Decision making is probably the most important fundamental task in dental clinical practice. This diagnostic and therapeutic decision making should be evidence-based and any deviation from that may negatively impact quality care in dentistry.  Cognitive biases represent systematic but unconscious distortions that cause errors in rational judgment, often leading to undesirable outcome.(1,2) These biases are subjective in nature and often generated from Type I thinking which is fast, intuitive, exerts low cognitive burden but essential for rapid assessments. Hence they are termed as ‘heuristic’ since they avail the route of mental shortcuts to aid quick diagnostic decisions. However, it lacks mindful and logical analysis of type II thinking and hence may cloud clinical judgement. (3) In dentistry, cognitive biases may impact both clinician’s diagnosis, treatment recommendations, patient communication as well as patient’s decision about treatment choices and adherence to healthcare interventions.(4) In health care probably more than 100 different biases have been identified.(5)  Understanding and mitigating these biases is crucial for improving diagnostic precision, optimizing patient care, and fostering evidence-based treatment approaches.

This review explores few common cognitive biases encountered in day-to-day dental practice, examining their effects on clinician decision-making and patient perceptions. Additionally, it discusses structured strategies to overcome bias-related errors in dental treatment and education.

Cognitive Biases in Clinical Decision-Making

In clinical dentistry diagnosis comprises a central part of the patients’ management protocol as inaccurate diagnosis can lead to incorrect treatment plan as well as may incur financial burden to the patient. Proper diagnosis essentially depends on accurate decision making.  Decisions should be made rationally analysing every available information and not based on any preconceived notion or initial impression. However, this process of decision making is dependent on dual process theory and not as rational as we assume. In dual process theory the type I process is the fast thinking process that relies on pattern recognition and allows one to make fast decision rapidly whereas type II thinking urges more critical and analytical thinking and looks beyond patterns. Depending solely on type I thinking is more likely to cause cognitive bias whereas activating type II thinking can mitigate those biases. Hence an appropriate balance between these two thought process is the key for the right decision making and optimal patient care.(5)

Followings are some cognitive biases encountered by dentists in daily clinical practice -

1. Confirmation Bias

Dentists may favour information that supports pre-existing beliefs, potentially overlooking alternative diagnoses  to avoid the mental stress for maintaining two opposite opinion at the same time caused by congnitive dissonance.(6,7)  It is probably one of the most common confirmation bias.   

- Example: Misdiagnosing recurrent oral ulcers purely as trauma-induced, disregarding systemic contributions such as autoimmune disorders. 

- Mitigation: Differential diagnosis frameworks by making a checklist and peer case discussions.

2. Anchoring Bias

Over-reliance on initial impressions- ‘the anchor’ that may limit comprehensive reassessments of subsequently gathered information and objective decision making.(8)

- Example: A dentist assumes a persistent toothache stems from failure of an already restored tooth or previously done RCT without investigating for neuralgic pain. 

- Mitigation: Implementing re-evaluation by preparing a list of differential diagnosis or by slowing down for activating proper reasonable thinking.

3. Availability Heuristic

Recent or easily recalled examples disproportionately influence clinical decisions (9) because it is readily available in the mind. This include frequency and recency bias. (5) This overemphasises on the more frequently available data overlooking the actual features presented by the patient.

- Example: Overdiagnosis of oral cancer due to recent exposure to similar cases, despite statistical rarity. 

- Mitigation: metacognition, panel discussion and dialectical bootstrapping can be helpful to look beyond the mostly available data and can help to reach the most probable diagnosis.

4. Overconfidence Bias

Excessive reliance on personal experience or an inflated judgement about a clinician’s  decision making ability can make him prone to overconfidence bias. This may even happen with experienced clinician who sometimes tend to rely on hunches overlooking the actual presentation of the patient.(10)

- Example : diagnosis of drug induced gingival enlargement in a hypertensive patient without checking the blood picture and thus underdiagnosing leukaemia as a cause of gingival enlargement.

- Mitigation: encouraging continuing dental education, activating self-reflection of their own thought process.

5. Hindsight Bias

Clinicians perceive past events as more predictable than they actually were, impacting future diagnostic confidence. (11)

- Example: A dentist retrospectively believes they “knew-it-all along” that a patient had oral cancer based on subtle symptoms, disregarding the actual complexity of the diagnosis. 

- Mitigation: Encouraging reflective practice that acknowledges uncertainty rather than reinforcing overconfidence in past decisions.

6. Belief Perseverance Bias

Dentists are inclined to  maintain their original diagnosis or treatment plan despite new evidence suggesting an alternative approach. (12)

- Example: A clinician continues recommending a fluoride-based treatment regimen despite emerging studies highlighting more effective alternatives for specific patient demographics. 

- Mitigation: Prioritizing an adaptive mindset through regular literature review and interdisciplinary discussions.

Following are some cognitive biases in patient communication

1. Framing Effect

Patients’ choices depend heavily on how information is presented or ‘framed’ rather than objective assessment. (13)

- Example: A dentist emphasizing a “90% success rate” might encourage acceptance, while stating a “10% failure rate” could lead to hesitation. 

- Mitigation: Communicating risks and benefits of treatment choices in a neutral manner.

2. Bandwagon Effect

Patients sometimes opt for treatments perceived as popular rather than those suited to their individual needs. (14)

- Example: Insisting on teeth whitening because of social trends despite sensitivity risks. 

- Mitigation: Highlighting personalized treatment plans over generalized trends and communicating potential harmful consequences.

3. Loss Aversion Bias

Patients tend to fear treatment-related losses more than they value potential health gains. Here the patient feels more emotionally vulnerable to the assumed loss that may happen with the treatment rather than focusing on or enjoying the gain that the treatment can bring along. (7)

- Example: Patients may opt for avoiding tooth extraction despite severe decay, prioritizing tooth preservation over oral health. 

- Mitigation: Using motivational strategies like videos of patients giving positive feedback to the certain treatment procedure or emphasizing on the long-term benefits of the treatment procedure can help.

4. Optimism Bias

Patients may underestimate their health risks, leading to neglect of preventive measures  under the garb of unrealistic optimism.(14)

- Example: A smoker disregards their susceptibility to oral cancer, assuming the fallacy of  “it won’t happen to me.” and rejects cancer screening or counselling regarding tobacco control.

- Mitigation: Providing data-driven risk assessments tailored to individual cases.

5. Status Quo Bias

Patients resist changes to familiar treatment practices, even when beneficial and likely to maintain their present or existing situation even if that is proven harmful to practice. Basically the patients like to maintain a ‘status quo’ and choose default options even if better treatment alternatives are available.(7)

- Example: A patient refuses to change their brushing technique despite clear advantages. 

- Mitigation: Gradually introducing transitions with behavioural reinforcement.

6. Dunning-Kruger Effect

Patients with limited knowledge and skills may overestimate their understanding of dental conditions or procedures, rejecting expert advice. This is called “double curse” because firstly the patient is unable to perform the task and also does not recognize their disability under the misconception of illusionary superiority.(15)

- Example: Patients may rely on Self-diagnosing cavities and demanding antibiotics despite professional recommendations. 

- Mitigation: Strengthening patient education without diminishing confidence.

Beyond these abovementioned most commonly encountered biases in day-to-day clinical practice some other relevant biases could also be there. (3,5,14)

To highlight the gamut of existing cognitive biases, we present a list of additional examples aimed at increasing clinician’s awareness.

Since there are multiple biases that can cloud our clinical decision making and thereby cause clinical error , mere knowledge of these cognitive biases is not sufficient to assess its relevance. The following heat map highlights the impact of common biases and may serve as a guide in clinical setup

Strategies For Bias Mitigation in Dentistry

Acknowledging the fact that biases can happen even with the most experienced clinician probably is the first step towards debiasing. Effective strategies for mitigating biases include: 

Cognitive Bias Education: it is judicious to include training regarding recognizing cognitive biases and the process to attenuate them into dental training. This could probably serve as the immediate attempt towards debiasing. However, the results of researches conducted on the same are not very encouraging. ⁽³⁾

Slowing Down: Slowing down is a strategy that can be easily followed in any clinical set-up during decision making. It allows enough time to activate type II thinking process, analysing the available data more critically and  rationally to find the answer of ‘why’ and ‘how’ and thus reducing error in diagnosis.⁽⁵⁾

Formal Checklist: It is Important and time saving to have a formal checklist available with the clinician so that the clinician can think in a structured fashion beyond the initial impression that come to their mind. It helps to avoid the initial impression translating into diagnosis (avoiding anchoring bias) and therefore other options could be examined that was not considered previously.⁽⁵⁾

Metacognition: it is the process of reflecting on one’s own thinking process engaging mostly type II thought process purposefully. Considering other alternatives or even the opposite diagnosis can help reducing bias and reach the final diagnosis.⁽³⁾

Group Or Panel Discussion: This helps in exchange of ideas among clinicians and thus they can come up with newer diagnosis.  Interdisciplinary approaches are also important to have a knowledge about other disciplines so that any novel diagnosis is not missed. (5)

Dialectical Bootstrapping:  This process could be another method where the initial diagnosis is thought to be incorrect intentionally so that other alternate and conflicting alternatives can be explored.⁽³⁾

Transparent communication: Proper communication to the patient is highly important. Dental practitioner is the responsible person to frame communication in such a way that can alleviate fear and make the patient hopeful about the treatment procedure. Also, communication to the patient calls for a certain amount of command on the part of dentist so that the Optimism bias or Dunning-Kruger effect can be bypassed.

AI Based Methods: Leveraging computerized or AI-based diagnostic tools can be proven to be very effective to reduce bias-driven errors.

 In addition to these mitigating approaches taken by the clinician use of state of the art technology is proving beneficial. Computers with huge data storage capacity of a large patient pool with the ability to analyse them is now a reality.⁽⁵⁾ Softwares with automated algorithms may minimise diagnostic  errors.

Decision making, though important may be in patient care, remains an underrated topic for research. Biases are undoubtedly the major cause for medical error hence Identifying and mitigating cognitive biases in dentistry is crucial. The key to mitigate biases is the successful application of rational thinking which can significantly improve clinical reasoning, diagnostic accuracy, and patient engagement. By integrating cognitive psychology principles into education and practice, dentists can refine their decision-making processes, ultimately advancing patient care standards.

Future Directions

- Empirical research to assess incorporation of cognitive bias awareness in dental curricula. 

- Designing AI-assisted cognitive bias detection models for clinical decision-making. 

- use of data analytics and bioinformatics to guide in decision making avoiding cognitive pitfalls.

- Longitudinal studies evaluating the impact of bias mitigation strategies on patient health outcomes.

- Studies to assess the prevalence and impact of biases in dental clinics.

1. Galli E. Kahneman, D., Thinking, Fast and Slow. Journal of Public Finance and Public Choice. 2011 Apr;29(1-3):214-5.
2. Moreira JC, Caneppele TM, Bresciani E. Cognitive Biases Influence Clinical Practice of Selective Removal of Carious Tissue by Dentists and Students: An Exploratory Study. Pesquisa Brasileira em Odontopediatria e Clínica Integrada. 2024 Aug 12;24:e230132.
3. O'Sullivan ED, Schofield SJ. Cognitive bias in clinical medicine. Journal of the Royal College of Physicians of Edinburgh. 2018 Sep;48(3):225-32.
4. Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science. 1974 Sep 27;185(4157):1124-31.
5. Webster CS, Taylor S, Weller JM. Cognitive biases in diagnosis and decision making during anaesthesia and intensive care. BJA education. 2021 Nov 1;21(11):420-5.
6. Patel VL, Yoskowitz NA, Arocha JF. Towards effective evaluation and reform in medical education: a cognitive and learning sciences perspective. Advances in Health Sciences Education. 2009 Dec;14:791-812.
7. Rehder O, Noack M, Zirkel C, Wicht C. Recognition and prevention of cognitive biases and judgment errors in diagnostics and dental therapy. Dtsch Zahnärztl Z Int. 2021;3:231-7.
8. Furnham A, Boo HC. A literature review of the anchoring effect. The journal of socio-economics. 2011 Feb 1;40(1):35-42.
9. Gilovich T, Griffin D, Kahneman D, editors. Heuristics and biases: The psychology of intuitive judgment. Cambridge university press; 2002 Jul 8.
10. Kruger J, Dunning D. Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of personality and social psychology. 1999 Dec;77(6):1121.
11. Fischhoff B. Hindsight is not equal to foresight: The effect of outcome knowledge on judgment under uncertainty. Journal of Experimental Psychology: Human perception and performance. 1975 Aug;1(3):288.
12. Nisbett, R. E., & Ross, L. (1980). Human Inference: Strategies and Shortcomings of Social Judgment. Prentice-Hall.
13. Tversky A, Kahneman D. Rational choice and the framing of decisions. InMultiple criteria decision making and risk analysis using microcomputers 1989 Sep 11 (pp. 81-126). Berlin, Heidelberg: Springer Berlin Heidelberg.
14. Sareen R. (2022) Cognitive Bias in Medical Decision Making. Journal of Pathology Research Reviews and Reports. SRC/JPR-153. DOI: doi.org/10.47363/JPR/2022(4)142
15. Surdilovic D, Adtani P, Fuoad SA, Abdelaal HM, D’souza J. Evaluation of the Dunning-Kruger effects among Dental students at an academic training Institution in UAE. Acta Stomatologica Croatica. 2022 Sep;56(3):299.