Patients and experts agree that potentially inappropriate medications should be reconsidered after adverse drug events (ADEs), yet emergency providers are often hesitant to discuss deprescribing in deference to outpatient prescribers. We sought to explore patient communication preferences for deprescribing in the emergency department (ED) after an ADE. We conducted a cross-sectional survey study of older adults aged 65 years and older presenting to a southeastern, academic ED from June 2024 to October 2024. While awaiting results, eligible participants completed a best-worst scaling survey comparing seven potential ED communication strategies for prompting deprescription of daily aspirin. The primary analysis tested whether an ED-initiated "therapeutic pause" ("Considering your bleeding, I would like you to hold your aspirin until you can discuss with your primary care provider") was preferred by > 50% of participants over a generic discharge referral to a primary care provider through a one-sided binomial test. Secondary analyses used conditional logistic regression to evaluate relative preference across all seven deprescribing phrases. In total, 102 patients completed the survey with a mean (SD) age of 75 years old (std dev 7). Among all respondents, 62% (95% CI, 52%-71%) preferred an ED-initiated 'therapeutic pause' of aspirin with primary care follow-up to the generic PCP deferral approach (p = 0.01). The least preferred statement was a strict deprescribing recommendation ("I do not think you need aspirin anymore"), which was selected as the least-favored communication approach in 65% of choice tasks. In conditional logistic regression, the therapeutic pause had greater odds of being selected as most preferred compared to the least preferred phrase (OR 9.3; 95% CI, 6.3-13.8). Our study suggests that ED physicians may take a proactive approach in addressing potential deprescribing in caring for patients with ADEs, such as initiating a therapeutic pause of aspirin after an episode of bleeding.

In select situations, patients experiencing out-of-hospital cardiac arrest (OHCA) may be candidates for extracorporeal cardiopulmonary resuscitation (ECPR). Eligibility criteria for ECPR typically include a maximum time (usually 30 min) from arrest to arrival at an ECPR-capable center, which may exclude populations based on geographic factors. Using geospatial modeling, we calculated drive times to ECPR-capable hospitals in Boston utilizing census block group centroid coordinates as proxy sites for OHCA locations. We used a fixed dispatch-to-scene arrival time of 7.4 min, extrapolated from Boston EMS median transport time data. We set conditions at the 50th (24 min), 25th (18 min), and 10th (13 min) percentiles for EMS on-scene time and, for each condition, determined access to ECPR with an arrest to arrival criterion of less than 30 min. We analyzed the effect of high- versus low-traffic conditions and then derived the arrest to arrival time necessary to achieve access for 90% of the city. The entire City of Boston was excluded from ECPR with median times and current eligibility criteria. Decreasing time-on-scene to the 25th percentile led to increased access: 16% of block groups with low traffic and 6% of block groups with high traffic. At the 10th percentile for time-on-scene, 55% of block groups had access with low traffic and 28% had access with high traffic. To achieve access for 90% of the city under high-traffic conditions at the 50th percentile for time-on-scene, the criterion for arrest to arrival would need to be extended to 55.8 min. The current arrest to arrival criterion for ECPR excludes the entire City of Boston using median transportation and on-scene times. Increasing access to ECPR should include efforts to decrease prehospital duration, such as minimizing time-on-scene for potential OHCA cases. Future study should examine potential levers to improve access, such as novel prehospital ECPR delivery models, air-based transport, and liberalized arrest to arrival criteria.

To examine trends in clinician staffing in the context of rising Emergency Department (ED) boarding we describe five-year national trends (2019-2023) in boarding hours, attending physician and PA/NP coverage, and hospitalist and nursing support across a multicenter cohort of U.S. EDs. We conducted a retrospective cohort study using data from the Association of Academic Chairs of Emergency Medicine (AACEM) and Academy of Administrators in Academic Emergency Medicine (AAAEM) annual benchmarking survey for academic years 2019 through 2023. The analysis included primary academic, academic affiliate, and community affiliate EDs. Boarding hours, attending physician and PA/NP hours, nursing support, and hospitalist care for patients boarding were evaluated over time. Outcomes were stratified by hospital classification. 63 EDs were included in our analysis. Median boarding hours/day increased 61.1% from 206.36 (IQR 89.87, 373.11) to 332.47 (137.43, 548.09) (p = 0.01), while median attending hours remained stable over this time frame from 72.00 (53.90, 91.50) to 72.00 (56.00, 88.00) (p = 0.56). The boarding-to-attending hour ratio increased 55.6% overall from 2.97 (1.33, 4.04) to 4.62 (2.15, 7.42) (p = 0.002). Hospitals providing nursing support for boarders increased 53.6% (95% CI 3.20%, 134.40%) which was statistically significant, whereas hospitalist management of boarding patients increased 28.7% (95% CI 2.60%, 68.10%) which was not statistically significant. ED boarding hours increased substantially over the study period without a proportionate rise in attending staffing, resulting in a marked increase in boarding burden per physician hour. While some hospitals provided funding for increased nursing and hospital coverage to care for boarding patients, these measures have not kept pace with rising boarding demands. These findings highlight a growing mismatch between workload and staffing, underscoring the need for updated staffing models and system-level strategies to address the operational and clinical challenges of ED boarding.

Patients with limited English proficiency (LEP) face disproportionate risks at emergency department (ED) discharge. Professional interpretation improves outcomes, but real-time written translations remain difficult to provide in many EDs. Modern transformer-based large language models (LLMs) may offer improved translation quality compared with older systems, yet their performance on ad hoc provider-written ED discharge instructions is not well established. We conducted a blinded cross-sectional non-inferiority study of English-language ED discharge instructions translated into Spanish, Brazilian Portuguese, and Simplified Chinese comparing Google Translate and ChatGPT-4o versus professional medical interpreters. Fifty-three randomly selected provider-written instructions (100-500 words, preserving spelling/grammar errors) were translated, yielding 477 unique translations. Professional medical interpreters, blinded to translation method, independently scored each translation on fluency, adequacy, meaning, and severity on a five-point Likert scale. Inter-rater reliability between the professional interpreter evaluations was calculated. A 0.5-point non-inferiority margin was pre-specified, and adjusted mean Likert rating differences generated by mixed effects models for each accuracy dimension were compared between translation methods for each language. The proportion of clinically significant translation errors was compared between methods, as was the ability of evaluators to guess the translation method. Inter-rater reliability was high across languages. Both machine translation methods were non-inferior to professional interpreters for adequacy, meaning, and severity in Spanish and Portuguese, and for all four domains in Chinese. For fluency, Google Translate and ChatGPT-4o were inferior in Spanish and Portuguese but non-inferior in Chinese. The frequency of clinically significant errors did not differ significantly by translation method. Evaluators, blinded to method, frequently misidentified machine translations as professional. In this multi-language evaluation of real-world ED discharge instructions, Google Translate and ChatGPT-4o were non-inferior to professional interpreters for most domains of translation accuracy.

Older adults account for a disproportionate share of emergency department (ED) visits and are often present with complex needs. Frailty is a key determinant of adverse outcomes in this population. We previously developed the Emergency Department Frailty Scale (ED-FraS), a novel clinician-judgment-based instrument. This study prospectively validated the ED-FraS when used by triage nurses and evaluated its predictive performance for hospital admission and ED length of stay (EDLOS). This prospective observational study was conducted at a tertiary academic medical center in Taiwan between February and July 2025. Triage nurses assessed patients aged ≥ 65 years using the ED-FraS during routine triage encounters. We evaluated the association between ED-FraS levels (1-5) and hospital admission and EDLOS. We compared the predictive performance of ED-FraS, the standard Taiwan Triage and Acuity Scale (TTAS), and a modified TTAS (mTTAS), which integrated frailty scores. Discriminative ability was measured using the area under the receiver operating characteristic curve (AUROC). A total of 550 older adults were enrolled (mean age 77.2 years). Higher ED-FraS levels were significantly associated with increased admission rates (25.0% in level 1 vs. 59.4% in level 5) and prolonged median EDLOS (2.6 vs. 27.3 h). The mTTAS demonstrated superior discriminatory ability for hospital admission (AUROC 0.720) compared to TTAS (0.657) or ED-FraS alone (0.638). Nurses reported the tool was feasible, taking < 30 s to complete. The ED-FraS is a feasible and valid tool for identifying older adults at risk of adverse outcomes during triage. Integrating frailty assessment into standard triage systems enhances risk stratification and may improve resource allocation for vulnerable older adults.

Multicenter evaluation of emergency department (ED) risk stratification tools is often limited by barriers to patient-level data sharing. We used the HEART score as a clinical use case to evaluate whether a federated diagnostic meta-analytic approach yields performance estimates comparable to those obtained from centralized patient-level analysis for predicting 30-day major adverse cardiovascular events (MACE30). We conducted a retrospective, multicenter observational study across six EDs between January 1, 2020, and December 31, 2023. Adult patients presenting with chest pain who had a documented HEART score were included. MACE30 was defined as acute myocardial infarction, coronary revascularization, or all-cause mortality. The diagnostic performance of the HEART score was evaluated using a federated bivariate random-effects meta-analysis based on site-level 2 × 2 tables, yielding pooled sensitivity, specificity, and hierarchical summary receiver operating characteristic (HSROC) estimates. These results were compared with performance metrics derived from centralized patient-level analysis. Among 57,906 ED encounters with documented HEART scores, MACE30 occurred in 2.2%. In federated meta-analysis, the HEART score demonstrated high specificity and negative predictive value, consistent with its intended rule-out function, with modest between-site variability. The pooled HSROC area under the curve was 0.759 (95% CI, 0.646-0.831). Centralized patient-level analysis yielded similar discrimination (AUROC 0.785; 95% CI, 0.776-0.794). Differences between federated and centralized estimates were small and clinically modest, reflecting preservation of site-level heterogeneity and variation in outcome prevalence across EDs. Federated diagnostic meta-analysis produced HEART score performance estimates closely aligned with those obtained from centralized patient-level data. This approach enabled scalable, privacy-preserving multicenter evaluation of ED risk-stratification tools while accommodating heterogeneity across practice settings.