Annals of emergency medicine
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Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research. ⋯ This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.
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We assessed whether the timing and order of patients over emergency shifts are associated with receiving diagnostic imaging in the emergency department and characterized whether changes in imaging are associated with changes in patients returning to the ED. ⋯ Imaging in the ED is associated with shift length and especially patient order, suggesting that physicians make different imaging decisions over the course of their shifts. Additional imaging does not translate into reductions in subsequent bouncebacks to the hospital.