Quantitative theories for clinical decision making

Clinical work involves a great many decisions in situations characterized by uncertainty and by the need for value judgments. Formal, mostly quantitative approaches to medical decision making have been developed over the last thirty years. The best known theories are discriminant analysis or supervised pattern recognition, decision analysis and, more recently, artificial intelligence. Discriminant analysis is used for estimating diagnostic probabilities on the basis of diagnostic data. Outcome, action, state, probability, utility, and choice are the main concepts of decision analysis. Problems of diagnosis and therapy choice can be structured within the framework of decision analysis. The quality of a decision analysis largely depends on a good structuring of the problem, and on the quality of the probability and utility assessments made during the analysis. Decision analysis is basically a prescriptive theory for decision making in individual patient management. Other areas of application include basic and advanced teaching, formulation, explication and analysis of clinical problems, detection of gaps in clinical knowledge, development of clinical protocols, etc.