CJEM
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We developed the Canadian Syncope Pathway (CSP) based on the Canadian Syncope Risk Score (CSRS) to aid emergency department (ED) syncope management. This pilot implementation study assessed patient inclusion, length of transition period, as well as process measures (engagement, reach, adoption, and fidelity) to prepare for multicenter implementation. ⋯ In this pilot study, we achieved all prespecified benchmarks for proceeding to the multicenter CSP implementation except reach. Our results indicate a 1-month transition period will be adequate though regular reminders will be needed during full-scale implementation.
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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.
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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.