INTRODUCTION: This study examines triage judgments in emergency settings and compares the outcomes of artificial intelligence models for healthcare professionals. It discusses the disparities in precision rates between subjective evaluations by healt...
STUDY OBJECTIVE: This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critic...
INTRODUCTION AND OBJECTIVE: Modern and intelligent triage systems are used today due to the growing trend of disasters and emergencies worldwide and the increase in the number of injured people facing the challenge of using traditional triage methods...
BACKGROUND: In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage ...
BACKGROUND: Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study de...
Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
39643547
BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is important for patient care and ED workflow practices.
BMC medical informatics and decision making
39633370
BACKGROUND: Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, prese...
OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe...
The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a n...