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 thesis, the implementation of CBME at McMaster's Royal College Emergency Medicine residency program was studied and the development of a machine learning algorithm (MLA) to identify patterns in resident performance was reported. 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 was evaluated. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27615 |
| Date | January 2017 |
| Creators | Ariaeinejad, Ali |
| Contributors | Samavi, Reza, eHealth |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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