scholarly journals Teaching emergency ultrasound to emergency medicine residents: a scoping review of structured training methods

Author(s):  
Leila L. PoSaw ◽  
Brandon M. Wubben ◽  
Nicholas Bertucci ◽  
Gregory A. Bell ◽  
Heather Healy ◽  
...  
CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.


Author(s):  
Teresa M. Chan ◽  
Stefanie S. Sebok‐Syer ◽  
Warren J. Cheung ◽  
Martin Pusic ◽  
Christine Stehman ◽  
...  

2014 ◽  
Vol 15 (3) ◽  
pp. 306-311 ◽  
Author(s):  
Daniel Kim ◽  
Jonathan Theoret ◽  
Michael Liao ◽  
John Kendall

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S109-S110 ◽  
Author(s):  
T. Suryavanshi ◽  
S. Lambert ◽  
T. Chan

Introduction: Today's emergency department sees healthcare system pressures manifest through longer wait times, increased costs, and provider burnout. In the face of questionable sustainability, there is a greater role for training future innovators and entrepreneurs in healthcare. However, there is currently little formal education or mentorship in these areas. The aim of this scoping review was to identify the current and ideal educational practices to foster innovative and entrepreneurial mindsets, with specific interest amongst emergency medicine trainees. Methods: Using a scoping review methodology, the relationship between healthcare and entrepreneurship was explored. OVID, PubMed and Google Scholar were searched using the keywords “entrepreneurship”, “health education” and “health personnel”, on March 8th, 2018. Results were screened by title, abstract and full text by a team of three calibrated researchers, based upon pre-defined exclusion and inclusion criteria. The final list of papers was reviewed using an extraction tool to identify demographics, details of the paper, and its attitudes and perceptions towards entrepreneurship and innovation. Results: After screening, 59 papers were identified for qualitative analysis. These papers ranged from 1970-2018, mainly from the USA (n = 36). Most papers were commentaries/opinions (n = 35); 11 papers described specific innovations. Entrepreneurship was viewed positively in 45 papers, negatively in 2 papers, and mixed in 12 papers. Common specialties discussed were surgery (n = 9), internal medicine (n = 3), and not specified (n = 44). Emergency medicine was described in one paper. Major themes were: entrepreneurial environment (n = 29), funding and capital (n = 12), idea generation (n = 9), and teaching entrepreneurship (n = 6). Of the 11 innovation papers, the discussion was focused on educational (n = 6) or system (n = 5) innovations. These innovations related to surgery (n = 1), public health (n = 1) and palliative care (n = 1). None of these innovations were specific to emergency medicine. Conclusion: This review indicates a small number of programs focused on promoting innovation and entrepreneurship amongst trainees, but no programs specific to the emergency department. There may be benefit for educators in emergency medicine to consider how to foster a greater innovative spirit in our speciality, so our next generation of physicians can help tackle problems affecting patient care.


CJEM ◽  
2017 ◽  
Vol 20 (2) ◽  
pp. 176-182 ◽  
Author(s):  
Paul Olszynski ◽  
Dan Kim ◽  
Jordan Chenkin ◽  
Louise Rang

Emergency ultrasound (EUS) is now widely considered to be a “skill integral to the practice of emergency medicine.”1The Canadian Association of Emergency Physicians (CAEP) initially issued a position statement in 1999 supporting the availability of focused ultrasound 24 hours per day in the emergency department (ED).2


2020 ◽  
Author(s):  
Maria Louise Gamborg ◽  
Mimi Mehlsen ◽  
Charlotte Paltved ◽  
Gitte Tramm ◽  
Peter Musaeus

Abstract Background: Clinical decision-making (CDM) is an important competency for young doctors, especially under complex and uncertain conditions, which is present in geriatric emergency medicine (GEM). Research in this field is however characterized by an unclear conceptualization of CDM. To evolve and evaluate evidence-based knowledge of CDM, it is thus important to identify different definitions and their operationalisations in studies on GEM.Objective: A scoping review of empirical articles was designed to provide an overview of the documented evidence of findings and conceptualizations of CDM in GEM.Methods: A detailed search for empirical studies focusing on CDM in a GEM setting was conducted in PubMed, ProQuest, Scopus, EMBASE and Web of Science. In total, 52 publications were included in the analysis, utilizing a data extraction sheet, following the PRISMA guidelines. Reported outcomes were summarized.Results: Four themes of operationalization of CDM emerged; CDM as dispositional decisions, CDM as cognition, CDM as a model, and CDM as clinical judgement. Study results and conclusions differed according to how CDM was conceptualized. It was evident how especially frailty- heuristics lead to biases in treatment of geriatric patients, and that the complexity of this patient group was seen as a challenge for CDM.Conclusions: This scoping review summarizes how different studies in GEM use the term CDM. It provides a snapshot of findings in GEM. Potentially, findings from CDM research can guide implementation of adequate CDM strategies in clinical practice but this requires application of more stringent definitions of CDM in future research.


10.2196/12368 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e12368 ◽  
Author(s):  
Brendan William Munzer ◽  
Mohammad Mairaj Khan ◽  
Barbara Shipman ◽  
Prashant Mahajan

2010 ◽  
Vol 38 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Anthony J. Dean ◽  
Michael J. Breyer ◽  
Bon S. Ku ◽  
Angela M. Mills ◽  
Jesse M. Pines

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