A performance predictive model for emergency medicine residents

Competency-based medical education (CBME) is a paradigm of assessing resident performance through well-defined tasks, objectives and milestones. A large number of data points are generated during a five-year period as a resident accomplishes the assigned tasks. However, no tool support exists to process this data for early identification of a resident-at-risk failing to achieve future milestones. In this paper, we study the implementation of CBME at McMaster's Royal College Emergency Medicine residency program and report the development of a machine learning algorithm (MLA) to identify patterns in resident performance. We evaluate the adaptivity of multiple MLAs to build a tool support for monitoring residents' progress and flagging those who are in most need of assistance in the context of emergency medicine education.