Overconfidence as a cause of diagnostic error in medicine

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives

INTRODUCTION

The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies.

METHODS

Focusing on four key areas-medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework.

RESULTS

BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9.

CONCLUSION

The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.

Unveiling variability: A systematic review of reproducibility in visual EEG analysis, with focus on seizures

OBJECTIVE

Reproducibility is key for diagnostic tests involving subjective evaluation by experts. Our aim was to systematically review the reproducibility of visual analysis in clinical electroencephalogram (EEG). In this paper, we give data on the scope of EEG features found, and detailed reproducibility data for the most studied feature.

METHODS

We searched four databases for articles reporting reproducibility in clinical EEG, until June 2023. Two raters screened 24 553 citations, and then 2736 full texts. Quality was assessed according to the GRRAS guidelines.

RESULTS

We found 275 studies (268 interrater and 20 intrarater), addressing 606 different EEG features. Only 38 EEG features had been studied in >2 studies. Most studies had <50 patients and EEGs. The most often addressed feature was seizure detection (62 papers). Interrater reproducibility of seizure detection was substantial-to-almost-perfect with experienced raters and raw EEG (kappa .62-.88). With experienced raters and transformed EEG, reproducibility was substantial (kappa .63-.70). Inexperienced raters had lower reproducibility. Seizure lateralization reproducibility was moderate to substantial (kappa .58-.77) but lower than for seizure detection.

SIGNIFICANCE

Most EEG reproducibility studies are done only once. Intrarater studies are rare. The reproducibility of visual EEG analysis is variable. Interrater reproducibility for seizure detection is substantial-to-perfect with experienced raters and raw EEG, less with inexperienced raters or transformed EEG. The results of visual EEG analysis vary within the same rater, and between raters. There is a need for larger collaborative studies, using improved methodology, as well as more intrarater studies of EEG interpretation.

Common contributing factors of diagnostic error: A retrospective analysis of 109 serious adverse event reports from Dutch hospitals

INTRODUCTION

Although diagnostic errors have gained renewed focus within the patient safety domain, measuring them remains a challenge. They are often measured using methods that lack information on decision-making processes given by involved physicians (eg, record reviews). The current study analyses serious adverse event (SAE) reports from Dutch hospitals to identify common contributing factors of diagnostic errors in hospital medicine. These reports are the results of thorough investigations by highly trained, independent hospital committees into the causes of SAEs. The reports include information from involved healthcare professionals and patients or family obtained through interviews.

METHODS

All 71 Dutch hospitals were invited to participate in this study. Participating hospitals were asked to send four diagnostic SAE reports of their hospital. Researchers applied the Safer Dx Instrument, a Generic Analysis Framework, the Diagnostic Error Evaluation and Research (DEER) taxonomy and the Eindhoven Classification Model (ECM) to analyse reports.

RESULTS

Thirty-one hospitals submitted 109 eligible reports. Diagnostic errors most often occurred in the diagnostic testing, assessment and follow-up phases according to the DEER taxonomy. The ECM showed human errors as the most common contributing factor, especially relating to communication of results, task planning and execution, and knowledge. Combining the most common DEER subcategories and the most common ECM classes showed that clinical reasoning errors resulted from failures in knowledge, and task planning and execution. Follow-up errors and errors with communication of test results resulted from failures in coordination and monitoring, often accompanied by usability issues in electronic health record design and missing protocols.

DISCUSSION

Diagnostic errors occurred in every hospital type, in different specialties and with different care teams. While clinical reasoning errors remain a common problem, often caused by knowledge and skill gaps, other frequent errors in communication of test results and follow-up require different improvement measures (eg, improving technological systems).

Role of communicating diagnostic uncertainty in the safety-netting process: insights from a vignette study

BACKGROUND

Safety-netting is intended to protect against harm from uncertainty in diagnosis/disease trajectory. Despite recommendations to communicate diagnostic uncertainty when safety-netting, this is not always done.

AIMS

To explore how and why doctors safety-netted in response to several clinical scenarios, within the broader context of exploring how doctors communicate diagnostic uncertainty.

METHODS

Doctors working in internal medical specialties (n=36) from five hospitals were given vignettes in a randomised order (all depicting different clinical scenarios involving diagnostic uncertainty). After reading each, they told an interviewer what they would tell a 'typical patient' in this situation. A follow-up semistructured interview explored reasons for their communication. Interviews were recorded, transcribed and coded. We examined how participants safety-netted using a content analysis approach, and why they safety-netting with thematic analysis of the semistructured follow-up interviews using thematic analysis.

RESULTS

We observed n=78 instances of safety-netting (across 108 vignette encounters). We found significant variation in how participants safety-netted. Safety-netting was common (although not universal), but clinicians differed in the detail provided about symptoms to be alert for, and the action advised. Although many viewed safety-netting as an important tool for managing diagnostic uncertainty, diagnostic uncertainty was infrequently explicitly discussed; most advised patients to return if symptoms worsened or new 'red flag' symptoms developed, but they rarely linked this directly to the possibility of diagnostic error. Some participants expressed concerns that communicating diagnostic uncertainty when safety-netting may cause anxiety for patients or could drive inappropriate reattendance/over-investigation.

CONCLUSIONS

Participants safety-netted variously, even when presented with identical clinical information. Although safety-netting was seen as important in avoiding diagnostic error, concerns about worrying patients may have limited discussion about diagnostic uncertainty. Research is needed to determine whether communicating diagnostic uncertainty makes safety-netting more effective at preventing harm associated with diagnostic error, and whether it causes significant patient anxiety.

A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer

Background

Cervical cancer, a severe threat to women's health, is experiencing a global increase in incidence, notably among younger demographics. With artificial intelligence (AI) making strides, its integration into medical research is expanding, particularly in cervical cancer studies. This bibliometric study aims to evaluate AI's role, highlighting research trends and potential future directions in the field.

Methods

This study systematically retrieved literature from the Web of Science Core Collection (WoSCC), employing VOSviewer and CiteSpace for analysis. This included examining collaborations and keyword co-occurrences, with a focus on the relationship between citing and cited journals and authors. A burst ranking analysis identified research hotspots based on citation frequency.

Results

The study analyzed 927 articles from 2008 to 2024 by 5,299 authors across 81 regions. China, the U.S., and India were the top contributors, with key institutions like the Chinese Academy of Sciences and the NIH leading in publications. Schiffman, Mark, featured among the top authors, while Jemal, A, was the most cited. 'Diagnostics' and 'IEEE Access' stood out for publication volume and citation impact, respectively. Keywords such as 'cervical cancer,' 'deep learning,' 'classification,' and 'machine learning' were dominant. The most cited article was by Berner, ES; et al., published in 2008.

Conclusions

AI's application in cervical cancer research is expanding, with a growing scholarly community. The study suggests that AI, especially deep learning and machine learning, will remain a key research area, focusing on improving diagnostics and treatment. There is a need for increased international collaboration to maximize AI's potential in advancing cervical cancer research and patient care.

Experience With Proximal Median Nerve Entrapment by the Lacertus Fibrosus

BACKGROUND

Unconscious bias of the clinician favors the diagnosis of carpal tunnel syndrome (CTS) in patients with median paresthesia. We hypothesized that more patients in this cohort would be diagnosed with proximal median nerve entrapment (PMNE) by strengthening our cognitive awareness of this alternative diagnosis. We also hypothesized that patients with PMNE may be successfully treated with surgical release of the lacertus fibrosus (LF).

METHODS

In this retrospective study, cases of median nerve decompression at the carpal tunnel and in the proximal forearm for the 2-year periods before and after adopting strategies to mitigate cognitive bias for CTS were enumerated. Patients diagnosed with PMNE and treated by LF release under local anesthesia were evaluated to determine surgical outcome at minimum 2-year follow-up. Primary outcome measures were changes in preoperative median paresthesia and proximal median-innervated muscle strength.

RESULTS

There was a statistically significant increase in PMNE cases identified after our heightened surveillance was initiated (z = 3.433, P < .001). In 10 of 12 cases, the patient had previous ipsilateral open carpal tunnel release (CTR) but experienced recurrent median paresthesia. In 8 cases evaluated an average of 5 years after LF release, there was improvement in median paresthesia and resolution of median-innervated muscle weakness.

CONCLUSIONS

Owing to cognitive bias, some patients with PMNE may be misdiagnosed with CTS. All patients with median paresthesia, particularly those with persistent or recurrent symptoms after CTR, should be assessed for PMNE. Surgical release limited to the LF may be an effective treatment for PMNE.

Diagnostic reproducibility of the 2018 Classification of Gingival Recessions: Comparing photographic and in-person diagnoses

BACKGROUND

To assess how the diagnostic reproducibility of the 2018 Classification of Gingival Recession Defects (GRD) could be applied when comparing in-person chairside measurements with photographic measurements.

METHODS

Thirty-four GRD were photographed and evaluated by 4 masked operators. For each case, the operators measured twice recession type (RT), recession depth (RD), keratinized tissue width (KTW), gingival thickness (GT), detectability of the cemento-enamel junction (CEJ), and presence of root steps (RSs), chairside, and on photographs. Intraclass correlation coefficient (ICC) with 95% confidence intervals (CI) was calculated for RD and KTW; Kappa with 95% CI was used for GT, CEJ, and RS; quadratic weighted Kappa with 95% CI was used for RT.

RESULTS

RD, KTW, and RT showed excellent overall intra-operator agreement (> 0.93), and from good to excellent overall inter-operator agreement (> 0.80), for both clinical and photographic measurements. Agreements were lower for GT, CEJ, and RS. Overall clinical and photographic agreements were within 0.1 difference for every variable, except for inter-operator agreement for RS which was 0.72 for clinical measurements and 0.45 for photographic measurements. The lowest overall agreement between clinical versus photographic measurements existed for CEJ (0.28) and RS (0.35).

CONCLUSIONS

Variables composing the 2018 Classification of GRD are reproducible, both clinically and on photographs, with comparable agreements. The overall agreement was higher for KTW, RD, and RT, and lower for GT, CEJ, and RS, for both clinical and photographic measurements. The comparison between chairside and photographic evaluations indicated fair to excellent agreement for most variables, with CEJ and RS showing fair agreement.

PLAIN LANGUAGE SUMMARY

As digital diagnostics evolve to facilitate clinical diagnostic measurement, we aimed to assess the effectiveness of intraoral photography for diagnosing gingival recession defects (GRD) according to the 2018 Classification of GRD, compared to traditional clinical examination. Standardized photographs of thirty-four GRD cases were captured. Four masked operators evaluated the same gingival recessions twice in a clinical setting and twice using photographs. Measurement repeatability within and between operators was calculated for both clinical and photographic settings, and the two settings were compared. Continuous measurements such as recession depth and keratinized tissue width, as well as the evaluation of interproximal attachment height (recession type), showed excellent agreement both clinically and photographically. Agreement was lower for gingival thickness and the detectability of tooth anatomical landmarks, such as the cemento-enamel junction and the presence of root steps. Overall, the agreement between chairside and photographic evaluations was generally good, but lower when evaluating tooth anatomical landmarks. The variables composing the 2018 Classification of GRD are reproducible in both clinical and photographic settings, with comparable levels of agreement. However, there was consistently worse agreement for gingival thickness and when evaluating tooth anatomical landmarks.

AI-Driven Diagnostic Assistance in Medical Inquiry: Reinforcement Learning Algorithm Development and Validation

BACKGROUND

For medical diagnosis, clinicians typically begin with a patient's chief concerns, followed by questions about symptoms and medical history, physical examinations, and requests for necessary auxiliary examinations to gather comprehensive medical information. This complex medical investigation process has yet to be modeled by existing artificial intelligence (AI) methodologies.

OBJECTIVE

The aim of this study was to develop an AI-driven medical inquiry assistant for clinical diagnosis that provides inquiry recommendations by simulating clinicians' medical investigating logic via reinforcement learning.

METHODS

We compiled multicenter, deidentified outpatient electronic health records from 76 hospitals in Shenzhen, China, spanning the period from July to November 2021. These records consisted of both unstructured textual information and structured laboratory test results. We first performed feature extraction and standardization using natural language processing techniques and then used a reinforcement learning actor-critic framework to explore the rational and effective inquiry logic. To align the inquiry process with actual clinical practice, we segmented the inquiry into 4 stages: inquiring about symptoms and medical history, conducting physical examinations, requesting auxiliary examinations, and terminating the inquiry with a diagnosis. External validation was conducted to validate the inquiry logic of the AI model.

RESULTS

This study focused on 2 retrospective inquiry-and-diagnosis tasks in the emergency and pediatrics departments. The emergency departments provided records of 339,020 consultations including mainly children (median age 5.2, IQR 2.6-26.1 years) with various types of upper respiratory tract infections (250,638/339,020, 73.93%). The pediatrics department provided records of 561,659 consultations, mainly of children (median age 3.8, IQR 2.0-5.7 years) with various types of upper respiratory tract infections (498,408/561,659, 88.73%). When conducting its own inquiries in both scenarios, the AI model demonstrated high diagnostic performance, with areas under the receiver operating characteristic curve of 0.955 (95% CI 0.953-0.956) and 0.943 (95% CI 0.941-0.944), respectively. When the AI model was used in a simulated collaboration with physicians, it notably reduced the average number of physicians' inquiries to 46% (6.037/13.26; 95% CI 6.009-6.064) and 43% (6.245/14.364; 95% CI 6.225-6.269) while achieving areas under the receiver operating characteristic curve of 0.972 (95% CI 0.970-0.973) and 0.968 (95% CI 0.967-0.969) in the scenarios. External validation revealed a normalized Kendall τ distance of 0.323 (95% CI 0.301-0.346), indicating the inquiry consistency of the AI model with physicians.

CONCLUSIONS

This retrospective analysis of predominantly respiratory pediatric presentations in emergency and pediatrics departments demonstrated that an AI-driven diagnostic assistant had high diagnostic performance both in stand-alone use and in simulated collaboration with clinicians. Its investigation process was found to be consistent with the clinicians' medical investigation logic. These findings highlight the diagnostic assistant's promise in assisting the decision-making processes of health care professionals.

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.

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department

In emergency department (ED) settings, rapid and precise diagnostic evaluations are critical to ensure better patient outcomes and efficient healthcare delivery. This study assesses the accuracy of differential diagnosis lists generated by the third-generation ChatGPT (ChatGPT-3.5) and the fourth-generation ChatGPT (ChatGPT-4) based on electronic health record notes recorded within the first 24 h of ED admission. These models process unstructured text to formulate a ranked list of potential diagnoses. The accuracy of these models was benchmarked against actual discharge diagnoses to evaluate their utility as diagnostic aids. Results indicated that both GPT-3.5 and GPT-4 reasonably accurately predicted diagnoses at the body system level, with GPT-4 slightly outperforming its predecessor. However, their performance at the more granular category level was inconsistent, often showing decreased precision. Notably, GPT-4 demonstrated improved accuracy in several critical categories that underscores its advanced capabilities in managing complex clinical scenarios.

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.

Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring

We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary toxicologic pathologists using a multinomial logistic model. In this study, we assessed both the inter-rater and intra-rater agreement of the pathologists and compared pathologists' ratings to the artificial intelligence (AI)-predicted scores. Pathologists and the AI algorithm were presented with 500 slides of rodent heart. They quantified the amount of cardiomyopathy in each slide. A total of 200 of these slides were novel to this study, whereas 100 slides were intentionally selected for repetition from the previous study. After a washout period of more than six months, the repeated slides were examined to assess intra-rater agreement among pathologists. We found the intra-rater agreement to be substantial, with weighted Cohen's kappa values ranging from k = 0.64 to 0.80. Intra-rater variability is not a concern for the deterministic AI. The inter-rater agreement across pathologists was moderate (Cohen's kappa k = 0.56). These results demonstrate the utility of AI algorithms as a tool for pathologists to increase sensitivity and specificity for the histopathologic assessment of the heart in toxicology studies.

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.

Public Preferences for Surgeon Gender in Saudi Arabia: A Cross-Sectional Analysis

BACKGROUND

Gender equity in healthcare ensures equal access to services and resources for all individuals, regardless of gender. Studies show that patients' gender influences their healthcare experiences and decisions, and societal gender preferences impact surgeon selection. Therefore, the objective of this study was to address the surgeons' gender preferences among Saudi Arabia's population.

METHODS

This study used a cross-sectional research design and survey methodology to gather data from a representative sample of adults aged 18 and older from the general population in Saudi Arabia. This study used SPSS version 26 for statistical data entry and analysis, employing descriptive and inferential statistics, frequency distributions, descriptive statistics, and multivariate analysis using multiple logistic regression for variables significantly associated with population gender in univariate analysis.

RESULTS

This study involved 2085 participants, with 51.2% aged 18-25, 18.4% aged 26-35, 16.7% aged 36-45, and 13.8% aged 45 and above. The majority were Saudi Arabian, with a majority holding a university degree. The majority worked in the healthcare sector, while the remaining 29.7% were unemployed. This study found that there were significant gender preferences among survey respondents for different types of surgical procedures. Male participants preferred male surgeons for routine non-emergency visits and sensitive obstetric, genital, sexual, and minor procedures (p < 0.001). Female participants, on the other hand, had no preference for emergency surgeries and major surgical procedures (p < 0.001). This study found that participants were more likely to prefer female surgeons for regular non-emergency visits to the surgery clinic (p < 0.001; aOR = 2.344). Additionally, participants had a high preference for female surgeons for sensitive cases (p < 0.001; aOR = 7.064) and minor surgical procedures (p < 0.001; aOR = 2.489).

CONCLUSION

This study underscores the significance of cognizance and the incorporation of a patient's gender preferences when selecting surgical procedures, thus fostering an environment that is more accommodating and focused on the patient.

Current status and ongoing needs for the teaching and assessment of clinical reasoning - an international mixed-methods study from the students` and teachers` perspective

BACKGROUND

Clinical reasoning (CR) is a crucial ability that can prevent errors in patient care. Despite its important role, CR is often not taught explicitly and, even when it is taught, typically not all aspects of this ability are addressed in health professions education. Recent research has shown the need for explicit teaching of CR for both students and teachers. To further develop the teaching and learning of CR we need to improve the understanding of students' and teachers' needs regarding content as well as teaching and assessment methods for a student and trainer CR curriculum.

METHODS

Parallel mixed-methods design that used web-surveys and semi-structured interviews to gather data from both students (nsurvey = 100; ninterviews = 13) and teachers (nsurvey = 112; ninterviews = 28). The interviews and surveys contained similar questions to allow for triangulation of the results. This study was conducted as part of the EU-funded project DID-ACT ( https://did-act.eu ).

RESULTS

Both the surveys and interview data emphasized the need for content in a clinical reasoning (CR) curriculum such as "gathering, interpreting and synthesizing patient information", "generating differential diagnoses", "developing a diagnostic and a treatment plan" and "collaborative and interprofessional aspects of CR". There was high agreement that case-based learning and simulations are most useful for teaching CR. Clinical and oral examinations were favored for the assessment of CR. The preferred format for a train-the-trainer (TTT)-course was blended learning. There was also some agreement between the survey and interview participants regarding contents of a TTT-course (e.g. teaching and assessment methods for CR). The interviewees placed special importance on interprofessional aspects also for the TTT-course.

CONCLUSIONS

We found some consensus on needed content, teaching and assessment methods for a student and TTT-course in CR. Future research could investigate the effects of CR curricula on desired outcomes, such as patient care.

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.

DiTA: helping you search for evidence of diagnostic test accuracy in physical therapy

BACKGROUND

Physical therapists use diagnostic tests in a variety of settings. Choosing the best diagnostic test to apply in a particular situation can be difficult. The choice of diagnostic test should be informed, at least in part, by evidence of test accuracy. Finding evidence of diagnostic test accuracy has, until recently, been challenging. Ideally, there would exist a database that comprehensively indexes evidence on diagnostic tests relevant to physical therapy practice, is free to access, and is easy to use.

OBJECTIVE

This Masterclass will describe the DiTA (Diagnostic Test Accuracy) database (dita.org.au) including its development and search interface, and provide advice on how to search and retrieve records.

DISCUSSION

DiTA indexes more than 2400 primary studies and systematic reviews of diagnostic test accuracy relevant to physical therapy practice. Users can search DiTA using text fields and dropdown lists to find evidence of diagnostic test accuracy. The database is freely accessible on the internet. Since its launch, DiTA has been accessed from almost every country in the world, the largest number of searches having been conducted from Brazil.

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting

BACKGROUND

General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS).

METHODS

We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure.

RESULTS

Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance.

DISCUSSION

Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice.

HIGHLIGHTS

We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes.

Experimental evidence on the role of shared protocols as coordination device on clinical best practices

Our experiment assesses the level of coordination on clinical best practice among physicians and investigates whether the release of guidelines helps in supporting coordination. Based on three clinical vignettes using current national guidelines, physicians evaluate the appropriateness of each of the proposed courses of action. Afterwards, physicians are allowed to ask which action corresponds to national guidelines and change their ratings, if desired. On average, slightly more than half of the sample coordinated on appropriateness evaluations. Empirical analysis indicates that several organizational and individual variables influence the level of coordination. Additionally, the release of national guidelines improved both the level of conformity and coordination. Our findings suggest changes in implementation practices to increase the impact of these shared protocols in the health field.

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.

The effects of gaze-display feedback on medical students' self-monitoring and learning in radiology

Self-monitoring is essential for effectively regulating learning, but difficult in visual diagnostic tasks such as radiograph interpretation. Eye-tracking technology can visualize viewing behavior in gaze displays, thereby providing information about visual search and decision-making. We hypothesized that individually adaptive gaze-display feedback improves posttest performance and self-monitoring of medical students who learn to detect nodules in radiographs. We investigated the effects of: (1) Search displays, showing which part of the image was searched by the participant; and (2) Decision displays, showing which parts of the image received prolonged attention in 78 medical students. After a pretest and instruction, participants practiced identifying nodules in 16 cases under search-display, decision-display, or no feedback conditions (n = 26 per condition). A 10-case posttest, without feedback, was administered to assess learning outcomes. After each case, participants provided self-monitoring and confidence judgments. Afterward, participants reported on self-efficacy, perceived competence, feedback use, and perceived usefulness of the feedback. Bayesian analyses showed no benefits of gaze displays for post-test performance, monitoring accuracy (absolute difference between participants' estimated and their actual test performance), completeness of viewing behavior, self-efficacy, and perceived competence. Participants receiving search-displays reported greater feedback utilization than participants receiving decision-displays, and also found the feedback more useful when the gaze data displayed was precise and accurate. As the completeness of search was not related to posttest performance, search displays might not have been sufficiently informative to improve self-monitoring. Information from decision displays was rarely used to inform self-monitoring. Further research should address if and when gaze displays can support learning.

Impact of performance and information feedback on medical interns' confidence-accuracy calibration

Diagnostic errors are a major, largely preventable, patient safety concern. Error interventions cannot feasibly be implemented for every patient that is seen. To identify cases at high risk of error, clinicians should have a good calibration between their perceived and actual accuracy. This experiment studied the impact of feedback on medical interns' calibration and diagnostic process. In a two-phase experiment, 125 medical interns from Dutch University Medical Centers were randomized to receive no feedback (control), feedback on their accuracy (performance feedback), or feedback with additional information on why a certain diagnosis was correct (information feedback) on 20 chest X-rays they diagnosed in a feedback phase. A test phase immediately followed this phase and had all interns diagnose an additional 10 X-rays without feedback. Outcome measures were confidence-accuracy calibration, diagnostic accuracy, confidence, and time to diagnose. Both feedback types improved overall confidence-accuracy calibration (R2No Feedback = 0.05, R2Performance Feedback = 0.12, R2Information Feedback = 0.19), in line with the individual improvements in diagnostic accuracy and confidence. We also report secondary analyses to examine how case difficulty affected calibration. Time to diagnose did not differ between conditions. Feedback improved interns' calibration. However, it is unclear whether this improvement reflects better confidence estimates or an improvement in accuracy. Future research should examine more experienced participants and non-visual specialties. Our results suggest that feedback is an effective intervention that could be beneficial as a tool to improve calibration, especially in cases that are not too difficult for learners.

Speech and language therapists' management practices, perceived effectiveness of current treatments and interest in neuromuscular electrical stimulation for acquired dysarthria rehabilitation: An international perspective

BACKGROUND

Research is beginning to shed light on the practices employed by speech-language therapists (SLTs) for the management of acquired dysarthria. However, studies that explore SLTs' satisfaction with the effectiveness of current therapies and their interest in new treatment methods for this population have not been carried out. One potential new method is neuromuscular electrical stimulation (NMES): the pool of evidence for its use in rehabilitation is increasing, yet it has not been widely explored for use with dysarthria.

AIM

To extend the understanding of acquired dysarthria management practices employed by SLTs across the globe and determine their satisfaction with current therapy options. To explore their interest in using NMES with this population.

METHODS AND PROCEDURES

A cross-sectional international online survey was developed and disseminated to SLTs working with adults with acquired dysarthria through international professional associations. The survey collected information on demographic characteristics, dysarthria management practices, satisfaction with treatment effectiveness and interest in and knowledge of NMES. Survey responses were analysed using descriptive and inferential statistics, and quantitative content analysis.

OUTCOMES AND RESULTS

A total of 211 SLTs (North America, 48.8%; Europe, 36%; Asia, 8.1%; Oceania, 5.7%; Africa, 0.9%; South America, 0.5%) completed the survey in full. Management practices varied considerably. There was a clear preference for informal assessments, mainly oral-motor examinations, focusing on body functions and structures. The majority of respondents rejected the use of non-speech oral motor exercises as a clinical or carryover exercise. Variable satisfaction with current speech subsystem treatments was noted; however, overall, there was a general dissatisfaction. Whilst a strong interest in the use of NMES for dysarthria was evidenced, it was noted that most SLTs lacked fundamental knowledge of NMES principles and application.

CONCLUSION

SLTs' management practices and satisfaction with acquired dysarthria treatments differed substantially. Investigations of the potential use of NMES for dysarthria treatment are of interest.

WHAT THIS PAPER ADDS

What is already known on the subject Recent country-specific surveys have explored speech-language therapists' (SLTs') assessment and intervention practices for acquired dysarthria. These studies indicate that although clinical management for this speech disorder mainly involves informal assessment tools and impairment-focused treatment, communication beyond the impairment, such as the activity and participation domains, is also frequently assessed and treated. What this paper adds to existing knowledge The majority of SLTs are dissatisfied with the overall benefits of current acquired dysarthria treatment. Phonatory, respiration and speech rate therapies are perceived to be more effective than prosody, articulation and resonance treatments. Despite a general lack of theoretical knowledge, most SLTs are interested in neuromuscular electrical stimulation treatment for acquired dysarthria. What are the potential or actual clinical implications of this work? New, evidence-based treatments are needed for SLTs to be confident in the effectiveness of their acquired dysarthria treatment.

Overconfidence, Time-on-Task, and Medical Errors: Is There a Relationship?

Background

Literature suggest that physicians' high level of confidence has a negative impact on medical decisions, and this may lead to medical errors. Experimental research is lacking; however, this study investigated the effects of high confidence on diagnostic accuracy.

Methods

Forty internal medicine residents from different hospitals in Saudi Arabia were divided randomly into two groups: A high-confidence group as an experimental and a low-confidence group acting as a control. Both groups solved each of eight written complex clinical vignettes. Before diagnosing these cases, the high-confidence group was led to believe that the task was easy, while the low-confidence group was presented with information from which it could deduce that the diagnostic task was difficult. Level of confidence, response time, and diagnostic accuracy were recorded.

Results

The participants in the high-confidence group had a significantly higher confidence level than those in the control group: 0.75 compared to 0.61 (maximum 1.00). However, neither time on task nor diagnostic accuracy significantly differed between the two groups.

Conclusion

In the literature, high confidence as one of common cognitive biases has a strong association with medical error. Even though the high-confidence group spent somewhat less time on the cases, suggesting potential premature decision-making, we failed to find differences in diagnostic accuracy. It is suggested that overconfidence should be studied as a personality trait rather than as a malleable characteristic.

'Insight' in medical training: what, why, and how?

The term 'insight' is generically defined in English language as the ability to perceive deeper truths about people and situations. In clinical practice, patient insight is known to have important implications in treatment compliance and clinical outcomes, and can be assessed clinically by looking for the presence of illness awareness, correct attribution of symptoms to underlying condition, and acceptance of treatment. In this article, we suggest that cultivating insight is actually a highly important, yet often overlooked, component of medical training, which may explain why some consistently learn well, communicate effectively, and quickly attain clinical competency, while others struggle throughout their clinical training and may even be difficult to remediate. We herein define 'insight' in the context of medical training as having an astute perception of personal cognitive processes, motivations, emotions, and ability (strengths, weaknesses, and limitations) that should drive self-improvement and effective behavioural regulation. We then describe the utility of cultivating 'insight' in medical training through three lenses of (i) promoting self-regulated, lifelong clinical learning, (ii) improving clinical competencies and person-centred care, and (iii) enhancing physician mental health and well-being. In addition, we review educational pedagogies that are helpful to create a medical eco-system that promotes the cultivation of insight among its trainees and practitioners. Finally, we highlight several tell-tale signs of poor insight and discuss psychological and non-psychological interventions that may help those severely lacking in insight to become more amenable to change and remediation.

Navigating Pathways to Diagnosis in Idiopathic Subglottic Stenosis: A Qualitative Study

OBJECTIVE

Idiopathic subglottic stenosis is a rare disease, and time to diagnosis is often prolonged. In the United States, some estimate it takes an average of 9 years for patients with similar rare disease to be diagnosed. Patient experience during this period is termed the diagnostic odyssey. The aim of this study is to use qualitative methods grounded in behavioral-ecological conceptual frameworks to identify drivers of diagnostic odyssey length that can help inform efforts to improve health care for iSGS patients.

METHODS

Qualitative study using semi-structured interviews. Setting consisted of participants who were recruited from those enrolled in a large, prospective multicenter trial. We use directed content analysis to analyze qualitative semi-structured interviews with iSGS patients focusing on their pathways to diagnosis.

RESULTS

Overall, 30 patients with iSGS underwent semi-structured interviews. The patient-reported median time to diagnosis was 21 months. On average, the participants visited four different health care providers. Specialists were most likely to make an appropriate referral to otolaryngology that ended in diagnosis. However, when primary care providers referred to otolaryngology, patients experienced a shorter diagnostic odyssey. The most important behavioral-ecological factors in accelerating diagnosis were strong social support for the patient and providers' willingness to refer.

CONCLUSION

Several factors affected time to diagnosis for iSGS patients. Patient social capital was a catalyst in decreasing time to diagnosis. Patient-reported medical paternalism and gatekeeping limited specialty care referrals extended diagnostic odysseys. Additional research is needed to understand the effect of patient-provider and provider-provider relationships on time to diagnosis for patients with iSGS.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:815-824, 2024.

Japanese Internists' Most Memorable Diagnostic Error Cases: A Self-reflection Survey

Objective The etiologies of diagnostic errors among internal medicine physicians are unclear. To understand the causes and characteristics of diagnostic errors through reflection by those involved in them. Methods We conducted a cross-sectional study using a web-based questionnaire in Japan in January 2019. Over a 10-day period, a total of 2,220 participants agreed to participate in the study, of whom 687 internists were included in the final analysis. Participants were asked about their most memorable diagnostic error cases, in which the time course, situational factors, and psychosocial context could be most vividly recalled and where the participant provided care. We categorized diagnostic errors and identified contributing factors (i.e., situational factors, data collection/interpretation factors, and cognitive biases). Results Two-thirds of the identified diagnostic errors occurred in the clinic or emergency department. Errors were most frequently categorized as wrong diagnoses, followed by delayed and missed diagnoses. Errors most often involved diagnoses related to malignancy, circulatory system disorders, or infectious diseases. Situational factors were the most cited error cause, followed by data collection factors and cognitive bias. Common situational factors included limited consultation during office hours and weekends and barriers that prevented consultation with a supervisor or another department. Conclusion Internists reported situational factors as a significant cause of diagnostic errors. Other factors, such as cognitive biases, were also evident, although the difference in clinical settings may have influenced the proportions of the etiologies of the errors that were observed. Furthermore, wrong, delayed, and missed diagnoses may have distinctive associated cognitive biases.

Teaching medical students to apply deliberate reflection

PURPOSE

Deliberate reflection on initial diagnosis has been found to repair diagnostic errors. We investigated the effectiveness of teaching students to use deliberate reflection on future cases and whether their usage would depend on their perception of case difficulty.

METHOD

One-hundred-nineteen medical students solved cases either with deliberate-reflection or without instructions to reflect. One week later, all participants solved six cases, each with two equally likely diagnoses, but some symptoms in the case were associated with only one of the diagnoses (discriminating features). Participants provided one diagnosis and subsequently wrote down everything they remembered from it. After the first three cases, they were told that the next three would be difficult cases. Reflection was measured by the proportion of discriminating features recalled (overall; related to their provided diagnosis; related to alternative diagnosis).

RESULTS

The deliberate-reflection condition recalled more features for the alternative diagnosis than the control condition (p = .013) regardless of described difficulty. They also recalled more features related to their provided diagnosis on the first three cases (p = .004), but on the last three cases (described as difficult), there was no difference.

CONCLUSION

Learning deliberate reflection helped students engage in more reflective reasoning when solving future cases.

Development and Validation of a Formative Assessment Tool for Nephrology Fellows' Clinical Reasoning

BACKGROUND

Diagnostic errors are commonly driven by failures in clinical reasoning. Deficits in clinical reasoning are common among graduate medical learners, including nephrology fellows. We created and validated an instrument to assess clinical reasoning in a national cohort of nephrology fellows and established performance thresholds for remedial coaching.

METHODS

Experts in nephrology education and clinical reasoning remediation designed an instrument to measure clinical reasoning through a written patient encounter note from a web-based, simulated AKI consult. The instrument measured clinical reasoning in three domains: problem representation, differential diagnosis with justification, and diagnostic plan with justification. Inter-rater reliability was established in a pilot cohort ( n =7 raters) of first-year nephrology fellows using a two-way random effects agreement intraclass correlation coefficient model. The instrument was then administered to a larger cohort of first-year fellows to establish performance standards for coaching using the Hofstee method ( n =6 raters).

RESULTS

In the pilot cohort, there were 15 fellows from four training program, and in the study cohort, there were 61 fellows from 20 training programs. The intraclass correlation coefficients for problem representation, differential diagnosis, and diagnostic plan were 0.90, 0.70, and 0.50, respectively. Passing thresholds (% total points) in problem representation, differential diagnosis, and diagnostic plan were 59%, 57%, and 62%, respectively. Fifty-nine percent ( n =36) met the threshold for remedial coaching in at least one domain.

CONCLUSIONS

We provide validity evidence for a simulated AKI consult for formative assessment of clinical reasoning in nephrology fellows. Most fellows met criteria for coaching in at least one of three reasoning domains, demonstrating a need for learner assessment and instruction in clinical reasoning.

Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs

OBJECTIVES

The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists.

MATERIALS AND METHODS

A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.

RESULTS

The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.

CLINICAL SIGNIFICANCE

We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.

Misconceptions about Oral Diagnosis and Radiology among Dental Students: A Questionnaire Study

BACKGROUND

Misconceptions are learning deficits that may cause clinical judgments in a dental practice to be made incorrectly.

AIM

This study aimed to identify and look into students' misconceptions regarding Oral Diagnosis and Radiology.

MATERIALS AND METHODS

The fourth and fifth-grade students at the Altınbaş University Faculty of Dentistry were given a self-administered questionnaire that included multiple-choice questions about Oral Diagnosis/Medicine and Radiology. 106 students participated in the questionnaire and 50.9% of the participants were fourth-grade students, 49.1% of them were fifth-grade students. The questions were classified into two parts: five scenario-based questions and ten knowledge-based questions. Students' replies were evaluated for accuracy and confidence, and when a student was certain of giving an inaccurate response, misconceptions were noted. Two groups were compared using the Mann-Whitney-U test (P = 0.05).

RESULTS

A total of 1590 responses were collected. Regarding the question type, scenario-based questions had more misconceptions (19.6%) than knowledge-based questions (18.4%). Depending on the question, the majority of misconceptions were seen in the questions about denture stomatitis (46.2%) and radiographic contrast (38.7%).

CONCLUSIONS

To prevent misdiagnosis, it is essential to recognize and overcome misconceptions in Oral Diagnosis/Medicine and Radiology. Therefore this study assists students and lecturers in defining misconceptions and organizing precautions to avoid and fix them.

Diagnostic error and bias in the department of radiology: a pictorial essay

Diagnostic imaging is an essential and indispensable part of medical diagnosis and treatment, and diagnostic errors or biases are also common in the department of radiology, sometimes even having a severe impact on the diagnosis and treatment of patients. There are various reasons for diagnostic errors or biases in imaging. In this review, we analyze and summarize the causes of diagnostic imaging errors and biases based on practical cases. We propose solutions for dealing with diagnostic imaging errors and reducing their probability, thereby helping radiologists in their clinical practice.Critical relevance statement Diagnostic errors or bias contribute to most medical errors in the radiology department. Solutions for dealing with diagnostic imaging errors are pivotal for patients.Key points• Diagnostic errors or bias contribute to most medical errors in radiology department.• Solutions for dealing with diagnostic imaging errors are pivotal for patients.• This review summarizes the causes of diagnostic errors and offers solutions to them.

Navigating the COVID-19 infodemic: the influence of metacognitive efficiency on health behaviours and policy attitudes

The COVID-19 pandemic has been accompanied by an infodemic of misinformation and increasing polarization around public health measures, such as social distancing and national lockdowns. In this study, I examined metacognitive efficiency-the extent to which the subjective feeling of knowing predicts the objective accuracy of knowledge-as a tool to understand and measure the assimilation of misleading misinformation in a balanced sample of Great Britain's population (N = 1689), surveyed at the end of the third national lockdown. Using a signal-detection theory approach to quantify metacognitive efficiency, I found that at the population level, metacognitive efficiency for COVID-19 knowledge was impaired compared with general knowledge, indicating a worse alignment between confidence levels and the actual ability to discern true and false statements. Crucially, individual differences in metacognitive efficiency related to COVID-19 knowledge predicted health-protective behaviours, vaccination intentions and attitudes towards public health measures, even after accounting for the level of knowledge itself and demographic covariates, such as education, income and political alignment. These results reveal the significant impact of misinformation on public beliefs and suggest that fostering confidence in accurate knowledge should be a key target for science communication efforts aimed at promoting compliance with public health and social measures.

Clinical reasoning in managing chronic hip pain: One in two Australian and New Zealand physiotherapists diagnosed a case vignette with clinical criteria for hip OA as hip OA. A cross-sectional survey

OBJECTIVES

Using a case vignette of an adult (George) presenting with hip pain consistent with hip OA, this study aimed to describe: (a) whether physiotherapists make diagnoses and identify bodily structures using either patient history and/or physical examination findings; (b) which diagnoses and bodily structures physiotherapists attribute to the hip pain; (c) how confident physiotherapists were in their clinical reasoning using patient history and physical examination findings; (d) what treatments physiotherapists would offer to George.

METHODS

We conducted a cross-sectional online survey of physiotherapists in Australia and New Zealand. We used descriptive statistics to analyse closed questions and content analysis for open-text responses.

RESULTS

Two hundred and twenty physiotherapists completed the survey (39% response-rate). After receiving the patient history, 64% diagnosed George's pain and 49% of these as hip OA; 95% attributed George's pain to a bodily structure(s). After receiving the physical examination, 81% diagnosed George's hip pain and 52% of these as hip OA; 96% attributed George's hip pain to a bodily structure(s). Ninety-six percent of respondents were at least somewhat confident in their diagnosis after the patient history, and 95% were similarly confident after the physical examination. Most respondents offered advice (98%) and exercise (99%), but fewer offered treatments for weight loss (31%), medication (11%), and psychosocial factors (<15%).

DISCUSSION

About half of the physiotherapists that diagnosed George's hip pain made a diagnosis of hip OA, despite the case vignette including clinical criteria for a diagnosis of OA. Physiotherapists offered exercise and education, but many physiotherapists did not offer other clinically indicated and recommended treatments, such as weight loss and sleep advice.

Cognitive perspectives on maintaining physicians' medical expertise: III. Strengths and weaknesses of self-assessment

Is self-assessment enough to keep physicians' cognitive skills-such as diagnosis, treatment, basic biological knowledge, and communicative skills-current? We review the cognitive strengths and weaknesses of self-assessment in the context of maintaining medical expertise. Cognitive science supports the importance of accurately self-assessing one's own skills and abilities, and we review several ways such accuracy can be quantified. However, our review also indicates a broad challenge in self-assessment is that individuals do not have direct access to the strength or quality of their knowledge and instead must infer this from heuristic strategies. These heuristics are reasonably accurate in many circumstances, but they also suffer from systematic biases. For example, information that feels easy to process in the moment can lead individuals to overconfidence in their ability to remember it in the future. Another notable phenomenon is the Dunning-Kruger effect: the poorest performers in a domain are also the least accurate in self-assessment. Further, explicit instruction is not always sufficient to remove these biases. We discuss what these findings imply about when physicians' self-assessment can be useful and when it may be valuable to supplement with outside sources.

Approach to Tendinopathies of the Upper Limb: What Works

Tendinopathies are some of the most common diagnoses treated by hand surgeons. Diagnoses such as trigger digit, de Quervain tenosynovitis, extensor carpi ulnaris tendinitis, and epicondylitis often resolve with nonoperative treatment and/or a single ambulatory procedure. When symptoms persist or worsen after surgery, patients are disappointed and treatment can be challenging. This article reviews practical points in evaluation of such cases, and surgical options that work in revision scenarios.

Cognitive biases in internal medicine: a scoping review

BACKGROUND

Medical errors account for up to 440,000 deaths annually, and cognitive errors outpace knowledge deficits as causes of error. Cognitive biases are predispositions to respond in predictable ways, and they don't always result in error. We conducted a scoping review exploring which biases are most prevalent in Internal Medicine (IM), if and how they influence patient outcomes, and what, if any, debiasing strategies are effective.

CONTENT

We searched PubMed, OVID, ERIC, SCOPUS, PsychINFO, and CINAHL. Search terms included variations of "bias", "clinical reasoning", and IM subspecialties. Inclusion criteria were: discussing bias, clinical reasoning, and physician participants.

SUMMARY

Fifteen of 334 identified papers were included. Two papers looked beyond general IM: one each in Infectious Diseases and Critical Care. Nine papers distinguished bias from error, whereas four referenced error in their definition of bias. The most commonly studied outcomes were diagnosis, treatment, and physician impact in 47 % (7), 33 % (5), and 27 % (4) of studies, respectively. Three studies directly assessed patient outcomes. The most commonly cited biases were availability bias (60 %, 9), confirmation bias (40 %, 6), anchoring (40 %, 6), and premature closure (33 %, 5). Proposed contributing features were years of practice, stressors, and practice setting. One study found that years of practice negatively correlated with susceptibility to bias. Ten studies discussed debiasing; all reported weak or equivocal efficacy.

OUTLOOK

We found 41 biases in IM and 22 features that may predispose physicians to bias. We found little evidence directly linking biases to error, which could account for the weak evidence of bias countermeasure efficacy. Future study clearly delineating bias from error and directly assessing clinical outcomes would be insightful.

Cognitive perspectives on maintaining physicians' medical expertise: I. Reimagining Maintenance of Certification to promote lifelong learning

Until recently, physicians in the USA who were board-certified in a specialty needed to take a summative test every 6-10 years. However, the 24 Member Boards of the American Board of Medical Specialties are in the process of switching toward much more frequent assessments, which we refer to as longitudinal assessment. The goal of longitudinal assessments is to provide formative feedback to physicians to help them learn content they do not know as well as serve an evaluation for board certification. We present five articles collectively covering the science behind this change, the likely outcomes, and some open questions. This initial article introduces the context behind this change. This article also discusses various forms of lifelong learning opportunities that can help physicians stay current, including longitudinal assessment, and the pros and cons of each.

Do malpractice claim clinical case vignettes enhance diagnostic accuracy and acceptance in clinical reasoning education during GP training?

BACKGROUND

Using malpractice claims cases as vignettes is a promising approach for improving clinical reasoning education (CRE), as malpractice claims can provide a variety of content- and context-rich examples. However, the effect on learning of adding information about a malpractice claim, which may evoke a deeper emotional response, is not yet clear. This study examined whether knowing that a diagnostic error resulted in a malpractice claim affects diagnostic accuracy and self-reported confidence in the diagnosis of future cases. Moreover, suitability of using erroneous cases with and without a malpractice claim for CRE, as judged by participants, was evaluated.

METHODS

In the first session of this two-phased, within-subjects experiment, 81 first-year residents of general practice (GP) were exposed to both erroneous cases with (M) and erroneous cases without (NM) malpractice claim information, derived from a malpractice claims database. Participants rated suitability of the cases for CRE on a five-point Likert scale. In the second session, one week later, participants solved four different cases with the same diagnoses. Diagnostic accuracy was measured with three questions, scored on a 0-1 scale: (1) What is your next step? (2) What is your differential diagnosis? (3) What is your most probable diagnosis and what is your level of certainty on this? Both subjective suitability and diagnostic accuracy scores were compared between the versions (M and NM) using repeated measures ANOVA.

RESULTS

There were no differences in diagnostic accuracy parameters (M vs. NM next step: 0.79 vs. 0.77, p = 0.505; differential diagnosis 0.68 vs. 0.75, p = 0.072; most probable diagnosis 0.52 vs. 0.57, p = 0.216) and self-reported confidence (53.7% vs. 55.8% p = 0.390) of diagnoses previously seen with or without malpractice claim information. Subjective suitability- and complexity scores for the two versions were similar (suitability: 3.68 vs. 3.84, p = 0.568; complexity 3.71 vs. 3.88, p = 0.218) and significantly increased for higher education levels for both versions.

CONCLUSION

The similar diagnostic accuracy rates between cases studied with or without malpractice claim information suggests both versions are equally effective for CRE in GP training. Residents judged both case versions to be similarly suitable for CRE; both were considered more suitable for advanced than for novice learners.

Gender Differences in Perceived Working Conditions of General Practitioners During the COVID-19 Pandemic-a Cross-Sectional Study

BACKGROUND

The ongoing COVID-19 pandemic has revealed gender-specific differences between general practitioners in adapting to the posed challenges. As primary care workforce is becoming increasingly female, in many countries, it is essential to take a closer look at gender-specific influences when the global health care system is confronted with a crisis.

OBJECTIVE

To explore gender-specific differences in the perceived working conditions and gender-specific differences in challenges facing GPs at the beginning of the COVID-19 pandemic in 2020.

DESIGN

Online survey in seven countries.

PARTICIPANTS

2,602 GPs from seven countries (Austria, Australia, Switzerland, Germany, Hungary, Italy, Slovenia). Of the respondents, 44.4% (n = 1,155) were women.

MAIN MEASURES

Online survey. We focused on gender-specific differences in general practitioners' perceptions of working conditions at the beginning of the COVID-19 pandemic in 2020.

KEY RESULTS

Female GPs rated their skills and self-confidence significantly lower than male GPs (f: 7.1, 95%CI: 6.9-7.3 vs. m: 7.6, 95%CI 7.4-7.8; p < .001), and their perceived risk (concerned about becoming infected or infecting others) higher than men (f: 5.7, 95%CI: 5.4-6.0 vs. m: 5.1, 95%CI: 4.8-5.5; p = .011). Among female GPs, low self-confidence in the treatment of COVID-19 patients appear to be common. Results were similar in all of the participating countries.

CONCLUSIONS

Female and male GPs differed in terms of their self-confidence when dealing with COVID-19-related issues and their perceptions of the risks arising from the pandemic. To ensure optimal medical care, it is important that GPs realistically assess their own abilities and overall risk.

Increasing the margin of patient safety for periodontal and implant treatments: The role of human factors

Early complications following periodontal and dental implant surgeries are typically attributed to technique or poor biological response, ignoring the possibility of the human element. Interestingly, significant experience is not correlated with increased success, whereas evidence supports the impact of clinical behavior on patient outcome. This is the result of errors, much like those scrutinized in other high-risk technical fields, such as aviation. What can be surprising is that those who make these errors are very well acquainted with best practices. Given this, how is it possible for the conscientious practitioner to fail to apply protocols that are nonetheless very well known? Recently, the concepts of human and organizational factors have been translated to medicine, though dentistry has been slow to recognize their potential benefit. This review lists specific human factor behaviors, such as use of checklists and crew resource management, which might improve postsurgical outcome.

Heuristics and biases in medical decision-making under uncertainty: The case of neuropronostication for consciousness disorders

Neuropronostication for consciousness disorders can be very complex and prone to high uncertainty. Despite notable advancements in the development of dedicated scales and physiological markers using innovative paradigms, these technical progressions are often overshadowed by factors intrinsic to the medical environment. Beyond the scarcity of objective data guiding medical decisions, factors like time pressure, fatigue, multitasking, and emotional load can drive clinicians to rely more on heuristic-based clinical reasoning. Such an approach, albeit beneficial under certain circumstances, may lead to systematic error judgments and impair medical decisions, especially in complex and uncertain environments. After a brief review of the main theoretical frameworks, this paper explores the influence of clinicians' cognitive biases on clinical reasoning and decision-making in the challenging context of neuroprognostication for consciousness disorders. The discussion further revolves around developing and implementing various strategies designed to mitigate these biases and their impact, aiming to enhance the quality of care and the patient safety.

Noise and transient ischaemic attacks - A challenge?

Consistency in medical decision-making is ideally expected. This includes consistency between different clinicians so that the same patient will receive the same diagnosis regardless of the assessing clinician. It also encompasses reliability as an individual clinician meaning at any given time or context, we apply the same process and principles to ensure the decisions we make do not deviate significantly from our peers or indeed our own past decisions. However, consistency in decision-making can be challenged when working within a busy healthcare system. We discuss the concept of 'noise' and explore how it affects decision-making in acute presentations of transient neurology where doctors can differ in terms of their diagnostic decisions.

Impact of diagnostic checklists on the interpretation of normal and abnormal electrocardiograms

OBJECTIVES

Checklists that aim to support clinicians' diagnostic reasoning processes are often recommended to prevent diagnostic errors. Evidence on checklist effectiveness is mixed and seems to depend on checklist type, case difficulty, and participants' expertise. Existing studies primarily use abnormal cases, leaving it unclear how the diagnosis of normal cases is affected by checklist use. We investigated how content-specific and debiasing checklists impacted performance for normal and abnormal cases in electrocardiogram (ECG) diagnosis.

METHODS

In this randomized experiment, 42 first year general practice residents interpreted normal, simple abnormal, and complex abnormal ECGs without a checklist. One week later, they were randomly assigned to diagnose the ECGs again with either a debiasing or content-specific checklist. We measured residents' diagnostic accuracy, confidence, patient management, and time taken to diagnose. Additionally, confidence-accuracy calibration was assessed.

RESULTS

Accuracy, confidence, and patient management were not significantly affected by checklist use. Time to diagnose decreased with a checklist (M=147s (77)) compared to without a checklist (M=189s (80), Z=-3.10, p=0.002). Additionally, residents' calibration improved when using a checklist (phase 1: R2=0.14, phase 2: R2=0.40).

CONCLUSIONS

In both normal and abnormal cases, checklist use improved confidence-accuracy calibration, though accuracy and confidence were not significantly affected. Time to diagnose was reduced. Future research should evaluate this effect in more experienced GPs. Checklists appear promising for reducing overconfidence without negatively impacting normal or simple ECGs. Reducing overconfidence has the potential to improve diagnostic performance in the long term.

Medical Student Perspectives on Sexual and Gender Minority Acceptance in Surgical Specialties and Sexual and Gender Minority Education

BACKGROUND

The topics of healthcare for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) patients and inclusion of LGBTQ+ health providers remain overlooked. Some specialties may be perceived as less inclusive to LGBTQ+ trainees. This study aimed to describe the perspectives of current medical students regarding LGBTQ+ education and the acceptance of LGBTQ+ trainees among different specialties.

MATERIALS AND METHODS

A cross-sectional voluntary and anonymous online survey was distributed through REDCap to all medical students (n = 495) at a state medical school. Medical students' sexuality and gender identity were queried. A descriptive statistical analysis was performed, and the responses were classified into two groups: LGBTQ+ and non-LGBTQ+.

RESULTS

A total of 212 responses were queried. Of the respondents who agreed that certain specialties are less welcoming to LGBTQ+ trainees (n = 69, 39%), orthopedic surgery, general surgery and neurosurgery were identified most frequently (84%, 76%, and 55%, respectively). After analyzing sexual orientation as an influence on choosing a future specialty for residency, only 1% of non-LGBTQ+ students indicated that their sexual orientation influences their specialty of choice in comparison with 30% of LGBTQ+ students (P < 0.001). Finally, more non-LGBTQ+ students indicated that they believe they are receiving appropriate education on caring for LGBTQ+ patients as compared to LGBTQ+ students (71% and 55%, respectively, P < 0.05).

CONCLUSIONS

LGBTQ+ students are still hesitant to pursue careers in General Surgery as compared to their non-LGBTQ+ peers. The perception that surgical specialties are the least welcoming to LGBTQ+ students continues to be a concern for all students. Further strategies of inclusivity and their effectiveness need to be studied.

Illusion of knowledge in statistics among clinicians: evaluating the alignment between objective accuracy and subjective confidence, an online survey

Healthcare professionals' statistical illiteracy can impair medical decision quality and compromise patient safety. Previous studies have documented clinicians' insufficient proficiency in statistics and a tendency in overconfidence. However, an underexplored aspect is clinicians' awareness of their lack of statistical knowledge that precludes any corrective intervention attempt. Here, we investigated physicians', residents' and medical students' alignment between subjective confidence judgments and objective accuracy in basic medical statistics. We also examined how gender, profile of experience and practice of research activity affect this alignment, and the influence of problem framing (conditional probabilities, CP vs. natural frequencies, NF). Eight hundred ninety-eight clinicians completed an online survey assessing skill and confidence on three topics: vaccine efficacy, p value and diagnostic test results interpretation. Results evidenced an overall consistent poor proficiency in statistics often combined with high confidence, even in incorrect answers. We also demonstrate that despite overconfidence bias, clinicians show a degree of metacognitive sensitivity, as their confidence judgments discriminate between their correct and incorrect answers. Finally, we confirm the positive impact of the more intuitive NF framing on accuracy. Together, our results pave the way for the development of teaching recommendations and pedagogical interventions such as promoting metacognition on basic knowledge and statistical reasoning as well as the use of NF to tackle statistical illiteracy in the medical context.

Undiagnosed and 'overdiagnosed' COPD using postbronchodilator spirometry in primary healthcare settings: a systematic review and meta-analysis

BACKGROUND

Despite chronic obstructive pulmonary disease (COPD) being a major global cause of mortality and hospitalisation, it is often undiagnosed or inaccurately diagnosed in clinical settings.

OBJECTIVE

To systematically synthesise all peer-reviewed papers from primary healthcare settings that have reported data on: (1) undiagnosed COPD, that is, patients with respiratory symptoms and postbronchodilator airflow obstruction consistent with COPD, without a formal clinician's diagnosis of COPD either documented in health records or reported by patients and (2) 'overdiagnosed COPD', that is, clinician's diagnosis without postbronchodilator airflow obstruction.

METHODS

Studies investigating these diagnostic metrics in patients from primary healthcare clinics (according to predefined inclusion/exclusion criteria) were sourced from Medline and Embase and assessed for bias (Johanna Briggs Institute tools for prevalence studies and case series). Meta-analyses of studies of adequate sample size used random effect modelling stratified by risk factor categories.

RESULTS

Of 26 eligible articles, 21 cross-sectional studies investigated 3959 cases of spirometry-defined COPD (with or without symptoms), and 5 peer-reviewed COPD case series investigated 7381 patients. The prevalence of spirometry-confirmed COPD without a diagnosis documented in their health records was 14%-26% in studies of symptomatic smokers (N=3). 1 in 4 patients taking inhaled therapies (25% (95% CI 22% to 28%), N=2) and 1 in 6 smokers irrespective of symptoms (16% (95% CI 14% to 18%), N=6) fulfilled diagnostic spirometry criteria but did not report receiving a COPD-related diagnosis. In an adequately powered series of COPD cases documented in primary healthcare records (N=4), only between 50% and 75% of subjects had any airflow obstruction on postbronchodilator spirometry performed by study researchers, therefore, COPD was clinically 'overdiagnosed' in 25%-50% of subjects.

DISCUSSION

Although data were heterogeneous and of modest quality, undiagnosed COPD was common in primary healthcare, especially for symptomatic smokers and patients treated with inhaled therapies. In contrast, frequent COPD 'overdiagnosis' may represent treatment of asthma/reversible component or another medical diagnosis.

PROSPERO REGISTRATION NUMBER

CRD42022295832.

Goal-Oriented and Habit-Oriented Reflective Models to Support Professional Identity Formation and Metacognitive Thinking

Professional identity formation (PIF) is a multi-year, complex, social, and intellectual process that culminates in trainees "thinking, acting, and feeling like a doctor." Some of the processes of PIF, particularly the Hidden Curriculum, may result in suboptimal outcomes in student's cognition, including implicit bias and poor clinical decision-making. Many have recognized the importance of reflective writing, particularly metacognition, in undergraduate medical education. However, there are few concrete examples and explanations for fully integrating reflective writing exercises across multiple situations, experiences, and levels of growth. The authors provide conceptual frameworks, concrete curricular structures, and reflective tools used at two medical schools.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40670-023-01752-9.

Impact of uncertainty intolerance on clinical reasoning: A scoping review of the 21st-century literature

RATIONALE, AIMS AND OBJECTIVES: Clinical reasoning is currently extensively studied to find out how to make proper diagnoses. Literature indicates that intolerance of uncertainty (IU) may have a strong negative impact on clinical reasoning. We summarize the various consequences of IU on clinical reasoning.

METHODS

A scoping review was conducted using relevant keywords to scientific databases (i.e., Google Scholar, Medline, PsycINFO and PBSC) from September to November 2021. Complementary research included relevant articles and articles retrieved through Google Scholar's alert system. We included articles about healthcare professionals as defined by the French Public Health Code (As defined here: https://www.vie-publique.fr/fiches/37855-categories-de-professionnels-de-sante-code-se-la-sante-publique), and articles reporting on the impact of IU or uncertainty management on clinical reasoning.

RESULTS

We retrieved 1853 articles, of which 8 were kept for final analysis considering our inclusion criteria. Two behaviour categories were affected by uncertainty intolerance: investigative and prescriptive behaviours. Regarding the investigation process, mismanagement of uncertainty appeared to lead to reasoning bias, potentially resulting in diagnostic errors. IU was associated with withholding information, more referrals to peers and less use of new medical interventions. Regarding prescription behaviours, IU among health professionals could lead to overprescribing unnecessary or dangerous tests. IU was also associated with more antibiotic prescriptions for conditions where antibiotics are to be used carefully.

CONCLUSION

Few studies have yet addressed the impact of IU on clinical reasoning. IU's influence is primarily observed on investigative and prescribing behaviours. More studies are needed to fully understand the impact of IU on clinical reasoning itself, and not only on practical consequences.