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The Importance of Cognitive Errors in Diagnosis and Strategies to Minimize Them
Abstract
In the area of patient safety, recent attention has focused on diagnostic error. The reduction of diagnostic error is an important goal because of its associated morbidity and potential preventability. A critical subset of diagnostic errors arises through cognitive errors, especially those associated with failures in perception, failed heuristics, and biases; collectively, these have been referred to as cognitive dispositions to respond (CDRs). Historically, models of decision-making have given insufficient attention to the contribution of such biases, and there has been a prevailing pessimism against improving cognitive performance through debiasing techniques. Recent work has catalogued the major cognitive biases in medicine; the author lists these and describes a number of strategies for reducing them ("cognitive debiasing"). Principle among them is metacognition, a reflective approach to problem solving that involves stepping back from the immediate problem to examine and reflect on the thinking process. Further research effort should be directed at a full and complete description and analysis of CDRs in the context of medicine and the development of techniques for avoiding their associated adverse outcomes. Considerable potential exists for reducing cognitive diagnostic errors with this approach. The author provides an extensive list of CDRs and a list of strategies to reduce diagnostic errors.
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In the citing paper
“Cognitive errors are an important source of diagnostic error35”
From Large Language Models lack essential metacognition for reliable medical reasoning (2025) · cites this paper as [35]
In the citing paper
“However, because prior research provides ample evidence that even experts making critical decisions resort to heuristic thinking (and fall prey to cognitive biases) [4, 6, 17, 42], we have some confidence that our results will generalize broadly.”
From To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making (2021) · cites this paper as [17]
In the citing paper
“When making decisions under time pressure, people may be more likely to use recognition of past experiences and heuristics instead of more analytical strategies, such as systematically evaluating different options [18, 38, 54].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [18]
In the citing paper
“Because of heightened time pressure, poor access to information, and increased number of decisions, emergency medicine providers often rely on heuristics when making decisions [18].”
From To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events (2025) · cites this paper as [18]
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Metadata
- Authors
- Croskerry, P.
- Affiliations
- Dalhousie University (CA)
- Corresponding author
- Croskerry, Pat
- ORCID
- Croskerry, Pat
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-08
- Volume
- 78
- Issue
- 8
- Pages
- 775-780
- ISSN
- 1040-2446 (ISSN-L)
- OpenAlex
- W1967291734
- Cited by
- 1,675
- References
- 20
- Citations by year
- 2026: 186 · 2025: 83 · 2024: 74 · 2023: 83 · 2022: 87 · 2021: 103 · 2020: 120 · 2019: 91 · 2018: 85 · 2017: 87 · 2016: 117 · 2015: 73 · 2014: 86 · 2013: 92 · 2012: 66
- FWCI
- 12.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agency for Healthcare Research and Quality (P20HS11592-02)
- Topics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- diagnostic errors, cognitive errors, cognitive biases, cognitive debiasing, metacognition, heuristics, perceptual errors, clinical decision-making, patient safety
- MeSH terms
- Cognition, Decision Support Techniques, Diagnostic Errors, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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