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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.

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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.

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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.

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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.

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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.

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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.

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Diagnostic overshadowing refers to a phenomenon whereby people with mental health conditions encounter inadequate or delayed medical attention and misdiagnosis. This occurs when physical symptoms are mistakenly attributed to their mental health condition. This paper presents a scoping review focusing on direct causes and background factors of diagnostic overshadowing in the context of hepatitis C infection in people who inject drugs and have concurrent mental health conditions. Despite significant strides in hepatitis C treatment with direct-acting antiviral drugs, the complex interplay of mental health conditions and physical symptoms necessitates a nuanced approach for accurate diagnosis and effective screening. This review was conducted using Joanna Briggs Institute's methodology for scoping reviews. The databases searched included Medline, Embase, PsycInfo, Global Health, CINAHL and Scopus. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The search strategies identified 1995 records. Overall, 166 studies were excluded. Forty-two (42) studies met the inclusion criteria. Three (n = 3) studies represented direct causes, and 39 (n = 39) with background factors related to diagnostic overshadowing. Studies highlighted six key themes encompassing diagnostic overshadowing, with communication barriers, stigma and knowledge deficiencies being the most prominent. Recognising and addressing diagnostic overshadowing in chronic hepatitis C will lead to increased screening, diagnosis and timely administration of life-saving antiviral therapy, resulting in profound enhancements in well-being and health outcomes. Moreover, this proactive approach will play a pivotal role in advancing the global effort towards eliminating hepatitis C by 2030.

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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.

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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.

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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.

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OBJECTIVES: Patients referred to general internal medicine (GIM) outpatient clinics may face a higher risk of diagnostic errors than non-referred patients. This difference in risk is assumed to be due to the differences in diseases and clinical presentations between referred and non-referred patients; however, clinical data regarding this issue are scarce. This study aimed to determine the frequency of diagnostic errors and compare the characteristics of referred and non-referred patients visit GIM outpatient clinics. METHODS: This study included consecutive outpatients who visited the GIM outpatient clinic at a university hospital, with or without referral. Data on age, sex, chief complaints, referral origin, and final diagnosis were collected from medical records. The Revised Safer Dx Instrument was used to detect diagnostic errors. RESULTS: Data from 534 referred and 599 non-referred patients were analyzed. The diagnostic error rate was higher in the referral group than that in the non-referral group (2.2 % vs. 0.5 %, p=0.01). The prevalence of abnormal test results and sensory disturbances was higher in the chief complaints, and the prevalence of musculoskeletal system disorders, connective tissue diseases, and neoplasms was higher in the final diagnoses of referred patients compared with non-referred patients. Among referred patients with diagnostic errors, abnormal test results and sensory disturbances were the two most common chief complaints, whereas neoplasia was the most common final diagnosis. Problems with data integration and interpretation were found to be the most common factors contributing to diagnostic errors. CONCLUSIONS: Paying more attention to patients with abnormal test results and sensory disturbances and considering a higher pre-test probability for neoplasms may prevent diagnostic errors in patients referred to GIM outpatient clinics.

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Medical practitioners are entrusted with the pivotal task of making optimal decisions in healthcare delivery. Despite rigorous training, our confidence in reasoning can fail when faced with pressures, uncertainties, urgencies, difficulties, and occasional errors. Day-to-day decisions rely on swift, intuitive cognitive processes known as heuristic or type 1 decision-making, which, while efficient in most scenarios, harbor inherent vulnerabilities leading to systematic errors. Cognitive biases receive limited explicit discussion during our training as junior doctors in the domain of paediatric cardiology. As pediatric cardiologists, we frequently confront emergencies necessitating rapid decision-making, while contending with the pressures of stress, fatigue, an earnest interest in "doing the right thing" and the impact of parental involvement. This article aims to describe cognitive biases in pediatric cardiology, highlighting their influence on therapeutic interventions for congenital heart disease. Whether future pediatric cardiologists or experienced professionals, understanding and actively combating cognitive biases are essential components of our ongoing medical education. Furthermore, it is our responsibility to thoroughly examine our own practices in our unwavering commitment to providing high-quality care.

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BACKGROUND: There is growing concern that pulse oximeters are routinely less accurate in hospitalized patients with darker skin pigmentation, in turn increasing risk of undetected (occult) hypoxemia and adverse clinical outcomes. The aim of this systematic review and meta-analysis was to synthesize evidence on racial and ethnic disparities in occult hypoxemia prevalence and clinical impacts of undetected hypoxemia. METHODS: Ovid MEDLINE, Embase, and CINAHL databases were searched for relevant articles published through January 2024. Eligible studies must have been conducted among adults in inpatient or outpatient settings and report occult hypoxemia prevalence stratified by patient race or ethnicity, or clinical outcomes stratified by patient race or ethnicity and occult hypoxemia status. Screening for inclusion was conducted independently by two investigators. Data extraction and risk of bias assessment were conducted by one investigator then checked by a second. Outcome data were synthesized using random-effects meta-analyses. RESULTS: Fifteen primary studies met eligibility criteria and reported occult hypoxemia prevalence in 732,505 paired oximetry measurements from 207,464 hospitalized patients. Compared with White patients, occult hypoxemia is likely more common among Black patients (pooled prevalence ratio = 1.67, 95% CI 1.47 to 1.90) and among patients identifying as Asian, Latinx, Indigenous, multiracial, or other race or ethnicity (pooled prevalence ratio = 1.39, 95% CI 1.19 to 1.64). Findings from studies reporting clinical outcomes suggest that Black patients with undetected hypoxemia may experience poorer treatment delivery outcomes than White patients with undetected hypoxemia. No evidence was found from outpatient settings. DISCUSSION: This review and included primary studies rely on self-identified race or ethnicity, which may obscure variability in occult hypoxemia risk. Findings underscore that clinicians should be aware of the risk of occult hypoxemia in hospitalized patients with darker skin pigmentation. Moreover, oximetry data from included studies suggests that the accuracy of pulse oximeters could vary substantially from patient to patient and even within individual patients. TRIAL REGISTRATION: PROSPERO ( CRD42023402152 ).

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Pregnancy-related morbidity and mortality remain high across the United States, with the majority of deaths being deemed preventable. Misdiagnosis and delay in diagnosis are thought to be significant contributors to preventable harm. These diagnostic errors in obstetrics are understudied. Presented here are five selected research methods to ascertain the rates of and harm associated with diagnostic errors and the pros and cons of each. These methodologies include clinicopathologic autopsy studies, retrospective chart reviews based on clinical criteria, obstetric simulations, pregnancy-related harm case reviews, and malpractice and administrative claim database research. We then present a framework for a future study of diagnostic errors and the pursuit of diagnostic excellence in obstetrics: (1) defining and capturing diagnostic errors, (2) targeting bias in diagnostic processes, (3) implementing and monitoring safety bundles, (4) leveraging electronic health record triggers for case reviews, (5) improving diagnostic skills via simulation training, and (6) publishing error rates and reduction strategies. Evaluation of the effectiveness of this framework to ascertain diagnostic error rates, as well as its impact on patient outcomes, is required.

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Aortic dissection (AD) remains a difficult diagnosis in the emergency setting. Despite its rare occurrence, it is a life-threatening pathology that, if missed, is typically fatal. Previous studies have documented minimal improvement in timely and accurate diagnoses despite the advancement of computed tomography. Previous literature has highlighted aortic dissections as a major cause of serious misdiagnosis-related harm. The aim of this article is to review the available literature on AD, discussing the diversity in presentations and the prevalence of historical and exam features to better aid in the diagnosis of AD. AD remains a difficult diagnosis, even with the widespread prevalence of computed tomography angiography usage. No single feature of the history or physical examination is enough to raise suspicion. The diagnosis should be strongly considered in any patient with chest pain that is severe and unexplained by other findings or testing. Those who do not present with acute pain are often complicated by neurologic deficits, hypotension, or syncope. These patients suffer from a change in mental status limiting their ability to participate in the history and physical examination and have a higher rate of complications and mortality. An educated understanding of the atypical presentations of aortic dissection helps the clinician to realistically rank it on the differential diagnosis, culminating in judicious use of definitive imaging.

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Background: Given the strikingly high diagnostic error rate in hospitals, and the recent development of Large Language Models (LLMs), we set out to measure the diagnostic sensitivity of two popular LLMs: GPT-4 and PaLM2. Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results, with GPT-4 demonstrating high accuracy in diagnosing test cases. However, larger evaluations on real electronic patient data are needed to provide more reliable estimates. Methods: To fill this gap in the literature, we used a deidentified Electronic Health Record (EHR) data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston. This data set contained blood, imaging, microbiology and vital sign information as well as the patients' medical diagnostic codes. Based on the available EHR data, doctors curated a set of diagnoses for each patient, which we will refer to as ground truth diagnoses. We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients. Results: Based on the proportion of correctly predicted ground truth diagnoses, we estimated the diagnostic hit rate of GPT-4 to be 93.9%. PaLM2 achieved 84.7% on the same data set. On these 1000 randomly selected EHRs, GPT-4 correctly identified 1116 unique diagnoses. Conclusion: The results suggest that artificial intelligence (AI) has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year. However, human oversight of AI remains essential: LLMs cannot replace clinicians, especially when it comes to human understanding and empathy. Furthermore, a significant number of challenges in incorporating AI into health care exist, including ethical, liability and regulatory barriers.

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INTRODUCTION: Diagnostic errors in the emergency departments can have major implications on patient outcomes. Preliminary Image Evaluation (PIE) is a brief comment written by a radiographer describing an acute or traumatic pathology on a radiograph and can be used to complement referrer's image interpretation in the absence of the radiologist report. Currently, no studies exist that focus their analysis on false-positive (FP) errors in PIE. The purpose of this study was to investigate the regions of the body that cause the most FP errors and recognise other areas in image interpretation that may need additional attention. METHODS: A longitudinal retrospective clinical audit was conducted to determine the accuracy of radiographer PIE's over 5 years from January 2016 to December 2020. PIE's were compared to the radiologist report to assess for diagnostic accuracy. FP and unsure errors were further categorised by anatomical region and age. RESULTS: Over this period, a sample size of 11,090 PIE audits were included in the study demonstrating an overall PIE accuracy of 87.7%. Foot, ankle and chest regions caused the most FP errors, while ankle, shoulder and elbow caused the most unsure cases. 76% of the unsure cases were negative for any pathology when compared to the radiologist report. The paediatric population accounted for 21.3% of FP cases and 33.6% of unsure cases. CONCLUSION: Findings in this study should be used to tailor education specific to radiographer image interpretation. Improving radiography image interpretation skills can assist in improving referrer diagnostic accuracy, thus improving patient outcomes.

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Background: Strangulated intestinal obstruction is a life-threatening condition that should be considered as a differential diagnosis in children with shock. However, it has pitfalls in diagnosis and can lead to diagnostic errors. Case Presentation: A 3-month-old male patient presented with a pale complexion lasting 2 h and abnormal crying. He was in shock with lactic acidosis, altered mental status, and slight abdominal distension. He required volume resuscitation, vasoactive agents, and transfusion. On Day 2, he had marked abdominal distension and acute kidney injury, which required continuous kidney replacement therapy. Contrast-enhanced computed tomography revealed extensive intestinal ischemia. It took 33.5 h from his arrival to the computed tomography, leading to operative management. The small intestine had entered a mesenteric hiatus, leading to ischemia. He was diagnosed with strangulated mesenteric hernia. Conclusion: In this case, four pitfalls led to delayed diagnosis. Factors for diagnostic errors specific to strangulated intestinal obstruction and intensive care should be noted.