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Doctors joining Emergency Departments (ED) have individual training needs based on their experience and background of working in different countries or hospitals, and a large proportion of junior doctors work for less than a year in a single ED. We designed the AWARE project to analyse the challenges associated with familiarity with the physical workplace and resuscitation equipment for doctors new to an ED environment. The goals of the project were to assess the diverse learning needs [1], impact of unfamiliarity with environment and equipment on physician confidence, ability to participate in resuscitation scenarios, and to develop a simulation-based intervention to support new doctors in ED. We developed a questionnaire for multidisciplinary staff to explore problems with workplace unfamiliarity and its impact on different aspects of performance during resuscitation. We included questions (tailored to professional background) about the management of resuscitation and the location of vital equipment under the broad headings of: preparation, airway, breathing, circulation, and other critical interventions. We collected 104 completed questionnaires (67 from doctors, 37 from nurses). Over 90% of staff felt that lack of workplace familiarity negatively affects performance and leads to delay in performing procedures. 92% of the nurses felt that it was easier and more efficient to work with doctors who were familiar with the workplace. Quantitative data revealed issues with locating equipment such as: 60% Doctors did not know where to find mechanical-CPR device (LUCAS). 81% of the Senior House Officers did not know where to find end-tidal CO We have identified familiarity with workplace and resuscitation equipment as a key learning need. The data from Phase 1 of the project have informed the development of scenarios for new induction processes in phase 2. Simulation is an important tool for education but also for induction and analysis of systems and pathways [2]. Phase 2 will also use novel technologies including 360° videos to allow staff new to the department to access ED environments and equipment virtually and at their convenience. Future work will involve monitoring the success of the interventions in phase 2. 1. Grant J. Learning needs assessment: Assessing the need. Br Med J. 2002;324(7330):156–9. 2. Health Education England. Simulation immersive technologies.

Background Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12–24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. Methods We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). Findings 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858–0·881, 95% CI 0·838–0·912, for CURIAL-Lab and 0·836–0·854, 0·814–0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5–85·7, for CURIAL-Lab and 83·5%, 81·8–85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9–71·8) for CURIAL-Lab and 63·6% (63·1–64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7–62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6–88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4–91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32–64), 16 min (26·3%) sooner than with LFDs (61 min, 37–99; log-rank p Interpretation Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas.