Hidden biases in clinical decision-making: potential solutions, challenges, and perspectives

<a href="https://it.freepik.com/vettori-gratuito/medico-barbuto-che-spiega-le-regole-di-protezione-dalla-pandemia-paziente-coronavirus-illustrazione-vettoriale-piatta-del-virus-quarantena-e-protezione_10172388.htm#query=clinical%20decision-making&position=1&from_view=search&track=ais">Immagine di pch.vector</a> su Freepik
Submitted: May 27, 2022
Accepted: September 2, 2022
Published: September 7, 2022
Abstract Views: 1242
PDF: 547
Supplementary: 138
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


Every day, we must make decisions that range from simple and risk-free to difficult and risky. Our cognitive sources' limitations, as well as the need for speed, can frequently impair the quality and accuracy of our reasoning processes. Indeed, cognitive shortcuts lead us to solutions that are sufficiently satisfying to allow us to make quick decisions. Unfortunately, heuristics frequently misguide us, and we fall victim to biases and systematic distortions of our perceptions and judgments. Because suboptimal diagnostic reasoning processes can have dramatic consequences, the clinical setting is an ideal setting for developing targeted interventions to reduce the rates and magnitude of biases. There are several approaches to bias mitigation, some of which may be impractical. Furthermore, advances in information technology have given us powerful tools for addressing and preventing errors in health care. Recognizing and accepting the role of biases is only the first and unavoidable step toward any effective intervention proposal. As a result, our narrative review aims to present some insights on this contentious topic based on both medical and psychological literature.



PlumX Metrics


Download data is not yet available.


Kobayashi K, Lee S, Filipowicz ALS, et al. Dynamic representation of the subjective value of information. J Neurosci 2021;41:8220–32. DOI: https://doi.org/10.1523/JNEUROSCI.0423-21.2021
Gigerenzer G, Hertwig R, Pachur T. Heuristics: The foundations of adaptive behaviour. New York, NY, US: Oxford University Press; 2011. xxv, pp 844. DOI: https://doi.org/10.1093/acprof:oso/9780199744282.001.0001
Gigerenzer G, Selten R. Bounded rationality: The adaptive toolbox. Cambridge: MIT Press; 2001. DOI: https://doi.org/10.7551/mitpress/1654.001.0001
Simon HA. A behavioral model of rational choice. Q J Econ 1955;69:99-118. DOI: https://doi.org/10.2307/1884852
Stanovich KE, West RF. On the relative independence of thinking biases and cognitive ability. J Pers Socl Psychol 2008;94:672–95. DOI: https://doi.org/10.1037/0022-3514.94.4.672
Friston K. The history of the future of the Bayesian brain. NeuroImage 2012;62:1230–3. DOI: https://doi.org/10.1016/j.neuroimage.2011.10.004
Perrotta G. Executive functions: Definition, contexts and neuropsychological profiles. J Neurosci Neurol Surg 2019:4:1–4. DOI: https://doi.org/10.33552/CTCMS.2019.01.000507
Vaidya AR, Fellows LK. The neuropsychology of decision-making: A view from the frontal lobes. In J-C Dreher, L Tremblay, Editors. Decision neuroscience: An integrative perspective. Cambridge: Elsevier Academic Press; 2017. p. 277–89. DOI: https://doi.org/10.1016/B978-0-12-805308-9.00022-1
Ouerchefani R. Ouerchefani N, Allain P, et al. Relationships between executive function, working memory, and decision-making on the Iowa Gambling Task: Evidence from ventromedial patients, dorsolateral patients, and normal subjects. J Neuropsychol 2019;13:432-61. DOI: https://doi.org/10.1111/jnp.12156
Kahneman D, Slovic P, Tversky A. Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press; 1982. DOI: https://doi.org/10.1017/CBO9780511809477
Friedman HH. Cognitive biases that interfere with critical thinking and scientific reasoning: A course module. SSRN J. 2017; Available at: https://ssrn.com/abstract=2958800 DOI: https://doi.org/10.2139/ssrn.2958800
Stanovich KE, Toplak ME, West RF. The development of rational thought: A taxonomy of heuristics and biases. In: J Benson, Editor. Advances in child development and behavior. Amsterdam: Elsevier; 2008. p. 251–85. DOI: https://doi.org/10.1016/S0065-2407(08)00006-2
Epstein S. Integration of the cognitive and the psychodynamic unconscious. Am Psychol 1994;49:709-24. DOI: https://doi.org/10.1037/0003-066X.49.8.709
Evans JStBT. In two minds: dual-process accounts of reasoning. Trends Cogn Sci 2003;7:454-9. DOI: https://doi.org/10.1016/j.tics.2003.08.012
Sloman SA. The empirical case for two systems of reasoning. Psychol Bull 1996;119:3-22. DOI: https://doi.org/10.1037/0033-2909.119.1.3
Pronin E, Lin DY, Ross L. The bias blind spot: Perceptions of bias in self versus others. Pers Soc Psychol Bull 2002;28:369–81. DOI: https://doi.org/10.1177/0146167202286008
Pronin E. Perception and misperception of bias in human judgment. Trends Cogn Sci 2007;11:37-43. DOI: https://doi.org/10.1016/j.tics.2006.11.001
Lau AYS, Coiera EW. Do people experience cognitive biases while searching for information? J Am Med Inform Assoc 2007;14:599-608. DOI: https://doi.org/10.1197/jamia.M2411
Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive biases associated with medical decisions: a systematic review. BMC Med Inform Decis Mak 2016;16:138. DOI: https://doi.org/10.1186/s12911-016-0377-1
Tversky A, Kahneman D. Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychol Rev 1983;90:293-315. DOI: https://doi.org/10.1037/0033-295X.90.4.293
Schaller-Paule MA, Steinmetz H, Vollmer FS, et al. Lessons in clinical reasoning – pitfalls, myths, and pearls: the contribution of faulty data gathering and synthesis to diagnostic error. Diagnosis 2021;8:515–24. DOI: https://doi.org/10.1515/dx-2019-0108
O’Sullivan E, Schofield S. Cognitive bias in clinical medicine. J R Coll Physicians Edinb 2018;48:225–32. DOI: https://doi.org/10.4997/jrcpe.2018.306
Newman-Toker DE, Wang Z, Zhu Y, et al. Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the “Big Three”. Diagnosis 2021;8:67–84. DOI: https://doi.org/10.1515/dx-2019-0104
Dubosh NM, Edlow JA, Lefton M, Pope JV. Types of diagnostic errors in neurological emergencies in the emergency department. Diagnosis 2015;2:21–8. DOI: https://doi.org/10.1515/dx-2014-0040
Shimizu T, Nemoto T, Tokuda Y. Effectiveness of a clinical knowledge support system for reducing diagnostic errors in outpatient care in Japan: A retrospective study. Int J Med Informs 2018;109:1–4. DOI: https://doi.org/10.1016/j.ijmedinf.2017.09.010
Phillips-Wren G, Adya M. Decision making under stress: the role of information overload, time pressure, complexity, and uncertainty. J Decis Syst 2020;29:213-225. DOI: https://doi.org/10.1080/12460125.2020.1768680
Ceschi A, Demerouti E, Sartori R, Weller J. Decision-making processes in the workplace: How exhaustion, lack of resources and job demands impair them and affect performance. Front Psychol 2017;8:313. DOI: https://doi.org/10.3389/fpsyg.2017.00313
World Health Assembly 72. Global action on patient safety. 2019. Available from: https://apps.who.int/iris/handle/10665/329284
Lal A, Ashworth H, Dada S, et al. Optimizing pandemic preparedness and response through health information systems: Lessons learned from Ebola to COVID-19. Disaster Med Public 2022;16:333–40. DOI: https://doi.org/10.1017/dmp.2020.361
Friedman CP, Gatti GG, Franz TM, et al. Do physicians know when their diagnoses are correct? Implications for decision support and error reduction. J Gen Intern Med 2005;20:334–9. DOI: https://doi.org/10.1111/j.1525-1497.2005.30145.x
Eva KW, Cunnington JPW. The difficulty with experience: Does practice increase susceptibility to premature closure? J Contin Educ Health Prof 2006;26:192–8. DOI: https://doi.org/10.1002/chp.69
Larrick RP, Feiler DC. Expertise in decision making. In: G Keren, G Wu, Editors. The Wiley Blackwell handbook of judgment and decision making. Chichester: J. Wiley & Sons; 2015. pp. 696–721. DOI: https://doi.org/10.1002/9781118468333.ch24
Poses RM, Anthony M. Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia. Med Decis Mak 1991;11:159–68. DOI: https://doi.org/10.1177/0272989X9101100303
Austin LC. Physician and nonphysician estimates of positive predictive value in diagnostic v. mass screening mammography: An examination of Bayesian reasoning. Med Decis Mak 2019;39:108-18. DOI: https://doi.org/10.1177/0272989X18823757
Bornstein BH, Emler AC. Rationality in medical decision making: a review of the literature on doctors’ decision-making biases: Rationality in medical decisions. J Eval Clin Pract 2001;7:97-107. DOI: https://doi.org/10.1046/j.1365-2753.2001.00284.x
Soll JB, Milkman KL, Payne JW. A user’s guide to debiasing. In: G Keren, G Wu, Editors. The Wiley Blackwell handbook of judgment and decision making. Chichester: J. Wiley & Sons; 2015. pp. 924–951. DOI: https://doi.org/10.1002/9781118468333.ch33
Chew KS, van Merrienboer J, Durning S. Teaching cognitive biases in clinical decision making: A case-based discussion. MedEdPORTAL 2015;11:10138. DOI: https://doi.org/10.15766/mep_2374-8265.10138
Thaler RH, Sunstein CR. Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press; 2008.
Montani S, Striani M. Artificial intelligence in clinical decision support: a focused literature survey. Yearb Med Inform 2019;28:120–7. DOI: https://doi.org/10.1055/s-0039-1677911
Bond WF, Schwartz LM, Weaver KR, et al. Differential diagnosis generators: an evaluation of currently available computer programs. J Gen Intern Med 2012;27:213–9. DOI: https://doi.org/10.1007/s11606-011-1804-8
Denny JC, Collins FS. Precision medicine in 2030 - seven ways to transform healthcare. Cell 2021;184:1415–9. DOI: https://doi.org/10.1016/j.cell.2021.01.015
Exarchos KP, Kostikas K. Artificial intelligence in COPD: Possible applications and future prospects. Respirology 2021;26:641-2. DOI: https://doi.org/10.1111/resp.14061
Goto T, Camargo CA Jr, Faridi MK, et al. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med 2018;36:1650-4. DOI: https://doi.org/10.1016/j.ajem.2018.06.062
Korteling JEH, van de Boer-Visschedijk GC, Blankendaal RAM, et al. Human- versus artificial intelligence. Front Artif Intell 2021;4:622364. DOI: https://doi.org/10.3389/frai.2021.622364
Cheraghi-Sohi S, Alam R, Hann M, et al. Assessing the utility of a differential diagnostic generator in UK general practice: a feasibility study. Diagnosis 2021;8:91–9. DOI: https://doi.org/10.1515/dx-2019-0033
Bostrom N. Superintelligence: paths, dangers, strategies. Oxford: Oxford University Press; 2014.
Bauman Z. Modernity and the Holocaust. Ithaca: Cornell University Press; 2000.
Last BS, Buttenheim AM, Timon CE, et al. Systematic review of clinician-directed nudges in healthcare contexts. BMJ Open 2021;11:e048801. DOI: https://doi.org/10.1136/bmjopen-2021-048801

How to Cite

Vitacca, Matteo, Anna Giardini, Lidia Gazzi, and Michele Vitacca. 2022. “Hidden Biases in Clinical Decision-Making: Potential Solutions, Challenges, and Perspectives”. Monaldi Archives for Chest Disease 93 (2). https://doi.org/10.4081/monaldi.2022.2339.