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This study analyzes hospital Emergency Department (ED) data from 2016 to 2024, examining trends in Waiting Times (WT), Lengths of Stay (LoS), and patient outcomes. WT and LoS increased after the pandemic, indicating operational issues, even though patient volumes remained consistent throughout the whole period. Longer delays were observed on weekends and throughout the colder months, according to temporal analysis. Younger age groups and NZ European/Pākehā and Māori populations dominated ED visits, with older patients experiencing higher mortality rates. Mortality analysis revealed an inverse relationship between WT and patient mortality, with extended LoS correlating with increased severity. The results emphasize the use of predictive analytics to enhance healthcare equity and optimize ED operations.
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http://dx.doi.org/10.3233/SHTI250891 | DOI Listing |
Am J Emerg Med
September 2025
Department of Surgical Education, Orlando Regional Medical Center, Orlando, FL, USA; Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando Regional Medical Center, Orlando, FL, USA. Electronic address:
Background: There is conflicting literature regarding mortality outcomes associated with REBOA usage in patients with severe thoracic or abdominal trauma. Our study aims to assess the benefits and negative implications of REBOA use in adult trauma patients in hemorrhagic shock with severe thoracic or abdominal injuries.
Methods: This retrospective cohort analysis utilized the American College of Surgeons Trauma Quality Improvement Program Participant Use File (ACS-TQIP-PUF) database from 2017 to 2023 to evaluate adult patients with severe isolated thoracic or abdominal trauma undergoing REBOA placement.
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Hum Factors
September 2025
Media Psychology Lab, Department of Communication Science, KU Leuven, Leuven, Belgium.
Background: Out-of-hospital cardiac arrests (OHCAs) are a leading cause of death worldwide, yet first responder apps can significantly improve outcomes by mobilizing citizens to perform cardiopulmonary resuscitation before professional help arrives. Despite their importance, limited research has examined the psychological and behavioral factors that influence individuals' willingness to adopt these apps.
Objective: Given that first responder app use involves elements of both technology adoption and preventive health behavior, it is essential to examine this behavior from multiple theoretical perspectives.
Crit Care Explor
September 2025
Department of Biostatistics, University of Florida Colleges of Medicine and Public Health and Health Professions, Gainesville, FL.
Objectives Background: Monocyte anisocytosis (monocyte distribution width [MDW]) has been previously validated to predict sepsis and outcome in patients presenting in the emergency department and mixed-population ICUs. Determining sepsis in a critically ill surgical/trauma population is often difficult due to concomitant inflammation and stress. We examined whether MDW could identify sepsis among patients admitted to a surgical/trauma ICU and predict clinical outcome.
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September 2025
Division of Tropical Medicine and Infectious Diseases, Department of Internal Medicine, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
Importance: Sepsis remains a leading cause of death in infectious cases. The heterogeneity of immune responses is a major challenge in the management and prognostication of patients with sepsis. Identifying distinct immune response subphenotypes using parsimonious classifiers may improve outcome prediction, particularly in resource-limited settings.
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