Cognitive debiasing strategies in otorhinolaryngology: a framework for clinical excellence and patient safety
DOI:
https://doi.org/10.18203/issn.2454-5929.ijohns20262388Keywords:
Cognitive bias, Otolaryngology, Clinical decision-making, Debiasing, Patient safetyAbstract
To present a comprehensive debiasing framework to mitigate systematic cognitive errors in Otorhinolaryngology Head and Neck Surgery (OHNS), establishing it as a fundamental requirement for patient safety and clinical excellence. This study utilizes a systematic review that identified 71 OHNS-relevant cognitive biases. It evaluates the dual-process theory of cognition, differentiating rapid, intuitive System 1 processing from analytical System 2 deliberation and synthesizes current evidence regarding structural safety protocols, educational mnemonics, and artificial intelligence (AI) applications. Cognitive biases span the entire patient care lifecycle and contribute to approximately 20% of surgical "never events," with confirmation bias being the most prevalent factor. Effective mitigation strategies include cognitive forcing functions such as the WHO surgical checklist, "Stop, Pause, and Agree" protocols, and the SKAIR mnemonic for resident assessments. Furthermore, explainable AI (XAI) and large language models (LLMs) demonstrate significant promise as "second observers" to objectively challenge subjective clinical assessments. Achieving clinical excellence in OHNS requires a paradigm shift from individual awareness to systemic institutional change. By embedding simulation-based education and Bayesian decision-making into daily surgical practice, the specialty can ensure accurate and precise decision-making, though future research is necessitated to experimentally validate these interventions in real-world clinical environments.
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Pulimoottil DT, Philip AC, Kaipuzha RR, Thomas IA, Joseph NA, Moideen SP, et al. Cognitive biases in otolaryngology: a systematic narrative review. Int J Otorhinolaryngol Head Neck Surg. 2025;11(5):625-37. DOI: https://doi.org/10.18203/issn.2454-5929.ijohns20253002
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. DOI: https://doi.org/10.1093/bjs/znad004
Tewfik G, Rivoli S, Harbell MW. Recognizing and combating cognitive bias in anesthesiology: implications for patient safety. APSF Newsletter. 2023;38:28-31.
Daniel M, Carney M, Khandelwal S, Merritt C, Cole M, Malone M, et al. Cognitive Debiasing Strategies: A Faculty Development Workshop for Clinical Teachers in Emergency Medicine. MedEdPORTAL. 2017;13:10646. DOI: https://doi.org/10.15766/mep_2374-8265.10646
Doherty TS, Carroll AE. Believing in Overcoming Cognitive Biases. AMA J Ethics. 2020;22(9):E773-8. DOI: https://doi.org/10.1001/amajethics.2020.773
Thammasitboon S, Cutrer WB. Diagnostic decision-making and strategies to improve diagnosis. Curr Probl Pediatr Adolesc Health Care. 2013;43(9):232-41. DOI: https://doi.org/10.1016/j.cppeds.2013.07.003
Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013;22(2):ii58-64. DOI: https://doi.org/10.1136/bmjqs-2012-001712
Croskerry P, Singhal G, Mamede S. Cognitive debiasing 2: impediments to and strategies for change. BMJ Qual Saf. 2013;22(2):ii65-72. DOI: https://doi.org/10.1136/bmjqs-2012-001713
Croskerry P. Achieving Quality in Clinical Decision Making: Cognitive Strategies and Detection of Bias. Acad Emerg Med. 2002;9(11):1184-204. DOI: https://doi.org/10.1197/aemj.9.11.1184
Alessa MM, Obaid SB, Aldayel F, Almetary R, Alhussinan K, Assiri H, Farahat M. Accuracy of Diagnosis of Benign Vocal Fold Lesions among Ear, Nose, and Throat Residents. Int Arch Otorhinolaryngol. 2024;28(4):e619-25. DOI: https://doi.org/10.1055/s-0044-1787168
McKinney M. Reducing Diagnostic Errors: Using Cognitive Science. EMOttawa Blog. 2021. Available at: https://emottawablog.com/2021/05/diagnostic-errors-what-cognitive-science-has-to-say/. Accessed on 2 February 2026.
Wang AZ, Leung RK, Roxbury CR. Left-Digit Bias in Surgical Management of Chronic Rhinosinusitis in Young Adults. Int Forum Allergy Rhinol. 2025;15(6):648-50. DOI: https://doi.org/10.1002/alr.23544
Raghoebar-Krieger HMJ, Barnhoorn PC, Verhoeven AAH. Reflection on medical errors: a thematic analysis. Med Teacher. 2023;45(12):1404-10. DOI: https://doi.org/10.1080/0142159X.2023.2221809
ENT Surgical Checklists. Exr.education. ExR; 2026. Available at: https://app.exr.education/lessons/dffe7746-7b41-45ad-be7d-0098d3d23f8d/. Accessed on 2 February 2026.
Dabholkar Y, Velankar H, Suryanarayan S. Evaluation and Customization of WHO Safety Checklist for Patient Safety in Otorhinolaryngology. Indian J Otolaryngol Head Neck Surg. 2018;70:149-55. DOI: https://doi.org/10.1007/s12070-018-1253-3
Local Safety Standards LocSSIPs for ENT Outpatients Clinic V2.0. 2025. Available at: https://doclibrary-rcht.cornwall.nhs.uk/DocumentsLibrary/RoyalCornwallHospitalsTrust/Clinical/EarNoseAndThroat/LocalSafetyStandardsLocSSIPSForENTOutpatientsClinic.pdf. Accessed on 2 February 2026.
Ren L, Han L, Ping F, Yin Y, Liang T, Wang F. Impact of Implementing Checklist Management Combined with SOP on Nursing Quality Among ENT Surgery Nurses. Risk Manag Healthc Policy. 2025;18:1735-46. DOI: https://doi.org/10.2147/RMHP.S508787
Meunier MR, Theofiles MG, Gonzalez CA. A Structured Approach to Mitigating Cognitive Bias in Educational Assessment. J Grad Med Educ. 2025;17(2):136-9. DOI: https://doi.org/10.4300/JGME-D-24-00734.1
Gilbert M, Keyser V. Implicit Bias Scenario Design: What Can We Learn from Cognitive Science? Clin Simulation Nursing. 2022;71:9-18. DOI: https://doi.org/10.1016/j.ecns.2022.08.003
Moll-Khosrawi P, Zöllner C, Cronje JS, Schulte-Uentrop L. The effects of simulation-based education on medical students’ motivation. Int J Med Educ. 2021;12:130-5. DOI: https://doi.org/10.5116/ijme.60c0.981e
Ayoub NF, Rameau A, Brenner MJ, Bur AM, Ator GA, Briggs SE, et al. American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Report on Artificial Intelligence. Otolaryngol Head Neck Surg. 2025;172(2):734-43. DOI: https://doi.org/10.1002/ohn.1080
Advancing Patient Safety and Quality Initiatives through Artificial Intelligence. AAO-HNS Bulletin. 2024. Available at: https://bulletin.entnet.org/home/article/22926504/advancing-patient-safety-and-quality-initiatives-through-artificial-intelligence. Accessed on 2 February 2026.
Benkirane K, Kay J, Perez-Ortiz M. How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making? Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies. 2025;1:2263-88. DOI: https://doi.org/10.18653/v1/2025.naacl-long.114
Kim SH, Ziegelmayer S, Busch F, Mertens CJ, Keicher M, Adams LC, et al. LLM Reasoning Does Not Protect Against Clinical Cognitive Biases-An Evaluation Using BiasMedQA. medRxiv. 2025. DOI: https://doi.org/10.1101/2025.06.22.25330078
Knudsen JE, Ghaffar U, Ma R, Hung AJ. Clinical applications of artificial intelligence in robotic surgery. J Robot Surg. 2024;18(1):102. DOI: https://doi.org/10.1007/s11701-024-01867-0
Fixation bias in healthcare Recommending Best Practices. 2023. Available at: https://www.has-sante.fr/upload/docs/application/pdf/2023-08/ssp_fixation.pdf. Accessed on 2 February 2026.
Simon, Schraffenberger H, Thill S, Haselager P. Questions: A Taxonomy for Critical Reflection in Machine-Supported Decision-Making. arXiv.org. 2025.
Maier M, Powell D, Murchie P, Allan JL. Systematic review of the effects of decision fatigue in healthcare professionals on medical decision-making. Health Psychol Rev. 2025;19(4):717-62. DOI: https://doi.org/10.1080/17437199.2025.2513916
Soukup T, Lamb BW, Weigl M, Green JSA, Sevdalis N. An Integrated Literature Review of Time-on-Task Effects with a Pragmatic Framework for Understanding and Improving Decision-Making in Multidisciplinary Oncology Team Meetings. Front Psychol. 2019;10:1245. DOI: https://doi.org/10.3389/fpsyg.2019.01245
Yang HH, Tsai M, Mukdad L, St John M. Positive Outcome Bias in the Influential Otolaryngology Clinical Trial Literature. Otolaryngol Head Neck Surg. 2024;170(3):812-20. DOI: https://doi.org/10.1002/ohn.562
Leclerc AA, Gillespie AI, Tadic SD, Smith LJ, Rosen CA. The prevalence of cognitive impairment in laryngology treatment-seeking patients. Laryngoscope. 2020;130(8):2003-7. DOI: https://doi.org/10.1002/lary.28355
Medical Mishaps Can Have Negative Effects on Otolaryngologists and Patients-ENTtoday. ENTtoday. 2023. Available at: https://www.enttoday.org/article/medical-mishaps-can-have-negative-effects-on-otolaryngologists-and-patients/2/?singlepage=1. Accessed on 28 February 2026.
Gettelfinger JD, Paulk PB, Schmalbach CE. Patient Safety and Quality Improvement. Otolaryngol Head Neck Surg. 2021;165:P46-54. DOI: https://doi.org/10.1177/01945998211030908g
The Movement to Address Quality in Healthcare. AAO-HNS Bulletin. 2013. Available at: https://bulletin.entnet.org/home/article/21245653/the-movement-to-address-quality-in-healthcare. Accessed on 28 February 2026.
2019-2020 AAO-HNS/F Committee Highlights. AAO-HNS Bulletin. 2020. Available from: https://bulletin.entnet.org/home/article/21248078/20192020-aaohnsf-committee-highlights
Track Cognitive Bias to Improve Patient Safety. Childrenshospitals.org. 2020. Available at: https://www.childrenshospitals.org/news/childrens-hospitals-today/2020/07/track-cognitive-bias-to-improve-patient-safety. Accessed on 28 February 2026.
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