Publications

When given a sample of 100 emergency department discharge instructions, Claude Sonnet, a large language model, produced accurate Spanish translations as evaluated by Spanish-speaking physicians and medical interpreters.

Non-evidence based factors influence post-surgical opioid prescribing practices. Delivering automated near real-time opioid prescribing feedback may encourage providers to prescribe opioid quantities which are more aligned with patient consumption and institutional guidelines. COVID-19 presented unprecedented challenges to healthcare delivery. We observed a substantial deviation in guideline-concordant opioids prescribing during the initial outbreak. However, our institution's pre-existing opioid prescribing feedback system and decision aid may have helped limit the duration and magnitude of the observed deviations by informing prescribers of atypically large opioid prescriptions and encouraging use of institutional data. Combined with provider education, a non-directive decision aid, in the form of near, real-time email feedback, may be an effective mechanism to advance evidence-based opioid prescribing, as it retains flexibility and provider autonomy while encouraging data-driven decision making.

BACKGROUND: Emergency physician-performed ultrasound-guided nerve blocks are becoming more commonplace as an integral component of multimodal analgesia. Ultrasound-guided nerve blocks (UGNBs) can be used to safely and effectively treat pain from commonly encountered pathologies or to facilitate procedures.

OBJECTIVES: There is, currently, variability in the use of nerve blocks in emergency departments (ED) based on training, comfort, and resources; however, it is likely that this method of analgesia will continue to expand into ED practice. In this piece, we review the evidence for several high-utility nerve blocks that have been successfully used in the ED.

DISCUSSION: In this article, we specifically review the superficial cervical plexus block, interscalene brachial plexus nerve block, serratus anterior plane block, erector spinae plane block, pericapsular nerve block, and transgluteal sciatic nerve block.

CONCLUSION: UGNBs are increasingly being incorporated into ED patient care and becoming a critical tool as a part of an opioid reduction strategy. The group of UGNBs listed in this article represent a list of commonly performed blocks and should be considered when offering optimal multimodal analgesia to the acutely injured patient.

BACKGROUND: Ultrasound-guided nerve blocks (UGNBs) are a core component of multimodal analgesia for acute pain management in emergency departments (EDs). In addition to using standard local anesthetics, adjuncts have been demonstrated to extend the duration of UGNBs. We evaluated the efficacy and safety of dexamethasone and epinephrine as anesthetic adjuncts in UGNBs in the ED.

METHODS: Data were analyzed from the National Ultrasound-guided neRVE (NURVE) Block Registry, a retrospective, multicenter, observational registry evaluating UGNBs performed in 11 EDs from January 1, 2022, to December 31, 2023. A generalized linear mixed effects model (GLMER) with a binomial family examined factors associated with pain reduction when comparing adjunct vs. non-adjunct UGNBs. The dependent variable and primary outcome were pain reduction. Secondary outcomes included safety, dosing of adjuncts, and complications.

RESULTS: A total of 29.6% (812/2742) of UGNBs received adjuncts, most commonly dexamethasone (72.5%, 589/812) and epinephrine (23.5%, 191/812). Dexamethasone had a 1.99 odds ratio of > 50% pain reduction versus isolated local anesthetic blocks, while epinephrine had an odds ratio of 0.99 for > 50% pain reduction. There was no association between adjunct use and complications.

CONCLUSION: Compared to isolated local anesthetic nerve blocks, dexamethasone had an association with improved pain control within 60 min; without additional safety concerns in a large retrospective dataset. Prospective studies are needed to further investigate these findings in the ED setting.

AIM: Point of Care Ultrasound (POCUS) excels in the assessment of patients with hypotension and shock. Whether using real patients or a manikin simulator to teach POCUS skills is preferable is not completely clear. We designed a randomized-controlled trial to compare these two different teaching methods of POCUS.

METHODS: We enrolled 47 medical students on an internal medicine sub-internship in this randomized-controlled trial. Twenty-four students were randomly assigned to the experimental group to learn from volunteer patients in the emergency department (ED), and 23 were randomly assigned to the control group to learn from a manikin simulator in a simulation center. All students received a didactic workshop focused on hypotension and shock, followed by supervised learning from either volunteer patients in the ED or a manikin simulator in a simulation center. Student knowledge and confidence were assessed through a pre-survey before the workshop, post-survey after the workshop, and a 3-month longitudinal survey after both the workshop and supervised POCUS learning were completed. The primary end point was assessment of student knowledge and confidence at the 3-month longitudinal time period.

RESULTS: At the 3-month longitudinal survey, there was no statistical difference in the primary end point of questions correctly answered by students in the experimental group compared to those in the control group (88% vs 86.5%, p = 0.713, NS), and no statistical difference in reported confidence between students in the experimental group from those in the control group (4.22 vs 4.10, p = 0.846, NS).

CONCLUSION: In this randomized-controlled trial using POCUS to assess hypotension and shock, there were no significant differences in learner knowledge and confidence between students in the ED experimental group learning from volunteer patients versus the control group learning from a manikin simulator indicating that the methods may be equally effective in teaching POCUS.

BACKGROUND: Point of care ultrasound (POCUS) is a critical skill for physicians across multiple medical specialties, yet substantial heterogeneity exists in how competency is assessed. Computer-based approaches can be used to deliver, grade, and analyze learner performance, and may be more objective and reliable than traditional approaches using expert assessments. This study aimed to systematically review and summarize the existing literature surrounding computer-based approaches to assessing POCUS competency.

METHODS: We searched six online databases (MEDLINE, IEEE Xplore Digital Library, Association for Computing Machinery Digital Library, PsycINFO (Ovid), EMBASE, Web of Science Core Collection). We included original peer-reviewed studies that assessed computer-based metrics of POCUS competence among any learner group performing POCUS. We also reviewed reference lists of all included studies. We extracted data elements that included the specialty of participants, POCUS experience, POCUS modality used, and type and results of computer-based competency assessments. At least two authors conducted title and abstract screening, full text review, and data extraction, with discrepancies adjudicated by a third author. We present a qualitative synthesis of study findings.

RESULTS: Of 7375 identified studies, we included 28 in our final analysis. Computer-based metrics were used to assess knowledge (n = 10), skills (n = 25), and cognitive load (n = 1) using hand tracking (n = 14), eye tracking (n = 7), image analysis (n = 6), and simulation scores (n = 1). In general, hand tracking analysis showed that experts had shorter probe path lengths, took less time to identify areas of interest, and had fewer discrete movements compared with novices. Eye tracking assessment showed increased dwell time was associated with successful completion of procedures and increased accuracy in interpreting images.

CONCLUSION: We identified four computer-based metrics for assessing POCUS competence, many of which demonstrated consistent performance in distinguishing skill level. Further work is needed to standardize and validate those approaches.

OBJECTIVES: Our primary objective was to estimate the realistic impact of an artificial intelligence (AI)-based trans-thoracic echocardiogram (TTE)-first strategy on the annual national cost savings among eligible adult emergency department (ED) patients presenting with syncope in the United States. Our secondary outcomes were the estimated reduction in avoidable ED bed hours and comprehensive TTE studies.

METHODS: Using publicly available estimates for inputs such as the size of the adult ED syncope population, typical disposition and risk stratification proportions, and frequency of comprehensive TTE studies, we created a model and ran 1000 trials of a Monte Carlo simulation. Using this simulation, we modeled the national annual cost savings and potential bed hours averted through the impact of avoiding comprehensive TTE studies. We report the descriptive statistics modeling the distribution of all endpoints.

RESULTS: An AI-assisted TTE-first strategy was estimated to save a mean (±SD) of $815 million (±$260 million) by avoiding 468,000 (±141,000) comprehensive TTE studies resulting in 12,500,000 (±4,600,000) bed hours saved.

CONCLUSION: If adopted widely, an AI-based TTE-first strategy applied to eligible ED patients presenting with syncope could yield substantial benefits by averting avoidable comprehensive TTE studies and saving bed hours.