In the use of medical device procedures, learning effects have been shown to have a significant impact on the outcome, and are a critical component of medical device safety surveillance. To support estimation of these effects, we evaluated our methods for modeling these rates within several different actual datasets representing patients treated by physicians clustered within institutions to show the flexibility of this method across applications. We employed our unique modeling for the learning curves to incorporate the hierarchy between institution and physicians, and then modeled them within established methods that work with hierarchical data. Within the actual datasets, we looked at two device types and also two procedure types. We also tried mediation analyses within the GEE framework for these various devices/procedures as well. We found that the choice of shape used to produce the "learning-free" dataset would still be dataset-specific and log or power series tended to work best. We were able to show the flexibility of applying our method in different data applications. This can, therefore, be used across the board for device and procedure safety.