THE HYPOTHETICO-DEDUCTIVE METHOD

DIAGNOSIS AS CATEGORIZATION OR PATTERN RECOGNITION

The finding of case specificity also challenged the hypothetico-deductive model as an adequate account of the process of clinical reasoning. Both successful and unsuccessful diagnosticians employed a hypothesis-testing strategy, and diagnostic accuracy depended more on mastery of the content in a domain than on the strategy employed. By the mid-1980s, the view of diagnostic reasoning as complex and systematic generation and testing of hypotheses was being criticized. Patel, Norman and their associates (e.g. Brooks et al 1991, Eva et al 1998, Groen & Patel 1985, Schmidt et al 1990) pointed out that the clinical reasoning of experts in familiar situations frequently does not display explicit hypothesis testing. It is rapid, automatic and often non-verbal. Not all cases seen by an experienced physician appear to require hypothetico-deductive reasoning (Davidoff 1996).

Expert reasoning in familiar situations looks more like pattern recognition or direct automatic retrieval from a well-structured network of stored knowledge (Groen & Patel 1985). Since experienced clinicians have a better sense of clinical realities and the likely diagnostic possibilities, they can also more efficiently generate an early set of plausible hypotheses to avoid fruitless and expensive pursuit of unlikely diagnoses. The research emphasis has shifted from the problem-solving process to the organization of knowledge in the long-term memory of experienced clinicians (Norman 1988).

Categorization of a new case can be based either on retrieval of and matching to specific instances or examplars, or to a more abstract prototype. In instance-based recognition, a new instance is classified by resemblance to memory of a past case (Brooks et al 1991, Medin & Schaffer 1978, Norman et al 1992, Schmidt et al 1990). This model is supported by the finding that clinical diagnosis is strongly affected by the context of events (for example the location of a skin rash on the body), even when this context is normatively irrelevant. Expert–novice differences are mainly explicable in terms of the size of the knowledge store of prior instances available for pattern recognition. This theory of clinical reasoning has been developed with particular reference to pathology, dermatology and radiology, where the clinical data are predominantly visual.

According to prototype models, clinical experience facilitates the construction of abstractions or prototypes (Bordage & Zacks 1984, Rosch & Mervis 1975). Better diagnosticians have constructed more diversified and abstract sets of semantic relations to represent the links between clinical features or aspects of the problem (Bordage & Lemieux 1991, Lemieux & Bordage 1992). Experts in a domain are more able to relate findings to each other and to potential diagnoses, and to identify what additional findings are needed to complete a picture (Elstein et al 1993). These capabilities suggest that experts are working with more abstract representations and are not simply trying to match a new case to a previous instance, although that matching process may occur with simple cases.

MULTIPLE REASONING STRATEGIES

Norman et al (1994) found that experienced physicians used a hypothetico-deductive strategy with difficult cases only, a view supported by Davidoff (1996). When a case is perceived to be less challenging, quicker and easier methods are used, such as pattern recognition or feature matching. Thus, controversy about the methods used in diagnostic reasoning can be resolved by positing that the method selected depends upon perceived characteristics of the problem. There is an interaction between the clinician’s level of skill and the perceived difficulty of the task (Elstein 1994). Easy cases are solved by pattern recognition and going directly from data to diagnostic classification – what Groen & Patel (1985) called forward reasoning. Difficult cases need systematic hypothesis generation and testing. Whether a problem is easy or difficult depends in part on the knowledge and experience of the clinician who is trying to solve it (Figure 20.1).

Figure 20.1 Impact on problem-solving strategy of case difficulty, clinician experience and case features

Both Norman and Eva have since championed the view that clinicians apply multiple reasoning strategies as necessary to approach diagnostic problems (Eva 2005, Norman 2005). Their research suggests that training physicians in multiple knowledge representations and reasoning modes may yield the best overall performance.

ERRORS IN HYPOTHESIS-GENERATION AND RESTRUCTURING

Neither pattern recognition nor hypothesis testing is an error-proof strategy, nor are they always consistent with statistical rules of inference with imperfect information. Errors that can occur in difficult cases in internal medicine were illustrated and discussed by Kassirer & Kopelman (1991) and classes of error were reviewed by Graber et al (2002). The frequency of errors in actual practice is unknown, but considering a number of studies as a whole, an error rate of 15% might be a good first approximation.

Looking at an instance of diagnostic reasoning retrospectively, it is easy to see that a clinician could err either by oversimplifying a complex problem or by taking a problem that could appropriately have been dealt with routinely and using a more effortful strategy of competing hypotheses. It has been far more difficult for researchers and teachers to prescribe an appropriate strategy in advance. Because so much depends on the interaction between case and clinician, prescriptive guidelines for the proper amount of hypothesis generation and testing are still unavailable for the student clinician. Perhaps the most useful advice is to emulate the hypothesis-testing strategy used by experienced clinicians when they are having difficulty, since novices will experience as problematic many situations that the former solve by routine pattern-recognition methods. In an era that emphasizes cost-effective clinical practice, gathering data unrelated to diagnostic hypotheses will be discouraged.

Many diagnostic problems are so complex that the correct solution is not contained within the initial set of hypotheses. Restructuring and reformulating must occur through time as data are obtained and the clinical picture evolves. However, as any problem solver works with a particular set of hypotheses, psychological commitment takes place and it becomes more difficult to restructure the problem (Janis & Mann 1977). Ideally, one might want to work purely inductively, reasoning only from the facts, but this strategy is never employed because it is inefficient and produces high levels of cognitive strain (Elstein et al 1978). It is much easier to solve a problem where some boundaries and hypotheses provide the needed framework. On the other hand, early problem formulation may also bias the clinician’s thinking (Barrows et al 1982, Voytovich et al 1985). Errors in interpreting the diagnostic value of clinical information have been found by several research teams (Elstein et al 1978, Friedman et al 1998, Gruppen et al 1991, Wolf et al 1985).

BAYES’ THEOREM

In the literature on medical decision making, reaching a diagnosis is conceptualized as a process of reasoning about uncertainty, updating an opinion with imperfect information (the clinical evidence). As new information is obtained, the probability of each diagnostic possibility is continuously revised. Each post-test probability becomes the pre-test probability for the next stage of the inference process. Bayes’ theorem, the formal mathematical rule for this operation (Hunink et al 2001, Sox et al 1988), states that the post-test probability is a function of two variables, pre-test probability and the strength of the new diagnostic evidence. The pre-test probability can be either the known prevalence of the disease or the clinician’s belief about the probability of disease before new information is acquired. The strength of the evidence is measured by a likelihood ratio, the ratio of the probabilities of observing a particular finding in patients with and without the disease of interest. This framework directs attention to two major classes of errors in clinical reasoning: errors in a clinician’s beliefs about pre-test probability or errors in assessing the strength of the evidence. Bayes’ theorem is a normative rule for diagnostic reasoning; it tells us how we should reason, but it does not claim that we actually revise our opinions in this way. Indeed, from the Bayesian viewpoint, the psychological study of diagnostic reasoning centres on errors in both components, which are discussed below (Kempainen et al 2003 provide a similar review).