CJEM
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Quality improvement and patient safety (QIPS) and clinician well-being work are interconnected and impact each other. Well-being is of increased importance in the current state of workforce shortages and high levels of burnout. The Canadian Association of Emergency Physicians (CAEP) Academic Symposium sought to understand the interplay between QIPS and clinician well-being and to provide practical recommendations to clinicians and institutions on ensuring that clinician well-being is integrated into QIPS efforts. ⋯ QIPS and clinician well-being are often closely linked. By incorporating these recommendations, QIPS strategies can enhance clinician well-being.
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Review
Heatstroke presentations to urban hospitals during BC's extreme heat event: lessons for the future.
Climate change is leading to more extreme heat events in temperate climates that typically have low levels of preparedness. Our objective was to describe the characteristics, treatments, and outcomes of adults presenting to hospitals with heatstroke during BC's 2021 heat dome. ⋯ Heatstroke patients were unable to activate 911 themselves, and most presented with a 48-h delay. This delay may represent a critical window of opportunity for pre-hospital and hospital systems to prepare for the influx of high-acuity resource-intensive patients.
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Older adults visit emergency departments (EDs) at higher rates than their younger counterparts. However, less is known about the rate at which older adults living with dementia visit and revisit EDs. We conducted a systematic review and meta-analysis to quantify the revisit rate to the ED among older adults living with a dementia diagnosis. ⋯ Existing literature on ED revisits among older adults living with dementia highlights the medical complexities and challenges surrounding discharge and follow-up care that may cause these patients to seek ED care at an increased rate. ED personnel may play an important role in connecting patients and caregivers to more appropriate medical and social resources in order to deliver an efficient and more rounded approach to care.
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Prompt diagnosis of acute coronary syndrome (ACS) using a 12-lead electrocardiogram (ECG) is a critical task for emergency physicians. While computerized algorithms for ECG interpretation are limited in their accuracy, machine learning (ML) models have shown promise in several areas of clinical medicine. We performed a systematic review to compare the performance of ML-based ECG analysis to clinician or non-ML computerized ECG interpretation in the diagnosis of ACS for emergency department (ED) or prehospital patients. ⋯ ML models have overall higher discrimination and sensitivity but lower specificity than clinicians and non-ML software in ECG interpretation for the diagnosis of ACS. ML-based ECG interpretation could potentially serve a role as a "safety net", alerting emergency care providers to a missed acute MI when it has not been diagnosed. More rigorous primary research is needed to definitively demonstrate the ability of ML to outperform clinicians at ECG interpretation.