Journal of clinical anesthesia
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Performing hip or knee arthroplasty as an outpatient surgery has been shown to be operationally and financially beneficial for selected patients. By applying machine learning models to predict patients suitable for outpatient arthroplasty, health care systems can better utilize resources efficiently. The goal of this study was to develop predictive models for identifying patients likely to be discharged same-day following hip or knee arthroplasty. ⋯ Machine learning models may utilize electronic health records to screen arthroplasty procedures for outpatient eligibility. Tree-based models demonstrated superior performance in this study.
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We performed a narrative review of articles applicable to anesthesiologists' and nurse anesthetists' choices of who works each statutory holiday for operating room and non-operating room anesthesia. We include search protocols and detailed supplementary annotated comments. Studies showed that holiday staff scheduling is emotional. ⋯ For example, fairness can be based on the difference between the maximum and minimum number of holidays for which practitioners of the same division are scheduled. Holidays can be given greater weights than other shifts when estimating fairness. Staff scheduling for holidays, when done simultaneously with regular workdays, nights, and weekends, can also use personalized weights, specifying practitioners' preferences to be satisfied if possible.