Browsing by Author "Rosenman, Elizabeth D."

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    Changing Systems Through Effective Teams: A Role for Simulation
    (Wiley, 2017) Rosenman, Elizabeth D.; Fernandez, Rosemarie; Wong, Ambrose H.; Cassara, Michael; Cooper, Dylan D.; Kou, Maybelle; Laack, Torrey A.; Motola, Ivette; Parsons, Jessica R.; Levine, Benjamin R.; Grand, James A.; Department of Emergency Medicine, School of Medicine
    Teams are the building blocks of the healthcare system, with growing evidence linking the quality of health care to team effectiveness, and team effectiveness to team training. Simulation has been identified as an effective modality for team training and assessment. Despite this, there are gaps in methodology, measurement, and implementation that prevent maximizing the impact of simulation modalities on team performance. As part of the 2017 Academic Emergency Medicine Consensus Conference “Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes,” we explored the impact of simulation on various aspects of team effectiveness. The consensus process included an extensive literature review, group discussions, and the conference “work-shop” involving emergency medicine physicians, medical educators, and team science experts. The objectives of this work are to: (1) explore the antecedents and processes that support team effectiveness, (2) summarize the current role of simulation in developing and understanding team effectiveness, and (3) identify research targets to further improve team-based training and assessment, with the ultimate goal of improving health care systems.
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    Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study
    (Wiley, 2021) Beiser, David G.; Jarou, Zachary J.; Kassir, Alaa A.; Puskarich, Michael A.; Vrablik, Marie C.; Rosenman, Elizabeth D.; McDonald, Samuel A.; Meltzer, Andrew C.; Courtney, D. Mark; Kabrhel, Christopher; Kline, Jeffrey A.; RECOVER Investigators; Emergency Medicine, School of Medicine
    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.