5. Limitations of Current Accounts of Medical Diagnosis
Throughout the preceding case study, we have discussed a number of
decisions made by the physicians regarding the generation and pursuit of
diagnostic hypotheses. In the follow two sections we, first, highlight
some limitations of the two primary frameworks used for discussing
diagnostic reasoning in the medical literature: (i) the normative,
probabilistic framework associated with the threshold approach and (ii)
descriptive frameworks based on cognitive psychology. Second, we then
offer our constructive proposal: to conceptualize the process of
diagnosis in terms of strategic reasoning.
The probabilistic framework is the most popular normative framework
employed in the methodological discussions of diagnosis in the medical
literature, especially among proponents of evidence-based medicine. This
approach is typically summarized as follows (e.g. Richardson and Wilson
2015): First, the physician identifies a plausible differential
diagnosis for the patient and assigns an initial prior (or “pretest”)
probability to each of the hypotheses in the differential diagnosis.
Second, the clinician compares the initial probabilities of the
hypotheses to the probability thresholds, as determined by the
decision-theoretic models of threshold approach, in order to decide
whether to test or treat for the disease. Third, as test-results become
available, the clinician should use Bayes’ Theorem together with
information about test reliability to update their probabilities.
While this probabilistic framework can highlight important lessons for
clinical reasoning,12 it does not provide a general
framework for explicating clinical reasoning; the probabilistic approach
presents an idealized, simplified picture of clinical decision-making
which leaves out many important aspects of the process of diagnosis. In
our case study, factors that eventually led to successful diagnoses
included: (i) decisions by the emergency room clinician and later by the
cardiologist about when to generate more diagnoses for consideration;
(ii) choosing effective and efficient strategies for generating relevant
hypotheses; (iii) recognizing whether the generated hypotheses can
explain the salient symptoms; (iv) recognizing the importance of subtle
clues, such as the dilated aortic root or the diastolic murmur, that may
initially appear puzzling or unimportant, as well as knowing which
features (most of them unmentioned in our description of the case) to
ignore; (v) the strategic choice of test (the CT-scan) which could
reveal important information for further inquiry even if it failed to
confirm the hypothesis tested.
This last point is crucial. The decision-theoretic models of the
threshold approach are limited to considering the direct benefits and
harms of testing or treating. They do not take into account the kinds of
downstream consequences highlighted in Section 3. However, these
considerations proved crucial to the successful resolution of our case:
the CT-scan produced the crucial clue that eventually led the
cardiologist on the right track. Considerations of this type are
difficult to represent directly in the probabilistic framework, since it
is difficult to assign meaningful probabilities or utilities to these
unknown unknowns. What is the probability that a given test will produce
a valuable clue for a diagnosis we have not thought of yet? What is the
utility of treating this as-yet-unknown disease? Successful diagnosis
depends, in part, on recognizing and considering these possibilities. Of
course, one can always add a term into the decision-theoretic calculus
to represent the weight these considerations are given relative to the
direct consequences of testing/treating. But this does not represent the
reasoning that leads physicians to give them that weight.
Finally, probabilistic models start from the assumption that one has
already formulated a diagnostic hypothesis. In its current form, it only
addresses the question of whether the hypotheses generated satisfy the
goal of being pursuit-worthy. To the extent that it succeeds in the
latter, it at best represents the aim of generative reasoning,
rather than describing this reasoning in itself.
When hypothesis generation is discussed in the medical literature, it is
done primarily within the framework of cognitive psychology. For
instance, while Kassirer, Wong and Kopelman (2010, Ch. 13), discuss
hypothesis generation in several case studies, their focus is on which
structures of memory allow (or prevent) physicians from recalling the
correct diagnosis—e.g. perhaps the physician’s memory is structured in
condition-action pairs, one of which state (say) that IF an adult has a
high serum cholesterol value, THEN consider the possibility of
hypothyroidism (ibid. , 75)? While much can no doubt be learned
about generative reasoning from cognitive psychology, these analyzes
currently lack a guiding normative framework. Correct diagnosis of
course requires physicians to have structures of memory which allow them
to recall the correct diagnosis, but as a normative account it amounts
to asking “how can we recall the right diagnosis?” As we have argued,
the relevant question is rather: which strategies for hypothesis
generation allow physicians to generate a manageable set of hypotheses
that are most important to consider at the given stage of inquiry?