Medical Decision-Making
April 22nd, 2011 | 
Author: Medical decision-making is an important responsibility of the physician and occurs at each stage of the diagnostic and treatment process. It involves the ordering of additional tests, requests for consults, and decisions regarding treatment and prognosis. This process requires an in-depth understanding of the pathophysiology and natural history of disease. As described above, medical decision-making should be evidence-based so that patients derive the full benefit of the scientific knowledge available to physicians. Formulating a differential diagnosis requires not only a broad knowledge base but also the ability to assess the relative probabilities of various diseases. Application of the scientific method, including hypothesis formation and data collection, is essential to the process of accepting or rejecting a particular diagnosis. Analysis of the differential diagnosis is an iterative process. As new information or test results are acquired, the group of disease processes being considered can be contracted or expanded appropriately.
Despite the importance of evidence-based medicine, much of medical decision-making relies on good clinical judgment—a process that is difficult to quantify or even to assess qualitatively. Physicians must use their knowledge and experience as a basis for weighing known factors along with the inevitable uncertainties and the need to use sound judgment; this is particularly important when a relevant evidence base is not available. Several quantitative tools may be invaluable in synthesizing the available information, including diagnostic tests, Bayes’ theorem, and multivariate statistical models. Diagnostic tests serve to reduce uncertainty about a diagnosis or prognosis in a particular individual and to help the physician decide how best to manage that individual’s condition. The battery of diagnostic tests complements the history and the physical examination. The accuracy of a given test is ascertained by determining its sensitivity (true positive rate) and specificity (true negative rate) as well as the predictive value of a positive and negative result. Bayes’ theorem uses information on a test’s sensitivity and specificity, in conjunction with the pretest probability of a diagnosis, to determine mathematically the posttest probability of the diagnosis. More complex clinical problems can be approached with multivariate statistical models, which generate highly accurate information even when multiple factors are acting individually or together to affect disease risk, progression, or response to treatment. Studies comparing the performance of statistical models with that of expert clinicians have documented equivalent accuracy, although the models tend to be more consistent. Thus, multivariate statistical models may be particularly helpful to less experienced clinicians.