Tapping into Efficient Learning: An Exploration of the Impact of Sequential Learning on Skill Gains and Learning Curves in Central Venous Catheterization Simulator Training - Haroula Tzamaras; Dailen Brown; Jason Moore; Scarlett R. Miller (Journal of Medical Education and Curricular Development)
OBJECTIVE Medical residents learn how to perform many complex procedures in a short amount of time. Sequential learning, or learning in stages, is a method applied to complex motor skills to increase skill acquisition and retention but has not been widely applied in simulation-based training (SBT). Central venous catheterization (CVC) training could benefit from the implementation of sequential learning. CVC is typically taught with task trainers such as the dynamic haptic robotic trainer (DHRT). This study aims to determine the impact of sequential learning on skill gains and learning curves in CVC SBT by implementing a sequential learning walkthrough into the DHRT. METHODS 103 medical residents participated in CVC training in 2021 and 2022. One group ( N  = 44) received training on the original DHRT system while the other group ( N  = 59) received training on the DHRT sequential with interactive videos and assessment activities. All residents were quantitatively assessed on (e.g. first trial success rate, distance to vein center, overall score) the DHRT or DHRT sequential systems. RESULTS Residents in the DHRT sequential group exhibited a 3.58 times higher likelihood of successfully completing needle insertion on their first trial than those in the DHRT only group and required significantly fewer trials to reach a pre-defined mastery level of performance. The DHRT sequential group also had fewer significant learning curves compared to the DHRT only group. CONCLUSION Implementing sequential learning into the DHRT system significantly benefitted CVC training by increasing the efficiency of initial skill gain, reducing the number of trials needed to complete training, and flattening the slope of the subsequent learning curve.