Aims and Objectives High-Acuity Low-Occurrence (HALO) procedures are critical, rare procedures for Emergency Physicians. Cadaveric and high fidelity HALO courses are resource and faculty intensive, creating a need for feasible alternatives to train and maintain skills. We piloted a novel approach to HALO procedure training for senior EM trainees using low fidelity, user-resettable, peer-supported simulation.

Method and Design Eleven HALO procedures were identified from the RCEM 2021 curriculum (table 1). For each procedure, user-resettable low fidelity models were designed using easily available resources, alongside key procedural equipment as required. Prompt sheets detailed the station setup, procedure steps, simulation scenarios, and debrief points for peer-directed training and simulation. EM trainees in South-East Scotland completed a one-day training course, rating aspects of the day, and self-assessed pre-and post-training procedural confidence, on 7-point Likert scales.

Results and Conclusion Thirteen ST3-ST6 EM trainees South-East Scotland participated in a one-day training course using simulation training tools prepared as outlined in table 1. Pre-course surveys showed most trainees had not practised most procedures in the preceding year and all trainees had at least one procedure that they had never seen or simulated. Across all procedures, the average confidence of trainees improved from 3.75 (pre-course) to 5.7 (post-course) on a 7-point Likert scale. Pre-course, all trainees reported at least two procedures where self-assessed confidence was 1 or 2, whereas after the course no procedures were rated at 1 or 2. 100% of trainees found the day “very useful” and 100% would want to repeat it annually.

This course used user-resettable, low-fidelity models with peer-directed simulation to successfully improve EM trainee confidence in HALO procedures. This shows such an approach is feasible in a typical simulation centre by a single faculty member for minimal material cost. Future work will evaluate longer-term skill maintenance and improve individual simulation models.