CJEM最新文献

Introduction: We evaluated how emergency department (ED) staff perceives the practice known as redirection, whereby triage nurses guide low-acuity patients to alternative care settings without evaluation by a physician. Our aim was to evaluate its use across Canada and to identify its key drivers and barriers to its implementation.

Methods: We conducted a cross-sectional survey of Canadian ED physicians, nurses and managers from September to December 2023. The survey tool was developed in French and English following a modified Dillman's tailored design method, including: (1) literature review to identify key themes on redirection; (2) semi-structured interviews with experts on redirection; (3) the development of a tool prototype; (4) scientific and linguistic revisions; and (5) pre-testing. The survey was distributed through the mailing list of the Canadian emergency medicine and nursing associations.

Results: Of the 719 respondents recruited, 47.0% were nurses, 44.2% were physicians and 5% were managers. The overall response rate was 10.2%. Most respondents endorsed redirection as safe, with this endorsement ranging from 75.5% in Ontario to 94.3% in Manitoba. Similarly, the view that first-line physicians can adequately manage redirected patients was supported by most respondents, with proportions ranging from 78.1% in Ontario to 92.1% in Québec. Redirection strategies reported by the majority of respondents were based on the Canadian Triage and Acuity Scale (65.2%). Insufficient opening hours of clinics (87.2%) and those with a CTAS score of 3 (62.7%) were identified as the main challenges. Professionals most suggested to receive redirected patients were family physicians (90.9%), nurse practitioners (86.4%), dentists (83.8%), social workers (71.9%), pharmacists (63.9%), and physiotherapists (58.0%).

Conclusions: In this pan-Canadian survey of ED personnel, the majority of respondents expressed support for redirecting low-acuity patients. These findings indicate an opportunity for further research on the development of redirection tools.

Introduction: As point-of-care ultrasound (POCUS) applications expand in the emergency department (ED), its utilization within the management of patients presenting with rapid atrial fibrillation (AF) or flutter (AFL) is unclear. The co-morbidity of heart failure and AF/AFL complicates the rate/rhythm control and disposition of this population. ED physicians diagnose heart failure by integrating POCUS left ventricular function assessment, but studies have not focused on the rapid AF/AFL population.

Methods: Our survey aimed to explore the use, integration, and perceived barriers of POCUS left ventricular function assessment in AF/AFL patients. Canadian ED physicians and residents who are members of the Canadian Association of Emergency Physicians (CAEP) completed an online survey through email between February and March 2024.

Results: We received 91 responses. For patients presenting with rapid AF/AFL with no prior imaging in the last 12 months, 51% of respondents indicated using cardiac POCUS for left ventricular function assessment. Half of respondents indicated that distinguishing between normal and severely reduced left ventricular function with POCUS would help guide management in rapid AF/AFL patients, and 77% responded that it would help guide disposition. A majority, 63% of respondents, agreed the assessment of left ventricular function would assist in determining if they will use calcium channel blockers. Perceived barriers included confidence in interpretation, skill level, machine accessibility, and accuracy of left ventricular function assessment in rapid AF/AFL patients.

Conclusion: Our survey demonstrated that half of ED physicians and residents are currently integrating POCUS left ventricular function assessment in rapid AF/AFL patients and most adapt their clinical decision making based on their findings, notably in whether or not to use calcium channel blockers. Among the perceived barriers, ED physicians most commonly reported low POCUS expertise level and the accuracy of left ventricular assessment in rapid AF/AFL.

Background: Recent advancements in artificial intelligence have shown promise in enhancing diagnostic precision within healthcare sectors. In emergency departments, artificial intelligence has demonstrated potential for improving triage, guiding the choice of radiologic imaging and crafting individualized medical notes and discharge summaries, including tailored care plans. Advances in generative artificial intelligence have led to the development of sophisticated models such as OpenAI's GPT-4. This study assessed the ability of generative artificial intelligence in diagnosis and management in emergency medicine. Specifically, we compared GPT-4 with the performance of emergency medicine trainees in Canada, as gauged by the Canadian In-Training Examination.

Methods: We compared the performance of emergency medicine residents to GPT-4 on the Canadian in-training exams for the years 2021 and 2022. Each question was entered into a fresh GPT-4 chat and the first response was recorded without any prompting. GPT-4's responses were then assessed using the same marking grid that is employed for evaluating medical trainees. We then compared GPT-4'sscores to the average scores of each post-graduate year (PGY) level of residents across all FRCPC training programs. Ethical approval was obtained, then Canadian In-Training Examination committee provided exam questions and anonymized national results.

Results: The participants in this study included 389 residents in 2021 and 333 residents in the 2022 exams. In 2021, mean trainee scores increased progressively across the levels, with PGY1 trainees scoring 48.0% (SD 15.6), PGY2 at 56.2% (SD 14.7), PGY3 at 59.8% (SD 16.7), PGY4 at 67.2% (12.3), and PGY5 at 70.1% (SD 12.5), whereas GPT-4 scored 88.7%. In 2022, a similar pattern, with PGY1 scoring 46.3% (SD 14.7), PGY2 at 51.8% (SD 14.7), PGY3 at 58.2% (SD 14.3), PGY4 at 66.2% (SD 15.3), and PGY5 at 64.3% (SD 8.5), while GPT-4 scored 82.0%.

Conclusions: GPT-4 has shown impressive capabilities, surpassing the performance of medical trainees at different postgraduate levels in the clinical written exam. These findings highlight the potential of artificial intelligence to serve as a valuable support tool in medical practice. However, it should be used with caution and must not substitute for established, evidence-based medical resources.

Objective: Urolithiasis is a common urological condition accounting for more than 1.3 million emergency department visits annually with costs exceeding $2.8 billion (Scales et al. in Eur Urol. 62:160-5, 2012;Eaton et al. in J Endourol 27:1535-1538, 2013;Antonelli et al. in Eur Urol 66:724-729, 2014;). Non-contrast computed tomography of the abdomen and pelvis remains the diagnostic gold standard. Studies assessing urinalysis and renal point-of-care ultrasound (PoCUS), individually, to diagnose symptomatic ureterolithiasis demonstrate inadequate sensitivities (Mefford et al. in West J Emerg Med 18:775, 2017;Eray et al. in Am J Emerg Med 21:152-4, 2003;Luchs et al. in Urology 59:839-842, 2002;Smith-Bindman et al. in N Engl J Med 371:1100-1110, 2014;Riddell et al. in West J Emerg Med 15:96-100, 2014;Rosen et al. in J Emerg Med 16:865-870, 1998;Gaspari and Horst in Acad Emerg Med 12:1180-1184, 2005;Watkins et al. in Emerg Med Australas 19:188-195, 2007;). The primary objective of this study was to assess the test characteristics of the U-PoCUS (urinalysis with renal point-of-care ultrasound) protocol.

Methods: This was an Institutional Review Board approved, multi-center, retrospective chart review at a university-based healthcare system. Study investigators included all patients who presented from January 1, 2016 through June 30, 2020, and underwent computed tomography of the abdomen and pelvis and had a urinalysis and PoCUS for suspected ureterolithiasis. Investigators utilized MedCalc (Version 19.1.6) and standard 2 × 2 tables to calculate test characteristics with 95% confidence intervals (CI).

Results: Study investigators enrolled 183 patients, including 122 patients diagnosed with computed tomography confirmed ureterolithiasis and 61 patients without it. The combination of hematuria and/or hydronephrosis on PoCUS had a sensitivity of 99.2% (95.6-100) and a specificity of 14.8% (7-26.2) for the presence of urolithiasis. Positive predictive value and negative predictive value were 69.9% (67.7-72.1) and 90% (53.9-98.6), respectively.

Conclusion: The presence of hematuria and/or hydronephrosis was 99.2% sensitive for the presence of ureterolithiasis diagnosed on computed tomography of the abdomen and pelvis. The U-PoCUS protocol missed only one symptomatic ureterolithiasis.