The American journal of emergency medicine
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The utilization of smartphone-based technology and applications to streamline patient care provides an exciting opportunity for quality improvement research. As traditional communication methods such as paging have repeatedly been shown to be susceptible to errors and inefficiency that can delay patient care, smartphones continue to be investigated as means of improving inter-hospital communication and patient outcomes. ⋯ The use of smartphones can positively impact patient care; however, these benefits must be balanced with the responsibility to protect patient privacy and confidentiality. In order to continue to support HCGM's expansion and integration into daily practice, further data-driven studies into HCGM-specific interventions must be pursued.
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Acute heart failure (AHF) accounts for a significant number of emergency department (ED) visits, and the disease may present along a spectrum with a variety of syndromes. ⋯ A variety of misconceptions surround the evaluation and management of heart failure including clinical assessment, natriuretic peptide use, chest radiograph and US use, nitroglycerin and diuretics, vasopressor choice, and disposition. This review evaluates these misconceptions while providing physicians with updates in evaluation and management of AHF.
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Thrombus burden in pulmonary embolism (PE) is associated with higher D-Dimer-levels and poorer prognosis. We aimed to investigate i) the influence of right ventricular dysfunction (RVD), deep venous thrombosis (DVT), and high-risk PE-status on D-Dimer-levels and ii) effectiveness of D-Dimer to predict RVD in normotensive PE patients. ⋯ Thrombus burden in PE is related to elevated D-Dimer levels, and D-Dimer values >1.18 mg/l were predictive for RVD in normotensive patients. D-Dimer levels were influenced by DVT, but not by cancer, pneumonia, age, or renal impairment.
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The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. We compared the performance of several machine learning approaches for predicting two clinical outcomes (critical care and hospitalization) among ED patients with asthma or COPD exacerbation. ⋯ Based on nationally-representative ED data, machine learning approaches improved the ability to predict disposition of patients with asthma or COPD exacerbation.