Cognitive shortcuts or rules of thumb, sometimes referred to as heuristics, can help solve complex problems, of the sort encountered daily in clinical medicine, with great efficiency. Clinicians rely on three basic types of heuristics. When assessing a particular patient, clinicians often weigh the probability that this patient’s clinical features match those of the class of patients with the leading diagnostic hypotheses being considered. In other words, the clinician is searching for the diagnosis for which the patient appears to be a representative example; this cognitive shortcut is called the representativeness heuristic.
It may take only a few characteristics from the history for an expert clinician using the representativeness heuristic to arrive at a sound diagnostic hypothesis. For example, an elderly patient with new-onset fever, cough productive of copious sputum, unilateral pleuritic chest pain, and dyspnea is readily identified as fitting the pattern for acute pneumonia, probably of bacterial origin. Evidence of focal pulmonary consolidation on the physical examination will increase the clinician’s confidence in the diagnosis because it fits the expected pattern of acute bacterial pneumonia. Knowing this allows the experienced clinician to conduct an efficient, directed, and therapeutically productive patient evaluation since there may be little else in the history or physical examination of direct relevance. The inexperienced medical student or resident, who has not yet learned the patterns most prevalent in clinical medicine, must work much harder to achieve the same result and is often at risk of missing the important clinical problem in a sea of compulsively collected but unhelpful data.
However, physicians using the representativeness heuristic can reach erroneous conclusions if they fail to consider the underlying prevalence of two competing diagnoses (i.e., the prior, or pretest, probabilities). Consider a patient with pleuritic chest pain, dyspnea, and a low-grade fever. A clinician might consider acute pneumonia and acute pulmonary embolism to be the two leading diagnostic alternatives. Using the representativeness heuristic, the clinician might judge both diagnostic candidates to be equally likely, although to do so would be wrong if pneumonia was much more prevalent in the underlying population. Mistakes may also result from a failure to consider that a pattern based on a small number of prior observations will likely be less reliable than one based on larger samples.
A second commonly used cognitive shortcut, the availability heuristic, involves judgments made on the basis of how easily prior similar cases or outcomes can be brought to mind. For example, the experienced clinician may recall 20 elderly patients seen over the past few years who presented with painless dyspnea of acute onset and were found to have acute myocardial infarction. The novice clinician may spend valuable time seeking a pulmonary cause for the symptoms before considering and then confirming the cardiac diagnosis. In this situation, the patient’s clinical pattern does not fit the expected pattern of acute myocardial infarction, but experience with this atypical presentation, and the ability to recall it, can help direct the physician to the diagnosis.
Errors with the availability heuristic can come from several sources of recall bias. For example, rare catastrophes are likely to be remembered with a clarity and force out of proportion to their value, and recent experience is, of course, easier to recall and therefore more influential on clinical judgments.
The third commonly used cognitive shortcut, the anchoring heuristic, involves estimating a probability by starting from a familiar point (the anchor) and adjusting to the new case from there. Anchoring can be a powerful tool for diagnosis but is often used incorrectly. For example, a clinician may judge the probability of coronary artery disease (CAD) to be very high after a positive exercise thallium test, because the prediction has been anchored to the test result (“positive test = high probability of CAD”). Yet, as discussed below, this prediction would be inaccurate if the clinical (pretest) picture of the patient being tested indicates a low probability of disease (e.g., a 30-year-old woman with no risk factors). As illustrated in this example, anchors are not necessarily the same as the pretest probability (see “Measures of Disease Probability and Bayes’ Theorem,” below).
