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This cohort study examines whether machine learning (ML) can enhance the ability of electronic triggers to identify possible missed opportunities in diagnosis.

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BACKGROUND: Retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S) is a rare autosomal dominant systemic microvascular disorder attributed to TREX1 (three-prime repair exonuclease-1) gene mutations, often proned to misdiagnosed. METHODS: We reported a case of RVCL-S coexisting with systemic lupus erythematosus due to a mutation in the TREX1 gene. This study provided a summary and discussion of previously documented cases related to TREX1 mutations or RVCL-S. RESULTS: A 39-year-old female patient visited the clinic due to progressive memory loss and speech difficulties. Magnetic resonance imaging results showed corpus callosum atrophy and multiple subcortical calcifications in both brain hemispheres. Genetic testing revealed a TREX1 gene mutation (c.294dupA). Treatment with immunosuppressive therapy for 2 months led to improvements in communication and mobility. We also summarized previously reported cases providing an overview of TREX1 gene mutation or RCVL-S. CONCLUSION: Our case establishes a compelling foundation for future RVCL-S diagnosis and treatment paradigms. Notably, conducting systemic immunity screening in patients with RVCL-S emerges as a strategic approach to prevent potential diagnostic oversights.

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BACKGROUND: Managing diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes. OBJECTIVE: Our aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice. DESIGN: This qualitative study used video and audio-recordings. Verbatim transcripts were coded with a modified, validated tool to capture GPs' actions and communication in primary care consultations that included diagnostic uncertainty. The tool includes items relating to advice regarding new symptoms or symptom deterioration (sometimes called 'safety netting'). Video data was analysed to identify GP and patient body postures during and after the delivery of the management plan. PARTICIPANTS: All patient participants had a consultation with a GP, were over the age of 50 and had (1) at least one new presenting problem or (2) one persistent problem that was undiagnosed. APPROACH: Data collection occurred in GP-patient consultations during 2017-2018 across 7 practices in UK during 2017-2018. KEY RESULTS: GPs used various management strategies to address diagnostic uncertainty, including (1) symptom monitoring without treatment, (2) prescribed treatment with symptom monitoring, and (3) addressing risks that could arise from administrative tasks. GPs did not make management plans for potential treatment side effects. Specificity of uncertainty management plans varied among GPs, with only some offering detailed actions and timescales. The transfer of responsibility for the management plan to patients was usually delivered rather than negotiated, with most patients confirming acceptance before concluding the discussion. CONCLUSIONS: We offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.

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Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promise for more routine medical tasks, may broaden one's differential diagnosis, and may be able to assist in the evaluation of images, such as radiographs and electrocardiograms, the technology is largely based on advanced algorithms akin to pattern recognition. One of the key questions raised in concert with these advances is: What does the growth of artificial intelligence mean for medical education, particularly the development of critical thinking and clinical reasoning? In this commentary, we will explore the elements of cognitive theory that underlie the ways in which physicians are taught to reason through a diagnostic case and compare hypothetico-deductive reasoning, often employing illness scripts, with inductive reasoning, which is based on a deeper understanding of mechanisms of health and disease. Issues of cognitive bias and their impact on diagnostic error will be examined. The constructs of routine and adaptive expertise will also be delineated. The application of artificial intelligence to diagnostic problem solving, along with concerns about racial and gender bias, will be delineated. Using several case examples, we will demonstrate the limitations of this technology and its potential pitfalls and outline the direction medical education may need to take in the years to come.

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In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, and David Newman-Toker, MD, PhD, discuss the prevalence and harms of diagnostic error in medicine; the role and causes of error in diagnostic radiology; and disease-specific examples, including in patients with stroke who present with dizziness.

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OBJECTIVES: Diagnostic errors contribute substantially to preventable medical errors. Especially, the emergency department (ED) is a high-risk environment. Previous research showed that in 15%-30% of the ED patients, there is a difference between the primary diagnosis assigned by the emergency physician and the discharge diagnosis. This study aimed to determine the number and types of diagnostic discrepancies and to explore factors predicting discrepancies. METHODS: A retrospective record review was conducted in an academic medical center. The primary diagnosis assigned in the ED was compared with the discharge diagnosis after hospital admission. For each patient, we gathered additional information about the diagnostic process to identify possible predictors of diagnostic discrepancies. RESULTS: The electronic health records of 200 patients were reviewed. The primary diagnosis assigned in the ED was substantially different from the discharge diagnosis in 16.0%. These diagnostic discrepancies were associated with a higher number of additional diagnostics applied for (2.4 versus 2.0 diagnostics; P = 0.002) and longer stay in the ED (5.9 versus 4.7 hours; P = 0.008). CONCLUSIONS: A difference between the diagnosis assigned by the emergency physician and the discharge diagnosis was found in almost 1 in 6 patients. The increased number of additional diagnostics and the longer stay at the ED in the group of patients with a diagnostic discrepancy suggests that these cases reflect the more difficult cases. More research should be done on predictive factors of diagnostic discrepancies.

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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.

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In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, introduces the concept of diagnostic excellence and its relevance to radiologists. Patient-centered definitions of diagnostic error and conceptualizations of the diagnostic process are discussed.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, introduces the concept of diagnostic excellence and its relevance to radiologists. Patient-centered definitions of diagnostic error and conceptualizations of the diagnostic process are discussed.

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BACKGROUND: Pitfalls in Pap test could be defined as false positive, false negative, or underdiagnosed results which can lead to unnecessary diagnostic procedures or delayed and inadequate treatment. It can be a consequence of misinterpretation of certain morphological entities which are described in this paper. SUMMARY: The paper presents an overview of the morphological features and look-alikes of the common sources of pitfalls such as atrophy, repair, intrauterine device change, tubal metaplasia, hyperchromatic crowded groups, and radiation changes. Rare causes of pitfalls such as Arias-Stella changes, pemphigus, tumor diathesis per se, rare types of cervical cancer, including verrucous and papillary squamous cell cancer, gastric type, and endometrioid adenocarcinoma are also described. KEY MESSAGES: The awareness of pitfalls in cervical cytology is important for cytopathologists and clinicians to avoid future errors. Review of Pap tests with erroneous diagnosis is important for quality control in cytology laboratory, and it must be considered an educational- and experience-building procedure. Cytopathologist should not pull back in significant diagnoses, especially in human papillomavirus-negative cases.

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Cognitive biases are systematic thought processes involving the use of a filter of personal experiences and preferences arising from the tendency of the human brain to simplify information processing, especially when taking in vast amounts of data such as from imaging studies. These biases encompass a wide spectrum of thought processes and frequently overlap in their concepts, with multiple biases usually in operation when interpretive and perceptual errors occur in radiology. The authors review the gamut of cognitive biases that occur in radiology. These biases are organized according to their expected stage of occurrence while the radiologist reads and interprets an imaging study. In addition, the authors propose several additional cognitive biases that have not yet, to their knowledge, been defined in the radiologic literature but are applicable to diagnostic radiology. Case examples are used to illustrate potential biases and their impact, with emergency radiology serving as the clinical paradigm, given the associated high imaging volumes, wide diversity of imaging examinations, and rapid pace, which can further increase a radiologist's reliance on biases and heuristics. Potential strategies to recognize and overcome one's personal biases at each stage of image interpretation are also discussed. Awareness of such biases and their unintended effects on imaging interpretations and patient outcomes may help make radiologists cognizant of their own biases that can result in diagnostic errors. Identification of cognitive bias in departmental and systematic quality improvement practices may represent another tool to prevent diagnostic errors in radiology. ©RSNA, 2024 See the invited commentary by Larson in this issue.

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Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

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The constant increase in the number of neurotraumas in the country leads to an increase in forensic examinations of a persons. In Russia, about 600 thousand people receive craniocerebral injuries annually, of which 50 thousand die, others are potentially will be in forensic examination during or after treatment. With an increase in the total number of such examinations, the number of erroneous conclusions is expected to increase. If it is impossible for the radiologist included in the commission to review the results of computed tomography of the head performed in the hospital, the experts are forced to use the data that are recorded in the medical documents. The present study revealed the percentage of erroneous interpretations in such descriptions, systematized typical errors, calculated the sensitivity, specificity and accuracy of computed tomography in craniocerebral injury.

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BACKGROUND: Fine-needle aspiration cytology (FNAC) is a cornerstone technique for the initial assessment of breast lesions, offering a rapid and minimally invasive option for cytological evaluation. While FNACs can forego the need for core needle biopsies (CNBs), variations in technique, subjective interpretation, and intrinsic limitations present diagnostic challenges. The International Academy of Cytology (IAC) established the Yokohama system and is developing the WHO Reporting System for Breast Cytopathology jointly with IARC, to standardize diagnostic criteria, aiming to enhance diagnostic precision and consistency. Due to the preference for CNBs, expertise in breast FNAC is low in the developed world. SUMMARY: This review assesses common pitfalls in breast cytopathology. These common and uncommon entities may easily lead to false-negative or false-positive diagnoses, due to morphological overlap or misleading clinical and radiological contexts. For instance, pauci-cellular lesions, such as lobular carcinomas, often lead to false-negative diagnoses, whereas complex sclerosing lesions, fibroadenomas, and papillary lesions may show concerning features, resulting in a false positive. The same is true for some benign inflammatory pathologies, such as steatonecrosis, and uncommon lesions, such as collagenous spherulosis. Ductal carcinoma in situ can lead to both false-negative and false-positive diagnoses, and high-grade lesions are impossible to tell apart from invasive carcinomas. These are discussed in detail. Procedural and preanalytical conditions, and the role of ancillary testing, are also briefly addressed. KEY MESSAGES: Breast FNAB is a powerful diagnostic technique, fast and minimally invasive. Even in contexts which lack expertise, this technique can be successfully adopted with a cautious approach and as long as pitfalls are kept in mind, benefiting patients and healthcare systems.

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Diagnosis education is explicitly included in the Draft Accreditation Standards 2025 and should be wholeheartedly supported. To address the staggering number of diagnostic errors in the US, the National Academy of Medicine advocates for diagnosis education for all health professions. Misperceptions of pharmacists' involvement in diagnosis are exacerbated by use of implicit language and euphemisms, and it's long overdue that we provide clarity. Pharmacists are engaged in diagnosis and diagnostic reasoning in everyday practice whether realized or not. Diagnosis education is implicitly included in US pharmacy curricula in the Pharmacists' Patient Care Process, and it is time to give students the language to engage in it responsibly to be practice-ready in all states. The explicit inclusion of diagnosis in the Draft Standards 2025 has positive implications for education, practice, and graduates.

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OBJECTIVES: Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts. METHODS: We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility. RESULTS: We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms. CONCLUSIONS: Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

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INTRODUCTION: Liver biopsy has become selective due to its invasiveness, potential adverse effects, patient acceptance and cost. Furthermore, the emergence of noninvasive tests (NITs) has challenged the necessity of liver biopsies in specific clinical situations. However, liver biopsy continues to play a crucial role in disease diagnosis, prognosis, and evaluating treatment compliance and response in selected patients. AREAS COVERED: In this narrative review, we discuss the errors and the shortcomings that can occur at various stages, from the initial patient selection for a liver biopsy to the final reporting phase, and strategies to address them. Clinicians and pathologists must take all necessary precautions to mitigate potential shortcomings that could compromise the value of liver biopsies. EXPERT OPINION: The increasing sophistication of NITs offers a safer, more convenient, and potentially more cost-effective approach to diagnosing chronic liver disease, especially for assessing the degree of liver fibrosis. As NITs continue to evolve, liver biopsy will likely transition to a more targeted role, ensuring optimal patient care in the ever-changing field of hepatology. However, liver biopsy will continue to have a pivotal role in assessing acute liver disease where the diagnostic yield of the liver biopsy still outweighs that of NITs.

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OBJECTIVE: The aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed to identify challenges in the process of diagnosing CVD, to identify discrepancies between patient and clinician experiences of CVD diagnosis, and to identify the requirements of future health technology solutions intended to improve CVD diagnosis. DESIGN: Semistructured focus groups and one-to-one interviews to generate qualitative data that were subjected to thematic analysis. PARTICIPANTS: UK-based individuals (N=32) with lived experience of diagnosis of CVD (n=23) and clinicians with experience in diagnosing CVD (n=9). RESULTS: We identified four key themes related to delayed or inaccurate diagnosis of CVD: symptom interpretation, patient characteristics, patient-clinician interactions and systemic challenges. Subthemes from each are discussed in depth. Challenges related to time and communication were greatest for both stakeholder groups; however, there were differences in other areas, for example, patient experiences highlighted difficulties with the psychological aspects of diagnosis and interpreting ambiguous symptoms, while clinicians emphasised the role of individual patient differences and the lack of rapport in contributing to delays or inaccurate diagnosis. CONCLUSIONS: Our findings highlight key considerations when developing digital technologies that seek to improve the efficiency and accuracy of diagnosis of CVD.

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