Type and Frequency of Misdiagnosis and Time Lag to Diagnosis in Patients with Chronic Progressive External Ophthalmoplegia.

Purpose: Since ptosis is an early feature of chronic progressive external ophthalmoplegia (CPEO), patients are commonly misdiagnosed with other causes of ptosis. This study aims to report the type and frequency of misdiagnosis and time lag to diagnosis and the palpebral fissure transfer (PFT) procedure in patients with CPEO. Methods: This is a retrospective analysis of consecutive patients with CPEO who underwent PFT between 2006 and 2017. The data on previous diagnoses and treatments, age at definitive diagnosis of CPEO, and clinical manifestations were recorded. While the diagnosis of CPEO was based on clinical examination, 75% (24/32) of patients had undergone a confirmatory muscle biopsy and genetic tests. Results: There were 32 patients (19 females) with a mean age of 24.8 years (range, 13-36) at the final diagnosis and 34.1 years (range, 15-56) at the time of PFT. Also, 78% (25/32) of patients had been initially misdiagnosed with congenital ptosis (60%; 15/25) and ocular myasthenia gravis (OMG) (40%; 10/25). The majority of patients (20/32) had one to three previous eyelid surgical procedures, of which 90% (18/20) were performed before the definitive diagnosis of CPEO. The mean time lag from the first surgical procedure to CPEO diagnosis and PFT was 6.2 and 14.7 years, respectively. Conclusion: In a referral center, 78% of the patients with CPEO were initially misdiagnosed with congenital ptosis and OMG, and 56% of them underwent ptosis repair before the diagnosis. While the onset of the disease was in the first or second decades of life, diagnosis was delayed up to a mean age of 25 years. Reviewing early family photos and paying attention to other signs of CPEO could prevent misdiagnosis.

Radiomic Analysis of Cohort-Specific Diagnostic Errors in Reading Dense Mammograms Using Artificial Intelligence.

OBJECTIVES: This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach. METHODS: Thirty-six radiologists from China and Australia read 60 dense mammograms. For each cohort, we identified normal areas that looked suspicious of cancer and the malignant areas containing cancers. Then radiomic features were extracted from these identified areas and random forest models were trained to recognize the areas that were most frequently linked to diagnostic errors within each cohort. The performance of the model and discriminatory power of significant radiomic features were assessed. RESULTS: We found that in the Chinese cohort, the AUC values for predicting false positives were 0.864 (CC) and 0.829 (MLO), while in the Australian cohort, they were 0.652 (CC) and 0.747 (MLO). For false negatives, the AUC values in the Chinese cohort were 0.677 (CC) and 0.673 (MLO), and in the Australian cohort, they were 0.600 (CC) and 0.505 (MLO). In both cohorts, regions with higher Gabor and maximum response filter outputs were more prone to false positives, while areas with significant intensity changes and coarse textures were more likely to yield false negatives. CONCLUSIONS: This cohort-based pipeline proves effective in identifying common errors for specific reader cohorts based on image-derived radiomic features. ADVANCES IN KNOWLEDGE: This study demonstrates that radiomics-based AI can effectively identify and predict radiologists' interpretation errors in dense mammograms, with distinct radiomic features linked to false positives and false negatives in Chinese and Australian cohorts.

Adverse diagnostic events in hospitalised patients: a single-centre, retrospective cohort study.

BACKGROUND: Adverse event surveillance approaches underestimate the prevalence of harmful diagnostic errors (DEs) related to hospital care. METHODS: We conducted a single-centre, retrospective cohort study of a stratified sample of patients hospitalised on general medicine using four criteria: transfer to intensive care unit (ICU), death within 90 days, complex clinical events, and none of the aforementioned high-risk criteria. Cases in higher-risk subgroups were over-sampled in predefined percentages. Each case was reviewed by two adjudicators trained to judge the likelihood of DE using the Safer Dx instrument; characterise harm, preventability and severity; and identify associated process failures using the Diagnostic Error Evaluation and Research Taxonomy modified for acute care. Cases with discrepancies or uncertainty about DE or impact were reviewed by an expert panel. We used descriptive statistics to report population estimates of harmful, preventable and severely harmful DEs by demographic variables based on the weighted sample, and characteristics of harmful DEs. Multivariable models were used to adjust association of process failures with harmful DEs. RESULTS: Of 9147 eligible cases, 675 were randomly sampled within each subgroup: 100% of ICU transfers, 38.5% of deaths within 90 days, 7% of cases with complex clinical events and 2.4% of cases without high-risk criteria. Based on the weighted sample, the population estimates of harmful, preventable and severely harmful DEs were 7.2% (95% CI 4.66 to 9.80), 6.1% (95% CI 3.79 to 8.50) and 1.1% (95% CI 0.55 to 1.68), respectively. Harmful DEs were frequently characterised as delays (61.9%). Severely harmful DEs were frequent in high-risk cases (55.1%). In multivariable models, process failures in assessment, diagnostic testing, subspecialty consultation, patient experience, and history were significantly associated with harmful DEs. CONCLUSIONS: We estimate that a harmful DE occurred in 1 of every 14 patients hospitalised on general medicine, the majority of which were preventable. Our findings underscore the need for novel approaches for adverse DE surveillance.

Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training.

Purpose: Accurate interpretation of mammograms presents challenges. Tailoring mammography training to reader profiles holds the promise of an effective strategy to reduce these errors. This proof-of-concept study investigated the feasibility of employing convolutional neural networks (CNNs) with transfer learning to categorize regions associated with false-positive (FP) errors within screening mammograms into categories of "low" or "high" likelihood of being a false-positive detection for radiologists sharing similar geographic characteristics. Approach: Mammography test sets assessed by two geographically distant cohorts of radiologists (cohorts A and B) were collected. FP patches within these mammograms were segmented and categorized as "difficult" or "easy" based on the number of readers committing FP errors. Patches outside 1.5 times the interquartile range above the upper quartile were labeled as difficult, whereas the remaining patches were labeled as easy. Using transfer learning, a patch-wise CNN model for binary patch classification was developed utilizing ResNet as the feature extractor, with modified fully connected layers for the target task. Model performance was assessed using 10-fold cross-validation. Results: Compared with other architectures, the transferred ResNet-50 achieved the highest performance, obtaining receiver operating characteristics area under the curve values of 0.933 ( ± 0.012 ) and 0.975 ( ± 0.011 ) on the validation sets for cohorts A and B, respectively. Conclusions: The findings highlight the feasibility of employing CNN-based transfer learning to predict the difficulty levels of local FP patches in screening mammograms for specific radiologist cohort with similar geographic characteristics.

Significance of early diagnosis and treatment of adult late-onset Pompe disease on the effectiveness of enzyme replacement therapy in improving muscle strength and respiratory function: a case report.

BACKGROUND: Pompe disease, a rare autosomal recessive disorder, is caused by mutations in the acid α-glucosidase gene. Pompe disease is a congenital metabolic disorder that affects all organs, particularly the striated muscle and nerve cells. Diagnosis is typically confirmed through enzyme assays that reveal reduced acid α-glucosidase activity. Enzyme replacement therapy utilizing human α-glucosidase is an available treatment option. Timely diagnosis and treatment in the early stages of the disease significantly impact the effectiveness of enzyme replacement therapy in enhancing patient condition. Here, we present a case of a patient with Pompe disease diagnosed 20 years after the onset of clinical symptoms. CASE PRESENTATION: A 38-year-old Iranian Baloch woman referred to our rheumatology clinic with progressive muscle weakness presents with a complex medical history. On mechanical ventilation for 12 years, she has endured fatigue and limb weakness since the age of 16, exacerbated following an abortion at 19. Despite undergoing corticosteroid and azathioprine therapies, the suspected diagnosis of inflammatory myopathy did not yield improvement. Hospitalization at 23 due to respiratory failure post-pregnancy led to her continued reliance on a ventilator. A dried blood spot test indicated reduced GAA enzyme activity, confirming a diagnosis of Pompe disease through genetic testing. Treatment with myozyme for 2 years demonstrated limited efficacy, as the patient experienced improved breathing but no significant overall improvement in limb-girdle muscular weakness. This case underscores the challenges and complexities involved in diagnosing and managing rare neuromuscular disorders like Pompe disease. CONCLUSION: Early intervention with enzyme replacement therapy plays a crucial role in halting further muscle loss and disease progression in Pompe disease patients. It is important to note that treatment during advanced stages of the disease may not yield substantial benefits. Nevertheless, enzyme instability and denaturation due to temperature and neutral pH levels, along with limited delivery to disease-relevant tissues, can pose challenges in treatment. However, timely diagnosis of Pompe disease is paramount for its effective management and improved outcomes.

Lessons in clinical reasoning - pitfalls, myths, and pearls: a case of persistent dysphagia and patient partnership.

OBJECTIVES: Diagnostic excellence underscores the patient-centered diagnosis and patient engagement in the diagnostic process. In contrast to a patient-centered diagnosis, a doctor-centered diagnosis with a lack of patient engagement may inhibit the diagnostic process due to the lack of responsibility, disrupted information, and increased effect of cognitive biases, particularly in a situation where multiple physicians are involved. In this paper, we suggest a promising idea to enhance patient engagement in the diagnostic process by using written information by a patient about their perspective and experience, which can fill the gaps needed for diagnosis that doctors cannot find alone. CASE PRESENTATION: A 38-year-old woman developed chest pain, which gradually worsened during the following two years. For two years, she was evaluated in multiple departments; however, no definitive diagnosis was made, and her condition did not improve. During this evaluation, she searched her symptoms and image findings online. She reached a possible diagnosis of 'esophageal achalasia.' Still, she could not tell her concerns to any physicians because she felt that her concerns were not correctly recognized, although she showed her notes that her symptoms were recorded. She finally consulted the department of internal medicine, where her notes and previous test results were thoroughly reviewed. The final diagnosis of esophageal achalasia was confirmed. CONCLUSIONS: Doctors must organize an environment where patients can freely express their thoughts, emotions, and ideas regarding their diagnosis. Cogenerating visit notes using patient input through written communication can be a promising idea to facilitate patient engagement in the diagnostic process.

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.

Charting Diagnostic Safety: Exploring Patient-Provider Discordance in Medical Record Documentation.

BACKGROUND: The 21st Century Cures Act enables patients to access their medical records, thus providing a unique opportunity to engage patients in their diagnostic journey. OBJECTIVE: To explore the concordance between patients' self-reported diagnostic concerns and clinician-interpreted information in their electronic health records. DESIGN: We conducted a mixed-methods analysis of a cohort of 467 patients who completed a structured data collection instrument (the Safer Dx Patient) to identify diagnostic concerns while reviewing their clinician's notes. We conducted a qualitative content analysis of open-ended responses on both the tools and the case summaries. Two clinical chart reviewers, blinded to patient-reported diagnostic concerns, independently conducted chart reviews using a different structured instrument (the Revised Safer Dx Instrument) to identify diagnostic concerns and generate case summaries. The primary outcome variable was chart review-identified diagnostic concerns. Multivariate logistic regression tested whether the primary outcome was concordant with patient-reported diagnostic concerns. SETTING: Geisinger, a large integrated healthcare organization in rural and semi-urban Pennsylvania. PARTICIPANTS: Cohort of adult patients actively using patient portals and identified as "at-risk" for diagnostic concerns using an electronic trigger algorithm based on unexpected visit patterns in a primary care setting. RESULTS: In 467 cohort patients, chart review identified 31 (6.4%) diagnostic concerns, of which only 11 (21.5%) overlapped with 51 patient-reported diagnostic concerns. Content analysis revealed several areas of discordant understanding of the diagnostic process between clinicians and patients. Multivariate logistic regression analysis showed that clinician-identified diagnostic concerns were associated with patients who self-reported "I feel I was incorrectly diagnosed during my visit" (odds ratio 1.65, 95% CI 1.17-2.3, p < 0.05). CONCLUSION: Patients and clinicians appear to have certain differences in their mental models of what is considered a diagnostic concern. Efforts to integrate patient perspectives and experiences with the diagnostic process can lead to better measurement of diagnostic safety.

Diagnostic performance of generative artificial intelligences for a series of complex case reports.

Background: Diagnostic performance of generative artificial intelligences (AIs) using large language models (LLMs) across comprehensive medical specialties is still unknown. Objective: We aimed to evaluate the diagnostic performance of generative AIs using LLMs in complex case series across comprehensive medical fields. Methods: We analyzed published case reports from the American Journal of Case Reports from January 2022 to March 2023. We excluded pediatric cases and those primarily focused on management. We utilized three generative AIs to generate the top 10 differential-diagnosis (DDx) lists from case descriptions: the fourth-generation chat generative pre-trained transformer (ChatGPT-4), Google Gemini (previously Bard), and LLM Meta AI 2 (LLaMA2) chatbot. Two independent physicians assessed the inclusion of the final diagnosis in the lists generated by the AIs. Results: Out of 557 consecutive case reports, 392 were included. The inclusion rates of the final diagnosis within top 10 DDx lists were 86.7% (340/392) for ChatGPT-4, 68.6% (269/392) for Google Gemini, and 54.6% (214/392) for LLaMA2 chatbot. The top diagnoses matched the final diagnoses in 54.6% (214/392) for ChatGPT-4, 31.4% (123/392) for Google Gemini, and 23.0% (90/392) for LLaMA2 chatbot. ChatGPT-4 showed higher diagnostic accuracy than Google Gemini (P < 0.001) and LLaMA2 chatbot (P < 0.001). Additionally, Google Gemini outperformed LLaMA2 chatbot within the top 10 DDx lists (P < 0.001) and as the top diagnosis (P = 0.010). Conclusions: This study demonstrated the diagnostic performance of generative AIs including ChatGPT-4, Google Gemini, and LLaMA2 chatbot. ChatGPT-4 exhibited higher diagnostic accuracy than the other platforms. These findings suggest the importance of understanding the differences in diagnostic performance among generative AIs, especially in complex case series across comprehensive medical fields, like general medicine.

Lessons learnt from a 2017 Irish national clinical claims review: a retrospective observational study.

OBJECTIVE: Learning from adverse outcomes in health and social care is critical to advancing a culture of patient safety and reducing the likelihood of future preventable harm to service users. This review aims to present an overview of all clinical claims finalised in one calendar year involving publicly funded health and social care providers in Ireland. DESIGN: This is a retrospective observational study. The Clinical Risk Unit (CRU) of the State Claims Agency identified all service-user clinical claims finalised between 1 January 2017 and 31 December 2017 from Ireland's National Incident Management System (n=713). Claims that had incurred financial damages were considered for further analysis (n=356). 202 claims underwent an in-depth qualitative review. Of these, 57 related to maternity and gynaecology, 64 to surgery, 46 to medicine, 20 to community health and social care and 15 related to children's healthcare. RESULTS: The services of surgery and medicine ranked first and second, respectively, in terms of a number of claims. Claims in maternity services, despite ranking third in terms of claims numbers, resulted in the highest claims costs. Catastrophic injuries in babies resulting in cerebral palsy or other brain injury accounted for the majority of this cost.Diagnostic errors and inadequate or substandard communication, either with service users and/or interprofessional communication with colleagues, emerged as common issues across all clinical areas analysed. Quantitative analysis of contributory factors demonstrated that the complexity and seriousness of the service user's condition was a significant contributory factor in the occurrence of incidents leading to claims. CONCLUSION: This national report identifies common issues resulting in claims. Targeting these issues could mitigate patient safety risks and reduce the cost of claims.

"I Had No Idea This Happened": Electronic Feedback on Clinical Reasoning for Hospitalists.

BACKGROUND: Feedback on the diagnostic process has been proposed as a method of improving clinical reasoning and reducing diagnostic errors. Barriers to the delivery and receipt of feedback include time constraints and negative reactions. Given the shift toward asynchronous, digital communication, it is possible that electronic feedback ("e-feedback") could overcome these barriers. OBJECTIVES: We developed an e-feedback system for hospitalists around episodes of care escalation (transfers to ICU and rapid responses). The intervention was evaluated by measuring hospitalists' satisfaction with e-feedback and commitment to change. DESIGN: A qualitative survey study conducted at one academic medical center from February to June 2023. PARTICIPANTS: Hospitalists - physicians and advanced practice providers. APPROACH: Two hospitalists, one internal medicine resident, and a nurse reviewed escalations of care on the hospitalist service each week using the Revised Safer Dx framework. Confidential feedback was emailed to the hospitalists involved in the patient's care. Hospitalists were asked to rate and explain their satisfaction with the e-feedback and whether they might modify their clinical practice based on the e-feedback. The open-ended text comments from the hospitalists were analyzed using a thematic analysis framework. RESULTS: Forty-nine out of fifty-eight hospitalists agreed to participate. One hundred five out of one hundred twenty-four (85%) e-feedback surveys that were sent were returned by the hospitalists. Hospitalists were highly satisfied with 67% (n = 70) of the e-feedback reports, moderately satisfied with 23% (n = 24), and not satisfied with 10% (n = 11). Six themes were identified based on analysis of the comments. Themes related to satisfaction with the intervention included appreciation for learning about patient outcomes, general appreciation of feedback on clinical care, and importance of detailed and specific feedback. Themes related to changing clinical practice included reflection on clinical decision-making, value of new insights, and anticipated future behavior change. CONCLUSIONS: E-feedback was well received by hospitalists. Their perspectives offer useful insights for enhancing electronic feedback interventions.

Minimizing the Risk of Diagnostic Errors in Acute Care for Older Adults: An Interdisciplinary Patient Safety Challenge.

Modern healthcare systems are increasingly organized according to diagnosis-specific clinical pathways and treatment protocols. At the same time, the number of patients with complex problems and needs that do not fit the single-diagnosis approach is rising, contributing to a high prevalence of diagnostic errors. In this article, we focus on the risk of diagnostic errors arising from missed or incomplete diagnosis and assessment of older adult patients' care needs in the first hours of acute hospitalizations in EDs. This focus is important for improving patient safety, as clinical decisions made in EDs impact patient safety in the subsequent steps of the process, thereby potentially causing new risks to arise. Based on our discussion of clinical decision-making and diagnostic errors in the acute care context, we propose a more comprehensive interdisciplinary approach to improvements in patient safety that integrates organizational and clinical research and examines where, when, how, and why risks to patient safety arise in and across different clinical-organizational contexts.

Misdiagnosis of Multiple Sclerosis: Past, Present, and Future.

PURPOSE OF REVIEW: Misdiagnosis of multiple sclerosis (MS) is a prevalent worldwide problem. This review discusses how MS misdiagnosis has evolved over time and focuses on contemporary challenges and potential strategies for its prevention. RECENT FINDINGS: Recent studies report cohorts with a range of misdiagnosis between 5 and 18%. Common disorders are frequently misdiagnosed as MS. Overreliance on MRI findings and misapplication of MS diagnostic criteria are often associated with misdiagnosis. Emerging imaging biomarkers, including the central vein sign and paramagnetic rim lesions, may aid diagnostic accuracy when evaluating patients for suspected MS. MS misdiagnosis can have harmful consequences for patients and healthcare systems. Further research is needed to better understand its causes. Concerted and novel educational efforts to ensure accurate and widespread implementation of MS diagnostic criteria remain an unmet need. The incorporation of diagnostic biomarkers highly specific for MS in the future may prevent misdiagnosis.

Economic Impact of Ambulatory Blood Pressure Monitoring Compared With Clinical Blood Pressure Monitoring: A Simulation Model.

Background: Ambulatory blood pressure monitoring (ABPM) is considered the gold standard for proper diagnosis of hypertension. Yet, access to ABPM in the U.S. is limited, and the extent of coverage by commercial health plans remains uncertain, potentially limiting access to ABPM among commercially insured patients. Objective: This study aims to assess the net cost impact of using ABPM in comparison to clinical blood pressure monitoring (CBPM) in the U.S. over a 5-year time period. Design methods: Using a Markov Model, we estimate the 5-year cumulative cost impact of using ABPM to confirm a prior diagnosis of primary hypertension using CBPM to avoid treatment for white-coat hypertension (WCH) in a hypothetical cohort of 1000 patients from a U.S. healthcare system perspective. The probability and cost inputs for the model were derived from available literature. Base-case model parameters were varied to account for different scenarios. Results: Base-case results indicate using ABPM instead of CBPM over 5 years saves a total of $348,028, reflecting an average per-person-per-year (PPPY) cost saving of $70. In sensitivity analyses, almost all cases reveal ABPM as a cost-saving approach compared to CBPM, with cost savings reaching up to $228 PPPY in the highest hypertension treatment cost model. Regression results reveal that ABPM was cost-saving compared to CBPM if ABPM annual payment rates are $100 or less and annual hypertension treatment costs are ⩾$300. Conclusion: The potential cost-savings of using ABPM instead of CBPM found in our simulation model underscores the need for confirmatory research using real-world data to support increased use of ABPM as the standard diagnostic approach for hypertension.

Initial signs in patients with ruptured abdominal aortic aneurysms: time for an expanded triad?

BACKGROUND AND OBJECTIVE: Misdiagnosis of ruptured abdominal aortic aneurysms (rAAA) contributes to delayed treatment and potentially higher mortality. The symptomatology in patients with rAAA is complex and challenging, 25-50% presumably fulfill the criteria of the standard triad of signs (STS). The objective was to determine the initial signs registered for patients with verified rAAAs, and to investigate if an expanded diagnostic triad could increase the diagnostic accuracy. METHODS: A population-based study was conducted among all patients presenting with verified rAAAs in Stockholm County, Sweden, from January 2010 to October 2021. Patients were identified with ICD code 171.3 (rAAA). The STS was defined as (1) abdominal pain, (2) syncope and (3) the finding of a pulsatile abdominal mass, the prevalence of STS was investigated. An expanded triad included similar and related signs commonly registered for patients with rAAA, and was referred to as the modified abdominal aortic aneurysm rupture signs (MARS). The MARS-signs encompassed (1) the registered pain-associated symptoms or signs, (2) all hypovolemic associated signs, and (3) pulsatile abdominal mass and/or ultrasound finding, and the prevalence was similarly investigated. Finally, the STS and MARS were compared to evaluate the usefulness and performance of the MARS-score. RESULTS: A total of 216 patients were identified. The majority were men (77%) with a median age of 78 years. The dominating symptom was abdominal pain (84%), followed by dizziness (50%). Few patients presented with three STS (13%), two STS were found in 37% and one STS in almost half of the patients (41%). By contrast, when applying MARS 35% presented with the complete expanded triad, 47% with two and 17% with one. Comparison of accuracy favored MARS (13 vs. 35% with 3 signs, P < 0.001 for STS vs. MARS) (2 or 3 signs, 48 vs. 82% STS vs. MARS, P < 0.001). CONCLUSIONS: The expanded MARS-signs could aid in easier and faster identification of rAAA patients, thus facilitating the first step with accurate diagnosis into the lifesaving rAAA care chain. Supportive diagnostic mnemonics and tools are especially important when targeting fatal diagnoses such as rAAA. Further studies are needed to investigate the implementation of the MARS-signs in various clinical settings.

Let us talk about mistakes.

Unfortunately, errors and mistakes are part of life. Errors and mistakes can harm patients and incur unplanned costs. Errors may arise from various sources, which may be classified as systematic, latent, or active. Intrinsic and extrinsic factors also contribute to incorrect decisions. In addition to cognitive biases, our personality, socialization, personal chronobiology, and way of thinking (heuristic versus analytical) are influencing factors. Factors such as overload from private situations, long commuting times, and the complex environment of information technology must also be considered. The objective of this paper is to define and classify errors and mistakes in radiology, to discuss the influencing factors, and to present strategies for prevention. Hierarchical responsibilities and team "well-being" are also discussed.

Achieving Diagnostic Excellence: Roadmaps to Develop and Use Patient-Reported Measures With an Equity Lens.

BACKGROUND: Diagnostic excellence refers to the optimal process to attain an accurate and precise explanation about a patient's condition and incorporates the perspectives of patients and their care partners. Patient-reported measures (PRMs), designed to capture patient-reported information, have potential to contribute to achieving diagnostic excellence. We aimed to craft a set of roadmaps illustrating goals and guiding the development of PRMs for diagnostic excellence ("Roadmaps"). METHODS: We used iterative inputs from environmental literature scans, expert consultations, and patient voice and employed human-centred design (HCD) and equity-focused road-mapping. The culminating activity of these approaches was an Expert Convening. RESULTS: Use of PRMs can achieve multiple goals for diagnostic excellence, including but not limited to: (1) PRMs for diagnostic continuity, (2) diagnostic PRM alerts, (3) PRM-based quality improvement, (4) PRMs for research, (5) PRMs for routine screening, (6) PRM-based diagnostic excellence population-level patterns, and (7) PRMs supporting patient storytelling. Equity is considered as a cross-cutting goal. Altogether these and future goals support operationalising a vision of patient-reported diagnostic excellence. Roadmaps were developed as a dynamic tool to illustrate PRMs in relation to specific steps with feedback loops to accomplish goals, anticipated timeframes (8-15 years), synergies to foster, and challenges to overcome. Roadmaps are practical in their following PRMs through the stages of development, endorsement, implementation and scaling, and acting upon those measures. Timeframe estimates assume immediate transitions between these stages and no acceleration through incentives and active coordination. CONCLUSION: PRMs for diagnostic excellence have potential to connect patient perspectives, equity, and achievable goals. Roadmaps offer a design approach to enable coordinating measurement activities among diverse stakeholders. Roadmaps also highlight versatility in ways patient-reported information can be collected and used, from clinical settings to public health contexts. Patient-reported diagnostic excellence cannot be established as a solely top-down endeavour, but inherently benefits from bottom-up approaches.

RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads.

Reader variability is intrinsic to radiologic oncology assessments, necessitating measures to enhance consistency and accuracy. RECIST 1.1 criteria play a crucial role in mitigating this variability by standardizing evaluations, aiming to establish an accepted "truth" confirmed by histology or patient survival. Clinical trials utilize Blind Independent Centralized Review (BICR) techniques to manage variability, employing double reads and adjudicators to address inter-observer discordance effectively. It is essential to dissect the root causes of variability in response assessments, with a specific focus on the factors influencing RECIST evaluations. We propose proactive measures for radiologists to address variability sources such as radiologist expertise, image quality, and accessibility of contextual information, which significantly impact interpretation and assessment precision. Adherence to standardization and RECIST guidelines is pivotal in diminishing variability and ensuring uniform results across studies. Variability factors, including lesion selection, new lesion appearance, and confirmation bias, can have profound implications on assessment accuracy and interpretation, underscoring the importance of identifying and addressing these factors. Delving into the causes of variability aids in enhancing the accuracy and consistency of response assessments in oncology, underscoring the role of standardized evaluation protocols and mitigating risk factors that contribute to variability. Access to contextual information is crucial. CRITICAL RELEVANCE STATEMENT: By understanding the causes of diagnosis variability, we can enhance the accuracy and consistency of response assessments in oncology, ultimately improving patient care and clinical outcomes. KEY POINTS: Baseline lesion selection and detection of new lesions play a major role in the occurrence of discordance. Image interpretation is influenced by contextual information, the lack of which can lead to diagnostic uncertainty. Radiologists must be trained in RECIST criteria to reduce errors and variability.