The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking

Selective processing of clinical information related to correct and incorrect diagnoses: An eye-tracking experiment

INTRODUCTION

Diagnostic errors are often attributed to erroneous selection and interpretation of patients' clinical information, due to either cognitive biases or knowledge deficits. However, whether the selection or processing of clinical information differs between correct and incorrect diagnoses in written clinical cases remains unclear. We hypothesised that residents would spend more time processing clinical information that was relevant to their final diagnosis, regardless of whether their diagnosis was correct.

METHODS

In this within-subjects eye-tracking experiment, 19 internal or emergency medicine residents diagnosed 12 written cases. Half the cases contained a correct diagnostic suggestion and the others an incorrect suggestion. We measured how often (i.e. number of fixations) and how long (i.e. dwell time) residents attended to clinical information relevant for either suggestion. Additionally, we measured confidence and time to diagnose in each case.

RESULTS

Residents looked longer and more often at clinical information relevant for the correct diagnostic suggestion if they received an incorrect suggestion and were able to revise this suggestion to the correct diagnosis (dwell time: M: 6.3 seconds, SD: 5.1 seconds; compared to an average of 4 seconds in other conditions; number of fixations: M: 25 fixations, SD: 20; compared to an average of 16-17 fixations). Accordingly, time to diagnose was longer in cases with an incorrect diagnostic suggestion (M: 86 seconds, SD: 47 seconds; compared to an average of 70 seconds in other conditions). Confidence (range: 64%-67%) did not differ depending on residents' accuracy or the diagnostic suggestion.

DISCUSSION

Selectivity in information processing was not directly associated with an increase in diagnostic errors but rather seemed related to recognising and revising a biased suggestion in favour of the correct diagnosis. This could indicate an important role for case-specific knowledge in avoiding biases and diagnostic errors. Future research should examine information processing for other types of clinical information.

Establishing the "North Star" for Clinical Education to Accelerate Practice Readiness: Implications for Leaders, Faculty, Educators, Nursing Professional Development Practitioners, and Preceptors

Ambiguity and confusion persist around the goal and purpose of clinical experiences in nursing education. As a result of this ambiguity and confusion, the potential exists to waste valuable clinical time, to create ineffective learning objectives, to focus on the tasks of nursing rather than the competencies required for nursing practice, and to perpetuate a mismatch of in-person clinical experiences with simulation decreasing the ability to achieve clinical practice readiness. Nurse leaders across academia, regulation and accreditation agencies, and practice settings must establish consensus about the purpose and goals of clinical education in order to align learning outcomes and expectations of clinical practice readiness. This article synthesizes Dr Patricia Benner's vast body of writings to create a universal "North Star" to align and guide all nurse educators and clinical education on appropriate learning outcomes, andragogies, and expectations for nursing students and new graduates to support clinical practice readiness and the formation of nurses' skilled know-how, notions of good practice, clinical reasoning and more that are required for excellent nursing practice. Leaders in academia, regulation, accreditation, and practice should adopt the goals delineated in this "North Star" as being the end-points of practice formation, create learning experiences to support practice formation from novice to expert, and create meaningful evaluation strategies that go beyond traditional psychometric testing. When academia, regulation, accreditation, and practice all adopt these goals of clinical education, we contend that there will be congruence and more seamless transitions across the academic-regulation-accreditation-practice spectrum.

Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback

BACKGROUND

Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.

OBJECTIVE

We aimed to show that VPs powered by large language models (LLMs) can generate authentic dialogues, accurately represent patient preferences, and provide personalized feedback on clinical performance. We also explored using LLMs to rate the quality of dialogues and feedback.

METHODS

We conducted an intrinsic evaluation study rating 60 VP-clinician conversations. We used carefully engineered prompts to direct OpenAI's generative pretrained transformer (GPT) to emulate a patient and provide feedback. Using 2 outpatient medicine topics (chronic cough diagnosis and diabetes management), each with permutations representing different patient preferences, we created 60 conversations (dialogues plus feedback): 48 with a human clinician and 12 "self-chat" dialogues with GPT role-playing both the VP and clinician. Primary outcomes were dialogue authenticity and feedback quality, rated using novel instruments for which we conducted a validation study collecting evidence of content, internal structure (reproducibility), relations with other variables, and response process. Each conversation was rated by 3 physicians and by GPT. Secondary outcomes included user experience, bias, patient preferences represented in the dialogues, and conversation features that influenced authenticity.

RESULTS

The average cost per conversation was US $0.51 for GPT-4.0-Turbo and US $0.02 for GPT-3.5-Turbo. Mean (SD) conversation ratings, maximum 6, were overall dialogue authenticity 4.7 (0.7), overall user experience 4.9 (0.7), and average feedback quality 4.7 (0.6). For dialogues created using GPT-4.0-Turbo, physician ratings of patient preferences aligned with intended preferences in 20 to 47 of 48 dialogues (42%-98%). Subgroup comparisons revealed higher ratings for dialogues using GPT-4.0-Turbo versus GPT-3.5-Turbo and for human-generated versus self-chat dialogues. Feedback ratings were similar for human-generated versus GPT-generated ratings, whereas authenticity ratings were lower. We did not perceive bias in any conversation. Dialogue features that detracted from authenticity included that GPT was verbose or used atypical vocabulary (93/180, 51.7% of conversations), was overly agreeable (n=56, 31%), repeated the question as part of the response (n=47, 26%), was easily convinced by clinician suggestions (n=35, 19%), or was not disaffected by poor clinician performance (n=32, 18%). For feedback, detractors included excessively positive feedback (n=42, 23%), failure to mention important weaknesses or strengths (n=41, 23%), or factual inaccuracies (n=39, 22%). Regarding validation of dialogue and feedback scores, items were meticulously developed (content evidence), and we confirmed expected relations with other variables (higher ratings for advanced LLMs and human-generated dialogues). Reproducibility was suboptimal, due largely to variation in LLM performance rather than rater idiosyncrasies.

CONCLUSIONS

LLM-powered VPs can simulate patient-clinician dialogues, demonstrably represent patient preferences, and provide personalized performance feedback. This approach is scalable, globally accessible, and inexpensive. LLM-generated ratings of feedback quality are similar to human ratings.

Benjamin's translation as dialectical abduction: a novel epistemic framework for diagnostic hypothesizing

In this paper I present a novel understanding of diagnostic hypothesis that draws ideas from Walter Benjamin's work on translation. My framework originates from previous literature that aligns diagnostic hypothesis with Peircean 'abduction.' I argue that the abductive step, rather than being an inference to the best explanation, is a strategic conjecture that is simultaneously interrogative and interpretive. While Peirce places the burden of interpretation solely on semiotic analysis, I develop a form of dialectical abduction that draws on Benjamin's distinction between semiotic and mimetic faculties of language. I further argue that while all abduction functions through language interpretation, diagnostic abduction works not simply as interpretation but is more accurately described as the translation of patient narrative and clinician investigation into the language of clinical medicine. I then analyze diagnostic translation within the dialectical framework for translation described by Benjamin, and use this model to develop suggestions for a methodology of clinical abduction.

Beyond thinking fast and slow: Implications of a transtheoretical model of clinical reasoning and error on teaching, assessment, and research

From dual process to a family of theories known collectively as situativity, both micro and macro theories of cognition inform our current understanding of clinical reasoning (CR) and error. CR is a complex process that occurs in a complex environment, and a nuanced, expansive, integrated model of these theories is necessary to fully understand how CR is performed in the present day and in the future. In this perspective, we present these individual theories along with figures and descriptive cases for purposes of comparison before exploring the implications of a transtheoretical model of these theories for teaching, assessment, and research in CR and error.

Sexual History-Taking in a Surgery Clerkship Assessment: A Stubborn Clinical Skills Gap With Reproductive Health Care Implications

PURPOSE

Patients present with sexual and reproductive health needs in various clinical settings, so knowing when and how to elicit a relevant sexual history is critical in any specialty. This work examined whether reinforcing the surgical relevance of sexual health with an integrated training improved third-year medical students' sexual history-taking.

METHOD

Third-year surgery clerkship standardized patient assessments were video coded from a 2021-2022 comparison and 2022-2023 intervention cohort (University of Louisville School of Medicine). The case used for both cohorts involved a 38-year-old patient assigned female at birth presenting with acute right lower abdominal pain. Before the assessment, the intervention cohort received an additional clerkship didactic session focused on evaluation and assessment of the acute abdomen emphasizing the importance of sexual history-taking for surgical patients. The frequency of sexual histories attempted, number of questions, topics discussed, and differential diagnoses were compared.

RESULTS

There was not a significant difference between cohorts' sexual history-taking frequency: 61% (72/119) of students in the comparison cohort, compared to 65% (86/132) in the intervention cohort. On average, students in the intervention group asked 6 questions related to sexual health, compared to 3 questions by students in the comparison group ( P < .001). Across cohorts, 66% (104/158) of students who took a sexual history considered sexual diagnoses on their differential, compared to only 23% (21/93) of students who did not take a sexual history ( P < .001).

CONCLUSIONS

Reinforcing the clinical relevance of sexual history-taking for surgical patients was associated with higher-quality histories and broader differentials but not higher rates of sexual history-taking. Because there is now variability in how reproductive health care can be delivered across the United States, medical educators must refocus on sexual history-taking skills in a variety of settings and work to address barriers preventing students from collecting this information.

Strategies for improving diagnostic safety and clinical reasoning

Diagnostic safety failures cause up to 15% of adverse health care-related events, many of which have serious consequences. The nature of diagnostic errors is complex and involves individual factors, such as cognitive and availability biases, as well as factors related to organizations and work dynamics. Through this document, the Health Care Quality and Patient Safety Committee of the Asociación Española de Pediatría (Spanish Association of Pediatrics) offers an updated review of the bases of diagnostic error and its characteristics in different health care settings, and proposes strategies for improving diagnostic safety and clinical reasoning, including educational and care delivery aspects and the application of novel technological resources, such as those based on artificial intelligence.

Exploring the perception of pre-clinical and clinical educators on clinical reasoning: A qualitative study

INTRODUCTION

Educators have differing perception on the definition and conceptualization of clinical reasoning. Even though clinical reasoning is important in making a sound diagnosis and reducing diagnostic error, educators proved to be a barrier in teaching clinical reasoning due to the lack of awareness of their own reasoning process.

OBJECTIVES

This study was conducted to investigate the perception and understanding of pre-clinical and clinical educators on what clinical reasoning entails, their experience, and educational strategies in teaching clinical reasoning.

METHOD

A semi-structured interview was conducted with fifteen educators encompassing pre-clinical (basic science, laboratory-based) and clinical (surgical-based, medical-based, community-based and emergency medicine) educators. The transcribed interview data was then analysed thematically.

RESULTS

Eight main themes were identified. Knowledge and experience were seen as important components in developing clinical reasoning. It was possible to teach clinical reasoning although there were some difficulties thus the need to have a train-the-trainer programme. Early introduction of clinical reasoning with its incorporation in various teaching and learning method; and the involvement of technological advances were also mentioned by the participants. However, pre-clinical educators did not perceive the importance of these technological advances. Role of educators; cognitive and non-cognitive attributes were also important in developing clinical reasoning.

CONCLUSION

The perception and understanding of pre-clinical and clinical educators on clinical reasoning did not really differ. They believed that clinical reasoning can be taught, and a train-the-trainer program may be of immeasurable benefit.

Diagnostic reasoning and cognitive error in emergency medicine: Implications for teaching and learning

BACKGROUND

Accurate diagnosis in emergency medicine (EM) is high stakes and challenging. Research into physicians' clinical reasoning has been ongoing since the late 1970s. The dual-process theory has established itself as a valid model, including in EM. It is based on the distinction between two information-processing systems. System 1 rapidly generates one or more diagnostic hypotheses almost instantaneously, driven by experiential knowledge, while System 2 proceeds more slowly and analytically, applying formal rules to arrive at a final diagnosis.

METHODS

We reviewed the literature on dual-process theory in the fields of cognitive science, medical education and emergency medicine.

RESULTS AND CONCLUSION

The literature reflects two prominent interpretations regarding the relationship between the fast and slow phases and these interpretations carry very different implications for the training of clinical learners. One interpretation, prominent in the EM community, presents it as a "check-and-balance" framework in which most diagnostic error is caused by cognitive biases originating within System 1. As a result, EM residents are frequently advised to deploy analytical (System 2) strategies to correct such biases. However, such teaching approaches are not supported by research into the nature of diagnostic reasoning. An alternative interpretation assumes a harmonious relationship between Systems 1 and 2 in which both fast and slow processes are driven by underlying knowledge that conditions performance and the occurrence of errors. Educational strategies corresponding to this alternative have not been explored in the EM literature. In this paper, we offer proposals for improving the teaching and learning of diagnostic reasoning by EM residents.

Bias Sensitivity in Diagnostic Decision-Making: Comparing ChatGPT with Residents

BACKGROUND

Diagnostic errors, often due to biases in clinical reasoning, significantly affect patient care. While artificial intelligence chatbots like ChatGPT could help mitigate such biases, their potential susceptibility to biases is unknown.

METHODS

This study evaluated diagnostic accuracy of ChatGPT against the performance of 265 medical residents in five previously published experiments aimed at inducing bias. The residents worked in several major teaching hospitals in the Netherlands. The biases studied were case-intrinsic (presence of salient distracting findings in the patient history, effects of disruptive patient behaviors) and situational (prior availability of a look-alike patient). ChatGPT's accuracy in identifying the most-likely diagnosis was measured.

RESULTS

Diagnostic accuracy of residents and ChatGPT was equivalent. For clinical cases involving case-intrinsic bias, both ChatGPT and the residents exhibited a decline in diagnostic accuracy. Residents' accuracy decreased on average 12%, while the accuracy of ChatGPT 4.0 decreased 21%. Accuracy of ChatGPT 3.5 decreased 9%. These findings suggest that, like human diagnosticians, ChatGPT is sensitive to bias when the biasing information is part of the patient history. When the biasing information was extrinsic to the case in the form of the prior availability of a look-alike case, residents' accuracy decreased by 15%. By contrast, ChatGPT's performance was not affected by the biasing information. Chi-square goodness-of-fit tests corroborated these outcomes.

CONCLUSIONS

It seems that, while ChatGPT is not sensitive to bias when biasing information is situational, it is sensitive to bias when the biasing information is part of the patient's disease history. Its utility in diagnostic support has potential, but caution is advised. Future research should enhance AI's bias detection and mitigation to make it truly useful for diagnostic support.

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement. AI can streamline the information-gathering process by automating data retrieval, reducing cognitive load, and providing clinicians with essential patient details quickly. AI-driven CDS systems enhance diagnostic decision making by offering real-time insights, reducing cognitive biases, and prioritizing differential diagnoses. Furthermore, AI-powered feedback loops can facilitate continuous learning and refinement of diagnostic processes by providing targeted education and outcome feedback to clinicians. By integrating AI into these areas, the potential for reducing diagnostic errors and improving patient safety in the ED is substantial. However, successfully implementing AI in the ED is challenging and complex. Developing, validating, and implementing AI as a safe, human-centered ED tool requires thoughtful design and meticulous attention to ethical and practical considerations. Clinicians and patients must be integrated as key stakeholders across these processes. Ultimately, AI should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.

Cognitive bias in the patient encounter: Part I. Background and significance

Cognitive bias may lead to diagnostic error in the patient encounter. There are hundreds of different cognitive biases, but certain biases are more likely to affect patient diagnosis and management. As during morbidity and mortality rounds, retrospective evaluation of a given case, with comparison to an optimal diagnosis, can pinpoint errors in judgment and decision-making. The study of cognitive bias also illuminates how we might improve the diagnostic process. In Part 1 of this series, cognitive bias is defined and placed within the background of dual process theory, emotion, heuristics, and the more neutral term judgment and decision-making bias.

Engaging in Lethal Means Safety (ELMS): An Evaluation of a Suicide Prevention Means Safety Training Program for Mental Health First Responders

Firearm-related suicide is a preventable yet leading cause of death in the United States, and screening for access to firearms in clinical settings is rare. The lack of screening in clinical practice may be compounded by the fact that clinicians are not often trained to effectively screen for access to lethal means. This article evaluates the effectiveness of the novel Engaging in Lethal Means Safety (ELMS) suicide prevention program. ELMS is a theoretically driven, empirically supported lethal means safety training program that trains mental health first responders in engaging clients in discussions of lethal means safety with a particular focus on firearm access and storage. Pretest, posttest, and one-month follow-up assessments measured knowledge of lethal means, confidence, and comfort in screening for access to lethal means, and future intent to counsel on means safety measures. All constructs increased significantly at posttest. Confidence was sustained at posttest. Comfort and intent to counsel levels decreased at follow-up but not to pretraining levels. Booster sessions are recommended for future iterations of the program to further sustain overall effects. Further, additional research using a treatment control is encouraged.

Hermeneutics as impediment to AI in medicine

Predictions that artificial intelligence (AI) will become capable of replacing human beings in domains such as medicine rest implicitly on a theory of mind according to which knowledge can be captured propositionally without loss of meaning. Generative AIs, for example, draw upon billions of written sources to produce text that most likely responds to a user's query, according to its probability heuristic. Such programs can only replace human beings in practices such as medicine if human language functions similarly and, like AI, does not rely on meta-textual resources to convey meaning. In this essay, I draw on the hermeneutic philosophy of Hans-Georg Gadamer to challenge this conception of human knowledge. I follow Gadamer in arguing that human understanding of texts is an interpretive process relying on previously received judgments that derive from the human person's situatedness in history, and these judgments differ from the rules guiding generative AI. Human understanding is also dialogical, as it depends on the 'fusion of horizons' with another person to the extent that one's own prejudices may come under question, something AI cannot achieve. Furthermore, artificial intelligence lacks a human body, which conditions human perception and understanding. I contend that these non-textual sources of meaning, which must remain obscure to AI, are important in moral practices such as medicine, particularly in history-taking, physical examination, diagnostic reasoning, and negotiating a treatment plan. Although AI can undoubtedly aid physicians in certain ways, it faces inherent limitations in replicating these core tasks of the physician-patient relationship.

Evaluating cognitive bias in clinical ethics supports: a scoping review

BACKGROUND

A variety of cognitive biases are known to compromise ethical deliberation and decision-making processes. However, little is known about their role in clinical ethics supports (CES).

METHODS

We searched five electronic databases (Pubmed, PsychINFO, the Web of Science, CINAHL, and Medline) to identify articles describing cognitive bias in the context of committees that deliberate on ethical issues concerning patients, at all levels of care. We charted the data from the retrieved articles including the authors and year of publication, title, CES reference, the reported cognitive bias, paper type, and approach.

RESULTS

Of an initial 572 records retrieved, we screened the titles and abstracts of 128 articles, and identified 58 articles for full review. Four articles were selected for inclusion. Two are empirical investigations of bias in two CES, and two are theoretical, conceptual papers that discuss cognitive bias during CES deliberations. Our main result first shows an overview of bias related to the working human environment and to information gathering that concerns different types of CES. Second, several determinants of cognitive bias were highlighted. Especially, stressful environments could be at risk of cognitive bias, whatever the clinical dilemma.

CONCLUSIONS

Whether a need for a better taxonomy of cognitive bias in CES is highlighted, a proposal is made to focus on individual, group, institutional and professional biases that can be present during clinical ethics deliberation. However, future studies need to focus on an ecological evaluation of CES deliberations, in order to better-characterize cognitive biases and to study how they impact the quality of ethical decision-making. This information would be useful in considering countermeasures to ensure that deliberation is as unbiased as possible, and allow the most appropriate ethical decision to emerge in response to the dilemma at hand.

Implicit bias assessment by career stage in medical education training: a narrative review

Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person based on individual characteristics. Early evaluation of implicit bias in medical training can prevent long-term adverse health outcomes related to racial bias. However, to our knowledge, no present studies examine the sequential assessment of implicit bias through the different stages of medical training. The objective of this narrative review is to examine the breadth of existing publications that assess implicit bias at the current levels of medical training, pre-medical, graduate, and postgraduate. Protocol for this study was drafted using the Scale for the Assessment of Narrative Reviews (SANRA). Keyword literature search on peer-reviewed databases Google Scholar, PubMed, Ebsco, ScienceDirect, and MedEd Portal from January 1, 2017, to March 1, 2022, was used to identify applicable research articles. The online database search identified 1,512 articles. Full screening resulted in 75 papers meeting the inclusion criteria. Over 50% of extracted papers (74%) were published between 2019 and 2021 and investigated implicit bias at the post-graduate level (43%), followed by the graduate level (34%), and pre-medical level (9.4%). Fourteen percent were classified as mixed. Studies at the medical and medical graduate level identified an implicit preference towards white, male, non-LGBTQIA+, thin, patients. Study findings highlight notable gaps within the sequential assessment of implicit bias, specifically at the pre-medical training level. Longitudinal epidemiological research is needed to examine the long-term effect of implicit biases on existing healthcare disparities.

Large Language Models lack essential metacognition for reliable medical reasoning

Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions. We evaluated twelve models on dimensions including confidence-based accuracy, missing answer recall, and unknown recall. Despite high accuracy on multiple-choice questions, our study revealed significant metacognitive deficiencies across all tested models. Models consistently failed to recognize their knowledge limitations and provided confident answers even when correct options were absent. In this work, we show that current models exhibit a critical disconnect between perceived and actual capabilities in medical reasoning, posing significant risks in clinical settings. Our findings emphasize the need for more robust evaluation frameworks that incorporate metacognitive abilities, essential for developing reliable Large Language Model enhanced clinical decision support systems.

On context specificity and management reasoning: moving beyond diagnosis

OBJECTIVES

Diagnostic error is a global emergency. Context specificity is likely a source of the alarming rate of error and refers to the vexing phenomenon whereby a physician can see two patients with the same presenting complaint, identical history and examination findings, but due to the presence of contextual factors, decides on two different diagnoses. Studies have not empirically addressed the potential role of context specificity in management reasoning and errors with a diagnosis may not consistently translate to actual patient care.

METHODS

We investigated the effect of context specificity on management reasoning in individuals working within a simulated internal medicine environment. Participants completed two ten minute back to back common encounters. The clinical content of each encounter was identical. One encounter featured the presence of carefully controlled contextual factors (CF+ vs. CF-) designed to distract from the correct diagnosis and management. Immediately after each encounter participants completed a post encounter form.

RESULTS

Twenty senior medical students participated. The leading diagnosis score was higher (mean 0.88; SEM 0.07) for the CF- encounter compared with the CF+ encounter (0.58; 0.1; 95 % CI 0.04-0.56; p=0.02). Management reasoning scores were higher (mean 5.48; SEM 0.66) for the CF- encounter compared with the CF+ encounter (3.5; 0.56; 95 % CI 0.69-3.26; p=0.01). We demonstrated context specificity in both diagnostic and management reasoning.

CONCLUSIONS

This study is the first to empirically demonstrate that management reasoning, which directly impacts the patient, is also influenced by context specificity, providing additional evidence of context specificity's role in unwanted variance in health care.

Dropping the baton: Cognitive biases in emergency physicians

INTRODUCTION

Clinical medicine is becoming more complex and increasingly requires a team-based approach to deliver healthcare needs. This dispersion of cognitive reasoning across individuals, teams and systems (termed "distributed cognition") means that our understanding of cognitive biases and errors must expand beyond traditional "in-the-head" individual mental models and focus on a broader "out-in-the-world" context instead. To our knowledge, no qualitative studies thus far have examined cognitive biases in clinical settings from a team-based sociocultural perspective. Our study therefore seeks to explore how cognitive biases and errors among emergency physicians (EPs) arise due to sociocultural influences and lapses in team cognition.

METHODOLOGY

Our study team comprised four EPs of different seniorities from three different institutions and local and international academics who provided qualitative methodological guidance. We adopted a constructivist paradigm and employed a reflexive thematic analysis approach which acknowledged our researcher reflexivity. We conducted seven focus group discussions with 25 EPs who were purposively sampled for maximum variation. Our research question was: How do sociocultural factors lead to cognitive biases and medical errors among EPs?

RESULTS

Our themes coalesce around sociocultural pressures related to team psychology. In theme one, the EP is compelled by sociocultural pressures to blindly trust colleagues. In the second, the EP is obliged by cultural norms to be "nice" and neatly summarise cases into illness scripts during handovers. In the last, the EP is under immense pressure to follow conventional wisdom, comply with clinical protocols and not challenge inpatient specialists.

CONCLUSION

Cognitive biases and errors in clinical decision-making can arise due to lapses in distributed team cognition. Although this study focuses on emergency medicine, these pitfalls in team-based cognition are relevant across the entire continuum of care and across all specialties of medicine. The hyperacute nature of emergency medicine merely exacerbates and condenses these into a compressed timeframe. Indeed, similar relays are run every day in every discipline of medicine, with the same unified goal of doing the best for our patients while not committing cognitive errors and dropping the baton.

The effect of a provisional diagnosis on intern diagnostic reasoning: a mixed methods study

OBJECTIVES

Competency in diagnostic reasoning is integral to medical training and patient safety. Situativity theory highlights the importance of contextual factors on learning and performance, such as being informed of a provisional diagnosis prior to a patient encounter. This study aims to determine how being informed of a provisional diagnosis affects an intern's approach to diagnostic reasoning.

METHODS

This mixed methods study was conducted in a real-time workplace learning environment at a large teaching hospital. Interns were randomized to the Chief Complaint (CC) only or chief complaint with Provisional Diagnosis (PD) group. One blinded researcher assessed intern diagnostic reasoning using a validated tool. Mean group scores were compared using the two-sample t-test. The researcher was unblinded for think aloud interviews analyzed via thematic analysis.

RESULTS

There was no difference in performance between the CC and PD groups (mean ± SD): 47.8 ± 8.1 vs. 43.9 ± 10.9, p=0.24. Thematic analysis identified that interns aware of the provisional diagnosis 1) invested less effort in diagnostic reasoning, 2) formulated a differential through a narrowly focused frame, 3) accepted a provisional diagnosis as definitive, and 4) sought to confirm rather than refute the provisional diagnosis.

CONCLUSIONS

Our discordant results highlight the complex interplay between a provisional diagnosis and diagnostic reasoning performance in early learners. Though an accurate provisional diagnosis may enhance diagnostic reasoning outcomes, our qualitative results suggest that it may pose certain risks to the diagnostic reasoning process. Metacognitive strategies may be a ripe field for exploration to optimize this complex interplay.

Exploring Differences in Clinical Decisions Between Medical Students and Expert Clinicians

Background

Numerous challenges exist in effectively bridging theory and practice in the teaching and assessment of clinical reasoning, despite an abundance of theoretical models. This study compares clinical reasoning practices and decisions between medical students and expert clinicians using a problem-solving framework from the learning sciences, which identifies clinical reasoning as distinct, observable actions in clinical case solving. We examined students at various training stages against expert clinicians to address the research question: How do expert clinicians and medical students differ in their practices and decisions during the diagnostic process?.

Methods

We developed a questionnaire about a pediatric infectious disease case based on the problem-solving framework from the learning sciences to probe clinical reasoning decisions. The questionnaire had four sections: medical history, physical examination, medical tests, and working diagnosis. The questionnaire was administered at Stanford University between January 2019 and June 2023 to collect data from 10 experts and 74 medical students. We recruited participants through maximum variation sampling. We applied deductive content analysis to systematically code responses to identify patterns in the execution of the practices and decisions across the questionnaire.

Results

This research introduces a highly detailed, empirically developed framework that holds potential to bridge theory and practice, offering practical insights for medical instructors in teaching clinical reasoning to students across various stages of their training. This framework involves nine practices, with a total of twenty-nine decisions that need to be made when carrying out these practices. Differences between experts and students centered on ten decisions across the practices: Differential diagnosis formulation, Diagnostic plan and execution, Clinical data reassessment, and Clinical solution review.

Conclusion

We were able to identify nuanced differences in clinical reasoning between students and expert physicians under one comprehensive problem-solving framework from the learning sciences. Identifying key clinical reasoning practices and decision differences could help develop targeted instructional materials and assessment tools, aiding instructors in fostering clinical reasoning in students.

Beyond thinking fast and slow: a Bayesian intuitionist model of clinical reasoning in real-world practice

Clinical reasoning is a quintessential aspect of medical training and practice, and is a topic that has been studied and written about extensively over the past few decades. However, the predominant conceptualisation of clinical reasoning has insofar been extrapolated from cognitive psychological theories that have been developed in other areas of human decision-making. Till date, the prevailing model of understanding clinical reasoning has remained as the dual process theory which views cognition as a dichotomous two-system construct, where intuitive thinking is fast, efficient, automatic but error-prone, and analytical thinking is slow, effortful, logical, deliberate and likely more accurate. Nonetheless, we find that the dual process model has significant flaws, not only in its fundamental construct validity, but also in its lack of practicality and applicability in naturistic clinical decision-making. Instead, we herein offer an alternative Bayesian-centric, intuitionist approach to clinical reasoning that we believe is more representative of real-world clinical decision-making, and suggest pedagogical and practice-based strategies to optimise and strengthen clinical thinking in this model to improve its accuracy in actual practice.

Use of diagnostic tests in elderly patients consulting the emergency department. Analysis of the emergency department and elder needs cohort (EDEN-8)

OBJECTIVE

Analyse the association between the use of diagnostic tests and the characteristics of older patients 65 years of age or more who consult the emergency department (ED).

METHODS

We performed an analysis of the EDEN cohort that includes patients who consulted 52 Spanish EDs. The association of age, sex, and ageing characteristics with the use of diagnostic tests (blood tests, electrocardiogram (ECG), microbiological cultures, X-ray, computed tomography, ultrasound, invasive techniques) was studied. The association was analysed by calculating the adjusted odds ratios (aOR) and their 95 % confidence intervals (CI) using a logistic regression model.

RESULTS

A total of 25,557 patients were analysed. There was an increase in the use of diagnostic tests based on age, with an aOR for blood test of 1.805 (95 %CI 1.671 - 1.950), ECG 1.793 (95 %CI 1.664 - 1.932) and X-ray 1.707 (95 %CI 1.583 - 1.840) in the group of 85 years or more. The use of diagnostic tests is lower in the female population. Most ageing characteristics (cognitive impairment, previous falls, polypharmacy, dependence, and comorbidity) were independently associated with increased use of diagnostic tests.

CONCLUSIONS

Age, and the characteristics of ageing itself are generally associated with a greater use of diagnostic tests in the ED.

Going beyond competencies: Building blocks for a patient- and population-centered medical curriculum

INTRODUCTION

Changing health care requires changing medical education. In this position paper it is suggested that subsequent innovations in medical education each had their specific strengths and shortcomings. What they have, however, in common is that they place the medical student and their competencies at their center. Innovation in medical education is inward looking.

DISCUSSION

The authors propose a perspective on the medical curriculum in which the patient, their family, and the surrounding community take center stage. They argue that present medical education cannot adequately respond to the great challenges to population health: an aging population, the obesity epidemic, and future pandemics of new diseases due to population growth, urbanization, and antimicrobial resistance, particularly because these challenges cannot be dealt with by the medical sciences alone but need deep understanding of the social sciences as well. In addition, the practice of health care is changing: effective health care demands a close partnership between the health care system and the medical school which is mostly lacking, cooperation with other health professions is becoming more and more necessary in response to the increasing complexity of health care, patients and their families are required to play a more active role in their health, medical error threatening patient safety is becoming to be seen as a huge problem, and the emergence of artificial intelligence in education and practice, all requiring transformation of medical education.

CONCLUSION

The present contribution suggests eight such transformations necessary to create a truly patient- and population-centered medical curriculum.

Assessment of the organ function as the primary intention of clinical reasoning applied to the critically ill patient

This article examines how clinical reasoning about the critical patient is currently treated and draws attention to some critical issues already often highlighted in the literature. Traditional approaches to clinical reasoning, even when applied to critical patients, prioritize identifying structured diseases. In contrast, the critical care setting demands an alternative approach that aligns with the intensivist's goal of supporting or substituting vital organ functions. In this manuscript, we emphasized the reasons that make it primary for intensivists to obtain a diagnosis of function in order to act therapeutically. Moreover, we highlighted the challenges posed by diagnostic errors, often attributed to cognitive biases and shortcomings in clinical reasoning, which can adversely affect patient outcomes and resource utilization. We also discussed the complexities of clinical decision-making in emergency medical services, where physicians must perform rapid actions in the face of incomplete information and high uncertainty. We underscore the limitations of traditional information technology tools in facilitating practical clinical reasoning, advocating for the integration of relevant data that directly informs on organ function and pathophysiological mechanisms. This discourse emphasizes a deep understanding of physiology and pathophysiology as foundational for practical clinical reasoning in critical care. Finally, we propose a structured assessment method that prioritizes pinpointing the compromised organ function, elucidating the pathophysiological mechanism responsible, hypothesizing potential causes, and testing these hypotheses to guide therapeutic interventions. This approach aligns clinical reasoning with the intensivist's goal: supporting and restoring vital functions in the critically ill patient.

Enhancing therapeutic reasoning: key insights and recommendations for education in prescribing

BACKGROUND

Despite efforts to improve undergraduate clinical pharmacology & therapeutics (CPT) education, prescribing errors are still made regularly. To improve CPT education and daily prescribing, it is crucial to understand how therapeutic reasoning works. Therefore, the aim of this study was to gain insight into the therapeutic reasoning process.

METHODS

A narrative literature review has been performed for literature on cognitive psychology and diagnostic and therapeutic reasoning.

RESULTS

Based on these insights, The European Model of Therapeutic Reasoning has been developed, building upon earlier models and insights from cognitive psychology. In this model, it can be assumed that when a diagnosis is made, a primary, automatic response as to what to prescribe arises based on pattern recognition via therapy scripts (type 1 thinking). At some point, this response may be evaluated by the reflective mind (using metacognition). If it is found to be incorrect or incomplete, an alternative response must be formulated through a slower, more analytical and deliberative process, known as type 2 thinking. Metacognition monitors the reasoning process and helps a person to form new therapy scripts after they have chosen an effective therapy. Experienced physicians have more and richer therapy scripts, mostly based on experience and enabling conditions, instead of textbook knowledge, and therefore their type 1 response is more often correct.

CONCLUSION

Because of the important role of metacognition in therapeutic reasoning, more attention should be paid to metacognition in CPT education. Both trainees and teachers should be aware of the possibility to monitor and influence these cognitive processes. Further research is required to investigate the applicability of these insights and the adaptability of educational approaches to therapeutic reasoning.

Mindfulness-Based Interventions and the Hypothalamic-Pituitary-Adrenal Axis: A Systematic Review

BACKGROUND

Numerous studies have evaluated the effect that mindfulness-based interventions (MBIs) have on multiple health outcomes. For its part, stress is a natural response to environmental disturbances and within the associated metabolic responses, alterations in cortisol levels and their measurement in different tissues are a way to determine the stress state of an individual. Therefore, it has been proposed that MBIs can modify cortisol levels.

METHODS AND RESULTS

The objective of this systematic review was to analyze and summarize the different studies that have evaluated the effect of MBIs on cortisol levels. The following databases were consulted: MEDLINE, AMED, CINAHL, Web of Science, Science Direct, PsycINFO, SocINDEX, PubMed, the Cochrane Library and Scopus. The search terms "mindfulness", "mindfulness-based interventions" and "cortisol" were used (and the search was limited to studies from January 1990 to May 2024). In order to reduce selection bias, each article was scrutinized using the JBI Critical Appraisal Checklist independently by two authors. We included those studies with specified intervention groups with at least one control group and excluded duplicate studies or those in which the intervention or control group was not adequately specified. Significant changes in cortisol following MBIs were found in 25 studies, while 10 found no changes. The small sample size, lack of randomization, blinding, and probable confounding and interaction variables stand out in these studies.

CONCLUSION

MBIs have biological plausibility as a means of explaining a positive effect on cortisol levels; however, the weakness of the studies and the absence of robust designs makes it difficult to establish a causal association between both variables.

REGISTRATION NUMBER

INPLASY2024110017.

Examining the impact of sleep deprivation on medical reasoning's performance among anaesthesiology residents and doctors: a prospective study

BACKGROUND

Working long consecutive hours' is common for anaesthesia and critical care physicians. It is associated with impaired medical reasoning's performance of anaesthesiology and serious medical errors. However, no study has yet investigated the impact of working long consecutive hours' on medical reasoning.

OBJECTIVE

The present study evaluated the impact of working long consecutive hours' on the medical reasoning's performance of anaesthesiology and intensive care physicians (residents and seniors).

METHODS

This multicentric, prospective, cross-over study was conducted in 5 public hospitals of Normandy region. Two groups of anaesthesia and critical care physicians were formed. One was in a rest group, RG (after a 48-hours weekend without hospital work) and the other in Sleep Deprivation Group (SDG) after a 24 h-consecutives-shift. Changes in medical reasoning's performance were measured by 69-items script concordance tests (SCT) through to the two tests. Group A completed the first part of the assessment (Set A) after a weekend without work and the second part (Set B) after a 24 h-shift; group B did the same in reverse order. The primary outcome was medical reasoning's performance as measured by SCT in RG and SDG. The secondary outcomes included association between the performance with the demographic data, variation of the KSS (Karolinska sleepiness scale) daytime alertness score, the number of 24 h-shift during the previous 30 days, the vacations during the previous 30 days, the presence of more or less than 4 h consecutives hours slept, the management of a stressful event during the shift, the different resident years, the place where the shift took place (University hospital or general hospitals) and the type of shift (anaesthesia or intensive care).

RESULTS

84 physicians (26 physicians and 58 residents) were included. RG exhibited significantly higher performance scores than SDG (68 ± 8 vs. 65 ± 9, respectively; p = 0.008). We found a negative correlation between the number of 24 h-shifts performed during the previous month and the variation of medical reasoning's performance and no significant variation between professionals who slept 4 h or less and those who slept more than 4 h consecutively during the shift (-4 ± 11 vs. -2 ± 11; p = 0.42).

CONCLUSION

Our study suggests that medical reasoning' performance of anaesthesiologists, measured by the SCT, is reduced after 24 h-shift than after rest period. Working long consecutive hours' and many shifts should be avoided to prevent the occurrence of medical errors.

Non-standard diagnostic assessment reliability in psychiatry: a study in a Brazilian outpatient setting using Kappa

The use of Structured Diagnostic Assessments (SDAs) is a solution for unreliability in psychiatry and the gold standard for diagnosis. However, except for studies between the 50 s and 70 s, reliability without the use of Non-SDAs (NSDA) is seldom tested, especially in non-Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries. We aim to measure reliability between examiners with NSDAs for psychiatric disorders. We compared diagnostic agreement after clinician change, in an outpatient academic setting. We used inter-rater Kappa measuring 8 diagnostic groups: Depression (DD: F32, F33), Anxiety Related Disorders (ARD: F40-F49, F50-F59), Personality Disorders (PD: F60-F69), Bipolar Disorder (BD: F30, F31, F34.0, F38.1), Organic Mental Disorders (Org: F00-F09), Neurodevelopment Disorders (ND: F70-F99) and Schizophrenia Spectrum Disorders (SSD: F20-F29). Cohen's Kappa measured agreement between groups, and Baphkar's test assessed if any diagnostic group have a higher tendency to change after a new diagnostic assessment. We analyzed 739 reevaluation pairs, from 99 subjects who attended IPUB's outpatient clinic. Overall inter-rater Kappa was moderate, and none of the groups had a different tendency to change. NSDA evaluation was moderately reliable, but the lack of some prevalent hypothesis inside the pairs raised concerns about NSDA sensitivity to some diagnoses. Diagnostic momentum bias (that is, a tendency to keep the last diagnosis observed) may have inflated the observed agreement. This research was approved by IPUB's ethical committee, registered under the CAAE33603220.1.0000.5263, and the UTN-U1111-1260-1212.

The influence of context on diagnostic reasoning: A narrative synthesis of experimental findings

AIMS AND OBJECTIVES

Contextual information which is implicitly available to physicians during clinical encounters has been shown to influence diagnostic reasoning. To better understand the psychological mechanisms underlying the influence of context on diagnostic accuracy, we conducted a review of experimental research on this topic.

METHOD

We searched Web of Science, PubMed, and Scopus for relevant articles and looked for additional records by reading the references and approaching experts. We limited the review to true experiments involving physicians in which the outcome variable was the accuracy of the diagnosis.

RESULTS

The 43 studies reviewed examined two categories of contextual variables: (a) case-intrinsic contextual information and (b) case-extrinsic contextual information. Case-intrinsic information includes implicit misleading diagnostic suggestions in the disease history of the patient, or emotional volatility of the patient. Case-extrinsic or situational information includes a similar (but different) case seen previously, perceived case difficulty, or external digital diagnostic support. Time pressure and interruptions are other extrinsic influences that may affect the accuracy of a diagnosis but have produced conflicting findings.

CONCLUSION

We propose two tentative hypotheses explaining the role of context in diagnostic accuracy. According to the negative-affect hypothesis, diagnostic errors emerge when the physician's attention shifts from the relevant clinical findings to the (irrelevant) source of negative affect (for instance patient aggression) raised in a clinical encounter. The early-diagnosis-primacy hypothesis attributes errors to the extraordinary influence of the initial hypothesis that comes to the physician's mind on the subsequent collecting and interpretation of case information. Future research should test these mechanisms explicitly. Possible alternative mechanisms such as premature closure or increased production of (irrelevant) rival diagnoses in response to context deserve further scrutiny. Implications for medical education and practice are discussed.

Teaching and Assessment of Clinical Reasoning Skills in a Case-Based Veterinary Cardiology Elective

Clinical reasoning (CR) is an important clinical competency for effective veterinary practice. We hypothesized that implementing an explicit 7-week CR curriculum taught in a large-enrollment elective veterinary cardiology course would improve students' awareness of clinical reasoning principles, self-efficacy of CR skills, and application of CR principles in clinical case analyses. A secondary aim was to assess the impact of peer review as a means of providing feedback in a large classroom setting. A mixed method approach was used with veterinary students (N = 78) in a cardiology elective course meeting twice weekly for a half-semester (7 weeks). Course content included a 1-week introduction to CR led by the instructor and 6 weeks of instructor-facilitated, case-based learning. Quantitative and qualitative data were collected, including pre- and post-course surveys, weekly peer reviews for six clinical case assignments, and instructor-graded clinical cases for three case assignments. Students reported improved self-efficacy across all CR skill categories (p < .001) and significant improvement in applied CR skills was demonstrated in both peer- (p < .001) and instructor-graded assignments (p < .001). Peer reviews provided a means for students to reflect on and internalize CR skills, which may play a role in improved self-efficacy. In an elective cardiology course, implementing an explicit CR curriculum resulted in improved student awareness and self-efficacy of CR, as well as improved applied CR skills.

Role of knowledge and reasoning processes as predictors of resident physicians' susceptibility to anchoring bias in diagnostic reasoning: a randomised controlled experiment

BACKGROUND

Diagnostic errors have been attributed to reasoning flaws caused by cognitive biases. While experiments have shown bias to cause errors, physicians of similar expertise differed in susceptibility to bias. Resisting bias is often said to depend on engaging analytical reasoning, disregarding the influence of knowledge. We examined the role of knowledge and reasoning mode, indicated by diagnosis time and confidence, as predictors of susceptibility to anchoring bias. Anchoring bias occurs when physicians stick to an incorrect diagnosis triggered by early salient distracting features (SDF) despite subsequent conflicting information.

METHODS

Sixty-eight internal medicine residents from two Dutch university hospitals participated in a two-phase experiment. Phase 1: assessment of knowledge of discriminating features (ie, clinical findings that discriminate between lookalike diseases) for six diseases. Phase 2 (1 week later): diagnosis of six cases of these diseases. Each case had two versions differing exclusively in the presence/absence of SDF. Each participant diagnosed three cases with SDF (SDF+) and three without (SDF-). Participants were randomly allocated to case versions. Based on phase 1 assessment, participants were split into higher knowledge or lower knowledge groups.

MAIN OUTCOME MEASUREMENTS

frequency of diagnoses associated with SDF; time to diagnose; and confidence in diagnosis.

RESULTS

While both knowledge groups performed similarly on SDF- cases, higher knowledge physicians succumbed to anchoring bias less frequently than their lower knowledge counterparts on SDF+ cases (p=0.02). Overall, physicians spent more time (p<0.001) and had lower confidence (p=0.02) on SDF+ than SDF- cases (p<0.001). However, when diagnosing SDF+ cases, the groups did not differ in time (p=0.88) nor in confidence (p=0.96).

CONCLUSIONS

Physicians apparently adopted a more analytical reasoning approach when presented with distracting features, indicated by increased time and lower confidence, trying to combat bias. Yet, extended deliberation alone did not explain the observed performance differences between knowledge groups. Success in mitigating anchoring bias was primarily predicted by knowledge of discriminating features of diagnoses.

Inter- and intraindividual variability in virtual single-tooth implant positioning

OBJECTIVES

The purpose of this prospective study was to determine the inter- and intraindividual variability in virtual single-tooth implant positioning based on the level of expertise, specialty, total time spent, and the use of a prosthetic tooth setup.

MATERIALS AND METHODS

Virtual implant planning was performed on matched pre- and post-extraction intraoral scans (IOS), and cone-beam computed tomography scans of 15 patients. Twelve individual examiners, involving six novices and experts from oral surgery and prosthodontics positioned the implants, first based on anatomical landmarks utilizing only the post-extraction, and second with the use of the pre-extraction IOS as a setup. The time for implant positioning was recorded. After 1 month, all virtual plannings were performed again. The individual implant positions were superimposed to obtain 3D deviations using a software algorithm.

RESULTS

An interindividual variability with mean angular, crestal, and apical positional deviations of 3.8 ± 1.94°, 1.11 ± 0.55, and 1.54 ± 0.66 mm, respectively, was found. When assessing intraindividual variability, deviations of 3.28 ± 1.99°, 0.78 ± 0.46, and 1.12 ± 0.61 mm, respectively, were observed. Implants planned by experts exhibited statistically lower deviations compared to those planned by novices. Longer planning times resulted in lower deviations in the experts' group but not in the novices. Oral surgeons demonstrated lower crestal, but not angular and apical deviations than prosthodontists. The use of a setup only led to minor adjustments.

CONCLUSIONS

Substantial inter- and intraindividual variability exists during implant positioning utilizing specialized software planning. The level of expertise and the time invested influenced the deviations of the implant position during the planning sequence.

Dual process models of clinical reasoning: The central role of knowledge in diagnostic expertise

RATIONALE

Research on diagnostic reasoning has been conducted for fifty years or more. There is growing consensus that there are two distinct processes involved in human diagnostic reasoning: System 1, a rapid retrieval of possible diagnostic hypotheses, largely automatic and based to a large part on experiential knowledge, and System 2, a slower, analytical, conscious application of formal knowledge to arrive at a diagnostic conclusion. However, within this broad framework, controversy and disagreement abound. In particular, many authors have suggested that the root cause of diagnostic errors is cognitive biases originating in System 1 and propose that educating learners about the types of cognitive biases and their impact on diagnosis would have a major influence on error reduction.

AIMS AND OBJECTIVES

In the present paper, we take issue with these claims.

METHOD

We reviewed the literature to examine the extent to which this theoretical model is supported by the evidence.

RESULTS

We show that evidence derived from fundamental research in human cognition and studies in clinical medicine challenges the basic assumptions of this theory-that errors arise in System 1 processing as a consequence of cognitive biases, and are corrected by slow, deliberative analytical processing. We claim that, to the contrary, errors derive from both System 1 and System 2 reasoning, that they arise from lack of access to the appropriate knowledge, not from errors of processing, and that the two processes are not essential to the process of diagnostic reasoning.

CONCLUSIONS

The two processing modes are better understood as a consequence of the nature of the knowledge retrieved, not as independent processes.

Using case vignettes to study the presence of outcome, hindsight, and implicit bias in acute unplanned medical care: a cross-sectional study

BACKGROUND AND IMPORTANCE

Various biases can impact decision-making and judgment of case quality in the Emergency Department (ED). Outcome and hindsight bias can lead to wrong retrospective judgment of care quality, and implicit bias can result in unjust treatment differences in the ED based on irrelevant patient characteristics.

OBJECTIVES

First, to evaluate the extent to which knowledge of an outcome influences physicians' quality of care assessment. Secondly, to examine whether patients with functional disorders receive different treatment compared to patients with a somatic past medical history.

DESIGN

A web-based cross-sectional study in which physicians received case vignettes with a case description and care provided. Physicians were informed about vignette outcomes in a randomized way (no, good, or bad outcome). Physicians rated quality of care for four case vignettes with different outcomes. Subsequently, they received two more case vignettes. Physicians were informed about the past medical history of the patient in a randomized way (somatic or functional). Physicians made treatment and diagnostic decisions for both cases.

SETTING AND PARTICIPANTS

One hundred ninety-one Dutch emergency physicians (EPs) and general practitioners (GPs) participated.

OUTCOME MEASURES AND ANALYSIS

Quality of care was rated on a Likert scale (0-5) and dichotomized as adequate (yes/no). Physicians estimated the likelihood of patients experiencing a bad outcome for hindsight bias. For the second objective, physicians decided on prescribing analgesics and additional diagnostic tests.

MAIN RESULTS

Large differences existed in rated quality of care for three out of four vignettes based on different case outcomes. For example, physicians rated the quality of care as adequate in 44% (95% CI 33-57%) for an abdominal pain case with a bad outcome, compared to 88% (95% CI 78-94%) for a good outcome, and 84% (95% CI 73-91%) for no outcome ( P  < 0.01). The estimated likelihood of a bad outcome was higher if physicians received a vignette with a bad patient outcome. Fewer diagnostic tests were performed and fewer opioids were prescribed for patients with a functional disorder.

CONCLUSION

Outcome, hindsight, and implicit bias significantly influence decision-making and care quality assessment by Dutch EPs and GPs.

'Walking together': How relationships shape physicians' clinical reasoning

INTRODUCTION

The clinical reasoning literature has increasingly considered context as an important influence on physicians' thinking. Physicians' relationships with patients, and their ongoing efforts to maintain these relationships, are important influences on how clinical reasoning is contextualised. The authors sought to understand how physicians' relationships with patients shaped their clinical reasoning.

METHODS

Drawing from constructivist grounded theory, the authors conducted semi-structured interviews with primary care physicians. Participants were asked to reflect on recent challenging clinical experiences, and probing questions were used to explore how participants attended to or leveraged relationships in conjunction with their clinical reasoning. Using constant comparison, three investigators coded transcripts, organising the data into codes and conceptual categories. The research team drew from these codes and categories to develop theory about the phenomenon of interest.

RESULTS

The authors interviewed 15 primary care physicians with a range of experience in practice and identified patient agency as a central influence on participants' clinical reasoning. Participants drew from and managed relationships with patients while attending to patients' agency in three ways. First, participants described how contextualised illness constructions enabled them to individualise their approaches to diagnosis and management. Second, participants managed tensions between enacting their typical approaches to clinical problems and adapting their approaches to foster ongoing relationships with patients. Finally, participants attended to relationships with patients' caregivers, seeing these individuals' contributions as important influences on how their clinical reasoning could be enacted within patients' unique social contexts.

CONCLUSION

Clinical reasoning is influenced in important ways by physicians' efforts to both draw from, and maintain, their relationships with patients and patients' caregivers. Such efforts create tensions between their professional standards of care and their orientations toward patient-centredness. These influences of relationships on physicians' clinical reasoning have important implications for training and clinical practice.

Clinical reasoning-the essentials for teaching medical students, trainees and non-medical healthcare professionals

Clinical reasoning is fundamental for effective clinical practice. Traditional consultation models for teaching clinical reasoning or conventional approaches for teaching students how to make a diagnosis or management plan that rely on learning through observation only, are increasingly recognised as insufficient. There are also many challenges to supporting learners in developing clinical reasoning over time as well as across different clinical presentations and contexts. These challenges are compounded by the differences in how experts and novices make sense of clinical information, and the different cognitive processes each use when processing and communicating this information using precise medical language. Diagnostic errors may be due to cognitive biases but also, in a majority of cases, due to a lack of clinical knowledge. Therefore, effective educational strategies to develop clinical reasoning include identifying learners' knowledge gaps, using worked examples to prevent cognitive overload, promoting the use of key features and practising the construction of accurate problem representations. Deliberate reflection on diagnostic justification is also recommended, and overall, contributes to a growing number of evidence-based and theory-driven educational interventions for reducing diagnostic errors and improving patient care.

Diagnostic errors during perceptual learning in dermatology: a prospective cohort study of Finnish undergraduate students

BACKGROUND

Perceptual learning modules (PLMs) have been shown to significantly improve learning outcomes in teaching dermatology.

OBJECTIVES

To investigate the quantity and quality of diagnostic errors made during undergraduate PLMs and their potential implications.

METHODS

The study data were acquired from 8 successive dermatology courses (2021-23) from 142 undergraduate medical students. Digital PLMs were held before, during and after the courses. We investigated the number and distribution of diagnostic errors, differences between specific skin conditions and classified the errors based on type.

RESULTS

Diagnostic errors were not randomly distributed. Some skin conditions were almost always correctly identified, whereas a significant number of errors were made for other diagnoses. Errors were classified into one of three groups: mostly systematic errors of relevant differential diagnoses ('similarity' errors); partly systematic errors ('mixed' errors); and 'random' errors. While a significant learning effect during the repeated measures was found in accuracy (P < 0.001, η²P = 0.64), confidence (P < 0.001, η²P = 0.60) and fluency (P < 0.001, η²P = 0.16), the three categories differed in all outcome measures (all P < 0.001, all η²P > 0.47). Visual learning was more difficult for diagnoses in the similarity category (all P < 0.001, all η²P > 0.12) than for those in the mixed and random categories.

CONCLUSIONS

Error analysis of PLMs provided relevant information about learning efficacy and progression, and systematic errors in tasks and more difficult-to-learn conditions. This information could be used in the development of adaptive, individual error-based PLMs to improve learning outcomes, both in dermatology and medical education in general.

Effects of interacting with a large language model compared with a human coach on the clinical diagnostic process and outcomes among fourth-year medical students: study protocol for a prospective, randomised experiment using patient vignettes

INTRODUCTION

Versatile large language models (LLMs) have the potential to augment diagnostic decision-making by assisting diagnosticians, thanks to their ability to engage in open-ended, natural conversations and their comprehensive knowledge access. Yet the novelty of LLMs in diagnostic decision-making introduces uncertainties regarding their impact. Clinicians unfamiliar with the use of LLMs in their professional context may rely on general attitudes towards LLMs more broadly, potentially hindering thoughtful use and critical evaluation of their input, leading to either over-reliance and lack of critical thinking or an unwillingness to use LLMs as diagnostic aids. To address these concerns, this study examines the influence on the diagnostic process and outcomes of interacting with an LLM compared with a human coach, and of prior training vs no training for interacting with either of these 'coaches'. Our findings aim to illuminate the potential benefits and risks of employing artificial intelligence (AI) in diagnostic decision-making.

METHODS AND ANALYSIS

We are conducting a prospective, randomised experiment with N=158 fourth-year medical students from Charité Medical School, Berlin, Germany. Participants are asked to diagnose patient vignettes after being assigned to either a human coach or ChatGPT and after either training or no training (both between-subject factors). We are specifically collecting data on the effects of using either of these 'coaches' and of additional training on information search, number of hypotheses entertained, diagnostic accuracy and confidence. Statistical methods will include linear mixed effects models. Exploratory analyses of the interaction patterns and attitudes towards AI will also generate more generalisable knowledge about the role of AI in medicine.

ETHICS AND DISSEMINATION

The Bern Cantonal Ethics Committee considered the study exempt from full ethical review (BASEC No: Req-2023-01396). All methods will be conducted in accordance with relevant guidelines and regulations. Participation is voluntary and informed consent will be obtained. Results will be published in peer-reviewed scientific medical journals. Authorship will be determined according to the International Committee of Medical Journal Editors guidelines.

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.

Ten misconceptions regarding decision-making in critical care

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

The inseparability of context and clinical reasoning

Early descriptions of clinical reasoning have described a dual process model that relies on analytical or nonanalytical approaches to develop a working diagnosis. In this classic research, clinical reasoning is portrayed as an individual-driven cognitive process based on gathering information from the patient encounter, forming mental representations that rely on previous experience and engaging developed patterns to drive working diagnoses and management plans. Indeed, approaches to patient safety, as well as teaching and assessing clinical reasoning focus on the individual clinician, often ignoring the complexity of the system surrounding the diagnostic process. More recent theories and evidence portray clinical reasoning as a dynamic collection of processes that takes place among and between persons across clinical settings. Yet, clinical reasoning, taken as both an individual and a system process, is insufficiently supported by theories of cognition based on individual clinicals and lacks the specificity needed to describe the phenomenology of clinical reasoning. In this review, we reinforce that the modern healthcare ecosystem - with its people, processes and technology - is the context in which health care encounters and clinical reasoning take place.

Characteristics differentiating problem representation synthesis between novices and experts

BACKGROUND

Formulating a thoughtful problem representation (PR) is fundamental to sound clinical reasoning and an essential component of medical education. Aside from basic structural recommendations, little consensus exists on what characterizes high-quality PRs.

OBJECTIVES

To elucidate characteristics that distinguish PRs created by experts and novices.

METHODS

Early internal medicine residents (novices) and inpatient teaching faculty (experts) from two academic medical centers were given two written clinical vignettes and were instructed to write a PR and three-item differential diagnosis for each. Deductive content analysis described the characteristics comprising PRs. An initial codebook of characteristics was refined iteratively. The primary outcome was differences in characteristic frequencies between groups. The secondary outcome was characteristics correlating with diagnostic accuracy. Mixed-effects regression with random effects modeling compared case-level outcomes by group.

RESULTS

Overall, 167 PRs were analyzed from 30 novices and 54 experts. Experts included 0.8 fewer comorbidities (p < .01) and 0.6 more examination findings (p = .01) than novices on average. Experts were less likely to include irrelevant comorbidities (odds ratio [OR] = 0.4, 95% confidence interval [CI] = 0.2-0.8) or a diagnosis (OR = 0.3, 95% CI = 0.1-0.8) compared with novices. Experts encapsulated clinical data into higher-order terms (e.g., sepsis) than novices (p < .01) while including similar numbers of semantic qualifiers (SQs). Regardless of expertise level, PRs following a three-part structure (e.g., demographics, temporal course, and clinical syndrome) and including temporal SQs were associated with diagnostic accuracy (p < .01).

CONCLUSIONS

Compared with novices, expert PRs include less irrelevant data and synthesize information into higher-order concepts. Future studies should determine whether targeted educational interventions for PRs improve diagnostic accuracy.

A Multicenter Evaluation of a Metacognitive Framework for Antimicrobial Selection Education

Antimicrobial selection is a complex task for veterinary students there is a need for both assessment tools and novel strategies to promote the proper use of antimicrobials. SODAPOP (Source-Organism-Decided to treat-Antimicrobials-Patient- Option-Plan) is a mnemonic previously designed to aid in developing antimicrobial selection skills by promoting metacognition. To assess the effect of this tool, we enrolled veterinary students (N = 238) from five veterinary teaching institutions in a study that consisted of an online survey that contained a video-based intervention. For the intervention, a video that presented principles of antimicrobial selection was embedded within the survey. For one-half of students, the video also included an explanation of SODAPOP. The survey included self-efficacy statements rated by participants pre-intervention and post-intervention. The survey also included cases, developed for this study, that were used to assess selection and plan competence. Cases were graded using two study-developed rubrics in a blinded fashion by veterinary educators. A statistically significant difference was found in participant-reported self-efficacy pre-scores and post-scores when asked about empiric prescribing (5.8 vs. 6.5; P = .0153) for the SODAPOP group but not the control group. No immediate impact on competence was found. When asked whether SODAPOP was an essential educational tool and likely to be used by participants in the future, the mean rank score (from 1-10) was 7.6 and 7.2, respectively. In addition to developing cases and rubrics, this study demonstrated that SODAPOP may be a useful tool for integration into approaches for teaching antimicrobial selection to veterinary students.

The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study

BACKGROUND

The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets.

OBJECTIVE

This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED.

METHODS

We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS.

RESULTS

The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred.

CONCLUSIONS

Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

Lessons in clinical reasoning - pitfalls, myths, and pearls: shoulder pain as the first and only manifestation of lung cancer

OBJECTIVES

Lung cancer is the leading cause of cancer-related death and poses significant challenges in diagnosis and management. Although muscle metastases are exceedingly rare and typically not the initial clinical manifestation of neoplastic processes, their recognition is crucial for optimal patient care.

CASE PRESENTATION

We present a case report in which we identify the unique scenario of a 60-year-old man with shoulder pain and a deltoid muscle mass, initially suggestive of an undifferentiated pleomorphic sarcoma. However, further investigations, including radiological findings and muscle biopsy, revealed an unexpected primary lung adenocarcinoma. We performed a systematic literature search to identify the incidence of SMM and reflect on how to improve and build on better diagnosis for entities as atypical as this. This atypical presentation highlights the importance of recognizing and addressing cognitive biases in clinical decision-making, as acknowledging the possibility of uncommon presentations is vital. By embracing a comprehensive approach that combines imaging studies with histopathological confirmation, healthcare providers can ensure accurate prognoses and appropriate management strategies, ultimately improving patient outcomes.

CONCLUSIONS

This case serves as a reminder of the need to remain vigilant, open-minded, and aware of cognitive biases when confronted with uncommon clinical presentations, emphasizing the significance of early recognition and prompt evaluation in achieving optimal patient care.

Application of Cardiovascular Physiology to the Critically Ill Patient

OBJECTIVES

To use the ventricular pressure-volume relationship and time-varying elastance model to provide a foundation for understanding cardiovascular physiology and pathophysiology, interpreting advanced hemodynamic monitoring, and for illustrating the physiologic basis and hemodynamic effects of therapeutic interventions. We will build on this foundation by using a cardiovascular simulator to illustrate the application of these principles in the care of patients with severe sepsis, cardiogenic shock, and acute mechanical circulatory support.

DATA SOURCES

Publications relevant to the discussion of the time-varying elastance model, cardiogenic shock, and sepsis were retrieved from MEDLINE. Supporting evidence was also retrieved from MEDLINE when indicated.

STUDY SELECTION, DATA EXTRACTION, AND SYNTHESIS

Data from relevant publications were reviewed and applied as indicated.

CONCLUSIONS

The ventricular pressure-volume relationship and time-varying elastance model provide a foundation for understanding cardiovascular physiology and pathophysiology. We have built on this foundation by using a cardiovascular simulator to illustrate the application of these important principles and have demonstrated how complex pathophysiologic abnormalities alter clinical parameters used by the clinician at the bedside.

Emergency department use of an electronic differential diagnosis generator in the evaluation of critically ill patients

BACKGROUND

Accurate diagnosis is an essential component of managing critically ill emergency department (ED) patients. Electronic diagnosis generators (EDGs) are software tools which assist clinicians in their diagnosis generation; however, they have not been evaluated for use for critical ED patients. We aimed to evaluate the use of an EDG for this population to determine its impact on diagnosis generation and diagnostic testing.

METHODS

We performed an observational study on usage of an EDG in the high-acuity area of a tertiary care ED. The EDG was used by residents evaluating each patient in the area. The resident used the EDG when the case was felt to have diagnostic uncertainty and completed a data collection tool. Data were summarized by frequencies. Chi-squared or Fisher's exact tests were used to assess the association of added value of the EDG for diagnosis generation and diagnostic testing.

RESULTS

Over the 8-month study period, the EDG was utilized to evaluate 98 critical ED patients, of whom 60% were female, 7% were pediatric, and 46% were elderly. It was used most commonly for gastroenterological, infectious disease/immunologic, metabolic/renal, and neuropsychiatric presentations, and was least used for trauma presentations. Use of the EDG led to a diagnosis not initially considered in 47% of cases and led to additional diagnostic testing in 4% of cases.

CONCLUSION

EDGs have some potential to improve diagnosis in critical EM patients by expanding the differential diagnosis and, to a lesser extent, altering diagnostic testing.