Decision trees: construction, uses, and limits.
Decision trees are models of the temporal and logical flow of clinical problems. Their purpose is to help the physician choose a clinical management strategy that offers the greatest expected value for the patient. Decision trees help answer questions such as:"should a risky diagnostic test be performed?" "Given our present knowledge, which of several available treatments is best for this patient?" "What are the expected benefits, risks, and financial costs of pursuing different stages of patient care?" Decision trees do not create new information, but they can provide new insights based on existing information. The principles of analyzing a clinical situation from a decision analytic perspective and of constructing and using a decision tree are illustrated with three clinical examples: a patient with possible urinary tract infection, a young man with Hodgkin's disease, and patients with chronic progressive liver failure. We present a simplified quantitative analysis to determine whether the patient with Hodgkin's disease should undergo a staging laparotomy. The last example serves as a prelude to R. Fuhrer's discussion of the expected value of test information. Following R. Fuhrer's presentation, we discuss some of the objections and advantages to medical decision analysis. Despite its limitations we believe decision analysis can be a powerful aid to medical practitioners.
D000293:Adolescent; D000328:Adult; D003657:Decision Making; D005260:Female; D006689:Hodgkin Disease / Q000628:therapy; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis / Q000628:therapy; D008297:Male; D008962:Models, Theoretical; D011336:Probability; D014552:Urinary Tract Infections / Q000175:diagnosis / Q000628:therapy