Articles: emergency-services.
-
Observational Study
Rapid Electroencephalography and Artificial Intelligence in the Detection and Management of Nonconvulsive Seizures.
Nonconvulsive status epilepticus is a commonly overlooked cause of altered mental status. This study assessed nonconvulsive status epilepticus prevalence in emergency department (ED) patients with acute neurologic presentations using limited electroencephalogram (EEG) coupled with artificial intelligence (AI)-enhanced seizure detection technology. We then compared the accuracy of the AI EEG interpretations to those performed by an epileptologist. ⋯ Limited AI-enhanced EEG can detect nonconvulsive status epilepticus in the ED; however, the technology tended to overestimate seizure burden in our cohort. This study found a lower nonconvulsive status epilepticus prevalence compared to prior literature reports.
-
Pediatric emergency care · Oct 2024
Caregiver Intent and Willingness to Accept COVID-19 Vaccine in the Pediatric Emergency Department.
While COVID-19 vaccine (CV) acceptance is improving, little is known about parental acceptance of CV in the pediatric emergency department (PED). ⋯ CV acceptance was low in this cohort. A gap population of unvaccinated children whose caregivers intend to vaccinate exists, and many of these would accept CV in the ED. This data supports the presence of CV programs in the ED to close this gap.
-
Presentation to the emergency department (ED) with self-harm provides an important opportunity for intervention. ⋯ The findings underline the importance of assessing all individuals who present with self-harm and highlight the need for comprehensively resourced 24hr services providing mental health care in the ED.
-
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 32.2% of total ED visits [2]. Our goal is to develop a machine learning framework that predicts patients' resource needs, thereby improving resource allocation during triage. ⋯ This study shows the high accuracy in predicting resource needs for patients in the ED using a machine learning model. This can greatly improve patient flow and resource allocation in already resource limited emergency departments.
-
Following standard syncope care, after exclusion of cardiac syncope, further workup is generally only recommended in cases of severe syncope due to consequential risk such that syncope is associated with injury or negative impacts on quality of life. This study is aimed to identify incidence and risk factors of severe syncope due to consequential risk, in a cohort of ED patients with non-cardiac syncope. ⋯ Syncope has a negative impact on a patient's life, through injuries or other personal consequences, in roughly one third of cases; to identity these patients, needing further investigation, emergency physicians should focus on episodes not preceded by prodromes, unwitnessed and with characteristic other than reflex syncope. Nonetheless, specific tools are needed to evaluate the impact of syncope on quality of life, to avoid clogging the path after ED discharge.