Articles: hospital-emergency-service.
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Multicenter Study
The multicenter impacts of an emergency physician lead on departmental flow and provider experiences.
Emergency department (ED) flow impacts patient safety, quality of care and ED provider satisfaction. Throughput interventions have been shown to improve flow, yet few studies have reported the impact of ED physician leadership roles on patient flow and provider experiences. The study objective was to evaluate the impacts of the emergency physician lead role on ED flow metrics and provider experiences. ⋯ In this study, the emergency physician lead impacted ED flow metrics variably at different sites, but important learnings from provider experiences can guide future emergency physician lead implementation.
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Head injury is a common reason children present to EDs. Guideline development to improve care for paediatric head injuries should target the information needs of ED clinicians and factors influencing its uptake. ⋯ Information needs of ED clinicians, factors influencing use of head CT in children with head injuries and the role of guidelines were identified. These findings informed the scope and implementation strategies for an Australasian guideline for mild-to-moderate head injuries in children.
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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.
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Domestic violence (DV) is a major cause of morbidity worldwide. The ED is a location recommended for opportunistic screening. However, screening within EDs remains irregular. ⋯ This study describes a culture of Queensland ED clinicians that believe DV screening in ED is important and interventions are effective. Most ED clinicians are willing to screen. In this setting, availability of social work and interpreter services are important mitigating resources. Clinician education focusing on duty to screen, coupled with a built-in screening tool, and e-links to a local management protocol may improve the uptake of screening and subsequently increase detection.