We have demonstrated using several examples how different test characteristics can be used to assist clinicians in making better decisions for their patients. These probabilistic models may seem confusing and difficult to implement. Some general rules may help, such as SnNout and SpPin. Clinicians should know the test characteristics and decision rules for the acute problems they may face. For chronic conditions, advanced planning may be helpful. Electronic medical record systems may be able to incorporate these at the user interface. The improvements in hand-held computers may bring clinical decision-support systems directly to the point of service. We may also begin to see laboratories report test characteristics for important conditions as likelihood ratios (we already see estimates of the risk of heart disease corresponding to different lipid ratios). We also suspect that the medical literature will report likelihood ratios more frequently. As practice networks develop more sophisticated disease-tracking mechanisms, clinicians will be able to obtain estimates of disease prevalence more appropriate to their practice. Ultimately, for physicians to make better decisions, appropriate data are needed, including accurate estimates of test characteristics and of disease probability.