Diagnostic Errors Research Articles

Identification of Potential Diagnostic Markers for Depressive Disorders using Urinary Biomarkers of N-Methylnicotinamide and Hippuric Acid

Introduction: The diagnostic methodology for depressive disorders, relying on symptom clusters, has inherent limitations in ensuring heterogeneity levels. Consequently, this presents a notable risk of inevitable diagnostic errors in mental health assessments. Therefore, advocating for objective diagnostic approaches through empirical testing in clinical settings becomes crucial for individuals dealing with depressive disorders. This study aims to identify the effectiveness of urine as a diagnostic support for depressive disorders using the N-Methylnicotinamide and Hippuric Acid biomarkers. Methods: This study used 13 urine samples from patients with depressive disorders and 13 normal urine samples. It used ELISA methods with observational analytic and cross-sectional designs. Results: The results showed that the N-methylnicotinamide biomarker had a relationship with depressive disorders with a correlation value of 0.867, while hippuric acid obtained a correlation value of 0.692. Besides, the N-Methylnicotinamide and Hippuric Acid biomarkers showed differences in the urine of depressive disorder and normal patients with significance values of 0.000 and 0.001 for the N-Methylnicotinamide and Hippuric Acid biomarkers, respectively. In addition, the Relative Operating Characteristics curve analysis showed that these two biomarkers had good sensitivity and specificity values in assisting the diagnosis of depressive disorders. N-methylnicotinamide has a sensitivity of 92.3% and a specificity of 100%, while hippuric acid has a sensitivity of 76.9% and a specificity of 84.6%. Conclusions: Significant differences in the biomarkers of N-methylnicotinamide and hippuric acid in the urine of depressed patients compared to normal patients. Therefore, these biomarkers can be the empirical laboratory methods to support the diagnosis of depressive.

Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning

Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency.

Clinical Reasoning Unveiled: Demystifying Heuristics and Hermeneutics in Neurological Clinical Practice

Introduction: Medical practice, particularly in Neurology, is often influenced by cognitive biases that can compromise diagnostic and therapeutic accuracy, leading to significant patient consequences. Clinical reasoning strategies are fundamental, yet the complexity of these processes is frequently underestimated. Understanding these strategies is crucial to ensure accurate diagnoses and effective treatments, thereby reducing clinical errors and optimizing patient outcomes. Despite extensive literature on cognitive biases, there is a need for a systematic synthesis addressing the peculiarities of neurological clinical reasoning, considering heuristics (mental shortcuts or rules of thumb that simplify decision-making) and hermeneutics (the theory and methodology of interpretation, especially of textual and symbolic content). Objectives: To review and systematize the main clinical reasoning strategies in Neurology, highlighting common biases and proposing methods to improve diagnostic and therapeutic accuracy. Methodology: A comprehensive literature review focusing on familiarity biases, Dual Process Theory, and heuristics (anchoring, availability, and representativeness). Additionally, the application of hermeneutics in hypothesis formulation and the importance of understanding the patient as a complex system were analyzed. Results: Specialized neurologists often rely on heuristics, which means a cognitive shortcut. Recognizing this process can mitigate anchoring and availability biases. The hermeneutic approach, on the other hand, is essential for interpreting variables in the patient’s context, integrating historical and social aspects. Conclusion: Understanding clinical reasoning processes and systematically teaching these strategies are crucial to enhancing the efficiency and effectiveness of clinical decision-making, reducing diagnostic errors, and improving patient outcomes.

The Malpractice Process: AJR Podcast Series on Diagnostic Excellence and Error, Episode 5.

The Malpractice Process: AJR Podcast Series on Diagnostic Excellence and Error, Episode 5.

Evaluation of Measure Dx, a Resource to Accelerate Diagnostic Safety Learning and Improvement.

Several strategies have been developed to detect diagnostic errors for organizational learning and improvement. However, few health care organizations (HCOs) have integrated these strategies into routine operations. To address this gap, the Agency for Healthcare Research and Quality released "Measure Dx: A Resource To Identify, Analyze, and Learn From Diagnostic Safety Events" in 2022. We conducted an evaluation of Measure Dx to measure feasibility of implementation and effects on short-term and intermediate outcomes related to diagnostic safety. Prospective observational study. Teams from 11 HCOs, primarily academic medical centers. Participants were asked to use Measure Dx over approximately 6months and attend monthly virtual learning collaborative sessions to share and discuss approaches to measuring diagnostic safety. Descriptive outcomes were gathered at the HCO level and included uptake of different case-finding strategies and the number of cases reviewed and confirmed to have diagnostic safety improvement opportunities. We collected information on organizational practices related to diagnostic safety at each HCO at baseline and at the conclusion of the project. The 11 HCOs completed all requirements for the evaluation. Each of the four diagnostic safety case finding strategies outlined in Measure Dx were used by at least three HCOs. Across the cohort, participants reviewed 703 cases using a standardized data collection instrument. Of those cases, 224 (31.8%) were identified as diagnostic safety events with improvement opportunities. Unexpectedly, self-ratings on the checklist assessment declined for several organizations. Use of Measure Dx can help accelerate implementation of systematic approaches to diagnostic error measurement and learning across a variety of HCOs, while potentially enabling HCOs to identify opportunities to improve diagnostic safety practices.

Utilizing Care Transitions for Diagnostic Error Detection in Hospital Medicine Patients.

Utilizing Care Transitions for Diagnostic Error Detection in Hospital Medicine Patients.

MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation.

A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency.

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

Accurate diagnosis in emergency medicine (EM) is high stakes and challenging. Research into physicians' clinical reasoning has been ongoing since the late 1970s. The dual-process theory has established itself as a valid model, including in EM. It is based on the distinction between two information-processing systems. System 1 rapidly generates one or more diagnostic hypotheses almost instantaneously, driven by experiential knowledge, while System 2 proceeds more slowly and analytically, applying formal rules to arrive at a final diagnosis. We reviewed the literature on dual-process theory in the fields of cognitive science, medical education and emergency medicine. The literature reflects two prominent interpretations regarding the relationship between the fast and slow phases and these interpretations carry very different implications for the training of clinical learners. One interpretation, prominent in the EM community, presents it as a "check-and-balance" framework in which most diagnostic error is caused by cognitive biases originating within System 1. As a result, EM residents are frequently advised to deploy analytical (System 2) strategies to correct such biases. However, such teaching approaches are not supported by research into the nature of diagnostic reasoning. An alternative interpretation assumes a harmonious relationship between Systems 1 and 2 in which both fast and slow processes are driven by underlying knowledge that conditions performance and the occurrence of errors. Educational strategies corresponding to this alternative have not been explored in the EM literature. In this paper, we offer proposals for improving the teaching and learning of diagnostic reasoning by EM residents.

From diagnostic errors to diagnostic excellence in emergency care: Time to flip the script.

From diagnostic errors to diagnostic excellence in emergency care: Time to flip the script.

Implementation of a bundle to improve diagnosis in hospitalized patients: lessons learned.

The inpatient setting is a challenging clinical environment where systems and situational factors predispose clinicians to making diagnostic errors. Environmental complexities limit trialing of interventions to improve diagnostic error in active inpatient clinical settings. Informed by prior work, we piloted a multi-component intervention designed to reduce diagnostic error to understand its feasibility and uptake. From September 2018 to June 2019, we conducted a prospective, pre-test/post-test pilot study of hospital medicine physicians during admitting shifts at a tertiary-care, academic medical center. Optional intervention components included use of dedicated workspaces, privacy barriers, noise cancelling headphones, application-based breathing exercises, a differential diagnosis expander application, anda checklist to enable a diagnostic pause. Participants rated their confidence in patient diagnoses and completed a survey on intervention component use. Data on provider resource utilization and patient diagnoses were collected, and qualitative interviews were held with a subset of participants in order to better understand experience with the intervention. Data from 37 physicians and 160 patients were included. No intervention component was utilized by more than 50 % of providers, and no differences were noted in diagnostic confidence or number of diagnoses documented pre-vs. post-intervention. Lab utilization increased, but there were no other differences in resource utilization during the intervention. Qualitative feedback highlighted workflow integration challenges, among others, for poor intervention uptake. Our pilot study demonstrated poor feasibility and uptake of an intervention designed to reduce diagnostic error. This study highlights the unique challenges of implementing solutions within busy clinical environments.

Interventions to improve timely cancer diagnosis: an integrative review.

Cancer will affect more than one in three U.S. residents in their lifetime, and although the diagnosis will be made efficiently in most of these cases, roughly one in five patients will experience a delayed or missed diagnosis. In this integrative review, we focus on missed opportunities in the diagnosis of breast, lung, and colorectal cancer in the ambulatory care environment. From a review of 493 publications, we summarize the current evidence regarding the contributing factors to missed or delayed cancer diagnosis in ambulatory care, as well as evidence to support possible strategies for intervention. Cancer diagnoses are made afterfollow-up of a positive screening test or an incidental finding, or most commonly, by following up and clarifying non-specific initial presentations to primary care. Breakdowns and delays are unacceptably common in each of these pathways, representing failures to follow-up on abnormal test results, incidental findings, non-specific symptoms, or consults. Interventions aimed at 'closing the loop' represent an opportunity to improve the timeliness of cancer diagnosis and reduce the harm from diagnostic errors. Improving patient engagement, using 'safety netting,' and taking advantage of the functionality offered through health information technology are all viable options to address these problems.

Revolution or risk?-Assessing the potential and challenges of GPT-4V in radiologic image interpretation.

ChatGPT-4 Vision (GPT-4V) is a state-of-the-art multimodal large language model (LLM) that may be queried using images. We aimed to evaluate the tool's diagnostic performance when autonomously assessing clinical imaging studies. A total of 206 imaging studies (i.e., radiography (n = 60), CT (n = 60), MRI (n = 60), and angiography (n = 26)) with unequivocal findings and established reference diagnoses from the radiologic practice of a large university hospital were accessed. Readings were performed uncontextualized, with only the image provided, and contextualized, with additional clinical and demographic information. Responses were assessed along multiple diagnostic dimensions and analyzed using appropriate statistical tests. With its pronounced propensity to favor context over image information, the tool's diagnostic accuracy improved from 8.3% (uncontextualized) to 29.1% (contextualized, first diagnosis correct) and 63.6% (contextualized, correct diagnosis among differential diagnoses) (p ≤ 0.001, Cochran's Q test). Diagnostic accuracy declined by up to 30% when 20 images were re-read after 30 and 90 days and seemed unrelated to the tool's self-reported confidence (Spearman's ρ = 0.117 (p = 0.776)). While the described imaging findings matched the suggested diagnoses in 92.7%, indicating valid diagnostic reasoning, the tool fabricated 258 imaging findings in 412 responses and misidentified imaging modalities or anatomic regions in 65 images. GPT-4V, in its current form, cannot reliably interpret radiologic images. Its tendency to disregard the image, fabricate findings, and misidentify details, especially without clinical context, may misguide healthcare providers and put patients at risk. Question Can Generative Pre-trained Transformer 4 Vision (GPT-4V) interpret radiologic images-with and without clinical context? Findings GPT-4V performed poorly, demonstrating diagnostic accuracy rates of 8% (uncontextualized), 29% (contextualized, most likely diagnosis correct), and 64% (contextualized, correct diagnosis among differential diagnoses). Clinical relevance The utility of commercial multimodal large language models, such as GPT-4V, in radiologic practice is limited. Without clinical context, diagnostic errors and fabricated findings may compromise patient safety and misguide clinical decision-making. These models must be further refined to be beneficial.

Impact of disclosing a working diagnosis during simulated patient handoff presentation in the emergency department: correctness matters.

Diagnostic errors in emergency departments (ED) are a significant concern and exacerbated by cognitive biases during patient handoffs. The timing and accuracy of disclosing working diagnoses during these handoffs potentially influence diagnostic decisions, yet empirical evidence remains limited. This parallel, quasi-experimental study involved 40 interns from Japanese teaching hospitals, randomly assigned to control or intervention groups. Each group reviewed eight audio-recorded patient handoff scenarios where working diagnoses were disclosed at the start (control) or end (intervention). Four cases presented correct diagnoses, while four featured incorrect ones. The main measure was diagnostic error rate, calculated as the proportion of incorrect post-handoff responses to total questions asked. No significant difference in diagnostic error rates emerged between the control (39.4 %, 63/160) and intervention (38.8 %, 62/160) groups (point estimate-0.6 %; 95 % CI:-11.3-10.1 %, p=0.91). However, a substantial difference was evident between diagnostic errors after correct (20.6 %, 33/160) and incorrect (57.5 %, 92/160) working diagnoses presented (point estimate: 36.9 %; 95 % CI: 27.0-46.8 %, p<0.001). Diagnostic momentum accounted for 52 % (48/92) of errors under incorrect diagnoses. While the timing of working diagnosis disclosure did not significantly alter diagnostic accuracy during ED handoffs, exposure to incorrect diagnoses markedly increased error rates. These findings underscore the imperative to refine diagnostic skills and reconsider ED handoff protocols to mitigate cognitive biases and optimize patient care outcomes.

Role of the Pharmacotherapeutic Monitoring Service (PMS) of the Community Pharmacy in the detection and resolution of adverse reactions to statins linked to possible diagnostic errors in elderly patients

The Medication Review in the Pharmacotherapeutic Follow-up Service (PFS) seems to be an effective method to study long-term drug safety in the outpatient setting. The adverse drug reactions (ADRs) that are not immediately obvious are difficult to identify and sometimes can be confused with a more common condition. Misdiagnosis by not associating the symptoms of AMR to its pharmacological cause causes its masking and hinders its detection. Detect in the SFT service the diagnostic errors related to the non-detection of adverse reactions to statins. The data obtained from the medication review at the Pharmacotherapeutic Follow-up Service (PFS) were pooled for analysis. The patients who received the service were selected with the "DLGM screening" tool, an acronym "Diagnosis load Generated by Medications", that allows us to describe adverse drug reactions (ADRs), when their symptoms are attributed to a pathology, without considering medication as a possible underlying cause.Only the results of patients over 60 years of age, who after a prolonged period of statin use gradually presented musculoskeletal disorders (MSD) and other symptoms theoretically described as possible ADRs, are shown. In 66 % of the cases, corresponding to 14 patients out of a total of 21 studied, the physician modified the treatment and in 92% of these cases there was improvement and a decrease of the consumption of analgesics drug, anti-inflammatory and other drugs used to treat ADR symptoms. DLGM screening identified hidden AMRs in 62 % of patients.

"So You Made a Mistake" - The Path Forward Through Surgical Pathology Errors by Extreme Ownership and a Focus on the Patient.

Our goal for medicine is to make zero mistakes, yet the reality is that mistakes are an unfortunate part of medical practice. And when it comes to surgical pathology, it is a special case where the diagnostic "bottom line" is provided starkly and directly for all to see in the final diagnosis of the pathology report. When this diagnosis is wrong, particularly when it has serious adverse consequences for the patient, the resulting physical, mental, and emotional effects on patient, provider, pathologist, and health care system can be extremely serious. Head and neck surgical pathology, based on large second review-type studies, is a subspecialty area with average rates of major diagnostic error, but with potential for severely negative impacts on patients. Studies have shown between 1% and 7% major error rates for head and neck practice. How then, as the pathologist, can we react to and manage things when we have made a serious diagnostic mistake? Through personal experience over more than two decades, the hard-won answer is through extreme ownership and a focus on the needs of the patients, who, in the words of William J. Mayo, should have their "needs come first". The emotional impact on us as pathologists and on the clinicians we work with should also be acknowledged and managed. This article will serve as a thorough and open examination of these mistake scenarios and, focusing specifically on diagnostic errors, serve as a practical guide for what you can do, moving forward, to "make things right" to the best of your ability.

HPV-Independent Cervical Adenocarcinoma Discovered by Cytology-Based Screening: A case report of gastric type adenocarcinoma of the uterine cervix from cytopathology to histopathology

Abstract Introduction/Objective In recent years, the relative prevalence of adenocarcinoma has increased, comprising 20–25% of all cervical carcinomas in developed countries. Approximately 15% of cervical adenocarcinomas are unrelated to human papilloma virus (HPV) infection. With the shift toward HPV primary testing, an increased understanding of HPV-independent cervical adenocarcinomas becomes more important. Methods/Case Report This case report demonstrates a rare case of gastric-type adenocarcinoma (GAS) of the cervix discovered during a routine well-woman exam. Patient-reported episodes of vaginal spotting were noted prior to screening. HPV testing by PCR on the initial pap smear was negative. Cytology findings raised concerns about endocervical glandular cell abnormalities. Subsequent cervical biopsy revealed GAS, a finding supported by positive CK7, MUC5AC, CDX2, and aberrant (mutant) p53 expression by immunohistochemistry. HPV independence was further confirmed by negative results on mRNA ISH for high-risk HPV and HPV16/18. Results (if a Case Study enter NA) NA Conclusion Here we discuss the impact of cytology-based screening on HPV-independent cervical cancers, with focus on adenocarcinoma. While the move to primary HPV screening has been shown to be effective at identifying HPV-related neoplasms, it ineffectively addresses HPV-independent malignancies. In this particular population, cytology-based screening remains indispensable. The described case demonstrated subtle cytologic features consistent with endocervical glandular abnormality, ultimately correlating with subsequent histologic findings of HPV-independent GAS. Negative HPV results combined with subtle cytomorphologic changes displayed by some adenocarcinomas on PAP smear could present a significant diagnostic pitfall. Additionally, p16+ expression attributable to aberrant p53 pathways (rather than HPV) may further contribute to potential diagnostic errors. While further research into best practices for early detection of HPV-independent cervical neoplasias should be conducted, this team suggests both clinical survey and referral for cytologic screening following reports of abnormal vaginal bleeding and/or watery discharge and screener education focused on cervical GAS.

Enhanced WGAN Model for Diagnosing Laryngeal Carcinoma

This study modifies the U-Net architecture for pixel-based segmentation to automatically classify lesions in laryngeal endoscopic images. The advanced U-Net incorporates five-level encoders and decoders, with an autoencoder layer to derive latent vectors representing the image characteristics. To enhance performance, a WGAN was implemented to address common issues such as mode collapse and gradient explosion found in traditional GANs. The dataset consisted of 8171 images labeled with polygons in seven colors. Evaluation metrics, including the F1 score and intersection over union, revealed that benign tumors were detected with lower accuracy compared to other lesions, while cancers achieved notably high accuracy. The model demonstrated an overall accuracy rate of 99%. This enhanced U-Net model shows strong potential in improving cancer detection, reducing diagnostic errors, and enhancing early diagnosis in medical applications.

NEW PERSPECTIVES ON URINALYSIS: RECOMMENDATIONS FROM THE EUROPEAN FEDERATION OF CLINICAL CHEMISTRY AND LABORATORY MEDICINE (EFLM)

This article highlights the significance of the updated recommendations from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) – Urinalysis Guideline 2023. The guidelines emphasize the need for standardizing laboratory procedures to enhance research quality and reduce diagnostic errors. Based on current scientific data and international standards, these recommendations aim to ensure high reliability and accuracy of laboratory results. The implementation of urinalysis guideline has the potential to significantly improve diagnostic effectiveness, positively impacting the quality of patient care. Attention is also given to new technologies considered in the recommendations, which can greatly facilitate advancements in laboratory medicine. EFLM guidelines are essential for enhancing laboratory practices and maintaining high standards in medical diagnostics.

Patients with Non-Specific Complaints in Emergency Departments: A Growing Patient Safety Concern in an Aging Population with Multimorbidity.

In this opinion, we offer a new perspective on the important and persistent problem of diagnostic errors for patients with non-specific complaints (NSCs). As an increasing number of complex patients present clinicians with challenging diagnostic work in the time-pressured and high-volume contexts of EDs, we need to improve how clinicians and healthcare organizations can understand and perform safe diagnostics for patients with NSCs. The combination of a growing number of patients with NSCs and the ways in which clinicians use the categories 'non-specific complaints' and 'non-specific diagnosis' in diagnostic work in emergency departments presents a growing patient safety concern especially for older patients with multimorbidity that require the integration of clinical and organizational research. We argue why the growing numbers of patients with NSCs and clinicians' use of these categories have implications for patient safety both within and beyond the acute care context. We end by pointing to the importance of an interdisciplinary patient safety research agenda, ideally followed by the development of targeted usable protocols for older multimorbid patients with non-specific complaints.

Modeling transmission mechanism to infer treatment efficacy of different drugs and combination therapy against Trichuris trichiura

Trichuris trichiura is one of four soil-transmitted helminth species that, collectively, are responsible for a considerable public health burden. The World Health Organization recommends preventive chemotherapy as the main intervention to eliminate soil-transmitted helminthiasis as a public health problem. Clinical trials estimated the efficacy of different drugs and treatment regimen against T. trichiura and other soil-transmitted helminth species, whilst meta-analyses and modeling efforts were conducted to determine the most efficacious drugs and drug combinations. Of note, the diagnostic error was often neglected, and hence, cure rates (CRs) might be overestimated. We developed a Bayesian model, which estimates drug efficacy against T. trichiura, taking into account the transmission mechanism and the diagnostic error. The model was fitted to individual-level egg count data from an ensemble of seven trials with 29 treatments. We estimated the ‘true’ CRs, which were consistently lower than those reported in the literature. In our analysis, the treatment with the highest CR was combination therapy of albendazole plus pyrantel pamoate plus oxantel pamoate with a CR of 79% and an egg reduction rate (ERR) of 91%. Albendazole plus oxantel pamoate showed the highest ERR of 97% and a CR of 69%. Additionally, we estimated the intensity-dependent sensitivity of the Kato-Katz technique. For 24 eggs per gram of stool, the sensitivity was around 50% for a single Kato-Katz thick smear and increased to almost 70% for duplicate Kato-Katz thick smears. Combination therapies against soil-transmitted helminthiasis should be considered and the evaluation of infection intensity in low transmission settings via multiple Kato-Katz thick smears is recommended.