Cognitive biases in otolaryngology: a systematic narrative review

INTRODUCTION

Adverse events related to surgical care are increasing, many of which are considered preventable. Research suggests that medical errors occur in approximately 1.7% to 6.5% of hospital admissions, contributing to substantial morbidity and mortality annually in the United States, with estimates reaching up to 100,000 preventable deaths and close to a million injuries each year.¹ Multiple research efforts have demonstrated that cognitive biases defined as systematic thinking errors play a significant role in the occurrence of adverse events in surgical practice. Specifically, researchers have shown that CBs underlie misdiagnosis, incorrect treatment and poor intraoperative technical performance that can lead to patient harm, such as complications, never events (for example surgery on wrong site or side or patient) and death.^2-6 Cognitive bias refers to a range of mental shortcuts, known as heuristics, that individuals use to simplify problem-solving, enhance decision-making efficiency and facilitate learning. Heuristics are practical strategies or rules of thumb that, while not always logically optimal or fully accurate, often provide sufficiently effective solutions for complex tasks.⁷

A growing body of literature has shown that most errors in healthcare are due to mistakes in thinking and highlighted that CBs are a major contributor.^8,9 In response to this evidence, the National Academy of Sciences issued a report specific to diagnostic failure and the WHO has called for explicit training in clinical reasoning, managing uncertainty, critical thinking and cognitive heuristics and biases.^10,11 The landmark report from the Institute of Medicine in 1999, “To Err is Human”, dramatically changed the perspective of the medical community and the public, but improvements in quality and progress in reducing harm have not proceeded at the desired rate.¹² The truth is that clinicians are human, with all the attendant weaknesses and vulnerabilities that term implies. Factors such as fatigue, burnout, high-pressure or high-risk clinical situations and increased time and productivity pressures, all reduce the ability to provide optimal and just care to every patient. The result is inconsistent decision making that directly affects the quality of the medical care they provide.

As per the Competency Based Undergraduate Curriculum 2018, the Indian Medical Graduate is expected to “demonstrate effective clinical problem solving, judgment and ability to interpret and integrate available data in order to address patient problems, generate differential diagnoses and develop individualized management plans that include preventive, promotive and therapeutic goals”.¹³ Unfortunately, the curriculum presently does not envisage training in cognitive and implicit biases in medicine and surgery and debiasing strategies. Hence, in light of the glaring need for greater awareness and training in this area, this paper attempts to systematically review and present the most common and important biases that may affect clinical diagnosis and management in Otolaryngology.

METHODS

This narrative review aimed to describe types of cognitive bias in surgical specialties especially Otolaryngology, their impact on surgical performance and patient outcomes, their source and mitigation strategies used to reduce their effect. A comprehensive literature search was conducted using databases such as MEDLINE, Embase, PsycINFO, Scopus, Web of Science, Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Reviews. Search terms included combinations of the following: “cognitive bias”, “otolaryngology”, “surgery”, “decision-making”, “diagnostic errors” and related terms. We focused on articles published in the last 15 years to ensure relevance but included seminal older works when appropriate. Studies examining the influence of cognitive biases on surgical practice, along with approaches to mitigate their effects, were included in the review.

Research focusing on medical decision-making, diagnostic inaccuracies and clinical reasoning related to cognitive bias was also considered. Articles that did not specifically pertain to Otolaryngology or surgical specialties were excluded. Titles and abstracts were screened to assess relevance and full-text reviews were conducted for those meeting the initial criteria. To reduce selection bias, a second reviewer independently evaluated study eligibility. An inductive thematic approach was employed to identify patterns related to the effects of cognitive biases on surgical performance. The synthesized findings present a comprehensive overview of the current understanding of cognitive biases within Otolaryngology.

RESULTS

This review identified 71 cognitive biases relevant to surgical decision-making, particularly within Otolaryngology. These biases were systematically categorized based on their definitions, sources, examples relevant to Otolaryngology and possible mitigation strategies (Table 1). Two interesting biases, the Rashomon effect and the Dunning-Kruger effect are dealt with separately in the discussion section. Additionally, biases were classified according to their impact on different phases of surgical care: diagnostic workup, treatment phase and post-treatment follow-up (Table 2).

The identified biases were classified into distinct categories based on their occurrence in different phases of surgical practice. Among the biases, those primarily affecting the diagnostic workup included anchoring bias, availability bias, confirmation bias and premature closure. These biases contributed to misdiagnosis by causing surgeons to fixate on initial impressions, rely too heavily on recent experiences and fail to adequately consider alternative diagnoses.

In the treatment phase, commission bias, omission bias and overconfidence bias played significant roles. Surgeons were more likely to act unnecessarily due to commission bias, hesitate in necessary interventions due to omission bias or overestimate their capabilities, leading to suboptimal treatment choices. In the post-treatment follow-up stage, biases such as hindsight bias, outcome bias and ego bias affected how clinicians interpreted patient outcomes, which could result in inaccurate evaluations of earlier decisions and the reinforcement of flawed clinical reasoning.

A comprehensive table was developed outlining each of the 71 cognitive biases, providing their definitions, sources, specific examples in Otolaryngology and mitigation strategies. This table serves as a practical resource for clinicians to recognize and address cognitive biases in their daily surgical practice. The classification of biases across different surgical phases provides further insight into when and how these biases are most likely to occur, enabling targeted strategies to improve decision-making. Several recurring themes emerged from the analysis of cognitive biases. First, diagnostic inertia seen in biases such as diagnosis momentum and premature closure was a common factor in misdiagnosis. Second, cognitive overload, including biases such as multiple alternatives bias and information bias, demonstrated how excessive data or too many diagnostic possibilities could lead to decision fatigue. Third, emotional and subjective influences, represented by visceral bias and reactance bias, highlighted the effect of personal emotions and resistance to guidelines on clinical decisions.

This structured approach to bias identification and classification underscores the need for targeted interventions, including cognitive debiasing strategies, enhanced clinical decision support systems and increased awareness among surgical practitioners to mitigate the negative impacts of these biases on patient care.

DISCUSSION

Tversky and Kahneman were among the first to describe two distinct modes of thinking involved in decision-making under uncertainty: Type 1 and Type 2 processes. Type 1 thinking is characterized by its speed, intuitive nature and reliance on pattern recognition, requiring minimal cognitive effort.

As noted by O’Sullivan and Schofield, this form of reasoning enables rapid decision-making but may lack depth. In contrast, Type 2 thinking is more deliberate, analytical and cognitively demanding, making it better suited for handling complex or unfamiliar problems. Research suggests that individuals rely predominantly up to 95% of the time on Type 1 thinking.¹⁵ While efficient, this mode is more prone to cognitive biases, which are mental shortcuts that simplify decisions but can lead to systematic errors. Many biases arise when intuitive (Type 1) responses override more reflective (Type 2) processes, underscoring the importance of balancing both systems in clinical reasoning.^16,17 The evidence suggests that cognitive biases significantly contribute to diagnostic errors, suboptimal treatment choices and delayed recognition of complications. Among the most frequently observed biases, anchoring bias, overconfidence bias and confirmation bias have been shown to be particularly influential in surgical settings.¹⁸ Anchoring bias leads surgeons to fixate on initial impressions, often resulting in misdiagnosis when alternative possibilities are not sufficiently explored.

Overconfidence bias, prevalent among experienced surgeons, fosters undue reliance on personal judgment, which may contribute to unwarranted surgical interventions or failure to seek second opinions. Similarly, confirmation bias perpetuates diagnostic errors by reinforcing pre-existing beliefs rather than integrating new, contradictory evidence. Cognitive biases can also be influenced by individual personality traits, such as a tendency to avoid risk or discomfort with uncertainty, which may consistently shape clinical judgments and choices.

Decisions affected by these biases may not only be incorrect but also less than optimal, increasing the likelihood of medical errors. Enhancing awareness, comprehension and modification of bias-influenced decisions holds promise for improving patient care and outcomes. A key contribution of this review is the classification of cognitive biases based on their occurrence in different surgical phases diagnostic workup, treatment phase and post-treatment follow-up. Diagnostic workup is particularly vulnerable to biases such as premature closure and diagnosis momentum, where clinicians accept an initial diagnosis too readily, leading to missed alternative conditions. In the treatment phase, commission bias and omission bias emerge as critical factors affecting surgical decision-making, with commission bias prompting unnecessary interventions and omission bias leading to delayed treatments due to an aversion to perceived harm. Post-treatment follow-up is notably affected by hindsight bias and outcome bias, where retrospective judgments on patient outcomes shape future decision-making in a way that may not align with evidence-based practice.^19,20

The literature highlights several strategies to mitigate cognitive biases in surgical decision-making. Implementing structured decision-support tools, promoting reflective practice through case reviews and fostering interdisciplinary discussions are crucial steps toward reducing bias-related errors.^20,21 Additionally, training programs that emphasize metacognition an awareness of one's cognitive processes have been shown to improve diagnostic accuracy and treatment planning.¹⁸ A key approach to reducing bias is cognitive forcing strategies, which involve deliberate steps to counteract common biases by prompting clinicians to slow down and consider alternative possibilities.

Checklists and clinical decision support systems (CDSS) can help standardize diagnostic approaches and prevent premature closure by ensuring that all possible differentials are considered. Debriefing sessions and peer discussions provide opportunities for clinicians to reflect on past decisions and recognize patterns of bias in their thinking.^22-24 Furthermore, simulation-based training has emerged as an effective method for mitigating biases by exposing surgeons to diverse scenarios where they must identify and correct cognitive errors in a controlled environment. Encouraging the use of Bayesian reasoning where probability estimates are updated as new information emerges can help counteract availability and base-rate neglect biases.²⁵ Finally, fostering a culture of humility and openness to second opinions can combat overconfidence bias, ensuring that clinical decisions are thoroughly vetted before implementation.²⁶

Despite the growing recognition of cognitive biases in surgery, research on their mitigation remains limited. Future studies should focus on evaluating the effectiveness of debiasing interventions, such as cognitive forcing strategies and structured diagnostic algorithms, in real-world clinical settings.^27,28 Moreover, the integration of artificial intelligence (AI) in surgical decision-making presents a promising avenue to counteract human biases by providing objective, data-driven recommendations.

CONCLUSION

Cognitive biases are an inherent aspect of human decision-making and significantly impact surgical outcomes. By understanding how these biases manifest across different phases of surgical care, Otolaryngologists and other surgical specialists can implement targeted mitigation strategies to improve diagnostic accuracy, optimize treatment plans and enhance patient safety. Continued research into cognitive bias reduction techniques will be essential in fostering a more reliable and evidence-based surgical practice.

Funding: No funding sources

Conflict of interest: None declared

Ethical approval: Not required

REFERENCES

1. 1. Saposnik G, Redelmeier, Ruff CC, Tobler PN. Cognitive biases associated with medical decisions: a systematic review. BMC Med Inform Decis Mak. 2016;16:138-60.
2. 2. Dawson N, Arkes HR. Systematic errors in medical decision making. J Gen Intern Med. 1987;2:183–7.
3. 3. Dawson N. Physician judgment in clinical settings: methodological influences and cognitive performance. Clin Chem. 1993;39:1468–80.
4. 4. Elstein AS. Heuristics and biases. Acad Med. 1999;74:791–4.
5. 5. Patel VL, Kaufman DR, Arocha JF. Emerging paradigms of cognition in medical decision-making. J Biomed Inform. 2002;35:52–75.
6. 6. Thiels CA, Lal TM, Nienow JM, Pasupathy KS, Blocker RC, Aho JM, et al. Surgical never events and contributing human factors. Surgery. 2015;158:515–21.
7. 7. Gigerenzer G, Gaissmaier W. "Heuristic Decision Making". Ann Rev Psychol. 2011;62:451–82.
8. 8. O’Sullivan E, Schofield S. Cognitive bias in clinical medicine. J R Coll Physicians Edinb. 2018;48:225–32.
9. 9. Croskerry P. From mindless to mindful practice-cognitive bias and clinical decision making. N Engl J Med. 2013;368:2445–8.
10. 10. Balogh EP, Miller BT, Ball JR, eds. Improving Diagnosis in Health Care. Washington, DC: National Academies Press. 2015.
11. 11. WHO. Diagnostic Errors: Technical Series on Safer Primary Care. Geneva, Switzerland. 2016.
12. 12. Institute of Medicine (US) Committee on Quality of Health Care in America. Linda T. Kohn, Janet M. Corrigan, Molla S. Donaldson, editors. To err is human: Building a safer health system. Washington (DC): National Academies Press (US). 2000.
13. 13. Banerjee A. Coaching classes competency-based curriculum… bed of Procrustes deck chairs on the titanic. Medical Journal of Dr. DY Patil Vidyapeeth. 2020;13(1):1-2.
14. 14. Tversky A, Kahneman. Judgement under un certainty: heuristics and biases. Science. 1974;185:1124-31.
15. 15. Lakoff G, Johnson M. Philosophy in the Flesh: the embodied mind and its challenge to western thought. New York: Basic Books. 1999.
16. 16. Evans JSBT, Curtis-Holmes J. Rapid responding increases belief bias: evidence for the dual-process theory of reasoning. Think Reason. 2005;11:382-9.
17. 17. Evans JSBT. Dual-processing accounts of reasoning judgement and social cognition. Ann Rev Psychol. 2008;59:255-78.
18. 18. Antonacci AC, Dechario SP, Antonacci C, Husk G, Patel V, Nicastro J, et al. Cognitive bias impact on management of postoperative complications, medical error and standard of care. J Surg Res. 2021;258:47-53.
19. 19. Armstrong BA, Dutescu IA, Tung A, Carter DN, Trbovich PL, Wong S, et al. Cognitive biases in surgery: systematic review. Br J Surg. 2023;110(6):645-54.
20. 20. Hughes TM, Dossett LA, Hawley ST, Telem DA. Recognizing heuristics and bias in clinical decision-making. Ann Surg. 2020;271(5):813-4.
21. 21. Shah RK, Kentala E, Healy GB, Roberson DW. Classification and consequences of errors in otolaryngology. Laryngoscope. 2004;114(8):1322-35.
22. 22. Balakrishnan K, Arjmand EM. The impact of cognitive and implicit bias on patient safety and quality. Otolaryngol Clin North Am. 2019;52(1):35-46.
23. 23. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013;22(2):58–64.
24. 24. Zestcott CA, Blair IV, Stone J. Examining the presence, consequences and reduction of implicit bias in health care: a narrative review. Group Process Intergroup Relat. 2016;19:528–42.
25. 25. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 2: impediments to and strategies for change. BMJ Qual Saf. 2013;22(2):65–72.
26. 26. Croskerry P. Cognitive forcing strategies in clinical decision making. Ann Emerg Med. 2003;41:110–20.
27. 27. Oliver G, Oliver G, Body R. BET 2: poor evidence on whether teaching cognitive debiasing or cognitive forcing strategies, lead to a reduction in errors attributable to cognition in emergency medicine students or doctors. Emerg Med J. 2017;34:553–4.
28. 28. Hisam B, Zogg CK, Chaudhary MA. From understanding to action: interventions for surgical disparities. J Surg Res. 2016;200:560–78.
29. 29. Berger Loic, Bleichrodt H, Eeckhoudt L. Treatment decisions under ambiguity. J Health Econ. 2013;32(3):559-69.
30. 30. Howard J. Bandwagon effect and authority bias. InCognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine: Springer International Publishing. 2018: 21-56.
31. 31. Sheffield E. Belief Bias. InDecision Making in Emergency Medicine: Biases, Errors and Solutions. Singapore: Springer Singapore. 2021: 65-70.
32. 32. Howard J. Blind Spot Bias. In: Cognitive Errors and Diagnostic Mistakes. Springer, Cham. 2019:56-76.
33. 33. Luckhoff C. Congruence bias. InDecision Making in Emergency Medicine: Biases, Errors and Solutions. Singapore: Springer Singapore. 2021: 89-96.
34. 34. Yeates P, Moreau M, Eva K. Are examiners’ judgements in OSCE-style assessments influenced by contrast effects. Acad Med. 2015;90(7):975-80.
35. 35. Chen Q, Xu Duo, Fu H, Yip W. Distance effects and home bias in patient choice on the Internet: evidence from an online healthcare platform in China. China Economic Review. 2022;72:101757.
36. 36. Etzioni DA, Fowl RJ, Wasif N, Donohue JH, Cima RR. Distance bias and surgical outcomes. Med Care. 2013;51(3):238-44.
37. 37. Pafitanis G, Nikkhah D, Myers S. The Dunning-Kruger effect: revisiting “the valley of despair” in the evolution of competency and proficiency in reconstructive microsurgery. J Plast Reconstr Aesthet Surg. 2020;73(4):783-808.
38. 38. Sri-Ganeshan M. Ego Bias. InDecision Making in Emergency Medicine: Biases, Errors and Solutions. Singapore: Springer Singapore. 2021: 129-134.
39. 39. Sheffield E. Expectation bias. InDecision Making in Emergency Medicine: Biases, Errors and Solutions Singapore: Springer Singapore. 2021: 135-139.
40. 40. Croskerry P. The feedback sanction. Acad Emerg Med. 2000;7(11):1232-8.
41. 41. Kosmidis M. Fundamental Attribution Error. In: Raz, M., Pouryahya, P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. 2021: 43-72.
42. 42. Lim WH, Wong C, Jain SR, Ng CH, Tai CH, Devi MK, et al. The unspoken reality of gender bias in surgery: A qualitative systematic review. PLoS ONE. 2021;16(2):246420.
43. 43. Berkhout C, Berba O, Favre J, Collins C, Calafiore M, Peremans L, et al. Defining and evaluating the Hawthorne effect in primary care, a systematic review and meta-analysis. Front Med (Lausanne). 2022;9:1033486.
44. 44. Bowles F. Illusory Correlation. In: Raz, M., Pouryahya, P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. 2021:30.
45. 45. Bytzer P. Information bias in endoscopic assessment. Am J Gastroenterol. 2007;102(8):1585-7.
46. 46. Roswarski TE, Murray MD. Supervision of students may protect academic physicians from cognitive bias: a study of decision making and multiple treatment alternatives in medicine. Med Decis Making. 2006;26(2):154-61.
47. 47. Wills P. Mere Exposure Effect. In: Raz, M., Pouryahya, P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. 2021:10:33.
48. 48. Rubin M, Paolini S, Crisp RJ. The relationship between the need for closure and deviant bias: an investigation of generality and process. Int J Psychol. 2011;46(3):206-13.
49. 49. Sam KM. Playing the Odds Bias. In: Raz, M., Pouryahya, P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. 2021:978-981.
50. 50. Shander A, Gross I. Rashomon effect and the contradiction of data, practice and regulations. Anesth Analg. 2018;127(2):325-8.
51. 51. Biggs N. Reactance Bias. In: Raz M, Pouryahya P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. 2021:47-48
52. 52. Kulkarni SS, Dewitt B, Fischhoff B, Rosengart MR, Angus DC, Saul M, et al. Defining the representativeness heuristic in trauma triage: A retrospective observational cohort study. PLoS ONE. 2019;14(2):212201.
53. 53. Huang K, Bernhard RM, Barak-Corren N, Bazerman MH, Greene JD. Veil-of-ignorance reasoning mitigates self-serving bias in resource allocation during the COVID-19 crisis. Judgment and Decision Making. 2021;16(1):1–19.
54. 54. Gupta VK, Saini C, Oberoi M, Kalra G, Nasir MI. Semmelweis reflex: an age-old prejudice. World Neurosurg. 2020;136:119-25.
55. 55. Pouryahya P. Zebra Retreat. In: Raz, M., Pouryahya, P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. 2021:57-61.
56. 56. Machens A, Hauptmann S, Dralle H. Referral bias in thyroid cancer surgery: direction and magnitude. Eur J Surg Oncol. 2008;34(5):556-62.