The physician who acknowledges the imperfections of a diagnostic test will ask, "In view of this test result, how uncertain should I be about this patient?" For

The physician who acknowledges the imperfections of a diagnostic test will ask, "In view of this test result, how uncertain should I be about this patient?" Fortunately, there is a method for answering this question: the theory of probability. [1] is a primer for applying probability theory to the interpretation of test results and deciding when to do a test rather than treat or do nothing. Physicians often must start treatment when still uncertain about whether the patient has the disease. If treatment is started, there is a risk of causing harm to a person who does not have the disease, as well as the prospect of benefiting the person who does. If treatment is withheld, a person who is diseased will be denied a chance at a rapid, effective cure. The method for solving this problem analytically is called the threshold model of medical decisionmaking (Pauker and Kassirer 1975, 1980; Doubilet 1983). The threshold model is an example of expected-value decisionmaking that is applied to a particular type of decision. The key idea is the treatment threshold probability, which is the probability of disease at which one is indifferent between treating and not treating. The basic principle of the threshold model is the following dictum: Do a test only if the probability of disease could change enough to cross the treatment threshold probability. [1] Sox H, Stern S, Owens D, et al.; Institute of Medicine (US) Council on Health Care Technology. Assessment of Diagnostic Technology in Health Care: Rationale, Methods, Problems, and Directions: Monograph of the Council on Health Care Technology. Washington (DC): National Academies Press (US); 1989. 2, The Use of Diagnostic Tests: A Probabilistic Approach. https://www.ncbi.nlm.nih.gov/books/NBK235178/ https://www.ncbi.nlm.nih.gov/books/NBK235178/