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Background Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, typical turnaround times for batch-processed laboratory PCR tests remain 12-24h. Although rapid antigen lateral flow testing (LFD) has been widely adopted in UK emergency care settings, sensitivity is limited. We recently demonstrated that AI-driven triage (CURIAL-1.0) allows high-throughput COVID-19 screening using clinical data routinely available within 1h of arrival to hospital. Here we aimed to determine operational and safety improvements over standard-care, performing external/prospective evaluation across four NHS trusts with updated algorithms optimised for generalisability and speed, and deploying a novel lab-free screening pathway in a UK emergency department. Methods We rationalised predictors in CURIAL-1.0 to optimise separately for generalisability and speed, developing CURIAL-Lab with vital signs and routine laboratory blood predictors (FBC, U&E, LFT, CRP) and CURIAL-Rapide with vital signs and FBC alone. Models were calibrated during training to 90% sensitivity and validated externally for unscheduled admissions to Portsmouth University Hospitals, University Hospitals Birmingham and Bedfordshire Hospitals NHS trusts, and prospectively during the second-wave of the UK COVID-19 epidemic at Oxford University Hospitals (OUH). Predictions were generated using first-performed blood tests and vital signs and compared against confirmatory viral nucleic acid testing. Next, we retrospectively evaluated a novel clinical pathway triaging patients to COVID-19-suspected clinical areas where either model prediction or LFD results were positive, comparing sensitivity and NPV with LFD results alone. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser (OLO; SightDiagnostics, Israel) to provide lab-free COVID-19 screening in the John Radcliffe Hospital's Emergency Department (Oxford, UK), as trust-approved service improvement. Our primary improvement outcome was time-to-result availability; secondary outcomes were sensitivity, specificity, PPV, and NPV assessed against a PCR reference standard. We compared CURIAL-Rapide's performance with clinician triage and LFD results within standard-care. Results 72,223 patients met eligibility criteria across external and prospective validation sites. Model performance was consistent across trusts (CURIAL-Lab: AUROCs range 0.858-0.881; CURIAL-Rapide 0.836-0.854), with highest sensitivity achieved at Portsmouth University Hospitals (CURIAL-Lab:84.1% [95% Wilson's score CIs 82.5-85.7]; CURIAL-Rapide:83.5% [81.8 - 85.1]) at specificities of 71.3% (95% Wilson's score CIs: 70.9 - 71.8) and 63.6% (63.1 - 64.1). For 3,207 patients receiving LFD-triage within routine care for OUH admissions between December 23, 2021 and March 6, 2021, a combined clinical pathway increased sensitivity from 56.9% for LFDs alone (95% CI 51.7-62.0) to 88.2% with CURIAL-Rapide (84.4-91.1; AUROC 0.919) and 85.6% with CURIAL-Lab (81.6-88.9; AUROC 0.925). 520 patients were prospectively enrolled for point-of-care FBC analysis between February 18, 2021 and May 10, 2021, of whom 436 received confirmatory PCR testing within routine care and 10 (2.3%) tested positive. Median time from patient arrival to availability of CURIAL-Rapide result was 45:00 min (32-64), 16 minutes (26.3%) sooner than LFD results (61:00 min, 37-99; log-rank p<0.0001), and 6:52 h (90.2%) sooner than PCR results (7:37 h, 6:05-15:39; p<0.0001). Sensitivity and specificity of CURIAL-Rapide were 87.5% (52.9-97.8) and 85.4% (81.3-88.7), therefore achieving high NPV (99.7%, 98.2-99.9). CURIAL-Rapide correctly excluded COVID-19 for 58.5% of negative patients who were triaged by a clinician to COVID-19-suspected (amber) areas. Impact CURIAL-Lab & CURIAL-Rapide are generalisable, high-throughput screening tests for COVID-19, rapidly excluding the illness with higher NPV than LFDs. CURIAL-Rapide can be used in combination with near-patient FBC analysis for rapid, lab-free screening, and may reduce the number of COVID-19-negative patients triaged to enhanced precautions (amber) clinical areas.

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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.

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