Faculty Bibliography

Background Emergency Department (ED) handoff to inpatient teams is a potential source of error. Generative Artificial Intelligence (AI) has shown promise in succinctly summarizing large quantities of clinical data and may help improve Emergency Department (ED) handoff. Objectives Our objectives were to: 1) evaluate the accuracy, clinical utility, and safety of AI-generated ED-to-inpatient handoff summaries; 2) identify patient and visit characteristics influencing summary effectiveness; and 3) characterize potential error patterns to inform implementation strategies. Methods This exploratory study evaluated AI-generated handoff summaries at an urban academic ED (February-April 2024). A HIPAA-compliant GPT-4 model generated summaries aligned with the IPASS framework; ED providers assessed summary accuracy, usefulness, and safety through on-shift surveys. Results Among 50 cases, median quality and usefulness scores were 4/5 (SE = 0.13). Safety concerns arose in 6% of cases, with issues including data omissions and mischaracterizations. Consultation status significantly affected usefulness scores (p < 0.05). Omissions of relevant medications, laboratory results, and other essential detailss were noted (n=6), and EM clinicians disagreed with some AI characterizations of patient stability, vitals and workup (n=8). The most common response was positive impressions of the technology incorporated into the handoff process (n=11). Conclusions This exploratory provider-in-the-loop model demonstrated clinical acceptability and highlighted areas for refinement. Future studies should incorporate recipient perspectives and examine clinical outcomes to scale and optimize AI implementation.

BACKGROUND:High-sensitivity cardiac troponin (hs-cTnI) assays can quantify troponin concentrations with low limits of detection, potentially expediting and enhancing myocardial infarction diagnoses. This study investigates the real-world impact of hs-cTnI implementation on operational metrics and downstream cardiac services in patients presenting to the emergency department with chest pain. METHODS AND RESULTS/RESULTS:[lt] 0.001) during the index encounter. CONCLUSION/CONCLUSIONS:Implementation of the hs-cTnI assay was associated with reduced hospital admissions, shorter length of stay, and decreases in most downstream cardiac testing.

INTRODUCTION/UNASSIGNED:With the move toward competency-based medical education (CBME), data from the electronic health record (EHR) for informed self-improvement may be valuable as a part of programmatic assessment. Personalized dashboards are one way to view these clinical data. The purpose of this concept paper is to summarize the current state of clinical dashboards as they can be utilized by emergency medicine (EM) residency programs. METHODS/UNASSIGNED:The author group consisted of EM physicians from multiple institutions with medical education and informatics backgrounds and was identified by querying faculty presenting on resident clinical dashboards at the 2024 Society for Academic Emergency Medicine conference. Additional authors were identified by members of the initial group. Best practice literature was referenced; if none was available, group consensus was used. CATEGORIES OF METRICS/UNASSIGNED:Clinical exposures as well as efficiency, quality, documentation, and diversity metrics may be included in a resident dashboard. Resident dashboard metrics should focus on resident-sensitive measures rather than those primarily affected by attendings or systems-based factors. CONSIDERATIONS FOR IMPLEMENTATION/UNASSIGNED:Implementation of these dashboards requires the technical expertise to turn EHR data into actionable data, a process called EHR phenotyping. The dashboard can be housed directly in the EHR or on a separate platform. Dashboard developers should consider how their implementation plan will affect how often dashboard data will be refreshed and how to best display the data for ease of understanding. IMPLICATIONS FOR EDUCATION & TRAINING/UNASSIGNED:Dashboards can provide objective data to residents, residency leadership and clinical competency committees as they identify areas of strength, growth areas, and set specific and actionable goals. The success of resident dashboards is reliant on resident buy-in and creating a culture of psychological safety through thoughtful implementation, coaching, and regular feedback. . CONCLUSION/UNASSIGNED:Personalized clinical dashboards can play a crucial role in programmatic assessment within CBME, helping EM residents focus their efforts as they advance and refine their skills during training.

IMPORTANCE/UNASSIGNED:The emergency department (ED) offers an opportunity to initiate palliative care for older adults with serious, life-limiting illness. OBJECTIVE/UNASSIGNED:To assess the effect of a multicomponent intervention to initiate palliative care in the ED on hospital admission, subsequent health care use, and survival in older adults with serious, life-limiting illness. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Cluster randomized, stepped-wedge, clinical trial including patients aged 66 years or older who visited 1 of 29 EDs across the US between May 1, 2018, and December 31, 2022, had 12 months of prior Medicare enrollment, and a Gagne comorbidity score greater than 6, representing a risk of short-term mortality greater than 30%. Nursing home patients were excluded. INTERVENTION/UNASSIGNED:A multicomponent intervention (the Primary Palliative Care for Emergency Medicine intervention) included (1) evidence-based multidisciplinary education; (2) simulation-based workshops on serious illness communication; (3) clinical decision support; and (4) audit and feedback for ED clinical staff. MAIN OUTCOME AND MEASURES/UNASSIGNED:The primary outcome was hospital admission. The secondary outcomes included subsequent health care use and survival at 6 months. RESULTS/UNASSIGNED:There were 98 922 initial ED visits during the study period (median age, 77 years [IQR, 71-84 years]; 50% were female; 13% were Black and 78% were White; and the median Gagne comorbidity score was 8 [IQR, 7-10]). The rate of hospital admission was 64.4% during the preintervention period vs 61.3% during the postintervention period (absolute difference, -3.1% [95% CI, -3.7% to -2.5%]; adjusted odds ratio [OR], 1.03 [95% CI, 0.93 to 1.14]). There was no difference in the secondary outcomes before vs after the intervention. The rate of admission to an intensive care unit was 7.8% during the preintervention period vs 6.7% during the postintervention period (adjusted OR, 0.98 [95% CI, 0.83 to 1.15]). The rate of at least 1 revisit to the ED was 34.2% during the preintervention period vs 32.2% during the postintervention period (adjusted OR, 1.00 [95% CI, 0.91 to 1.09]). The rate of hospice use was 17.7% during the preintervention period vs 17.2% during the postintervention period (adjusted OR, 1.04 [95% CI, 0.93 to 1.16]). The rate of home health use was 42.0% during the preintervention period vs 38.1% during the postintervention period (adjusted OR, 1.01 [95% CI, 0.92 to 1.10]). The rate of at least 1 hospital readmission was 41.0% during the preintervention period vs 36.6% during the postintervention period (adjusted OR, 1.01 [95% CI, 0.92 to 1.10]). The rate of death was 28.1% during the preintervention period vs 28.7% during the postintervention period (adjusted OR, 1.07 [95% CI, 0.98 to 1.18]). CONCLUSIONS AND RELEVANCE/UNASSIGNED:This multicomponent intervention to initiate palliative care in the ED did not have an effect on hospital admission, subsequent health care use, or short-term mortality in older adults with serious, life-limiting illness. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT03424109.

Clinical documentation in the United States has grown longer and more difficult to read, a phenomenon described as "note bloat." This issue is especially pronounced in emergency medicine, where high diagnostic uncertainty and brief evaluations demand focused, efficient chart review to inform decision-making. Note bloat arises from multiple factors: efforts to enhance billing, mitigate malpractice risk, and leverage electronic health record tools that improve speed and completeness. We discuss best practices based on available evidence and expert opinion to improve note clarity and concision. Recent E/M coding reforms aim to streamline documentation by prioritizing medical decision-making over details of historical and physical examination, though implementation varies. New technologies such as generative artificial intelligence present opportunities and challenges for documentation practices. Addressing note bloat will require ongoing effort from clinical leadership, electronic health record vendors, and professional organizations.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

OBJECTIVES/OBJECTIVE: This study aimed to highlight the necessity of developing and implementing appropriate reference ranges for transgender and nonbinary (TGNB) patient populations to minimize misinterpretation of laboratory results and ensure equitable health care. CASE REPORT/METHODS: We describe a situation where a TGNB patient's abnormal laboratory values were not flagged due to undefined reference ranges for gender "X" in the Laboratory Information System (LIS). Implementation of additional reference ranges mapped to sex label "X" showed significant improvement in flagging abnormal lab results, utilizing sex-invariant reporting as an interim solution while monitoring developments on TGNB-specific reference ranges. CONCLUSION/CONCLUSIONS: Informatics professionals should assess their institution's policies for registration and lab reporting on TGNB patients as nonimplementation poses significant patient safety risks. Best practices include using TGNB-specific reference ranges emerging in the literature, reporting both male and female reference ranges for clinical interpretation and sex-invariant reporting.

OBJECTIVES/OBJECTIVE:To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (AI)-generated, patient-facing summaries of these findings. MATERIALS AND METHODS/METHODS:Radiology reports extracted from the electronic health record of a large academic medical center were manually reviewed to identify non-emergent, incidental findings with high likelihood of requiring follow-up, further sub-stratified as "definitely actionable" (DA) or "possibly actionable-clinical correlation" (PA-CC). Instruction prompts to GPT-4 were developed and iteratively optimized using a validation set of 50 reports. The optimized prompt was then applied to a test set of 430 unseen reports. GPT-4 performance was primarily graded on accuracy identifying either DA or PA-CC findings, then secondarily for DA findings alone. Outputs were reviewed for hallucinations. AI-generated patient-facing summaries were assessed for appropriateness via Likert scale. RESULTS:For the primary outcome (DA or PA-CC), GPT-4 achieved 99.3% recall, 73.6% precision, and 84.5% F-1. For the secondary outcome (DA only), GPT-4 demonstrated 95.2% recall, 77.3% precision, and 85.3% F-1. No findings were "hallucinated" outright. However, 2.8% of cases included generated text about recommendations that were inferred without specific reference. The majority of True Positive AI-generated summaries required no or minor revision. CONCLUSION/CONCLUSIONS:GPT-4 demonstrates proficiency in detecting actionable, incidental findings after refined instruction prompting. AI-generated patient instructions were most often appropriate, but rarely included inferred recommendations. While this technology shows promise to augment diagnostics, active clinician oversight via "human-in-the-loop" workflows remains critical for clinical implementation.

BACKGROUND:The EQUIPPED (Enhancing Quality of Prescribing Practices for Older Adults Discharged from the Emergency Department) medication safety program is an evidence-informed quality improvement initiative to reduce potentially inappropriate medications (PIMs) prescribed by Emergency Department (ED) providers to adults aged 65 and older at discharge. We aimed to scale-up this successful program using (1) a traditional implementation model at an ED with a novel electronic medical record and (2) a new hub-and-spoke implementation model at three new EDs within a health system that had previously implemented EQUIPPED (hub). We hypothesized that implementation speed would increase under the hub-and-spoke model without cost to PIM reduction or site engagement. METHODS:We evaluated the effect of the EQUIPPED program on PIMs for each ED, comparing their 12-month baseline to 12-month post-implementation period prescribing data, number of months to implement EQUIPPED, and facilitators and barriers to implementation. RESULTS:The proportion of PIMs at all four sites declined significantly from pre- to post-EQUIPPED: at traditional site 1 from 8.9% (8.1-9.6) to 3.6% (3.6-9.6) (p < 0.001); at spread site 1 from 12.2% (11.2-13.2) to 7.1% (6.1-8.1) (p < 0.001); at spread site 2 from 11.3% (10.1-12.6) to 7.9% (6.4-8.8) (p = 0.045); and at spread site 3 from 16.2% (14.9-17.4) to 11.7% (10.3-13.0) (p < 0.001). Time to implement was equivalent at all sites across both models. Interview data, reflecting a wide scope of responsibilities for the champion at the traditional site and a narrow scope at the spoke sites, indicated disproportionate barriers to engagement at the spoke sites. CONCLUSIONS:EQUIPPED was successfully implemented under both implementation models at four new sites during the COVID-19 pandemic, indicating the feasibility of adapting EQUIPPED to complex, real-world conditions. The hub-and-spoke model offers an effective way to scale-up EQUIPPED though a speed or quality advantage could not be shown.