Search

Introduction: In the 2023 National Resident Matching Program (NRMP) match, there were 554 unfilled emergency medicine (EM) positions before the Supplemental Offer and Acceptance Program (SOAP). We sought to describe features of EM programs that participated in the match and the association between select program characteristics and unfilled positions. Methods: The primary outcome measures included the proportion of positions filled in relation to state and population density, hospital ownership type, and physician employment model. Secondary outcome measures included comparing program-specific attributes between filled and unfilled programs, including original accreditation type, year of original accreditation, the total number of approved training positions, length of training, urban-rural designation, hospital size by number of beds, resident-to-bed ratio, and the percentage of disproportionate share patients seen. Results: The NRMP Match had 276 unique participating EM programs with 554 unfilled positions. Six states offered 52% of the total NRMP positions available. Five states were associated with two-thirds of the unfilled positions. Public hospitals had a statistically significant higher match rate (88%) when compared to non-profit and for-profit hospitals, which had match rates of 80% and 75%, respectively (P

Introduction: Medicine is increasingly influenced by politics, but physicians have historically had lower voter turnout than the general public. Turnout is even lower for younger voters. Little is known about the political interests, voting activity, or political action committee (PAC) involvement of emergency physicians in training. We evaluated EM trainees’ political priorities, use of and barriers to voting, and engagement with an emergency medicine (EM) PAC. Methods: Resident/medical student Emergency Medicine Residents’ Association members were emailed a survey between October–November 2018. Questions involved political priorities, perspective on single-payer healthcare, voting knowledge/behavior, and EM PACs participation. We analyzed data using descriptive statistics. Results: Survey participants included 1,241 fully responding medical students and residents, with a calculated response rate of 20%. The top three healthcare priorities were as follows: 1) high cost of healthcare/price transparency; 2) decreasing the number of uninsured; and 3) quality of health insurance. The top EM-specific issue was ED crowding and boarding. Most trainees (70%) were supportive of single-payer healthcare: “somewhat favor” (36%) and “strongly favor” (34%). Trainees had high rates of voting in presidential elections (89%) but less frequent use of other voting options: 54% absentee ballots; 56% voting in state primary races; and 38% early voting. Over half (66%) missed voting in prior elections, with work cited as the most frequent (70%) barrier. While overall, half of respondents (62%) reported awareness of EM PACs, only 4% of respondents had contributed. Conclusion: The high cost of healthcare was the top concern among EM trainees. Survey respondents had a high level of knowledge of absentee and early voting but less frequently used these options. Encouragement of early and absentee voting can improve voter turnout of EM trainees. Concerning EM PACs, there is significant room for membership growth. With improved knowledge of the political priorities of EM trainees, physician organizations and PACs can better engage future physicians.

Abstract Objectives Identification of patients with coronavirus disease 2019 (COVID‐19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID‐19 at risk for return and hospital admission within 30 days of ED discharge. Methods We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS‐CoV‐2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID‐19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. Results Among patients with COVID‐19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine‐learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71–0.78), with a sensitivity of 0.46 (95% CI, 0.39–0.54) and a specificity of 0.84 (95% CI, 0.82–0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. Conclusions A predictive model identifying adult ED patients with COVID‐19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot‐strapped classification methods (eg, GBM, RF, and LASSO) outperform the single‐tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow‐up resources.

Introduction: Patients with coronavirus disease 2019 (COVID-19) can develop rapidly progressive respiratory failure. Ventilation strategies during the COVID-19 pandemic seek to minimize patient mortality. In this study we examine associations between the availability of emergency department (ED)-initiated high-flow nasal cannula (HFNC) for patients presenting with COVID-19 respiratory distress and outcomes, including rates of endotracheal intubation (ETT), mortality, and hospital length of stay. Methods: We performed a retrospective, non-concurrent cohort study of patients with COVID-19 respiratory distress presenting to the ED who required HFNC or ETT in the ED or within 24 hours following ED departure. Comparisons were made between patients presenting before and after the introduction of an ED-HFNC protocol. Results: Use of HFNC was associated with a reduced rate of ETT in the ED (46.4% vs 26.3%, P

Abstract In the spring of 2020, emergency physicians found themselves in new, uncharted territory as there were few data available for understanding coronavirus disease 2019 (COVID‐19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) virus. In response, knowledge was being crowd sourced and shared across online platforms. The “wisdom of crowds” is an important vehicle for sharing information and expertise. In this article, we explore concepts related to the social psychology of group decisionmaking and knowledge translation. We then analyze a scenario in which the American College of Emergency Physicians (ACEP), a professional medical society, used the wisdom of crowds (via the EngagED platform) to disseminate clinically relevant information and create a useful resource called the “ACEP COVID‐19 Field Guide.” We also evaluate the crowd‐sourced approach, content, and attributes of EngagED compared to other social media platforms. We conclude that professional organizations can play a more prominent role using the wisdom of crowds for augmenting pandemic response efforts.