Cognitive Errors in Clinical Decision Making

Availability error is when clinicians choose the first thing that comes to their mind. This can underestimate the actual pre-test probability of a disease because a recent or memorable experience makes a given diagnosis more "available" to come to mind.

Experience often leads to overestimation of probability when there is memory of a case that was dramatic, involved a patient who fared poorly, or a lawsuit. For example, a clinician who recently missed the diagnosis of pulmonary embolism in a healthy young woman who had vague chest discomfort but no other findings or apparent risk factors might then overestimate the risk of pulmonary embolism in similar patients and become more likely to order chest CT angiography for similar patients despite the low probability of disease.

Limited experience can also lead to underestimation. For example, a junior resident who has seen only a few patients with chest pain, all of whom turned out to have benign causes, may do a cursory evaluation for cardiovascular or thromboembolic disease even among patient populations in whom the disease prevalence is not low.

Representation error is when clinicians focus on the presence or absence of classic manifestations of a disease without taking into account disease prevalence. For example, although several hours of vague chest discomfort in a thin, athletic, healthy-appearing 60-year-old man who has no known medical problems and who now looks and feels well does not match the typical profile of a myocardial infarction, it would be unwise to dismiss that possibility because myocardial infarction is common among men of that age and has highly variable manifestations. Conversely, a 20-year-old healthy man with sudden onset of severe, sharp chest pain and back pain may be suspected of having a dissecting thoracic aortic aneurysm because those clinical features are common in aortic dissection. The cognitive error is not taking into account the fact that aortic dissections are exceptionally rare in a 20-year-old, otherwise healthy patient without a family history of such events; thus, other, more likely causes (eg, pneumothorax, pleuritis) should be considered first. Representation error also occurs when clinicians fail to recognize that positive test results (for any test with a less than 100% specificity) in a population where the tested disease is rare are more likely to be false positive than true positive.

Premature closure is jumping to and holding on to a presumptive diagnosis. This is one of the most common errors; clinicians make a quick diagnosis (often based on pattern recognition), fail to consider other possible diagnoses, and prematurely stop collecting data. The suspected diagnosis may not even have been confirmed by appropriate testing. Premature closure errors may occur in any case but are particularly common when patients seem to be having an exacerbation of a known disorder—eg, if a woman with a long history of migraine presents with a severe headache (and actually has a new subarachnoid hemorrhage), the headache may be mistakenly assumed to be another attack of migraine. A variation of premature closure occurs when subsequent clinicians (eg, consultants on a complicated case) unquestioningly accept a previous working diagnosis without independently collecting and reviewing relevant data. Electronic medical records may exacerbate premature closure errors because the incorrect diagnoses, which may have been initially listed as probable, are often propagated in many notes and become increasingly entrenched as correct.

Anchoring errors are when clinicians steadfastly cling to an initial impression even as conflicting and contradictory data accumulate. For example, a working diagnosis of acute pancreatitis is quite reasonable in a 60-year-old man who has epigastric pain and nausea, who is sitting forward clutching his abdomen, and who has a history of several bouts of alcoholic pancreatitis that he states have felt similar to what he is currently feeling. However, despite the fact that the patient states that he has had no alcohol in many years and has normal blood levels of pancreatic enzymes, clinicians may minimize these data (eg, the patient is lying, his pancreas is burned out, the laboratory made a mistake), thus committing an anchoring error and delaying an accurate diagnosis of a penetrating gastric ulcer. Clinicians should regard any conflicting data as evidence of the need to continue to seek an alternative diagnosis rather than as anomalies to be disregarded. Initially, there may be no supporting evidence (ie, for the misdiagnosis) in some cases in which anchoring errors are committed, but those errors are usually recognized later.

Confirmation bias is "cherry-picking," which means clinicians selectively accept clinical data that support a desired hypothesis and ignore data that do not. Confirmation bias often compounds an anchoring error when the clinician uses confirmatory data to support the anchored hypothesis even when contradictory evidence is also available. For example, a clinician may steadfastly cling to patient history elements suggesting acute coronary syndrome (ACS) to confirm the original suspicion of ACS even when serial ECGs and cardiac enzymes are normal.

Attribution errors involve making decisions based on negative stereotypes, which can lead clinicians to ignore or minimize the possibility of serious disease. For example, clinicians might assume that an unconscious patient with an odor of alcohol is intoxicated and miss hypoglycemia, diabetic ketoacidosis, or intracranial injury, or they might assume that a patient with substance use disorder who has back pain is simply seeking drugs and miss an epidural abscess. Psychiatric patients who develop a physical disorder are particularly subject to attribution errors because not only may they be subject to negative stereotyping but they often describe their symptoms in unclear, inconsistent, or confusing ways, leading unwary clinicians to assume their complaints are functional in nature.

Affective error involves letting personal feelings (positive or negative) about a patient affect decisions. For example, avoiding unpleasant but necessary tests or examinations because of fondness or sympathy for the patient (eg, avoiding a pelvic examination and testing for sexually transmitted infections on a patient who is religious or a highly positioned executive or avoiding blood cultures on a seriously ill patient who has poor veins). Similarly, affective error includes failing to pursue a standard evaluation on an unlikeable patient (eg, minimizing the significance of dyspnea in a verbally abusive patient or in someone with chronic obstructive pulmonary disease who continues to smoke).