To a medical student who requires 2 hours to collect a patient's history and perform a physical examination and several additional hours to organize that information into a coherent presentation, an experienced clinician's ability to decide on a diagnosis and management plan in a fraction of the time seems extraordinary. What separates the experienced clinician's performance from that of the novice is an elusive quality called "expertise." The first part of this chapter will provide a brief introduction to what is known about the development of expertise in clinical reasoning.
Equally bewildering to the student are the proper use of diagnostic tests and the integration of the results into the patient's clinical assessment. A novice medical practitioner typically uses a "shotgun" approach to testing, hoping to hit a target without knowing exactly what that target is. The expert, in contrast, usually has a specific target in mind and adjusts the testing strategy to it. The second part of the chapter will review briefly some of the crucial basic statistical concepts useful in the interpretation of diagnostic tests. Quantitative tools available to assist in clinical decision-making also will be discussed.
Evidence-based medicine (EBM) is the term used to describe the integration of the best available research evidence with clinical judgment and experience as applied to the care of individual patients. The third part of the chapter will provide a brief overview of some of the tools of EBM.
Brief Introduction to Clinical Reasoning
It is surprisingly difficult to define clearly what is meant by "clinical expertise." Chess has its masters, music its virtuoso performers, and athletics its Olympians. But in medicine, once training is complete and the boards are passed, there are no further tests or benchmarks of performance or ability that can be used to identify those who have attained the highest level of abilities in their clinical roles. Of course, there are always a few clinicians who are believed by their colleagues to have special problem-solving abilities: the "elite" who are consulted when particularly difficult or obscure cases have baffled everyone else. But for all their expertise, such doctors typically cannot explain what processes and methods they use to achieve their impressive results. Furthermore, it is not clear that their diagnostic virtuosity can be generalized. In other words, an expert on hypertrophic cardiomyopathy may be no better (and possibly worse) than a first-year resident at diagnosing and managing a patient with neutropenia, fever, and hypotension.
Broadly construed, clinical expertise includes not only cognitive dimensions and the integration of verbal and visual cues or information but also complex motor skills that are required in the performance of various invasive and noninvasive procedures and tests. In addition, the ability to communicate effectively with patients and work effectively with members of the medical team could be included as important aspects of "the complete package" of expertise in medicine. In this chapter, however, the focus will be on the cognitive elements (clinical reasoning), particularly as they relate to diagnosis. This focus is driven by two factors. First, the most important "actions" in clinical medicine are not procedures or prescriptions but the judgments (both diagnoses and treatment choices) from which all other aspects of medical care flow. Second, although the research on medical expertise is relatively sparse overall, it is best developed in the area of diagnostic decision-making. Much less work has been done on treatment decisions or the technical skills involved in the performance of procedures.
The obvious difficulty involved in the study of clinical reasoning is that it takes place in the heads of doctors and is therefore not readily observable. Further, doctors may not even be aware of how they reason in many cases, and so they may be unable to describe the processes they use. To overcome this difficulty, one line of research has focused on how doctors should reason diagnostically rather than on how they actually do reason. In addition, because of the difficulties of empirical research in this area, much of what is known about clinical reasoning comes from empirical studies of nonmedical problem-solving behavior. The field has been influenced by important work from cognitive psychology, sociology, medical education, economics, informatics, and decision sciences. However, because of this diversity of perspectives, no single integrated model of clinical reasoning exists, and not infrequently, different terms and models are proposed for similar phenomena.
Intuitive versus Analytic Reasoning
One useful contemporary model of reasoning (dual-process theory) distinguishes two general systems of cognitive processes. Intuition (System 1) provides rapid effortless judgments from memorized associations—for example, African-American women and hilar adenopathy equals sarcoid—or from the reduction of complex data by means of pattern recognition and other heuristics. Typically, the clinician is unable to say how those judgments were formulated. Analysis (System 2), the other form of reasoning in the dual-process model, is slow, methodical, and effortful. These are, of course, idealized extremes of the cognitive continuum. The way these systems interact in different decision problems and the way they differ between experts and novices remain the subject of considerable debate. Much work has also been done to identify how each of these systems can lead to errors in judgment.
Pattern recognition is a complex cognitive process that appears largely intuitive. One can recognize people's faces, the breed of a dog, or the model of an automobile without necessarily being able to say what specific features prompted the recognition. Analogously, an experienced clinician often can recognize the pattern of a diagnosis she or he is very familiar with after a very short amount of time with the patient. The student, who does not have that stored repertoire of diagnostic patterns, must use a more laborious analytic approach along with much more intensive data collection to reach the diagnosis.
The following three brief scenarios ...