CJEM
-
Many emergency department (ED) patients with opioid use disorder are candidates for home buprenorphine/naloxone initiation with to-go packs. We studied patient opinions and acceptance of buprenorphine/naloxone to-go packs, and factors associated with their acceptance. ⋯ Although less than half of our study population accepted buprenorphine/naloxone to-go when offered, most thought this intervention was beneficial. In isolation, ED buprenorphine/naloxone to-go will not meet the needs of all patients with opioid use disorder. Clinicians and policy makers should consider buprenorphine/naloxone to-go as a low-barrier option for opioid use disorder treatment from the ED when integrated with robust addiction care services.
-
With the launch of competence by design (CBD) in emergency medicine (EM) in Canada, there are growing recommendations on the use of simulation for the training and assessment of residents. Many of these recommendations have been suggested by educational leaders and often exclude the resident stakeholder. This study sought to explore their experiences and perceptions of simulation in CBD. ⋯ EM residents strongly support using simulation in CBD and acknowledge its ability to bridge educational gaps and fulfill specific EPAs. However, this study suggests some unintended consequences of CBD and conflicting views around simulation-based assessment that challenge resident perceptions of simulation as a safe learning space. As CBD evolves, educational leaders should consider these impacts when making future curricular changes or recommendations.
-
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.