Automated detection of heuristics and biases among pathologists in a computer-based system

Debiasing Judgements Using a Distributed Cognition Approach: A Scoping Review of Technological Strategies

OBJECTIVE

To review and synthesise research on technological debiasing strategies across domains, present a novel distributed cognition-based classification system, and discuss theoretical implications for the field.

BACKGROUND

Distributed cognition theory is valuable for understanding and mitigating cognitive biases in high-stakes settings where sensemaking and problem-solving are contingent upon information representations and flows in the decision environment. Shifting the focus of debiasing from individuals to systems, technological debiasing strategies involve designing system components to minimise the negative impacts of cognitive bias on performance. To integrate these strategies into real-world practices effectively, it is imperative to clarify the current state of evidence and types of strategies utilised.

METHODS

We conducted systematic searches across six databases. Following screening and data charting, identified strategies were classified into (i) group composition and structure, (ii) information design and (iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into eight categories.

RESULTS

Eighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen were ineffective and six were partially effective. Information design strategies were studied most, followed by procedural debiasing, and group structure and composition. Gaps and directions for future work are discussed.

CONCLUSION

Through the lens of distributed cognition theory, technological debiasing represents a reconceptualisation of cognitive bias mitigation, showing promise for real-world application.

APPLICATION

The study results and debiasing classification presented can inform the design of high-stakes work systems to support cognition and minimise judgement errors.

The Role of Artificial Intelligence in the Diagnosis of Melanoma

The incidence of melanoma, the most aggressive form of skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, and recent advancements in artificial intelligence (AI) have shown promise in enhancing the accuracy and efficiency of melanoma diagnosis and management. This review examines the role of AI in skin lesion diagnostics, highlighting two main approaches: machine learning, particularly convolutional neural networks (CNNs), and expert systems. AI techniques have demonstrated high accuracy in classifying dermoscopic images, often matching or surpassing dermatologists' performance. Integrating AI into dermatology has improved tasks, such as lesion classification, segmentation, and risk prediction, facilitating earlier and more accurate interventions. Despite these advancements, challenges remain, including biases in training data, interpretability issues, and integration of AI into clinical workflows. Ensuring diverse data representation and maintaining high standards of image quality are essential for reliable AI performance. Future directions involve the development of more sophisticated models, such as vision-language and multimodal models, and federated learning to address data privacy and generalizability concerns. Continuous validation and ethical integration of AI into clinical practice are vital for realizing its full potential for improving melanoma diagnosis and patient care.

Identifying, Understanding, and Minimizing Unconscious Cognitive Biases in Perioperative Crisis Management: A Narrative Review

Rapid clinical decision-making behavior is often based on pattern recognition and other mental shortcuts. Although such behavior is often faster than deliberative thinking, it can also lead to errors due to unconscious cognitive biases (UCBs). UCBs may contribute to inaccurate diagnoses, hamper interpersonal communication, trigger inappropriate clinical interventions, or result in management delays. The authors review the literature on UCBs and discuss their potential impact on perioperative crisis management. Using the Scale for the Assessment of Narrative Review Articles (SANRA), publications with the most relevance to UCBs in perioperative crisis management were selected for inclusion. Of the 19 UCBs that have been most investigated in the medical literature, the authors identified 9 that were judged to be clinically relevant or most frequently occurring during perioperative crisis management. Formal didactic training on concepts of deliberative thinking has had limited success in reducing the presence of UCBs during clinical decision-making. The evolution of clinical decision support tools (CDSTs) has demonstrated efficacy in improving deliberative clinical decision-making, possibly by reducing the intrusion of maladaptive UCBs and forcing reflective thinking. Anesthesiology remains a leader in perioperative crisis simulation and CDST implementation, but spearheading innovations to reduce the adverse impact of UCBs will further improve diagnostic precision and patient safety during perioperative crisis management.

Exploring Knowledge of Cognitive Disposition to Respond in Clinical Decision-Making among Early Clinical Learners

Introduction: Background: Errors in clinical medicine are a constant occurrence leading to litigations. Cognitive disposition to respond (CDR) was considered the underlying cause of errors in the decision-making process. The rapid thinking system used by early clinical learners leads to cognitive errors. The scenario gets compounded by a limited awareness and lack of exposure to cognitive dispositions to respond (CDRs) in the formal medical graduate curriculum. Variation in the frequency of occurrence in a real-life context, contradictory claims over the influence of clinical expertise, specialty, and methods of teaching about cognitive biases led to this exploratory study among early clinical learners. Methods:In the present research, subjects' recruitment was based on voluntary participation of medical graduates, interns, and residents, who were assigned to equal groups of 30. All study groups were first exposed to six commonly occurring CDRs as role-play-based videos, followed by a case scenario-based exposure to 13 uncommon CDRs in the clinical setting. Participants were then asked to reflect on the experience. Data was analyzed by descriptive and thematic analysis. Results:Among the 19 tested biases, 75.6% of participants identified Hindsight bias. The sunk cost was found out by 34.4% of participants only. Information processing bias was identified more frequently than psychological processing bias. Intergroup variability showed that residents were not identifying biases as frequently as medical graduates and interns. The role play video with reflective writing was voiced as the suitable training method for cognitive bias. Conclusion:The study concludes that medical graduates must be sensitized to CDRs using role-play-based reflection methods.

Minimal invasiveness in gingival augmentation and root coverage procedures

Minimally invasive surgical procedures aim at optimal wound healing, a reduction of postoperative morbidity and, thus, at increased patient satisfaction. The present article reviews the concept of minimal invasiveness in gingival augmentation and root coverage procedures, and critically discusses the influencing factors, technical and nontechnical ones, and relates them to the underlying biological mechanisms. Furthermore, the corresponding outcomes of the respective procedures are assessed and evaluated in relation to a possible impact of a minimized surgical invasiveness on the clinical, aesthetic, and patient-related results.

The Impact of Cognitive Biases on Professionals' Decision-Making: A Review of Four Occupational Areas

The author reviewed the research on the impact of cognitive biases on professionals' decision-making in four occupational areas (management, finance, medicine, and law). Two main findings emerged. First, the literature reviewed shows that a dozen of cognitive biases has an impact on professionals' decisions in these four areas, overconfidence being the most recurrent bias. Second, the level of evidence supporting the claim that cognitive biases impact professional decision-making differs across the areas covered. Research in finance relied primarily upon secondary data while research in medicine and law relied mainly upon primary data from vignette studies (both levels of evidence are found in management). Two research gaps are highlighted. The first one is a potential lack of ecological validity of the findings from vignette studies, which are numerous. The second is the neglect of individual differences in cognitive biases, which might lead to the false idea that all professionals are susceptible to biases, to the same extent. To address that issue, we suggest that reliable, specific measures of cognitive biases need to be improved or developed.

Cue utilisation reduces the impact of response bias in histopathology

Histopathologists make diagnostic decisions that are thought to be based on pattern recognition, likely informed by cue-based associations formed in memory, a process known as cue utilisation. Typically, the cases presented to the histopathologist have already been classified as 'abnormal' by clinical examination and/or other diagnostic tests. This results in a high disease prevalence, the potential for 'abnormality priming', and a response bias leading to false positives on normal cases. This study investigated whether higher cue utilisation is associated with a reduction in positive response bias in the diagnostic decisions of histopathologists. Data were collected from eighty-two histopathologists who completed a series of demographic and experience-related questions and the histopathology edition of the Expert Intensive Skills Evaluation 2.0 (EXPERTise 2.0) to establish behavioural indicators of context-related cue utilisation. They also completed a separate, diagnostic task comprising breast histopathology images where the frequency of abnormality was manipulated to create a high disease prevalence context for diagnostic decisions relating to normal tissue. Participants were assigned to higher or lower cue utilisation groups based on their performance on EXPERTise 2.0. When the effects of experience were controlled, higher cue utilisation was specifically associated with a greater accuracy classifying normal images, recording a lower positive response bias. This study suggests that cue utilisation may play a protective role against response biases in histopathology settings.

Clinical peer Review; A mandatory process with potential inherent bias in desperate need of reform

Clinical peer review, a process mandated across all hospitals in the USA, originated as a measure to protect patients by ensuring a standardized level of medical service that is provided by all practicing physicians. The process involves retrospective chart reviewing to assess the quality of patients' care provided by physicians as well as adherence to the most appropriate guidelines. The process of clinical peer review almost entirely serves its ultimate purpose in quality preservation; However, certain laws gave immunity to reviewers resulting in abuse and using the clinical peer review process for secondary gain. Some notable cases of abuse were discussed in the article, we also shed light on two forms of bias that can potentially interfere with the review process and the dreaded outcomes that come along a negative peer review. We also propose methods to overcome these biases to further standardize and improve this crucial process.

Clinician Conceptualization of the Benefits of Treatments for Individual Patients

IMPORTANCE

Knowing the expected effect of treatment on an individual patient is essential for patient care.

OBJECTIVE

To explore clinicians' conceptualizations of the chance that treatments will decrease the risk of disease outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This survey study of attending and resident physicians, nurse practitioners, and physician assistants was conducted in outpatient clinical settings in 8 US states from June 2018 to November 2019. The survey was an in-person, paper, 26-item survey in which clinicians were asked to estimate the probability of adverse disease outcomes and expected effects of therapies for diseases common in primary care.

MAIN OUTCOMES AND MEASURES

Estimated chance that treatments would benefit an individual patient.

RESULTS

Of 723 clinicians, 585 (81%) responded, and 542 completed all the questions necessary for analysis, with a median (interquartile range [IQR]) age of 32 (29-44) years, 287 (53%) women, and 294 (54%) White participants. Clinicians consistently overestimated the chance that treatments would benefit an individual patient. The median (IQR) estimated chance that warfarin would prevent a stroke in the next year was 50% (5%-80%) compared with scientific evidence, which indicates an absolute risk reduction (ARR) of 0.2% to 1.0% based on a relative risk reduction (RRR) of 39% to 50%. The median (IQR) estimated chance that antihypertensive therapy would prevent a cardiovascular event within 5 years was 30% (10%-70%) vs evidence of an ARR of 0% to 3% based on an RRR of 0% to 28%. The median (IQR) estimated chance that bisphosphonate therapy would prevent a hip fracture in the next 5 years was 40% (10%-60%) vs evidence of ARR of 0.1% to 0.4% based on an RRR of 20% to 40%. The median (IQR) estimated chance that moderate-intensity statin therapy would prevent a cardiovascular event in the next 5 years was 20% (IQR 5%-50%) vs evidence of an ARR of 0.3% to 2% based on an RRR of 19% to 33%. Estimates of the chance that a treatment would prevent an adverse outcome exceeded estimates of the absolute chance of that outcome for 60% to 70% of clinicians. Clinicians whose overestimations were greater were more likely to report using that treatment for patients in their practice (eg, use of warfarin: correlation coefficient, 0.46; 95% CI, 0.40-0.53; P < .001).

CONCLUSIONS AND RELEVANCE

In this survey study, clinicians significantly overestimated the benefits of treatment to individual patients. Clinicians with greater overestimates were more likely to report using treatments in actual patients.

Accuracy of Practitioner Estimates of Probability of Diagnosis Before and After Testing

IMPORTANCE

Accurate diagnosis is essential to proper patient care.

OBJECTIVE

To explore practitioner understanding of diagnostic reasoning.

DESIGN, SETTING, AND PARTICIPANTS

In this survey study, 723 practitioners at outpatient clinics in 8 US states were asked to estimate the probability of disease for 4 scenarios common in primary care (pneumonia, cardiac ischemia, breast cancer screening, and urinary tract infection) and the association of positive and negative test results with disease probability from June 1, 2018, to November 26, 2019. Of these practitioners, 585 responded to the survey, and 553 answered all of the questions. An expert panel developed the survey and determined correct responses based on literature review.

RESULTS

A total of 553 (290 resident physicians, 202 attending physicians, and 61 nurse practitioners and physician assistants) of 723 practitioners (76.5%) fully completed the survey (median age, 32 years; interquartile range, 29-44 years; 293 female [53.0%]; 296 [53.5%] White). Pretest probability was overestimated in all scenarios. Probabilities of disease after positive results were overestimated as follows: pneumonia after positive radiology results, 95% (evidence range, 46%-65%; comparison P < .001); breast cancer after positive mammography results, 50% (evidence range, 3%-9%; P < .001); cardiac ischemia after positive stress test result, 70% (evidence range, 2%-11%; P < .001); and urinary tract infection after positive urine culture result, 80% (evidence range, 0%-8.3%; P < .001). Overestimates of probability of disease with negative results were also observed as follows: pneumonia after negative radiography results, 50% (evidence range, 10%-19%; P < .001); breast cancer after negative mammography results, 5% (evidence range, <0.05%; P < .001); cardiac ischemia after negative stress test result, 5% (evidence range, 0.43%-2.5%; P < .001); and urinary tract infection after negative urine culture result, 5% (evidence range, 0%-0.11%; P < .001). Probability adjustments in response to test results varied from accurate to overestimates of risk by type of test (imputed median positive and negative likelihood ratios [LRs] for practitioners for chest radiography for pneumonia: positive LR, 4.8; evidence, 2.6; negative LR, 0.3; evidence, 0.3; mammography for breast cancer: positive LR, 44.3; evidence range, 13.0-33.0; negative LR, 1.0; evidence range, 0.05-0.24; exercise stress test for cardiac ischemia: positive LR, 21.0; evidence range, 2.0-2.7; negative LR, 0.6; evidence range, 0.5-0.6; urine culture for urinary tract infection: positive LR, 9.0; evidence, 9.0; negative LR, 0.1; evidence, 0.1).

CONCLUSIONS AND RELEVANCE

This survey study suggests that for common diseases and tests, practitioners overestimate the probability of disease before and after testing. Pretest probability was overestimated in all scenarios, whereas adjustment in probability after a positive or negative result varied by test. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

Diagnostic Errors Induced by a Wrong a Priori Diagnosis: A Prospective Randomized Simulator-Based Trial

Preventive strategies against diagnostic errors require the knowledge of underlying mechanisms. We examined the effects of a wrong a priori diagnosis on diagnostic accuracy of a focussed assessment in an acute myocardial infarction scenario. One-hundred-and-fifty-six medical students (cohort 1) were randomized to three study arms differing in the a priori diagnosis revealed: no diagnosis (control group), myocardial infarction (correct diagnosis group), and pulmonary embolism (wrong diagnosis group). Forty-four physicians (cohort 2) were randomized to the control group and the wrong diagnosis group. Primary endpoint was the participants’ final presumptive diagnosis. Among students, the correct diagnosis of an acute myocardial infarction was made by 48/52 (92%) in the control group, 49/52 (94%) in the correct diagnosis group, and 14/52 (27%) in the wrong diagnosis group (p < 0.001 vs. both other groups). Among physicians, the correct diagnosis was made by 20/21 (95%) in the control group and 15/23 (65%) in the wrong diagnosis group (p = 0.023). In the wrong diagnosis group, 31/52 (60%) students and 6/23 (19%) physicians indicated their initially given wrong a priori diagnosis pulmonary embolism as final diagnosis. A wrong a priori diagnosis significantly increases the likelihood of a diagnostic error during a subsequent patient encounter.

Artificial intelligence in dermatopathology: Diagnosis, education, and research

Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, highlighting the potential for deep learning algorithms to improve diagnostic workflow in dermatopathology of highly routine diagnoses. Additionally, convolutional neural networks can support the diagnosis of melanoma and may help predict disease outcomes. Capabilities of machine learning in dermatopathology can extend beyond clinical diagnosis to education and research. Intelligent tutoring systems can teach visual diagnoses in inflammatory dermatoses, with measurable cognitive effects on learners. Natural language interfaces can instruct dermatopathology trainees to produce diagnostic reports that capture relevant detail for diagnosis in compliance with guidelines. Furthermore, deep learning can power computation- and population-based research. However, there are many limitations of deep learning that need to be addressed before broad incorporation into clinical practice. The current potential of AI in dermatopathology is to supplement diagnosis, and dermatopathologist guidance is essential for the development of useful deep learning algorithms. Herein, the recent progress of AI in dermatopathology is reviewed with emphasis on how deep learning can influence diagnosis, education, and research.

If in Doubt Don't Act Out! Exploring Behaviours in Clinical Decision Making by General Surgeons Towards Surgical Procedures

BACKGROUND

Clinical decision-making (CDM) plays an integral role to surgeons work and has ramifications for patient outcomes and experience. The factors influencing a surgeons decision-making and the utility of cognitive decisional short cuts used in CDM known as 'heuristics' remains unknown. The aim of this paper is to explore how general surgeons make decisions in high-stake biliary tract clinical scenarios.

METHODS

This was a cross sectional survey comprising of two sections-a 'demographics section' and a 'clinical vignettes section'. Participants were recruited by an email distributed by the Royal College of Surgeons in Ireland. Non-parametric testing examined relationships and content analysis was applied for clinical reasoning.

RESULTS

73 participants or 37.6% of the overall population completed the survey. 71.4% of these were male. Most (50%) were higher trainees with moderate levels of overall reflective practice in decision-making. A majority of participants chose conservatively in high-stake biliary tract clinical cases with disease factors (43.5%) weighted highest, followed by personal factors (41.1%) and patient factors (15.4%) in clinical reasoning. The presence of a 'hook' associated with commonly used heuristics did not significantly change decision-making behaviour.

CONCLUSION

In high-stake scenarios, surgeons make conservative clinical decisions, predominantly dominated by disease and personal justifications. The utility of heuristics in lower-stake scenarios should be explored regarding clinical decision-making rationale and outcomes. Practitioners should consider use of patient factors in high-stake decisions to enable shared decision-making when appropriate which can reduce post-decisional regret and support the vision of patient-centred care.

[Effect of the use of heuristics on diagnostic error in Primary Care: Scoping review]

OBJECTIVE

To assess the use of representativeness, availability, overconfidence, anchoring and adjustment heuristics in clinical practice, specifically in Primary Care setting.

DESIGN

Panoramic review (scope review).

DATA SOURCES

OvidMedline, Scopus, PsycoINFO, Cochrane Library and PubMed databases. Each one of the selected studies was reviewed applying TIDIER criteria (Template for Description of the Intervention and Replication) to facilitate their understanding and replicability.

SELECTION OF STUDIES

A total of 48 studies were selected that analyzed availability heuristics (26), anchoring and adjustment (9), overconfidence (9) and representativeness (8).

RESULTS

From the 48 studies selected, 26 analyzed availability heuristics, 9 anchoring and adjustment, 9 overconfidence; and 8 representativeness. The study population included physicians (35.4%), patients (27%), trainees (20.8%), nurses (14.5%) and students (14.5%). The studies conducted in clinical practice setting were 17 (35.4%). In 33 of the 48 studies (68,7%) it was observed heuristic use in the population studied. Heuristics use on diagnostic process was found in 27 studies (54.1%); 5 of them (18%) were carried out in clinical practice setting. Of the 48 studies, 6 (12,5%) were performed in Primary Care, 3 of which studied diagnostic process: only one of them analyzed the use of heuristics in clinical practice setting, without demonstrating bias as consequence of the use of heuristic.

CONCLUSION

The evidence about heuristic use in diagnostic process on clinical practice setting is limited, especially in Primary Care.

Confidence level of pediatric trainees in management of shock states

AIM

To assess overall confidence level of trainees in assessing and treating shock, we sought to improve awareness of recurrent biases in clinical decision-making to help address appropriate educational interventions.

METHODS

Pediatric trainees on a national listserv were offered the opportunity to complete an electronic survey anonymously. Four commonly occurring clinical scenarios were presented, and respondents were asked to choose whether or not they would give fluid, rank factors utilized in decision-making, and comment on confidence level in their decision.

RESULTS

Pediatric trainees have a very low confidence level for assessment and treatment of shock. Highest confidence level is for initial assessment and treatment of shock involving American College of Critical Care Medicine/Pediatric Advanced Life Support recommendations. Children with preexisting cardiac comorbidities are at high risk of under-resuscitation.

CONCLUSION

Pediatric trainees nationwide have low confidence in managing various shock states, and would benefit from guidance and teaching around certain common clinical situations.

Errors in Mammography Cannot be Solved Through Technology Alone

Mammography has been the frontline screening tool for breast cancer for decades. However, high error rates in the form of false negatives (FNs) and false positives (FPs) have persisted despite technological improvements. Radiologists still miss between 10% and 30% of cancers while 80% of woman recalled for additional views have normal outcomes, with 40% of biopsied lesions being benign. Research show that the majority of cancers missed is actually visible and looked at, but either go unnoticed or are deemed to be benign. Causal agents for these errors include human related characteristics resulting in contributory search, perception and decision-making behaviours. Technical, patient and lesion factors are also important relating to positioning, compression, patient size, breast density and presence of breast implants as well as the nature and subtype of the cancer itself, where features such as architectural distortion and triple-negative cancers remain challenging to detect on screening. A better understanding of these causal agents as well as the adoption of technological and educational interventions, which audits reader performance and provide immediate perceptual feedback, should help. This paper reviews the current status of our knowledge around error rates in mammography and explores the factors impacting it. It also presents potential solutions for maximizing diagnostic efficacy thus benefiting the millions of women who undergo this procedure each year.

Quantifying Heuristic Bias: Anchoring, Availability, and Representativeness

Construct: Authors examined whether a new vignette-based instrument could isolate and quantify heuristic bias.

BACKGROUND

Heuristics are cognitive shortcuts that may introduce bias and contribute to error. There is no standardized instrument available to quantify heuristic bias in clinical decision making, limiting future study of educational interventions designed to improve calibration of medical decisions. This study presents validity data to support a vignette-based instrument quantifying bias due to the anchoring, availability, and representativeness heuristics.

APPROACH

Participants completed questionnaires requiring assignment of probabilities to potential outcomes of medical and nonmedical scenarios. The instrument randomly presented scenarios in one of two versions: Version A, encouraging heuristic bias, and Version B, worded neutrally. The primary outcome was the difference in probability judgments for Version A versus Version B scenario options.

RESULTS

Of 167 participants recruited, 139 enrolled. Participants assigned significantly higher mean probability values to Version A scenario options (M = 9.56, SD = 3.75) than Version B (M = 8.98, SD = 3.76), t(1801) = 3.27, p = .001. This result remained significant analyzing medical scenarios alone (Version A, M = 9.41, SD = 3.92; Version B, M = 8.86, SD = 4.09), t(1204) = 2.36, p = .02. Analyzing medical scenarios by heuristic revealed a significant difference between Version A and B for availability (Version A, M = 6.52, SD = 3.32; Version B, M = 5.52, SD = 3.05), t(404) = 3.04, p = .003, and representativeness (Version A, M = 11.45, SD = 3.12; Version B, M = 10.67, SD = 3.71), t(396) = 2.28, p = .02, but not anchoring. Stratifying by training level, students maintained a significant difference between Version A and B medical scenarios (Version A, M = 9.83, SD = 3.75; Version B, M = 9.00, SD = 3.98), t(465) = 2.29, p = .02, but not residents or attendings. Stratifying by heuristic and training level, availability maintained significance for students (Version A, M = 7.28, SD = 3.46; Version B, M = 5.82, SD = 3.22), t(153) = 2.67, p = .008, and residents (Version A, M = 7.19, SD = 3.24; Version B, M = 5.56, SD = 2.72), t(77) = 2.32, p = .02, but not attendings.

CONCLUSIONS

Authors developed an instrument to isolate and quantify bias produced by the availability and representativeness heuristics, and illustrated the utility of their instrument by demonstrating decreased heuristic bias within medical contexts at higher training levels.

Immersive high fidelity simulation of critically ill patients to study cognitive errors: a pilot study

BACKGROUND

The majority of human errors in healthcare originate from cognitive errors or biases. There is dearth of evidence around relative prevalence and significance of various cognitive errors amongst doctors in their first post-graduate year. This study was conducted with the objective of using high fidelity clinical simulation as a tool to study the relative occurrence of selected cognitive errors amongst doctors in their first post-graduate year.

METHODS

Intern simulation sessions on acute clinical problems, conducted in year 2014, were reviewed by two independent assessors with expertise in critical care. The occurrence of cognitive errors was identified using Likert scale based questionnaire and think-aloud technique. Teamwork and leadership skills were assessed using Ottawa Global Rating Scale.

RESULTS

The most prevalent cognitive errors included search satisfying (90%), followed by premature closure (PC) (78.6%), and anchoring (75.7%). The odds of occurrence of various cognitive errors did not change with time during internship, in contrast to teamwork and leadership skills (x² = 11.9, P = 0.01). Anchoring appeared to be significantly associated with delay in diagnoses (P = 0.007) and occurrence of PC (P = 0.005). There was a negative association between occurrence of confirmation bias and the ability to make correct diagnosis (P = 0.05).

CONCLUSIONS

Our study demonstrated a high prevalence of anchoring, premature closure, and search satisfying amongst doctors in their first post-graduate year, using high fidelity simulation as a tool. The occurrence of selected cognitive errors impaired clinical performance and their prevalence did not change with time.

A Bayesian perspective on constructing a written assessment of probabilistic clinical reasoning in experienced clinicians

RATIONALE

Decision-making performance assessments have proven problematic for assessing clinical reasoning.

AIMS AND OBJECTIVES

A Bayesian approach to designing an advanced clinical reasoning assessment is well grounded in mathematical and cognitive theory and may offer significant psychometric advantages. Probabilistic logic plays an important role in medical problem solving, and performances on Bayesian-type tasks appear to be causally-related to the ability to make sound clinical decisions.

METHODS

A validity argument is used to guide the design of an assessment of medical reasoning using clinical probabilities.

RESULTS/CONCLUSIONS

The practical advantage of using a Bayesian approach to item design relates to the fact that probability theory provides a rationally optimal method for managing uncertain information and provides the criteria for objective correct answer scoring. Potential item formats are discussed.

Cognitive biases associated with medical decisions: a systematic review

Background

Cognitive biases and personality traits (aversion to risk or ambiguity) may lead to diagnostic inaccuracies and medical errors resulting in mismanagement or inadequate utilization of resources. We conducted a systematic review with four objectives: 1) to identify the most common cognitive biases, 2) to evaluate the influence of cognitive biases on diagnostic accuracy or management errors, 3) to determine their impact on patient outcomes, and 4) to identify literature gaps.

Methods

We searched MEDLINE and the Cochrane Library databases for relevant articles on cognitive biases from 1980 to May 2015. We included studies conducted in physicians that evaluated at least one cognitive factor using case-vignettes or real scenarios and reported an associated outcome written in English. Data quality was assessed by the Newcastle-Ottawa scale. Among 114 publications, 20 studies comprising 6810 physicians met the inclusion criteria. Nineteen cognitive biases were identified.

Results

All studies found at least one cognitive bias or personality trait to affect physicians. Overconfidence, lower tolerance to risk, the anchoring effect, and information and availability biases were associated with diagnostic inaccuracies in 36.5 to 77 % of case-scenarios. Five out of seven (71.4 %) studies showed an association between cognitive biases and therapeutic or management errors. Of two (10 %) studies evaluating the impact of cognitive biases or personality traits on patient outcomes, only one showed that higher tolerance to ambiguity was associated with increased medical complications (9.7 % vs 6.5 %; p = .004). Most studies (60 %) targeted cognitive biases in diagnostic tasks, fewer focused on treatment or management (35 %) and on prognosis (10 %). Literature gaps include potentially relevant biases (e.g. aggregate bias, feedback sanction, hindsight bias) not investigated in the included studies. Moreover, only five (25 %) studies used clinical guidelines as the framework to determine diagnostic or treatment errors. Most studies (n = 12, 60 %) were classified as low quality.

Conclusions

Overconfidence, the anchoring effect, information and availability bias, and tolerance to risk may be associated with diagnostic inaccuracies or suboptimal management. More comprehensive studies are needed to determine the prevalence of cognitive biases and personality traits and their potential impact on physicians’ decisions, medical errors, and patient outcomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0377-1) contains supplementary material, which is available to authorized users.

Decision-making in rectal and colorectal cancer: systematic review and qualitative analysis of surgeons' preferences

Surgeons are experiencing difficulties implementing recommendations not only owing to incomplete, confusing or conflicting information but also to the increasing involvement of patients in decisions relating to their health. This study sought to establish which common factors including heuristic factors guide surgeons' decision-making in colon and rectal cancers. We conducted a systematic literature review of surgeons' decision-making factors related to colon and rectal cancer treatment. Eleven of 349 identified publications were eligible for data analyses. Using the IRaMuTeQ (Interface of R for the Multidimensional Analyses of Texts and Questionnaire), we carried out a qualitative analysis of the significant factors collected in the studies reviewed. Several validation procedures were applied to control the robustness of the findings. Five categories of factors (i.e. patient, surgeon, treatment, tumor and organizational cues) were found to influence surgeons' decision-making. Specifically, all decision criteria including biomedical (e.g. tumor information) and heuristic (e.g. surgeons' dispositional factors) criteria converged towards the factor 'age of patient' in the similarity analysis. In the light of the results, we propose an explanatory model showing the impact of heuristic criteria on medical issues (i.e. diagnosis, prognosis, treatment features, etc.) and thus on decision-making. Finally, the psychosocial complexity involved in decision-making is discussed and a medico-psycho-social grid for use in multidisciplinary meetings is proposed.

Do We Know Why We Make Errors in Morphological Diagnosis? An Analysis of Approach and Decision-Making in Haematological Morphology

BACKGROUND

The laboratory interpretation of blood film morphology is frequently a rapid, accurate, and cost-effective final-stage of blood count analysis. However, the interpretation of findings often rests with a single individual, and errors can carry significant impact. Cell identification and classification skills are well supported by existing resources, but the contribution and importance of other skills are less well understood.

METHODS

The UK external quality assurance group in haematology (UK NEQAS(H)) runs a Continued Professional Development scheme where large digital-images of abnormal blood smears are presented using a web-based virtual microscope. Each case is answered by more than 800 individuals. Morphological feature selection and prioritisation, as well as diagnosis and proposed action, are recorded. We analysed the responses of participants, aiming to identify successful strategies as well as sources of error.

FINDINGS

The approach to assessment by participants depended on the affected cell type, case complexity or skills of the morphologist. For cases with few morphological abnormalities, we found that accurate cell identification and classification were the principle requirements for success. For more complex films however, feature recognition and prioritisation had primary importance. Additionally however, we found that participants employed a range of heuristic techniques to support their assessment, leading to associated bias and error.

INTERPRETATION

A wide range of skills together allow successful morphological assessment and the complexity of this process is not always understood or recognised. Heuristic techniques are widely employed to support or reinforce primary observations and to simplify complex findings. These approaches are effective and are integral to assessment; however they may also be a source of bias or error. Improving outcomes and supporting diagnosis require the development of decision-support mechanisms that identify and support the benefits of heuristic strategies while identifying or avoiding associated biases.

FUNDING

The CPD scheme is funded by participant subscription.

Evidence-informed person-centered healthcare part I: do 'cognitive biases plus' at organizational levels influence quality of evidence?

INTRODUCTION

There is increasing concern about the unreliability of much of health care evidence, especially in its application to individuals.

HYPOTHESIS

Cognitive biases, financial and non-financial conflicts of interest, and ethical violations (which, together with fallacies, we collectively refer to as 'cognitive biases plus') at the levels of individuals and organizations involved in health care undermine the evidence that informs person-centred care.

METHODS

This study used qualitative review of the pertinent literature from basic, medical and social sciences, ethics, philosophy, law etc.

RESULTS

Financial conflicts of interest (primarily industry related) have become systemic in several organizations that influence health care evidence. There is also plausible evidence for non-financial conflicts of interest, especially in academic organizations. Financial and non-financial conflicts of interest frequently result in self-serving bias. Self-serving bias can lead to self-deception and rationalization of actions that entrench self-serving behaviour, both potentially resulting in unethical acts. Individuals and organizations are also susceptible to other cognitive biases. Qualitative evidence suggests that 'cognitive biases plus' can erode the quality of evidence.

CONCLUSIONS

'Cognitive biases plus' are hard wired, primarily at the unconscious level, and the resulting behaviours are not easily corrected. Social behavioural researchers advocate multi-pronged measures in similar situations: (i) abolish incentives that spawn self-serving bias; (ii) enforce severe deterrents for breaches of conduct; (iii) value integrity; (iv) strengthen self-awareness; and (v) design curricula especially at the trainee level to promote awareness of consequences to society. Virtuous professionals and organizations are essential to fulfil the vision for high-quality individualized health care globally.

Reexamining our bias against heuristics

Using heuristics offers several cognitive advantages, such as increased speed and reduced effort when making decisions, in addition to allowing us to make decision in situations where missing data do not allow for formal reasoning. But the traditional view of heuristics is that they trade accuracy for efficiency. Here the authors discuss sources of bias in the literature implicating the use of heuristics in diagnostic error and highlight the fact that there are also data suggesting that under certain circumstances using heuristics may lead to better decisions that formal analysis. They suggest that diagnostic error is frequently misattributed to the use of heuristics and propose an alternative view whereby content knowledge is the root cause of diagnostic performance and heuristics lie on the causal pathway between knowledge and diagnostic error or success.

Bias: a normal operating characteristic of the diagnosing brain

People diagnose themselves or receive advice about their illnesses from a variety of sources ranging from their family or friends, alternate medicine, or through conventional medicine. In all cases, the diagnosing mechanism is the human brain which normally operates under the influence of a variety of biases. Most, but not all biases, reside in intuitive decision making, and no individual or group is immune from them. Two biases in particular, bias blind spot and myside bias, have presented obstacles to accepting the impact of bias on medical decision making. Nevertheless, there is now a widespread appreciation of the important role of bias in the majority of medical disciplines. The dual process model of decision making now seems well accepted, although a polarization of opinions has arisen with some arguing the merits of intuitive approaches over analytical ones and vice versa. We should instead accept that it is not one mode or the other that enables well-calibrated thinking but the discriminating use of both. A pivotal role for analytical thinking lies in its ability to allow decision makers the means to detach from the intuitive mode to mitigate bias; it is the gatekeeper for the final diagnostic decision. Exploring and cultivating such debiasing initiatives should be seen as the next major research area in clinical decision making. Awareness of bias and strategies for debiasing are important aspects of the critical thinker’s armamentarium. Promoting critical thinking in undergraduate, postgraduate and continuing medical education will lead to better calibrated diagnosticians.

Cognitive Processes in Anesthesiology Decision Making

The quality and safety of health care are under increasing scrutiny. Recent studies suggest that medical errors, practice variability, and guideline noncompliance are common, and that cognitive error contributes significantly to delayed or incorrect diagnoses. These observations have increased interest in understanding decision-making psychology.

Many nonrational (i.e., not purely based in statistics) cognitive factors influence medical decisions and may lead to error. The most well-studied include heuristics, preferences for certainty, overconfidence, affective (emotional) influences, memory distortions, bias, and social forces such as fairness or blame.

Although the extent to which such cognitive processes play a role in anesthesia practice is unknown, anesthesia care frequently requires rapid, complex decisions that are most susceptible to decision errors. This review will examine current theories of human decision behavior, identify effects of nonrational cognitive processes on decision making, describe characteristic anesthesia decisions in this context, and suggest strategies to improve decision making.

Cognitive debiasing 2: impediments to and strategies for change

In a companion paper, we proposed that cognitive debiasing is a skill essential in developing sound clinical reasoning to mitigate the incidence of diagnostic failure. We reviewed the origins of cognitive biases and some proposed mechanisms for how debiasing processes might work. In this paper, we first outline a general schema of how cognitive change occurs and the constraints that may apply. We review a variety of individual factors, many of them biases themselves, which may be impediments to change. We then examine the major strategies that have been developed in the social sciences and in medicine to achieve cognitive and affective debiasing, including the important concept of forcing functions. The abundance and rich variety of approaches that exist in the literature and in individual clinical domains illustrate the difficulties inherent in achieving cognitive change, and also the need for such interventions. Ongoing cognitive debiasing is arguably the most important feature of the critical thinker and the well-calibrated mind. We outline three groups of suggested interventions going forward: educational strategies, workplace strategies and forcing functions. We stress the importance of ambient and contextual influences on the quality of individual decision making and the need to address factors known to impair calibration of the decision maker. We also emphasise the importance of introducing these concepts and corollary development of training in critical thinking in the undergraduate level in medical education.