    • KATE demonstrated significantly higher accuracy (26.9%) compared to average nurse accuracy (P <.001).
    • For ESI levels 2 and 3, critical for decompensation risk, KATE's accuracy was 80%, a 93.2% improvement over triage nurses (41.4%, P <.001).

    Conclusions:

    • The KATE algorithm provides more accurate triage acuity assignments than human nurses in the studied sample.
    • KATE's independence from contextual factors may mitigate biases and external pressures leading to undertriage.
    • Future research should explore KATE's real-time feedback impact on patient outcomes, ED throughput, and resource optimization.