title: Tips for teachers of evidence-based medicine: making sense of decision analysis using a decision tree.
abstract: Decision analysis is a tool that clinicians can use to choose an option that maximizes the overall net benefit to a patient. It is an explicit, quantitative, and systematic approach to decision making under conditions of uncertainty. In this article, we present two teaching tips aimed at helping clinical learners understand the use and relevance of decision analysis. The first tip demonstrates the structure of a decision tree. With this tree, a clinician may identify the optimal choice among complicated options by calculating probabilities of events and incorporating patient valuations of possible outcomes. The second tip demonstrates how to address uncertainty regarding the estimates used in a decision tree. We field tested the tips twice with interns and senior residents. Teacher preparatory time was approximately 90 minutes. The field test utilized a board and a calculator. Two handouts were prepared. Learners identified the importance of incorporating values into the decision-making process as well as the role of uncertainty. The educational objectives appeared to be reached. These teaching tips introduce clinical learners to decision analysis in a fashion aimed to illustrate principles of clinical reasoning and how patient values can be actively incorporated into complex decision making.
authors.affiliation: Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, NT, Hong Kong, China.
authors.forename: Anna
authors.initials: A
authors.lastname: Lee
authors.forename: Gavin M
authors.initials: GM
authors.lastname: Joynt
authors.forename: Anthony M H
authors.initials: AM
authors.lastname: Ho
authors.forename: Sheri
authors.initials: S
authors.lastname: Keitz
authors.forename: Thomas
authors.initials: T
authors.lastname: McGinn
authors.forename: Peter C
authors.initials: PC
authors.lastname: Wyer
mesh_terms: D003661:Decision Support Techniques; D003663:Decision Trees; D019317:Evidence-Based Medicine / Q000193:education* / Q000379:methods; D005180:Faculty, Medical; D006801:Humans