Background: No recent data have investigated rates of diagnostic testing for pulmonary embolism (PE) in US emergency departments (EDs), and no data have examined computed tomographic pulmonary angiography (CTPA) rates in subgroups at high risk for adverse imaging outcomes, including young women and children. We hypothesized that over-testing for PE remains a problem. Methods and Results: We used electronic health record and billing data for 16 EDs in Indiana and 11 hospitals in the Dallas-Fort Worth area from 2016 to 2019 to locate ED patients who had any of the following: D-dimer, CTPA, scintillation ventilation perfusion lung scanning or formal pulmonary angiography. The primary outcomes were ED encounter volume-adjusted CTPA rate, PE yield rate with subgroup reporting for children (<18 years) and women under 45 years. We also examined the most frequent diagnoses. From a total visit volume of 1 828 010 patient encounters, 97 125 (5.3% of the total volume) had a diagnostic test for PE, including 25 870 patients who had CTPA order without D-dimer (59% of all tests for PE). The yield rate for PE from CTPA scans was 1.3% (1.1%–1.5%) in Indiana and 4.8% (4.2%–5.1%) in Dallas-Fort Worth (pooled rate 3.1%). Linear regression showed that increased D-dimer ordering correlated with increased PE yield rate (Pearson’s R ² =0.43; P <0.001). From the pooled sample, 59% of CTPAs done were in women, with 21% of all CTPAs performed on women under 45 years of age, and 1.4% (1.3%–1.5%) on children. The most frequent diagnoses were symptom-based descriptions of chest pain (34%) and shortness of breath (6.5%) and the condition-based diagnosis of pneumonia (4.1%). Conclusions: Over-testing for PE in American EDs remains a major public health problem. Centers with higher D-dimer ordering had higher yield of PE on CTPA. These data suggest the potential for implementation of D-dimer based protocols to reduce low-yield CTPA ordering.

Endotracheal intubation (ETI) is a frequently used life-saving procedure. In the US annually, 15 million operating room intubations and 650 000 hospital intubations outside the operating room are performed, including 346 000 emergency department (ED) intubations.^1,2 Despite its frequency, ETI is a high-risk procedure, with significant rates of respiratory complications, hemodynamic instability, and cardiac arrest.^3-7 Interventions to improve the safety and success of ETI could thus have a substantial effect on public health. Checklists are a form of cognitive strategy intended to force operators to ensure appropriate preparation before a procedure. Checklists have been associated with improved outcomes in multiple aspects of health care^8-13 and have been endorsed as a means to reduce complications during ETI.^6,14-16

Ophthalmologic emergencies account for approximately 1.3 million emergency department (ED) visits in the United States annually.¹ Many such cases are secondary to trauma, and more than 40% of ED ocular conditions are emergent.^2,3 Diagnoses involving the external eye and anterior chamber can typically be made on the basis of history and physical examination alone. However, visualizing and diagnosing posterior chamber abnormalities can be more challenging for nonspecialists because of inexperience, limitations of available equipment, and less-than-ideal examination conditions. Most physicians agree that fundoscopy is an important physical examination skill; however, many do not perform the examination when indicated or lack confidence in their ability to perform it effectively.^4,5

Background: The goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation. Methods: This was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert. Results: Fifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51–0.62), and 0.82 (CI 0.73–0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48–0.82). Conclusion: After a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.

Background: While COVID-19 has had far-reaching consequences on society and health care providers, there is a paucity of research exploring frontline emergency medicine (EM) provider wellness over the course of a pandemic. The objective of this study was to assess the well-being, resilience, burnout, and wellness factors and needs of EM physicians and advanced practice providers (e.g., nurse practitioners and physician assistants; APPs) during the initial phase of the COVID-19 pandemic. Methods: A descriptive, prospective, cohort survey study of EM physicians and APPs was performed across ten emergency departments in a single state, including academic and community settings. Participants were recruited via email to complete four weekly, voluntary, anonymous questionnaires comprised of customized and validated tools for assessing wellness (Well Being Index), burnout (Physician Work Life Study item), and resilience (Brief Resilience Scale) during the initial acceleration phase of COVID-19. Univariate and multivariate analysis with Chi-squared, Fisher’s Exact, and logistic regression was performed. Results: Of 213 eligible participants, response rates ranged from 31 to 53% over four weeks. Women comprised 54 to 60% of responses. Nonrespondent characteristics were similar to respondents. Concern for personal safety decreased from 85 to 61% (p < 0.001). Impact on basic self-care declined from 66 to 32% (p < 0.001). Symptoms of stress, anxiety, or fear was initially 83% and reduced to 66% (p = 0.009). Reported strain on relationships and feelings of isolation affected > 50% of respondents initially without significant change (p = 0.05 and p = 0.30 respectively). Women were nearly twice as likely to report feelings of isolation as men (OR 1.95; 95% CI 1.82–5.88). Working part-time carried twice the risk of burnout (OR, 2.45; 95% CI, 1.10–5.47). Baseline resilience was normal to high. Provider well-being improved over the four weeks (30 to 14%; p = 0.01), but burnout did not significantly change (30 to 22%; p = 0.39). Conclusion: This survey of frontline EM providers, including physicians and APPs, during the initial surge of COVID-19 found that despite being a resilient group, the majority experienced stress, anxiety, fear, and concerns about personal safety due to COVID-19, putting many at risk for burnout. The sustained impact of the pandemic on EM provider wellness deserves further investigation to guide targeted interventions.

A suspected diagnosis of cancer in the emergency department (ED) may be associated with poor outcomes, related to health disparities, however data are limited. This is a retrospective observational cohort of the Indiana State Department of Health Cancer Registry, and the Indiana Network for Patient Care. First time cancer diagnoses appearing in the registry between January 2013 and December 2017 were included. Cases identified as patients who had an ED visit in the 6 months before their cancer diagnosis; controls had no preceding ED visits. The primary outcome was mortality, comparing ED-associated mortality to non-ED-associated. 134,761 first-time cancer patients were identified, including 15,432 (11.5%) cases. The mean age was same at 65, more of the cases were Black than the controls (12.4% vs 7.4%, P < .0001) and more were low income (36.4%. vs 29.3%). The top 3 ED-associated cancer diagnoses were lung (18.4%), breast (8.9%), and colorectal cancers (8.9%), whereas the controls were breast (17%), lung (14.9%), and prostate cancers (10.1%). Cases observed an over three-fold higher mortality, with cumulative death rate of 32.9% for cases vs 9.0% for controls (P < .0001). Regression analysis predicting mortality, controlling for many confounders produced an odds ratio of 4.12 (95% CI 3.72–4.56 for cases). This study found that an ED visit within 6 months prior to the first time of ICD-coded cancer is associated with Black race, low income and an overall three-fold increased adjusted risk of death. The mortality rates for ED-associated cancers are uniformly worse for all cancer types. These data suggest that additional work is needed to reduce disparities among ED-associated cancer diagnoses.