The American journal of emergency medicine
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Victims of violence are at high risk for unmet mental and physical health care needs which can translate into increased Emergency Department (ED) visits. We investigated the effectiveness of participation in a psychosocial, case management-based trauma recovery program on ED utilization. ⋯ Despite high engagement, a multidisciplinary Trauma Recovery Center did not reduce ED utilization. ED utilization prior to TRC was the most predictive factor of ED utilization afterwards.
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Acute Descending Necrotizing Mediastinitis is a rare but serious illness that carries a high mortality rate. It is not commonly part of the Emergency Physician's differential diagnoses for the chief complaint of chest pain when there has been no recent instrumentation to the area. Because the disease is so uncommon, there is a relative paucity of reports of the illness. ⋯ We report the case of a 58-year-old male with a past medical history of HIV and history of intravenous drug use (IVDU) who presented to the Emergency Department with anterior chest pain for several days in addition to 3 days of fever and chills. The patient's presentation raised concern for intrathoracic infection and the diagnosis of Descending Necrotizing Mediastinitis complicated by internal jugular thrombosis was confirmed by contrast enhanced computed tomography and sonography.
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Administrators and clinicians alike have attempted to predict emergency department visits for many years. The ability to predict or "forecast" ED visit volume can allow for more efficient resource allocation, including up-staffing or down-staffing, changing OR schedules, and predicting the need for significant resources. The goal of this study is to examine combinations of variables via machine learning to increase prediction accuracy and determine the factors that are most predictive of overall ED visits. As compared to a simple univariate time series model, we hypothesize that machine learning models will predict St. Joseph Mercy Ann Arbor's patient visit load for the emergency department (ED) with higher accuracy than a simple univariate time series model. ⋯ Machine learning models perform better at predicting daily patient volumes as compared to simple univariate time series models, though not by a substantial amount. Further research can help confirm these limited initial results. Gathering more training data and additional feature engineering could also be beneficial to training the models and potentially improving predictive accuracy.