The Fallacy of a Single Diagnosis

Abstract

Background Diagnostic reasoning requires clinicians to think through complex uncertainties. We tested the possibility of a bias toward an available single diagnosis in uncertain cases. Design We developed 5 different surveys providing a succinct description of a hypothetical individual patient scenaric. Each scenario was formulated in 2 versions randomized to participants, with the versions differing only in whether an alternative diagnosis was present or absent. The 5 scenarios were designed as separate tests of robustness using diverse cases, including a cautious scenario, a risky scenario, a sophisticated scenario, a validation scenario, and a comparative scenario (each survey containing only 1 version of 1 scenario). Participants included community members ( n = 1104) and health care professionals ( n = 200) who judged the chances of COVID infection in an individual patient. Results The first scenario described a cautious patient and found a 47% reduction in the estimated odds of COVID when a flu diagnosis was present compared with absent (odds ratio = 0.53, 95% confidence interval 0.30 to 0.94, P = 0.003). The second scenario described a less cautious patient and found a 70% reduction in the estimated odds of COVID in the presence of a flu diagnosis (odds ratio = 0.30, 95% confidence interval 0.13 to 0.70, P < 0.001). The third was a more sophisticated scenario presented to medical professionals and found a 73% reduction in the estimated odds of COVID in the presence of a mononucleosis diagnosis (odds ratio = 0.27, 95% confidence interval 0.10 to 0.75, P < 0.001). Two further scenarios—avoiding mention of population norms—replicated the results. Limitations Brief hypothetical scenarios may overestimate the extent of bias in more complicated medical situations. Conclusions These results demonstrate that an available simple diagnosis can lead individuals toward premature closure and a failure to fully consider additional severe diseases. Highlights Occum’s razor has been debated for centuries yet rarely subjected to experimental testing for evidence-based medicine. This article offers direct evidence that people favor an available simple diagnosis, thereby neglecting to consider additional serious diseases. The bias can lead individuals to mistakenly lower their judged likelihood of COVID or another disease when an alternate diagnosis is present. This misconception over the laws of probability appears in judgments by community members and by health care workers. The pitfall in reasoning extends to high-risk cases and is not easily attributed to information, incentives, or random chance.