Teaching & Training
Training the next generation of health data researchers
Graduate and Professional Training
The lab contributes to graduate and professional training in health data science and informatics. Graduate and trainee work covers chronic disease surveillance, epidemiology, adverse events, patient-oriented research, ICD methods, statistical methods, artificial intelligence methods (e.g. natural language processing, machine learning, deep learning, and large language models), classical machine learning, deep learning, natural language processing, chart review methods, qualitative methods, and systematic & scoping reviews.
MDCH 664: Administrative Data Methodology
Dr. Hude Quan believed that advancing health informatics required investing in the people who would carry the work forward. His teaching was inseparable from his research, both grounded in the conviction that researchers and health system partners must work together to advance research and build Alberta's analytics capacity for health services.
A course built around real research, not textbook exercises
MDCH 664: Administrative Data Methodology was first piloted as MDSC 659.07 Administrative Data Analysis Methodology in Winter 2006. In 2012, it was renamed MDCH 664. The course continues today as one of Dr. Quan's most tangible legacies.
Projects are designed in consultation with each student and their research or supervisory team. Students engage directly with the health system to obtain approvals, then work with course instructors and supervisors to carry projects through to completion. Projects are often presented at conferences, shared with health system partners, and developed into publications and reports.