Translation of research into clinical practice remains a barrier, with inconsistent adoption of effective treatments and useful tests. Clinical decision rules (CDRs) integrate information from several clinical or laboratory findings to provide quantitative estimates of risk for a diagnosis or clinical outcome. They are increasingly reported in the literature and have the potential to provide a bridge that helps translate findings from original research studies into clinical practice. Unlike formal aids for shared decision making, they are pragmatic solutions that provide discrete quantitative data to aid clinicians and patients in decision making. These quantitative data can help inform the informal episodes of shared decision making that frequently take place at the point of care. Methods used to develop CDRs include expert opinion, multivariate models, point scores, and classification and regression trees Desirable CDRs are valid (make accurate predictions of risk), relevant (have been shown to improve patient-oriented outcomes), are easy to use at the point of care, are acceptable (with good face validity and transparency of recommendations), and are situated in the clinical context. The latter means that the rule places patients in risk groups that are clinically useful (i.e., below the test threshold or above the treatment threshold) and does so in adequate numbers to make use of the CDR a worthwhile investment in time. CDRs meeting these criteria should be integrated with electronic health records, populating the point score or decision tree with individual patient data and performing calculations automatically to streamline decision making.