ABSTRACT Background The transition from trainee to new attending physician can be overwhelming. Simulation has been shown to be effective in orienting trainees to the emergency department (ED) but there is limited literature on simulation for the orientation of newly hired attending physicians. Objective of the Innovation The objective of this innovation was to develop, implement, and evaluate a simulation curriculum to supplement the orientation of newly hired attending physicians in our emergency department. Development Process and Implementation We developed a year‐long quarterly simulation curriculum guided by Kern's six‐step curricular design model. Based on a targeted needs assessment, nine scenarios were chosen for inclusion. These scenarios were arranged to start the curriculum with those simulation scenarios that highlight systems and processes particular to our institution, with a transition towards scenarios requiring more complex team leadership and medical management decisions in the latter portions of the curriculum. Outcomes We have completed two years of this curriculum and have had 14 participants in total. Thirteen participants completed the end‐of‐year evaluation. The curriculum has been well received by participants with unanimous agreement that the curriculum helped them lead acute patient scenarios in the resuscitation bay during their first year as a new hire. Conclusions Simulation for the orientation of new attending physicians can be implemented successfully and received well by the targeted learners and by leadership invested in supporting new attending physicians. While the specific scenario topics and institutional procedures are site‐specific, the approach to curricular design and implementation is widely generalizable to other EDs.
ABSTRACT Background Pediatric critical resuscitations are high‐acuity, low‐occurrence events for emergency medicine (EM) providers in the United States (US). Concerns exist regarding graduating US EM resident preparedness due to limited exposure to critically ill children and variability of pediatric EM (PEM) curricula. This study sought to benchmark EM resident performance during simulated pediatric resuscitations, correlate performance and self‐efficacy, and compare differences between three‐ and four‐year EM residencies. Methods This was a prospective, cross‐sectional, cohort study (2019–2024) across eight US EM residencies. Participants were graduating residents leading pediatric resuscitation simulations (septic shock, status epilepticus, and cardiac arrest) and completed a self‐efficacy survey before participating. Simulations were scored using case‐specific checklists. Primary outcome was Total Performance Score (TPS), percent compliance with established guidelines, for combined performance data. Secondary outcomes included the correlation between TPS and self‐efficacy and the comparison between 3‐ and 4‐year programs. Results Of 103 eligible residents, 69 (67%) participated (56% PGY3, 44% PGY4). Performance gaps were consistent across cohorts. For the sepsis case, 9% delivered 60 mL/kg fluid within 15 min, and 9% initiated vasopressors afterward. For the seizure case, 73% checked glucose appropriately and 61% dosed dextrose correctly. For cardiac arrest, the compression rate was correct in 48%, a backboard was used in 25%, and correct epinephrine dosing approximated 60% of simulations. Mean TPS was 60.5 (±9.8), with no difference between 3‐ and 4‐year programs ( p = 0.956). Self‐efficacy scores reflected moderate confidence, with 3‐year residents reporting higher confidence in trauma ( p = 0.031) and respiratory failure ( p = 0.008). Correlations between self‐efficacy and performance did not meet statistical significance. Conclusions Graduating US EM residents demonstrated moderate pediatric resuscitation performance with critical deficits in basic life support, medication dosing, and hemodynamic management. Program length did not impact outcomes. Findings underscore the need for structured, simulation‐based PEM curricula to improve pediatric education during EM training.
ABSTRACT Objective To explore the minority tax experienced by underrepresented in medicine (URiM) emergency medicine residents. We aimed to (1) compare diversity, equity and inclusion (DEI) activity involvement between URiM and non‐URiM residents, (2) describe the support received by URiM residents for DEI activities, and (3) analyze associations between minority tax burden and perceived mentorship, depressive and anxiety symptoms, burnout, workplace discrimination, and institutional alignment. Methods A cross‐sectional survey of emergency medicine residents was conducted from July 2023 to May 2024. Data were analyzed using descriptive statistics and multiple linear regressions to assess differences in outcomes for those with minority tax burden. Minority tax burden was defined as spending more than the median number of hours per week on DEI as reported by non‐URiM residents. Results A total of 150 URiM and 50 non‐URiM residents completed the survey. URiM residents reported spending more time per week on DEI work (5.0 vs. 0.0 h; p < 0.01). Of the URiM residents involved in DEI work, 28.0% held titled positions; 2.4% received compensation; 5.4% had DEI‐related training opportunities. Residents with minority tax burden reported less effective mentorship relationships ( β = −35.65, 95% CI (−40.81, −30.48); p < 0.01), increased discrimination ( β = 4.90, 95% CI (3.98, 5.8)); p < 0.01), and decreased institutional alignment ( β = −3.17, 95% CI (−4.93, −1.41); p < 0.01). There was no statistically significant association between minority tax burden and depressive and anxiety symptoms or burnout. Conclusion URiM emergency medicine residents were more likely to experience a minority tax through substantial DEI work. This was associated with less time for other clinical and academic responsibilities and less favorable workplace experiences. Minority tax burden was not associated with depressive and anxiety symptoms or burnout.
ABSTRACT Competency based medical education (CBME) is an outcome‐driven training model that assesses individual learners based on competencies and observable abilities. Although implementation of competencies and milestones by the Accreditation Council of Graduate Medical Education has provided some ground work, full adoption of CBME is in its infancy in emergency medicine. Successful implementation of CBME will require dedicated attention to faculty and learner development; however, there is limited data or best practices available for guidance. To address this need, the Society of Academic Emergency Medicine convened a consensus conference to establish a research agenda for CBME with one workgroup specifically focused on faculty and learner development. In this paper, we present a consensus research agenda for faculty and learner development in CBME designed to advance faculty development and learner engagement within CBME in EM over the next decade.
ABSTRACT Background Artificial intelligence, particularly large language models, has the potential to enhance the evaluation of residents, yet few studies have compared artificial intelligence to humans in the analysis of large volumes of clinical evaluations. The objective of this study was to evaluate the use of large language model (LLM) artificial intelligence (AI) on the evaluation of resident feedback. Methods The LLM Llama‐3.1 70B was used on a secure cluster to conduct an analysis of emergency medicine (EM) educator feedback for 31 EM residents at a residency program in the Southeastern United States. The large language model generated assessments summarizing resident strengths, weaknesses, and milestone‐based performance. Members of a clinical competency committee (CCC) blinded to the study hypothesis were surveyed to assess their perceptions on the quality, accuracy, specificity, and usefulness of the AI‐generated content compared to the human‐generated content. Results Human‐generated assessments averaged 79 words, while AI‐generated assessments averaged 391 words. Seventy percent of the CCC completed the survey, rating the AI‐generated content more favorably for quality, accuracy, and specificity compared to the human‐generated content. Usefulness of the human‐generated content was reported as good or very good by 71.4%, while usefulness of the AI‐generated content was rated as acceptable (54.1%) or unfavorable (28.6%). Conclusions Artificial intelligence can generate EM resident assessments with comparable or superior ratings of quality, accuracy, and specificity relative to human‐generated assessments. This highlights the potential of AI‐driven evaluations to streamline educator review processes, reducing workload without sacrificing integrity.
ABSTRACT Background and Objectives There is currently limited research in unstructured trainee‐supervisor relationships within graduate medical education despite extensive literature on formal mentor/mentee relationships. These informal interactions and socializing are relatively common, but their prevalence and social costs and benefits remain poorly characterized. We performed a review of the existing literature to understand the breadth and depth of both online and in‐person informal interactions, and their effects on the learner‐supervisor experience within the clinical learning environment. Methods We searched PubMed in December 2024 for articles discussing informal or out‐of‐work socializing between medical trainees and supervisors both in‐person and online environments. Results A total of seven studies were found addressing our research question out of the 745 studies returned. These studies took multiple formats including semi‐structured interviews, surveys of current residents and attendings, and social media analysis. Several studies described benefits of informal/unstructured socializing such as resident wellness and a positive impact on the work environment, while multiple studies raised concerns about the blurring of professional boundaries. There was minimal discussion of how socializing between trainees and supervisors may influence evaluations or may lead to favoritism that may harm the clinical learning environment. Conclusion Informal/unstructured friendships and social relationships between trainees and supervisors have the potential to promote wellness for trainees and supervisors but also risk blurring of professional boundaries. More research is needed on these types of informal and out‐of‐office relationships to develop best practices and better understand their impact.
ABSTRACT The aging process is an inevitable and universal phenomenon characterized by a decline in function that all living organisms must undergo. The challenges posed by population aging, along with senile degeneration and cognitive disability, have emerged as pressing public health issues that require urgent attention on a global scale. The ability of older adults to maintain robust physiological function may confer resistance against the risk factors associated with senile degeneration and cognitive disability. Investigating centenarians who exemplify healthy longevity and their underlying mechanisms of functional resilience can aid in formulating preventive strategies aimed at achieving health, extending the lifespan, and identifying the pathways for promoting healthy longevity in the context of population aging. Hainan is the province in China where the largest number of centenarians and the highest density of centenarians are distributed. There are unique advantages to studying the centenarian population in Hainan. Hainan centenarians not only live in the natural environment with high oxygen concentration and dense greenery coverage but also inherit favorable genetic traits and adopt healthy lifestyles, including social participation, good education, stable emotion, balanced nutrition, moderate exercise, reasonable sleep, optimized senses, coordinated metabolism, improved immunity, prevention and treatment of chronic diseases, and joint treatment of comorbidities. These factors work together to form the Hainan longevity model (HLM), which is expected to play key roles in promoting healthy longevity and provide scientific evidence for achieving healthy longevity in the older populations. It will also contribute to the construction of a healthy human society and has important social value and practical significance.
ABSTRACT Background Surgical education and skill assessments are important in improving surgical skills. However, instructors' comments tend to be complex and unorganized, with varying content and categories. This study aimed to develop a natural language processing (NLP) model to automatically classify feedback comments on surgical procedures and evaluate the performance of the developed model. An NLP model was used to automatically analyze the tendency of differences in feedback comments for each surgeon's skill. Methods Evaluation comments provided to 1813 applicants seeking endoscopic surgical skill qualification for colorectal cases were used. As a ground truth for model training, comments were manually categorized into five competencies—tissue handling, psychomotor skills, dissection quality, exposure quality, and efficiency—and two emotional polarities—positive or negative. The competency and emotional polarity percentages were compared between applicants who did and did not qualify. An automatic feedback comment classification model based on NLP was constructed and verified. Feedback comments on surgical videos were analyzed using an NLP model. Results Training data comprised 5846 sentences from 175 cases. The overall accuracies of the comment analysis task were 0.75 and 0.94 for competency and emotional polarity classifications, respectively. The remaining 1526 cases were analyzed using the NLP model. The proportion of positive polarity was significantly higher in the qualified surgeon group than in the unqualified surgeon group. Conclusions The NLP model developed to evaluate feedback comments related to surgical skill efficiently organized feedback comments on surgical procedures and may contribute to enhanced skill evaluation and surgical education.