Novel 4W (When-Where-What-What) Approach of Training Point-of-Care Ultrasound (POCUS) Application in Resuscitation With High-Fidelity Simulator

Aim Point-of-care ultrasound (POCUS) is a valuable tool in anesthesiology used for evaluating and managing cardiopulmonary pathology. Implications of this modality are extensive. Seamless integration into advanced cardiac life support (ACLS) has potential to improve resuscitation outcomes, and there is growing impetus for its implementation during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. However, it remains underutilized largely due to limited training. We use high-fidelity simulation and a novel 4W approach (when to apply POCUS, where to place the ultrasound probe, what images mean, what to do next) to incorporate this technique into resuscitation training. This study aims to evaluate the efficacy of a novel 4W POCUS approach for training anesthesiology residents in the setting of resuscitation. Methods Our approach teaches learners when and where to implement POCUS, how to interpret their findings, and how to apply these findings in a clinical setting. Learners apply this method in high-fidelity simulation to diagnose and treat cardiopulmonary pathologies. Assessments were administered before and after training to evaluate efficacy. Results Post-test improvements were appreciated across all residency classes (n = 23), with achieved significance of P < 0.001 in the first-year clinical anesthesia class (CA-1) and P = 0.02 in the second-year clinical anesthesia class (CA-2). Performance was further subdivided into five categories: resuscitation integration, lung ultrasound, transthoracic echo, disease recognition, and treatment. Post-test scores also improved in each category, with lung ultrasound being the most significant improvement (P = 0.04). Conclusions Our initial data demonstrate the effectiveness of this approach to POCUS training. Performance is improved and learners are more likely to use POCUS in the future. The application of this method to larger sample sizes is an appropriate next step to demonstrate its utility.