Articles: emergency-services.
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Multicenter Study Comparative Study Observational Study
Prognostic accuracy of emergency department triage tools for adults with suspected COVID-19: the PRIEST observational cohort study.
The WHO and National Institute for Health and Care Excellence recommend various triage tools to assist decision-making for patients with suspected COVID-19. We aimed to compare the accuracy of triage tools for predicting severe illness in adults presenting to the ED with suspected COVID-19. ⋯ CURB-65, PMEWS and the NEWS2 score provide good but not excellent prediction for adverse outcome in suspected COVID-19, and predicted death without organ support better than receipt of organ support. PMEWS, the WHO criteria and NEWS2 (using a lower threshold than usually recommended) provide good sensitivity at the expense of specificity.
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Multicenter Study Comparative Study
To prescribe or not to prescribe for paediatric sore throat: a retrospective cohort study comparing clinician-led antibiotic prescriptions to FeverPAIN and Centor scoring in a tertiary paediatric emergency department and a national review of practice.
Tonsillopharyngitis is a common presentation to paediatric emergency departments (PEDs). FeverPAIN (FP) and Centor scoring systems are recommended in the UK to help delineate bacterial aetiology, despite being primarily evidenced in adult populations. We investigate how the use of FP or Centor compares to actual clinician practice in guiding antibiotic prescription rates in PED. We establish current national practice in English PEDs. ⋯ Current guidance is variably interpreted and inconsistently implemented in paediatric populations. FeverPAIN and Centor scoring systems may not rationalise antibiotics as much as previously reported compared with judicious clinician practice. Producing clear paediatric-specific national guidelines, especially for under-5s who are omitted from NICE sore throat guidance, may help further rationalise and standardise antibiotic use in paediatric tonsillopharyngitis.
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Multicenter Study
Clinical teachers' motivations for feedback provision in busy emergency departments: a multicentre qualitative study.
Feedback is an effective pedagogical tool in clinical teaching and learning, but the actual perception by learners of clinical feedback is often described as unsatisfactory. Unlike assessment feedback or teaching sessions, which often happen within protected time and space, clinical feedback is influenced by numerous clinical factors. Little is known about clinical teachers' motivations to provide feedback in busy clinical settings. We aimed to investigate the motivations behind feedback being given in emergency departments (EDs). ⋯ In this qualitative study, motivations for clinical feedback were identified. Although the motivations are mostly extrinsic, the elicitation of internal motivation is possible once true satisfaction is fostered during the feedback-giving process. This understanding can be used to develop interventions to enable clinical feedback to be provided in a sustained manner.
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Multicenter Study
Predictors of readmission requiring hospitalization after discharge from emergency departments in patients with COVID-19.
Little is known on prevalence of early return hospital admission of subjects with COVID-19 previously evaluated and discharged from emergency departments (EDs). This study aims to describe readmission rate within 14 days of patients with COVID-19 discharged from ED and to identify predictors of return hospital admission. ⋯ Several factors are associated with 14-day return hospital admission in COVID-19 subjects. These should be considered when assessing discharge risk in ED clinical practice.
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Multicenter Study
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.
This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models. ⋯ Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.