Heuristic decision making in medicine
Open accessAbstract
Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.
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Not all heuristics result in poor decisions; simple heuristics enable individuals to make accurate and efficient choices with limited yet relevant information, proving effective and reducing errors in real-world, uncertain environments like healthcare.
“Gigerenzer and Brighton (2012) suggest that simple heuristics enable individuals to make accurate and efficient choices with limited yet relevant information, proving effective and reducing errors in real-world, uncertain environments like healthcare [95].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [95]
Simple models that are moderately but not strongly calibrated often have practical advantages such as transparency or ease of use.
“While still providing sensible risk predictions, simple models that are for example moderately but not strongly calibrated often have many practical advantages such as transparency or ease of use [33].”
From A calibration hierarchy for risk models was defined: from utopia to empirical data (2016) · cites this paper as [33]
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References 51 · Cited by 377
Research works printed but not linked: 8
- [6]Gigerenzer G. Todd PM. the ABC Research Group. Simple Heuristics That Make Us Smart. New York, NY: Oxford University Press. 1999
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- Authors
- Marewski, J. N., & Gigerenzer, G.
- Affiliations
- University of Lausanne (CH)
- Max Planck Society (DE)
- Corresponding author
- Marewski, Julian N.
- Venue
- Dialogues in Clinical Neuroscience
- Publisher
- Informa UK Limited
- Published
- 2012-03-31
- Volume
- 14
- Issue
- 1
- Pages
- 77-89
- ISSN
- 1294-8322 (ISSN-L)
- 1958-5969
- PMC
- PMC3341653
- OpenAlex
- W1542200596
- Cited by
- 377
- References
- 51
- Citations by year
- 2026: 34 · 2025: 27 · 2024: 44 · 2023: 34 · 2022: 27 · 2021: 34 · 2020: 42 · 2019: 23 · 2018: 17 · 2017: 22 · 2016: 23 · 2015: 17 · 2014: 17 · 2013: 14 · 2012: 2
- FWCI
- 13.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Dialogues in Clinical Neuroscience (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- IRIS (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- SERVAL (Université de Lausanne) (submittedVersion)
- 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 › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- heuristics, clinical decision-making, physician decision-making, patient decision-making, diagnosis, regression analysis, transparency, cognitive biases
- MeSH terms
- Mental Disorders, Decision Making, Decision Support Techniques, Decision Trees, Humans, Models, Psychological, Psychiatry
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