Learning In Emergency Medicine Research Articles

Artificial Intelligence and Machine Learning in Emergency Medicine and Intensive Care

Artificial Intelligence and Machine Learning in Emergency Medicine and Intensive Care

Evaluation of learning in emergency medicine: An umbrella IRB protocol for education outcomes research.

Evaluation of learning in emergency medicine: An umbrella IRB protocol for education outcomes research.

A case for lifelong learning in emergency medicine: The perspective from a rural state.

The expansive practice of emergency medicine cannot be fully taught or learned in 3 or 4 years. Lifelong learning, self-assessment,1 and ongoing skill acquisition and retention are necessary. How do we teach and acknowledge this during residency training and how do we support our isolated, rural colleagues? Importantly, there is a growing shortage of rural emergency physicians and many of our trainees may work in these environments.2 As educators, we must study potential solutions and bridge these gaps for our trainees entering independent practice and for our colleagues far removed from formal training. Recent cases, and many like them, illustrate this point. A rural emergency physician expertly places a small-bore chest tube for an atraumatic pneumothorax, the pneumothorax resolved, they requested transfer to a larger center for ongoing management. The tertiary care center is at full capacity. A phone consultation with a specialist recommends removal of the chest tube in the emergency department (ED) and discharge home if a follow up chest x-ray shows sustained resolution. The emergency physician has never removed a chest tube before and the patient is transferred to a larger regional ED for uncomplicated removal. In a second case, a patient is transferred from an hour away, during a snowstorm, for a peritonsillar abscess. The procedure is within an emergency physician's scope of practice. The referring physician had never performed this procedure in their decades of practice. The patient is transferred to the tertiary care center where it is drained by the attending emergency physician and emergency medicine resident. On a winter night, an emergency physician intubates a patient with a head injury in their critical-access emergency department. They request lifesaving medications and the respiratory therapist to set the ventilator. It is nighttime; there is no respiratory therapist or pharmacist. The physician must assist in mixing the medications and setting up the ventilator. The skills required for an emergency physician are ever expanding, particularly as access to tertiary care becomes more difficult. Lifelong learning and practice development to best serve our patients is a necessity in our specialty. Solutions may include regional conferences, collaborative case reviews, real-time peer-based decision support with telemedicine, maintenance of board certification, asynchronous, self-directed learning through free open-access medical education (FOAM), podcasts, textbooks or journal articles, and academic–rural partnerships that create shared faculty positions between rural and tertiary sites and rural rotations for trainees. Each of these has its limitations and time represents a major barrier.3-5 The American Board of Emergency Medicine requires continuous learning for maintenance of certification; this is beneficial but is not tailored to the physician's practice environment.6, 7 Our own work in the rural state of Vermont and upstate New York has led us to offer high-acuity, low occurrence (HALO) courses at the regional academic center twice a year for both residents and practicing emergency physicians within our region and create a rural-specific simulation lab at one of our critical-access sites. Additionally, we established a tele-emergency medicine program to offer a virtual resource from the tertiary care center in times of high-acuity, high-complexity care, or volume surge. The skill of delivering and accepting tele-emergency medicine assistance must also be learned and taught. Our education must continue to adapt to meet the needs of our unique and evolving practice environments. We must also acknowledge that many scenarios cannot be predicted. Instilling the motivation for lifelong learning, self-assessment1 and exposure to resource limited settings during training may help prepare trainees for these challenges. Although these issues are not unique to emergency medicine, we are a specialty that prides itself on adaptability. We must confront this challenge with creative solutions. Skyler Lentz, Ashley Weisman, Jordan Ship, and Matthew S. Siket conceived the idea for this manuscript and contributed substantially to the content, design, writing and editing of the commentary. The authors declare no conflicts of interest.

An Evaluation of Emergency Medicine Core Content Covered by Podcasts

Podcasts are used broadly for learning in emergency medicine (EM); however, there is concern about the potential for knowledge gaps for learners who rely on podcasts for their learning. The extent to which EM podcasts cover the core curriculum of EM is not known; thus, we sought to quantify the extent to which podcasts represent the core content of our specialty. We performed a retrospective review of all EM podcast episodes published in 2019. All podcasts were given credit for the content they covered as it related to the 2016 American Board of Emergency Medicine (ABEM) Model of Clinical Practice in Emergency Medicine (EM Model). The primary outcome was a description of how podcasts represented the ABEM EM Model content topics compared to the topic representation of the ABEM Qualifying Exam. We included 54 unique EM podcast programs and 1,193 podcast episodes. They covered 2,965 total EM Model core content topics. The topics most covered were "other" (which includes interpersonal skills and professionalism), procedures, and signs and symptoms. Musculoskeletal, hematology, and environmental each accounted for less than 1% of all topics covered. Almost three-quarters of podcast episodes covered other core competencies of the practice of EM. Podcasts had a broad yet imbalanced coverage of the ABEM EM Model core content subtopics in 2019, with a notable coverage of other core competencies of the practice of EM. Learners, educators, and scholars should be mindful of these gaps and focus future work on exploring how podcasts should best be used in EM education.

Artificial intelligence and machine learning in emergency medicine: a narrative review.

AimThe emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine.MethodsWe undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments.ResultsThis review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI.ConclusionWe intend that this review serves as an introduction to AI and machine learning in emergency medicine.

179 A Cross-Sectional Study on the Effectiveness of Simulation-Based Learning in Emergency Medicine for Medical Undergraduates in a Low-Middle Income Country During the COVID-19 Pandemic

Learning emergencies is a challenge during COVID-19 pandemic for medical students. Managing a real patient in an emergency exposes the medical students and patients to risks now more than ever before. Simulation-based learning (SBL) is a proven safer teaching method to improve technical and non-technical medical knowledge, skills, and to enhance confidence in high-income countries. There is limited literature on the effectiveness of SBL in low-middle income countries (LMICs) This study evaluates final-year medical undergraduates’ knowledge, skills and confidence improvement through a novel SBL in an LMIC during the COVID-19 pandemic.Four simulation scenarios were conducted by an instructor to a small group of five to six participants. The instructors were Emergency Medicine Senior Registrars or Registrars, who had prior knowledge in teaching techniques through a formal instructor development course elsewhere. The simulation sessions were based on four scenarios. A high-fidelity mannequin, basic airway devices, IV access, monitoring devices and a defibrillator were used. Pendleton Model Final-year medical students of the University of Colombo were trained on medical emergency care skills and subsequently they were given the opportunity to apply skills in simulation. This course was conducted twice a week, 4-hour sessions, for 6 weeks in March and April 2021. There were four skills stations, including ABCDE assessment, airway management, defibrillation with BLS and non-technical skills. A pre- and post-MCQ was used to assess improvement of knowledge and confidence level on performing each skill. Likert-scale questionnaires were administered before and after each simulation session to assess the level of confidence in performing each task of the simulated scenario. The normal distribution of data was tested with the Shapiro–Wilk test. If the distribution of data was not normal, Wilcoxon signed-rank test was used to compare pre- and post-test scores. Paired sample t-test was used to compare pre- and post-test data if the distribution of data is normal.All 42 participants experienced SBL for the first time ever. Post-test MCQ score significantly improved compared with pre-test score (p < 0.001). Confidence in skills increased in all 17 domains following the skills sessions in all participants. Confidence to manage cardiac arrest increased in all 10 tasks of the cardiac arrest simulation and the total average confidence score rose from 17.1 (±4.7) to 32.0 (±7.7) after the simulation-based intervention (p < 0.001). Confidence increased significantly in all 12 domains of asthma and anaphylaxis management with the total average confidence score rising from 21.4 (±0.8) to 39.2 (±2.1) (p < 0.001). Satisfaction and attitudes towards simulation-based learning were very positive.The course has shown a statistically significant improvement of students’ knowledge and confidence in skills with a high level of satisfaction. Therefore, SBL is an effective, safe and feasible alternative to train emergency medicine for the students of LMICs during COVID-19 pandemic.

Machine Learning in Emergency Medicine: Keys to Future Success.

An era for artificial intelligence has arrived for emergency medicine. In the systematic review by Kareemi et al. (Ref 1.) published in this issue of Academic Emergency Medicine, the authors evaluate the performance of machine learning (ML) models versus standard care (e.g. clinical decision rules, provider judgement) in emergency medicine across a variety of clinical scenarios and outcomes. The systematic review concludes that ML has superior performance in almost all tasks, but also calls attention to several widespread shortcomings including limited adherence to reporting guidelines and the lack of evaluation through interventional trials. These findings highlight the need for a new phase in clinical decision support (CDS) for emergency care with research and practice focused on integrated, machine learning-driven CDS systems that are usable, interpretable, and effective. In this commentary, we review key concept areas for enhancing the performance, promoting the adoption, and studying the impact of ML within emergency medicine. We also discuss the interpretation and application of machine learning studies and projects, dividing key concepts into two domains: intrinsic - elements of the model and its task-based performance - and extrinsic - the ability for the model to achieve a desired objective with respect to patient care.

A Near-Peer Educational Model for Online, Interactive Learning in Emergency Medicine.

IntroductionThe COVID-19 pandemic led to a large disruption in the clinical education of medical students, particularly in-person clinical activities. To address the resulting challenges faced by students interested in emergency medicine (EM), we proposed and held a peer-led, online learning course for rising fourth-year medical students.MethodsA total of 61 medical students participated in an eight-lecture EM course. Students were evaluated through pre- and post-course assessments designed to ascertain perceived comfort with learning objectives and overall course feedback. Pre- and post-lecture assignments were also used to increase student learning.ResultsMean confidence improved in every learning objective after the course. Favored participation methods were three-person call-outs, polling, and using the “chat” function. Resident participation was valued for “real-life” examples and clinical pearls.ConclusionThis interactive model for online EM education can be an effective format for dissemination when in-person education may not be available.

Sociocultural learning in emergency medicine: a holistic examination of competence.

Objectives In the medical community of practice, the resident is situated in systems of professional and cultural activities. How diagnostic competence manifests in their sociocultural context is examined through situativity and systems theories. A holistic model is proposed that could examine diagnostic competence across micro (individual), meso (activity), and macro (cultural) systems. Methods Two short scenarios are presented resulting from observations of emergency medicine residents and their supervising physicians. These scenarios are analyzed using a trans-theoretical model of situativity and systems theories to understand how diagnostic competence manifests in practice (activity system). Results Assessment of diagnostic competence in a sociocultural context may require assessment of responses to contextual factors that seem immaterial to clinical reasoning. This information may signal that the resident also has the skills to identify appropriate information channels within an activity system and can accurately collect and prioritize clinical information within those channels. Therefore, the formal assessment of competent clinical reasoning performance, as a situated practice, may benefit from delineating how much of the context of an activity system is required to be competently managed and synthesized across the competency milestones. Conclusions The examination of diagnostic competence as a sociocultural practice can provide a unique and holistic examination of clinical reasoning performance and assessment.

Deep learning in Emergency Medicine: Recent contributions and methodological challenges

In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.

Artificial intelligence and machine learning in emergency medicine.

Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

Social-media-enabled learning in emergency medicine: a case study of the growth, engagement and impact of a free open access medical education blog

BackgroundClinicians are increasingly using social media for professional development and education. In 2012, we developed the St.Emlyn’s blog, an open access resource dedicated to providing free education in the field...

What's the Evidence: Self-Assessment Implications for Life-Long Learning in Emergency Medicine

BackgroundThe 2012 Academic Emergency Medicine Consensus Conference, “Education Research in Emergency Medicine: Opportunities, Challenges, and Strategies for Success” noted that emergency medicine (EM) educators often rely on theory and tradition when molding their approaches to teaching and learning, and called on the EM education community to advance the teaching of our specialty through the performance and application of research in teaching and assessment methods, cognitive function, and the effects of education interventions. ObjectiveThe purpose of this article was to review the research-based evidence for the effectiveness of self-assessment and to provide suggestions for its use in clinical teaching and practice in EM. DiscussionThis article reviews hypothesis-testing research related to self-assessment behaviors and learning. Evidence indicates that self-assessment is inherently flawed when used in isolation. We review a multi-dimensional approach to informed self-assessment that can serve as the basis for life-long learning and development. ConclusionsAdvancing EM education will require that high-quality education research results be translated into actual curricular, pedagogical, assessment, and professional development changes. The informed self-assessment framework is a method that is applicable to teaching and practice in EM.

Evaluation of the use of MOODLE-based e-learning for faculty development in Emergency Medicine

Background: Today, e-learning is of strategic importance to teaching and learning in Emergency Medicine (EM). We adopted an interactive e-learning platform based on the open source software called MOODLEa using a blended learning strategy to assist the implementation of a dedicated faculty development program known as the EM consolidation program. This 12-month program was designed to meet the developmental needs of 20 EM specialists who qualified from different parts of the world. The program is delivered through an innovative blend of workplace and simulation-based assessments, educational portfolio and individualized training in selected clinical areas. The e-learning platform was used by the program participants to share learning resources, communicate, and coordinate various educational activities over the last 6 months. Methods: We sought feedback from the participants through a MOODLE-based online questionnaire to evaluate their satisfaction, usage patterns and feedback of their e-learning experience. Results: Out of 20 participants, 90% (n = 18) completed the questionnaire. Two-thirds declared proficiency in computer skills and were enthusiastic about the technology. Their prior experience was related to certified courses rather than interactive forum-based e-learning. About their current experience with MOODLE, 86.7% of participants found it easy to use without any major technical problems. Most of them preferred to access the platform once or twice a week, with 20% accessing on a daily basis. Of the participants, 75% of them accessed MOODLE through their smartphones and 12.5 % through tablet devices. All areas of the e-learning course were found to be useful. The forum-based discussion area was highly rated. The overall learning material was perceived as appropriate and well designed. Among the participants, 62.50% liked the current methodology of blended learning, and 31.25% wished to replace traditional method with e-learning. Also 93.75% of the respondents stated that e-learning was time saving. Conclusion: Learners involved in this e-learning initiative were overall satisfied by the current implementation of MOODLE and with a third of users preferring e-learning to traditional learning. The use of smartphones and tablet devices was widespread in this small study and hence we recommend support for these devices in implementing similar e-learning initiatives. Reference: [1] Cook DA, Yvonne S. Online learning for faculty development: A review of the literature. Med Teach. 2013;35(11):930–937. a MOODLE – Modular Object Orientated Dynamic Learning Environment. http://moodle.org.

What's the Evidence: A Review of the One-Minute Preceptor Model of Clinical Teaching and Implications for Teaching in the Emergency Department

BackgroundThe 2012 Academic Emergency Medicine Consensus Conference, “Education Research in Emergency Medicine: Opportunities, Challenges, and Strategies for Success” noted that emergency medicine (EM) educators often rely on theory and tradition in molding their approaches to teaching and learning, and called on the EM education community to advance the teaching of our specialty through the performance and application of research in teaching and assessment methods, cognitive function, and the effects of education interventions. ObjectiveThe purpose of this article is to review the research-based evidence for the effectiveness of the one-minute preceptor (OMP) teaching method, and to provide suggestions for its use in clinical teaching and learning in EM. DiscussionThis article reviews hypothesis-testing education research related to the use of the OMP as a pedagogical method applicable to clinical teaching. Evidence indicates that the OMP prompts the teaching of higher level concepts, facilitates the assessment of students' knowledge, and prompts the provision of feedback. Students indicate satisfaction with this method of clinical case–based discussion teaching. ConclusionAdvancing EM education will require that high quality education research results be translated into actual curricular, pedagogical, assessment, and professional development changes. The OMP is a pedagogical method that is applicable to teaching in the emergency department.

Blended learning in emergency medicine: implementing the e-learning component

The South-East Asian Journal of Medical Education (SEAJME) is the culmination of the SEARAME's vision to bring together medical educationists in the region to promote collaborative efforts toward uplifting the standards of medical education in the region and beyond through dissemination of knowledge. This journal will be the ideal platform to disseminate research findings and stimulate discussion among experts in the field. Open access policy of the journal will facilitate this ideal.From November 2019, The South-East Asian Journal of Medical Education (SEAJME) is indexed in EuroPub

Rethinking Education: The Wiscopkins Model - Novel Curriculum and Milestone Deployment Using iTunesU

Didactic learning in emergency medicine (EM) education has changed. Textbooks are being replaced with online materials, blogs and podcasts as learners are moving to an asynchronous learning style that constantly promotes problem-based, up-to-date and interactive learning. Given the paucity of formal, online, EM-based educational materials–it is up to educators to develop and adopt new and innovative media-based instruction methods. At the same time, both educators and clinicians are looking for novel ways to integrate ABEM's newly adopted milestones into daily practice.

Undergraduate emergency medicine in an African medical school – Experiences from Botswana

Abstract Introduction In August 2011, the first cohort of students at the University of Botswana School of Medicine (UBSOM) began their third year of undergraduate medical training. As part of their 2011–2012 curriculum they each completed a 10week rotation in Emergency Medicine (EM), involving problem based learning (PBL) sessions, maintenance of log books, case presentations, and clinical time at the Emergency Centre (EC) of the main tertiary referral hospital in Gaborone, Princess Marina Hospital. Methods Since EM rotations are often undertaken in the final year of medical training, students were given an anonymous voluntary survey to study the effect of an EM experience in their first clinical year of training. A 5-point Likert scale was used to evaluate learning opportunities and the overall EM experience. Students described which procedures they had observed or performed in their rotation. The survey concluded with open-ended questions seeking additional feedback. Results These were very encouraging for both early exposure to EM and the PBL approach. More than 90% of students agreed or strongly agreed that they felt more confident in clinical settings, improved their clinical decision making process, and found the PBL model to be effective over the rotation. 22 different procedures were observed in the EC with 100% observing venesection and plaster applications. 14 different procedures were performed with 83% students performing venesection and over 65% performing chest compressions and venous cannulation. Discussion It is hoped that this study will provide valuable practical information about undergraduate EM learning in a PBL based Medical School within Sub Saharan Africa that can be replicated across other medical education institutions throughout the region.

Experience Curves as an Organizing Framework for Deliberate Practice in Emergency Medicine Learning

Deliberate practice is an important skill-training strategy in emergency medicine (EM) education. Learning curves display the relationship between practice and proficiency. Forgetting curves show the opposite, and demonstrate how skill decays over time when it is not reinforced. Using examples of published studies of deliberate practice in EM we list the properties of learning and forgetting curves and suggest how they can be combined to create experience curves: a longitudinal representation of the relationship between practice, skill acquisition, and decay over time. This framework makes explicit the need to avoid a piecemeal, episodic approach to skill practice and assessment in favor of more emphasis on what can be done to improve durability of competence over time. The authors highlight the implications for both educators and education researchers.

Simulation in Emergency Medicine Training

Simulation provides a range of educational tools that have increasingly been incorporated into emergency medicine (EM) curricula. Standardized patients and some partial task trainers, such as intubation heads, have been used for decades. More recently, a growing number of computer-screen simulations, high-fidelity mannequins, and virtual-reality simulators have expanded the number of procedures and conditions, which can be effectively simulated.The Accreditation Council for Graduate Medical Education transitioned to a competency-based assessment of residency programs in 2001 and included simulation as a method for incorporating the 6 core competencies into graduate medical education curricula. Over the past decade, numerous peer-reviewed publications have promoted simulation as an effective educational tool for each of the core competencies.The advanced technology used to operate many current simulators can erroneously become the focus of efforts to create a simulation-based curriculum. Simulation can most effectively be incorporated into EM curricula through the use of time-proven concepts, which start with defining the targeted learners, assessing their general and specific educational needs, defining learning objectives, and selecting the best educational strategy for achieving each objective. In many, but not all, instances, simulation can be the best tool for achieving EM learning objectives.