Bayes' Theorem and the Physical Examination: Probability Assessment and Diagnostic Decision Making

TLDR: All three groups tended to similarly underestimate the impact of examination findings on condition probability assessment, especially negative findings, and often ordered more tests when probabilities indicated that additional testing was unnecessary.

Abstract: Despite the key role that the physical examination occupies in patient care,1–8 the decline in examination skills has been well documented.9–20 In a 1996 commentary, Mangione and Peitzman argued that one way to improve examination skills is for “teachers of physical diagnosis [to] separate wheat from chaff and discard signs or maneuvers of little value.”6 This approach requires examination findings to be viewed as diagnostic tests each with their own test characteristics.21–25 Fortunately, the recent focus on the principles of evidence-based medicine has led to the determination of these characteristics for a number of findings.21–25 However, the quality of the available data is variable24–25 and the extent to which educators have incorporated this literature into their teaching is not known. Bayes’ theorem (see the Appendix) allows clinicians to apply the published test characteristics of examination findings to their probability assessment.26–30 When using a Bayesian approach, clinicians develop an initial probability that a patient has a disorder. This probability is then sequentially revised using information obtained from the history, physical examination, and diagnostic testing to arrive at a final probability estimate. Although the frequency with which clinicians employ a Bayesian approach in their decision-making is not known,29 research dating back to the 1970s has highlighted several commonly made mistakes.26,29,31–35 First, clinicians often inaccurately form their initial probability estimates by overestimating the prevalence of rare conditions and underestimating the prevalence of common conditions.26,31,35 Second, clinicians often do not revise their initial probability estimate as much as would be suggested by Bayes’ theorem.26,31 This observation may be due to “anchoring,” in which an individual’s final probability estimate is highly sensitive to the probability at which he or she starts.26,31 Third, clinicians tend to give more weight to items encountered later on in a patient interaction than to those items encountered earlier on.32,33 Finally, physicians tend to overemphasize the importance of diagnostic testing, perhaps due to believing that diagnostic tests are more accurate than historical items and examination findings.34 Currently, little is known about how clinicians apply examination findings to their probability assessments. In this cross-sectional, multi-institutional, Web-based survey study, we sought to determine and compare how third- and fourth-year medical students, internal medicine residents, and academic general internists apply examination findings to their probability assessments and to determine the impact that these findings have on the ordering of diagnostic tests.