Articles: emergency-department.
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This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously validated risk stratification scores using variables extracted from the electronic health record at the time of diagnosis. The combination of these approaches yielded an natural language processing-based clinical decision support tool that can identify patients presenting to the emergency department (ED) with low-risk PE as candidates for outpatient management. ⋯ This study demonstrates that a natural language processing-based model utilizing real-time radiology reports can accurately identify patients with PE. Further, this model, used in combination with a validated risk stratification score (sPESI), provides a clinical decision support tool that accurately identifies patients in the ED with low-risk PE as candidates for outpatient management.
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Observational Study
The injury pattern and outcomes among elephant attack survivors presenting to the emergency department: A retrospective observational study.
Human-elephant conflicts (HECs) are becoming a disturbing public health concern in eastern India. This study highlights the pattern of injuries, epidemiological factors, and outcomes among the victims who survived an elephant attack (EA). ⋯ Middle-aged men were the most common victims of EA occurring during the early morning hours. Extremity and soft tissue injuries were most common, followed by chest and abdominal injuries. Severe chest injury resulted in ICU admission and extended hospitalization.
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Identify high-risk clinical characteristics for a serious cause of vertigo in patients presenting to the emergency department (ED). ⋯ The Sudbury Vertigo Risk Score identifies the risk of a serious diagnosis as a cause of a patient's vertigo and if validated could assist physicians in guiding further investigation, consultation, and treatment decisions, improving resource utilization and reducing missed diagnoses.
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Violence in the emergency department (ED) has been escalating for decades worldwide. High-stress situations are commonplace in the ED and can lead to intentional and unintentional aggression from patients. Staff must be educated on the signs of violence and escalation to recognize potentially dangerous situations early. ⋯ Formalized procedures and policies should clearly assign roles for each staff member in the event of a violent patient. Training programs should be instituted and may include self-defense classes or crisis intervention courses. Emergency medicine residency programs and EDs around the country must address the rising incidence of violence within EDs through interdisciplinary policy, procedure development, and prevention and mitigation programs.
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Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle symptoms in critical patients, thus leading to delayed treatment. Unlike previous rivalry-focused approaches, our study aimed to establish a collaborative machine learning (ML) model that renders risk scores for severe illness, which may assist the triage staff to provide a better patient stratification for timely critical cares. ⋯ The traditional 5-level triage system often falls short, leading to under-triage of critical patients. Our models include a score-based differentiation within a triage level to offer advanced risk stratification, thereby promoting patient safety.