Development and validation of a machine learning framework for improved resource allocation in the emergency department.

Objective: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.

Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.

Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the advantage of ML-assisted sepsis alert in expediting clinical decisions leading to enhanced quality for emergency department (ED) patients remains unclear. A cluster-randomized trial was conducted in a tertiary-care hospital.

Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study.

Background: Early recognition and treatment of sepsis are crucial for improving patient outcomes. However, the diagnosis of sepsis remains challenging because of vague clinical presentations.

Objectives: We aim to developed novel sepsis screening tools with machine learning models and compared their performance with traditional methods.

Difficult airway predictors were associated with decreased use of neuromuscular blocking agents in emergency airway management: a retrospective cohort study in Thailand.

Background: It is recommended that difficult airway predictors be evaluated before emergency airway management. However, little is known about how patients with difficult airway predictors are managed in emergency departments. We aimed to explore the incidence, management and outcomes of patients with difficult airway predictors in an emergency department.