Diagnosis最新文献

Objectives: Missed opportunities for diagnostic excellence are common and can lead to delayed diagnoses and initiation of treatment. Medical teams incorporate many elements into the diagnostic process, including patient factors, medical knowledge, data gathering, care environment, subspecialized personnel, and hospital processes. A case-based discussion describes how search satisfying - the tendency to stop searching once something has been found - and authority gradients can lead to delays in diagnosis.

Case presentation: A 2-year-old girl repeatedly presents to the emergency department with vomiting and periorbital swelling progressing to esotropia, initially found to have normal short-sequence MR brain imaging. After nonspecific labs and reassuring initial imaging, ophthalmologic consultation obtained during the child's third emergency department visit proposed plausible diagnoses of benign post-viral esotropia or decompensated esotropia. When her exam worsened at ophthalmology clinic follow up, she was referred back to the emergency department for complete MR brain and orbit imaging leading to a diagnosis of bilateral orbital myositis.

Conclusions: Examining the diagnostic process through integrated commentary, this case emphasizes the importance of recognizing limitations of short-sequencing advanced imaging and continuing the diagnostic pursuit in collaboration with consultants. A fishbone diagram visually demonstrates the factors that lead to a missed opportunity for diagnostic excellence. The case concludes with clinical teaching points in addition to a pitfall, myth, and pearl specific to search satisfying and authority gradients.

Objectives: The focus on improving patient safety has mainly been by learning from errors and near misses (Safety-I). We applied a novel Safety-II approach to identify and learn from practice variations in the diagnostic process in the emergency department (ED), and subsequently design and implement practice changes.

Methods: In this single-center study, we used action research (cycles of micro-experiments to study and improve processes with active stakeholder involvement) in the diagnostic process. We used three subsequent observation cycles of the following six steps: observations, gathering of follow-up data, analyses, a co-creation session with involved stakeholders, sharing of best practices, and updating the observation form. The observations and analyses focused on identifying practice variation in everyday practice rather than on what went right or wrong. During co-creation sessions, stakeholders discussed whether practice variations were reflective of possible improvements in the diagnostic process. Promising best practices were identified, and subsequently implemented as practice changes aiming to improve the diagnostic process. Implemented practice changes were evaluated in subsequent cycles.

Results: Forty diagnostic processes were observed. We identified practice variations that reflected the resilience and adaptability of clinicians, as well as variations revealing opportunities to improve the diagnostic process. Five identified best practices were implemented as practice changes: a template for the documentation of ED preannouncements, a document with relevant digital information resources for residents, face-to-face supervision by Internal Medicine consultants during office hours, blood sampling at triage, and adding lipase to the standard ED blood tests. These changes were well-received by stakeholders, also shown by an adoption-rate of 67-100 % of observed cases after implementation.

Conclusions: A Safety-II approach with action research and direct observations of the diagnostic process in the ED can be successfully applied to identify and learn from practice variation, and can lead to well-received practice changes.

In the decade since the National Academies of Sciences, Engineering, and Medicine (NASEM) report Improving Diagnosis in Health Care, substantial progress has been made in understanding and teaching diagnostic reasoning. This manuscript reviews key advancements in the science and theory of clinical reasoning, including the shift from exclusive focus on individual cognitive models to those that embrace context and team-based approaches. Recent innovations in diagnosis education, such as development of formal competencies, use of structured reflection, and approaches to assessment are discussed. Despite these gains, challenges remain in translating theory into practice, particularly in curricular innovation and implementation, faculty development, and assessment. The emergence of generative artificial intelligence presents both opportunities and imperatives for reimagining diagnosis education. The authors call for sustained efforts to embed diagnostic excellence across health professions education, emphasizing interprofessional collaboration, patient engagement, and system-level reform to reduce diagnostic error and improve outcomes.

Objectives: Hepatic sclerosing hemangioma (SH) is a rare benign liver lesion that poses a significant diagnostic challenge due to its ability to mimic malignant hepatic tumors on imaging.

Case presentation: We report the case of a 67-year-old woman with diabetes, hypertension, and chronic kidney disease who presented with incidental liver lesions discovered during the workup of elevated alkaline phosphatase. Cross-sectional imaging, including MRI and PET-FDG, revealed multiple atypical hypodense hepatic lesions with perihilar biliary obstruction, initially raising concern for cholangiocarcinoma. Multiple tissue biopsies including Spyglass-guided and percutaneous attempts were non-diagnostic, and the clinical suspicion of malignancy persisted. Diagnostic laparoscopy and targeted incisional biopsy ultimately confirmed the diagnosis of sclerosing hemangioma. The patient underwent laparoscopic wedge resection and has remained well on follow-up for more than two years.

Conclusions: This case highlights the importance of maintaining a broad differential diagnosis in the evaluation of atypical hepatic lesions, the limitations of radiological findings, and the value of a stepwise multidisciplinary approach in avoiding unnecessary major resections. A focused review of the literature supports the rarity of this entity and emphasizes the need for tissue diagnosis in ambiguous cases.

Objectives: Clinical reasoning skills are required for safe care, yet they are not consistently taught to advanced practice providers (APPs). In hospital medicine, where APPs work semi-independently, gaps in clinical reasoning can increase the likelihood of error. To address this, we developed a module that uses diagnostic root cause analysis (RCA) to teach clinical reasoning skills to hospital medicine APP fellows.

Methods: The curriculum was delivered from July 2021 to March 2025. Fellows selected real-world diagnostic errors encountered during clinical rotations, created cognitive fishbone diagrams, and presented their analysis in small-group.

Results: Twenty-seven fellows completed the module and pre-post assessment surveys. Statistically significant improvements were observed across all six domains of knowledge and confidence related to identifying error contributors, analyzing cases, and setting goals. Free-text responses highlighted the module's emotional safety, peer learning value, and normalization of diagnostic reflection. Two learners published their projects as academic posters, and one graduate now co-facilitates the sessions.

Conclusions: This module offers a scalable, time-efficient approach to clinical reasoning education that is adaptable across learner levels and specialties. Its peer-led design fosters psychological safety, reflective practice, and creates a natural pathway for APPs to engage in microscholarship - addressing a critical gap in both education and academic inclusion.

Since the National Academies of Sciences, Engineering, and Medicine (NASEM) report Improving Diagnosis in Health Care, various research efforts have accelerated progress to understand and improve diagnostic safety. In this opinion piece, we summarize two decades of progress in methods for identifying and learning from diagnostic errors and provide recommendations for future research. Multiple methods have been used to quantify diagnostic errors in various clinical settings, thereby facilitating a deeper understanding of the nature and magnitude of the problem and enabling studies of contributing factors. However, the use of standardized definitions of a diagnostic error and/or diagnostic safety event, a shared mental model for measurement, and more universal application of tools to measure these events across the research enterprise are still needed. We highlight progress in selected research methods and applications, such as co-development with patients, inclusion of multidisciplinary perspectives (such as those from informatics, human factors, and social and cognitive sciences), and the use of sociotechnical approaches. Specific areas where research should be prioritized include the application of cognitive science to the real-world study of diagnostic errors, understanding the costs associated with diagnostic safety, developing and implementing interventions related to patient engagement, evaluating and integrating artificial intelligence, and implementing system-related interventions to improve diagnosis. To promote broad-scale improvement in diagnostic safety over the next decade, we provide several actionable steps and recommendations for various audiences, including researchers, research funders, safety professionals, and policymakers, involved in research and implementation activities for reducing preventable diagnostic harm.

Introduction: Diagnostic errors in acute cholecystitis significantly impact patient safety. Misdiagnosis, including both false negatives and false positives, is common due to the complex nature of abdominal pain and the overlapping symptoms of various conditions. This scoping review examines the definition, prevalence, and contributing factors of diagnostic errors in acute cholecystitis, emphasizing the need for improved diagnostic processes.

Content: Employing the PRISMA-ScR framework, this review examined definitions and factors contributing to diagnostic errors in cholecystitis. It highlights the variability in diagnostic criteria, with many studies relying on clinical judgment rather than standardized guidelines such as the Tokyo Guidelines. False negative diagnoses are often due to hypoalbuminemia, inconspicuous imaging findings, and the clinical setting, whereas false positives often result from misinterpreted imaging findings and failure to differentiate from other conditions.

Summary: This review found that approximately 30 % of acute cholecystitis cases may be missed (false negative), and 20-36 % of cases initially diagnosed can be incorrect (false positive). Adherence to standardized guidelines and improved recognition of atypical presentations could reduce diagnostic errors.

Outlook: Future research should aim for large-scale studies with clear diagnostic criteria and detailed clinical data to enhance diagnostic accuracy and patient safety.

Diagnostic radiologists are uniquely positioned to make a difference in the reduction of diagnostic errors in medicine, which was identified as a national priority in a special report of the National Academies of Medicine in 2015. Interest in diagnostic error reduction within the specialty of diagnostic radiology has been accelerated in recent years by the adoption of rapidly evolving technological and process-based solutions for previously identified vulnerabilities (i.e., potential failure modes) in the radiology workflow that are known to increase the risk of errors. Here we describe a range of such potential failure modes contributing to diagnostic error in the practice of diagnostic radiology and summarize evolving efforts to mitigate them. These include fostering the adoption of peer learning and other feedback and education programs, a range of measures under development for ongoing performance evaluation of radiologists and their work-processes, and the deployment of new technologies, most notably artificial intelligence tools, to augment human performance and thereby improve diagnostic accuracy in Radiology.