Course Schedule: 21 May 2023, 09:00-12:30
Course Director: Beate Jahn, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria
Additional Faculty: Uwe Siebert, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria
Course Level: Basic
Course Prerequisites: None. Please bring a simple pocket calculator.
Duration: Half Day
Course Description and Objectives:
Medical decision making is an essential part of health care. It involves choosing an action after weighing the risks and benefits of the options available to the individual patient or the patient population. While all decisions in health care are made under conditions of uncertainty, the degree of uncertainty depends on the availability, validity, and generalizability of clinical data. Medical decision analysis (or decision-analytic modeling) is a systematic approach to decision making under uncertainty that is used widely in medical decision making, clinical guideline development, and health technology assessment of preventive, diagnostic or therapeutic procedures. These analyses can support equity discussions of distribution of health, and it may lead to further ethical discussions. It involves combining evidence for different outcomes and from different sources. Outcome parameters may include disease progression, treatment efficacy/effectiveness, safety, quality of life, and individual patient preferences. Sources may include epidemiological studies, randomized clinical trials, observational studies etc.
The short course objectives are:
- To understand the key concepts and goals of medical decision analysis,
- To know the basic methods of decision tree analysis and Markov modeling and be able to choose the appropriate model type for a given research question.
- To understand why and when decision-analytic modeling should be used in clinical evaluation
- To be able to critically judge the conclusions derived from a decision-analytic model and know the strengths and limitations of modeling.