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Abstract 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.

Abstract In the spring of 2020, emergency physicians found themselves in new, uncharted territory as there were few data available for understanding coronavirus disease 2019 (COVID‐19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) virus. In response, knowledge was being crowd sourced and shared across online platforms. The “wisdom of crowds” is an important vehicle for sharing information and expertise. In this article, we explore concepts related to the social psychology of group decisionmaking and knowledge translation. We then analyze a scenario in which the American College of Emergency Physicians (ACEP), a professional medical society, used the wisdom of crowds (via the EngagED platform) to disseminate clinically relevant information and create a useful resource called the “ACEP COVID‐19 Field Guide.” We also evaluate the crowd‐sourced approach, content, and attributes of EngagED compared to other social media platforms. We conclude that professional organizations can play a more prominent role using the wisdom of crowds for augmenting pandemic response efforts.