Objectives Learning retention after the deliberate practice of radiograph interpretation may be challenging for image types which are common enough to be clinically relevant but not so common that proficiency can be sustained through clinical practice. This is especially relevant for radiographs such as paediatric elbow films (EXR) that carry high risks for the patient if pathology is missed. Building on our prior research in this area, this study will develop an innovative computer learning application for paediatric EXR interpretation. After an initial learning period, this study will quantitatively determine the rate of skill decay and the optimal timing for refresher-education.~Design A prospective cohort design with purposive sampling will be used to develop the EXR education intervention. A four arm randomized control trial will be used to test for the outcomes of skill decay and optimal timing of refresher education.~Setting The education intervention will be developed at two tertiary care children's hospitals. Study participants will include physicians from two large paediatric education networks in the United States (Paediatric Outcomes in Simulation Education) and Canada (Paediatric Emergency Research Canada).~Statistical analysis For the primary analysis, a four-group multi-level model with time-points nested within participants will be used. Secondary analyses will include a time series approach within each block of 20 test EXR to check for sequential effects, and between each block to determine whether we can efficiently model skill decay curves that can separate the underlying learning attrition curve from the effects of boluses of education.~Conclusion This study anticipates that learners will increase their learning of elbow radiographs by participating in the deliberate practice of radiographs, with skill decay over time that will be mitigated by refresher education. The results will allow evidence based recommendations regarding refresher education for learning from item banks of radiographs.
Learning retention after the deliberate practice of radiograph interpretation may be challenging for image types which are common enough to be clinically relevant but not so common that proficiency can be sustained through clinical practice. This is especially relevant for radiographs such as paediatric elbow films (EXR) that carry high risks for the patient if pathology is missed. Building on prior research in this area, this study will develop an innovative computer learning application for paediatric EXR interpretation. After an initial learning period, this study will quantitatively determine the rate of skill decay and the optimal timing for refresher-education.