31 Development of a Mastery Learning Checklist and Minimal Passing Standard for Emergency Medicine Resident EFAST Training

By providing for individualized learning with standardized outcomes, Mastery Learning (ML) provides a sophisticated and feasible educational method within today’s competency based residency training. ML has already been demonstrated to reduce complications and improve skill retention for multiple procedures. Yet, ML has not been adopted widely in Emergency Ultrasound (US). The objective of this multicenter educational research study, is to develop a robust mastery learning (ML) checklist for the requisite knowledge and skill of image acquisition, and then determine the needed minimal passing standard for independent performance of the Extended Focused Assessment with Sonography in Trauma (EFAST) by Emergency Medicine residents. The authors created an initial EFAST ML checklist based on the published American College of Emergency Physicians (ACEP) Imaging Compendium. Next, the checklist underwent methodical revisions via two rounds of a modified delphi technique with ten fellowship trained ultrasound faculty experts at seven institutions. The final wording of the preliminary checklist was then tested during actual EFAST training. Lastly, a second panel of ten new experts at the same seven institutions established a minimal passing standard for an adequately trained EMR using the Mastery Angoff Standard Setting method. The first group of ten experts created an EFAST ML checklist that included twenty four distinct actions with completion marked as Yes/No utilizing a modified delphi technique. Each EFAST view requires that the trainee select the appropriate probe, properly adjust depth and gain to adequately visualize relevant anatomy, then verbally and physically identify relevant anatomy and potential spaces. The second group of ten new experts then set a minimal passing standard requiring that 94% of checklist items be completed in order for an Emergency Medicine Resident to be considered proficient in the EFAST image acquisition. Utilizing best practices in ML, we created a rigorous expert consensus EFAST Checklist and minimal passing standard. Although local practice may vary, starting with the published ACEP US imaging compendium, then refining with ten experts at seven institutions, we believe that our EFAST checklist adequately reflects current performance standards. The next steps for our educational research network are to determine interobserver reliability, utilize the EFAST checklist as both a pre and post-assessment within a ML curriculum, and compare performance on the EFAST checklist versus completion of traditional set number benchmarks. When rigorously developed and validated, ML may supplement and ultimately even replace traditional set number procedure benchmarks for each application within Emergency Ultrasound.