RESUMEN
Residents have a limited time to be trained. Although having a highly variable caseload should be beneficial for resident training, residents do not necessarily get a uniform distribution of cases. By developing a dashboard where residents and their attendings can track the procedures they have done and cases that they have seen, we hope to give residents a greater insight into their training and into where gaps in their training may be occurring. By taking advantage of modern advances in NLP techniques, we process medical records and generate statistics describing each resident's progress so far. We have built the system described and its life within the NYP ecosystem. By creating better tracking, we hope that caseloads can be shifted to better close any individual gaps in training. One of the educational pain points for radiology residency is the assignment of cases to match a well-balanced curriculum. By illuminating the historical cases of a resident, we can better assign future cases for a better educational experience.