This course will provide an introduction to the principles and applications of decision sciences as they relate to clinical decision-making. The major themes will be a method of evaluating diagnostic and therapeutic strategies in order to optimize individualized patient care and inform policy decision, including those in which a fixed amount of resources are an important consideration. The basic building blocks of decision analysis (Bayes theorem, test and test-treatment thresholds, tree building, utility measurement, Markov processes and cost-effectiveness) will be reviewed and synthesised. Students will use decision analysis software to build and test their own decision analyses.

Objectives: 1) To learn the principles of decision analysis. 2) To learn how to use decision analysis software. 3) To perform a decision analysis by developing a model, gathering the relevant data, and performing complete sensitivity analyses. 4) To learn how to present a decision analysis orally and in writing.