Achieving quality in clinical decision making: cognitive strategies and detection of bias
Abstract
Clinical decision making is a cornerstone of high‐quality care in emergency medicine. The density of decision making is unusually high in this unique milieu, and a combination of strategies has necessarily evolved to manage the load. In addition to the traditional hypothetico‐deductive method, emergency physicians use several other approaches, principal among which are heuristics. These cognitive short‐cutting strategies are especially adaptive under the time and resource limitations that prevail in many emergency departments (EDs), but occasionally they fail. When they do, we refer to them as cognitive errors. They are costly but highly preventable. It is important that emergency physicians be aware of the nature and extent of these heuristics and biases, or cognitive dispositions to respond (CDRs). Thirty are catalogued in this article, together with descriptions of their properties as well as the impact they have on clinical decision making in the ED. Strategies are delineated in each case, to minimize their occurrence. Detection and recognition of these cognitive phenomena are a first step in achieving cognitive de‐biasing to improve clinical decision making in the ED.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A researcher's own biases, perspectives and experience may be reflected in the way in which a system's performance or an analysis' results are assessed and interpreted.
“A researcher own biases, perspectives and experience may be reflected in the way in which a system performance or an analysis' results are assessed and interpreted (Croskerry, 2002)”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Croskerry, 2002
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Importance of Cognitive Errors in Diagnosis and Strategies to Minimize Them
Croskerry, P. (2003) · Academic Medicine · doi:10.1097/00001888-200308000-00003 · Cited by 1,675 · see citing works
Self-citationA Universal Model of Diagnostic Reasoning
Croskerry, P. (2009) · Academic Medicine · doi:10.1097/acm.0b013e3181ace703 · Cited by 1,016 · see citing works
Self-citationSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Implicit bias in healthcare: clinical practice, research and decision making
Gopal, D. P., Chetty, U., O'Donnell, P., Gajria, C., & Blackadder-Weinstein, J. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0233 · Cited by 298 · see citing works
Dual-process cognitive interventions to enhance diagnostic reasoning: a systematic review
Lambe, K. A., O'Reilly, G., Kelly, B. D., & Curristan, S. (2016) · BMJ Quality & Safety · doi:10.1136/bmjqs-2015-004417 · Cited by 215 · see citing works
Understanding diagnostic errors in medicine: a lesson from aviation
Singh, H. (2006) · Quality and Safety in Health Care · doi:10.1136/qshc.2005.016444 · Cited by 156 · see citing works
Diagnostic error in medical education: where wrongs can make rights
Eva, K. W. (2009) · Advances in Health Sciences Education · doi:10.1007/s10459-009-9188-9 · Cited by 69 · see citing works
Beldhuis, I. E., Marapin, R. S., Jiang, Y. Y., Simões de Souza, N. F., Georgiou, A., Kaufmann, T., Castela Forte, J., & van der Horst, I. C. C. (2021) · Journal of Critical Care · doi:10.1016/j.jcrc.2021.04.012 · Cited by 58 · see citing works
Recognizing and reducing cognitive bias in clinical and forensic neurology
Satya-Murti, S., & Lockhart, J. (2015) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000181 · Cited by 42 · see citing works
Cognitive bias and decision aid use under stressful conditions
Phillips-Wren, G., Jefferson, T., & McKniff, S. (2019) · Journal of Decision Systems · doi:10.1080/12460125.2019.1643695 · Cited by 22 · see citing works
Yager, J., Ritvo, A. D., & MacPhee, E. R. (2022) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0000000000001548 · Cited by 7 · see citing works
Mastrianni, A., Kim, M. S., Sullivan, T. M., Sippel, G. J., Burd, R. S., Gajos, K. Z., & Sarcevic, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757512 · Cited by 6 · see citing works
Yazdanpanah, A., Jung, H., Soltani, A., & Wager, T. D. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2513856123 · Cited by 1 · see citing works
- Authors
- Croskerry, P.
- Affiliations
- Dalhousie University (CA)
- Corresponding author
- Croskerry, Pat
- ORCID
- Croskerry, Pat
- Venue
- Academic Emergency Medicine
- Publisher
- Wiley
- Published
- 2002-11
- Volume
- 9
- Issue
- 11
- Pages
- 1184-1204
- ISSN
- 1069-6563 (ISSN-L)
- 1553-2712
- OpenAlex
- W1964143014
- Cited by
- 938
- References
- 1
- Citations by year
- 2026: 42 · 2025: 50 · 2024: 42 · 2023: 44 · 2022: 50 · 2021: 61 · 2020: 86 · 2019: 76 · 2018: 58 · 2017: 44 · 2016: 43 · 2015: 36 · 2014: 46 · 2013: 46 · 2012: 40
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Emergency Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Keywords
- clinical decision-making, cognitive biases, heuristics, cognitive debiasing, emergency medicine, cognitive errors, emergency department, decision strategies
- MeSH terms
- Decision Making, Emergency Medical Services, Emergency Service, Hospital, Humans, Judgment, Pattern Recognition, Automated, Quality of Health Care
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 ›
Are you an author, or do you see a problem with this page? Request a review
