In patient management, clinical decisions follow a logical sequence which can be formally expressed as a decision tree in which the uncertainties associated with each alternative outcome may be made explicit using Bayes' theorem. Where test data is used in the formulation of a decision, the uncertainty associated with the information it conveys may be modified by changing the pass/fail criterion to alter the false positive and false negative error rate. Classical procedures based on information theory are described to illustrate how this may be achieved for any test. When hard data is not available to permit such an approach, the clinician must rely on his own past experience or that of a colleague. Several methods are available for quantifying such experience by estimating subjective probabilities associated with an action or test result. Two simple methods are described for deriving subjective probabilities for subsequent use within a Bayesian decision model.