Diagnostic Errors Research Articles

Characteristics and contributing factors of diagnostic error in surgery: analysis of closed medico-legal cases and complaints in Canada.

Diagnostic errors lead to patient harm; however, most research has been conducted in nonsurgical disciplines. We sought to characterize diagnostic error in the pre-, intra-, and postoperative surgical phases, describe their contributing factors, and quantify their impact related to patient harm. We performed a retrospective analysis of closed medico-legal cases and complaints using a database representing more than 95% of all Canadian physicians. We included cases if they involved a legal action or complaint that closed between 2014 and 2018 and involved a diagnostic error assigned by peer expert review to a surgeon. We identified 387 surgical cases that involved a diagnostic error. The surgical specialties most often associated with diagnostic error were general surgery (n = 151, 39.0%), gynecology (n = 71, 18.3%), and orthopedic surgery (n = 48, 12.4%), but most surgical specialties were represented. Errors occurred more often in the postoperative phase (n = 171, 44.2%) than in the pre- (n = 127, 32.8%) or intra-operative (n = 120, 31.0%) phases of surgical care. More than 80% of the contributing factors for diagnostic errors were related to providers, with clinical decision-making being the principal contributing factor. Half of the contributing factors were related to the health care team (n = 194, 50.1%), the most common of which was communication breakdown. More than half of patients involved in a surgical diagnostic error experienced at least moderate harm, with 1 in 7 cases resulting in death. In our cohort, diagnostic errors occurred in most surgical disciplines and across all surgical phases of care; contributing factors were commonly attributed to provider clinical decision-making and communication breakdown. Surgical patient safety efforts should include diagnostic errors with a focus on understanding and reducing errors in surgical clinical decision-making and improving communication.

Incorporation of Eye Tracking and Gaze Feedback to Characterize and Improve Radiologist Search Patterns of Chest X-Rays: A Randomized Controlled Clinical Trial

Diagnostic errors in radiology often result from incomplete visual assessments attributed to insufficient training in search patterns. Radiologists lack consistent feedback, relying on ad-hoc strategies, leading to suboptimal patterns and potential false negatives. This study used eye tracking to analyze search patterns, quantify performance metrics, and evaluate the impact of an automated continuous improvement feedback-driven framework on detection accuracy. In a controlled trial with ten residents, the intervention group (received feedback) demonstrated a significant 38.9% absolute improvement in detecting suspicious pulmonary nodules compared to the control group (5.6%, p=0.006). While improvements were more rapid over sessions (p=0.0001), other metrics showed no significant changes. The findings highlight the potential of automated feedback systems to enhance radiologist accuracy and reduce errors, emphasizing the need for further research and broader implementation in radiology training.

674 - Diagnostic error in the evaluation of abusive head trauma

674 - Diagnostic error in the evaluation of abusive head trauma

Deployment of a human-centred clinical decision support system for pulmonary embolism: evaluation of impact on quality of diagnostic decisions

Pulmonary embolism (PE) is a serious condition that presents a diagnostic challenge for which diagnostic errors often happen. The literature suggests that a gap remains between PE diagnostic guidelines and...

A systematic evaluation of the performance of GPT‐4 and PaLM2 to diagnose comorbidities in MIMIC‐IV patients

AbstractBackgroundGiven 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.MethodsTo 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.ResultsBased 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.ConclusionThe 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.

Al-Based Detection of Dental Caries: Comparative Analysis with Clinical Examination

ABSTRACT Dental caries pose a significant public health concern, affecting a vast population globally. Traditional clinical examination methods, although reliable, can be subject to human error and time-consuming. Artificial intelligence (AI) technologies have emerged as promising tools to enhance diagnostic accuracy and efficiency. This study explores the potential of AI in revolutionizing dental caries detection. Materials and Methods: A cohort of 50 patients with varying degrees of dental caries participated in this comparative analysis. Clinical examination by dental professionals served as the gold standard for caries detection. AI algorithms were trained using dental images, and their performance was evaluated against the clinical examination results. Results: The AI-based detection system demonstrated a sensitivity of 92% and a specificity of 85% in identifying dental caries, with an overall accuracy of 88%. The clinical examination yielded a sensitivity of 86% and a specificity of 90%, resulting in an overall accuracy of 88%. Statistical analysis indicated no significant difference between AI-based detection and clinical examination (P > 0.05). Conclusion: AI technology exhibits promise as an adjunctive tool for dental practitioners, potentially reducing diagnostic errors and improving efficiency. Integrating AI into routine dental practice may aid in early caries detection and promote better oral health outcomes.

Automatic feature segmentation in dental panoramic radiographs.

The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography. This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988). Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning. The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals.

An Update Summary on the Learning Sciences Within an Ophthalmic Context.

Clinical reasoning, specifically diagnostic decision-making, has been a subject of fragmented literature since the 1970s, marked by diverse theories and conflicting perspectives. This article reviews the latest evidence in medical education, drawing from scientific literature, to offer ophthalmologists insights into optimal strategies for personal learning and the education of others. It explores the historical development of clinical reasoning theories, emphasising the challenges in understanding how doctors formulate diagnoses. The importance of clinical reasoning is underscored by its role in making accurate diagnoses and preventing diagnostic errors. The article delves into the dual process theory, distinguishing between type 1 and type 2 thinking and their implications for clinical decision-making. Cognitive load theory is introduced as a crucial aspect, highlighting the limited capacity of working memory and its impact on the diagnostic process. The zone of proximal development (ZPD) is explored as a framework for optimal learning environments, emphasising the importance of scaffolding and deliberate practice in skill development. The article discusses semantic competence, mental representation, and the interplay of different memory stores-semantic, episodic, and procedural-in enhancing diagnostic proficiency. Self-regulated learning (SRL) is introduced as a student-centric approach, emphasising goal setting, metacognition, and continuous improvement. Practical advice is provided for minimising cognitive errors in clinical reasoning, applying dual process theory, and considering cognitive load theory in teaching. The relevance of deliberate practice in ophthalmology, especially in a rapidly evolving field, is emphasised for continuous learning and staying updated with advancements. The article concludes by highlighting the importance of clinical supervisors in recognising and supporting trainees' self-regulated learning and understanding the principles of various teaching and learning theories. Ultimately, a profound comprehension of the science behind clinical reasoning is deemed fundamental for ophthalmologists to deliver high-quality, evidence-based care and foster critical thinking skills in the dynamic landscape of ophthalmology.

Refining a Framework to Enhance Communication in the Emergency Department During the Diagnostic Process: An eDelphi Approach

Emergency departments (EDs) are susceptible to diagnostic error. Suboptimal communication between the patient and the interdisciplinary care team increases risk to diagnostic safety. The role of communication remains underrepresented in existing diagnostic decision-making conceptual models. The authors used eDelphi methodology, whereby data are collected electronically, to achieve consensus among an expert panel of 18 clinicians, patients, family members, and other participants on a refined ED-based diagnostic decision-making framework that integrates several potential opportunities for communication to enhance diagnostic quality. This study examined the entire diagnostic process in the ED, from prehospital to discharge or transfer to inpatient care, and identified where communication breakdowns could occur. After four iterative rounds of the eDelphi process, including a final validation round by all participants, the project's a priori consensus threshold of 80% agreement was reached. The authors developed a final framework that positions communication more prominently in the diagnostic process in the ED and enhances the original National Academies of Sciences, Engineering, and Medicine (NASEM) and ED-adapted NASEM frameworks. Specific points in the ED journey were identified where more attention to communication might be helpful. Two specific types of communication-information exchange and shared understanding-were identified as high priority for optimal outcomes. Ideas for communication-focused interventions to prevent diagnostic error in the ED fell into three categories: patient-facing, clinician-facing, and system-facing interventions. This project's refinement of the NASEM framework adapted to the ED can be used to develop communications-focused interventions to reduce diagnostic error in this highly complex and error-prone setting.

Don't interrupt me! development of a handoff education bundle to simulate the real world.

Ineffective nurse-to-nurse handoff communication is associated with information omissions, diagnostic errors, treatment errors, and delays. New nurses report a lack of confidence and ability in handoff communication, which may stem from inadequate training in prelicensure nursing programs. Our objective was to introduce prelicensure nursing students to a standardized, theory-based method for handoff, including behavioral strategies employed by nurses during interrupted handoff. A handoff education bundle (HEB) was developed. Kern's six-step curriculum model was utilized to design, implement, and evaluate the handoff curriculum. Student feedback highlighted the importance of integrating multiple, varying distractors during learning cycles and recognition of the impact of distractors on handoff. Implementing a HEB at the prelicensure nursing level could promote competency in handoff communication for new graduate nurses. Handoff is an international patient safety priority, as inadequate communication has been linked to adverse patient events.

Rare Case of Chronic Coxiella Endocarditis Without Fever or Classic Risk Factors

Background: “Q-fever” is a zoonotic infectious disease caused by Coxiella burnetii which is most commonly transmitted globally through unpasteurized animal products or aerosolized fluid. This zoonosis is not thought to be common in developed countries due to modern utilization of pasteurization but risk for transmission remains high in those with extended contact with livestock and cattle. Acute Q-fever presents with an illness characterized by high fevers, myalgias, and segmental pneumonia, but rarely it may progress to chronic Q-fever. Chronic Q-fever most often presents with a culture-negative endocarditis with or without renal impairment and can be fatal if left untreated. Case Report: In this case report, we present a 72-year-old male with a 3-month history of body pain, cough, and weight loss who was eventually discovered to have an afebrile presentation of Coxiella endocarditis. This patient was found to have a mitral valve vegetation diagnosed through transesophageal echocardiography and had positive serology for chronic Q-fever. Surprisingly, this patient had no classical exposures, no history of valvular dysfunction or prosthesis, and no clinical signs suggesting endocarditis, making his case markedly atypical. He was placed on a 12 -18-month course of antibiotics and was monitored on an outpatient basis. Conclusion: Although chronic Q-fever is rare its variable presentation may cause diagnostic error if it is not kept on the differential. A transesophageal echocardiogram should be strongly preferred due to its superior visualization of heart valves. Serology remains the gold standard for diagnosis of Coxiella. When dealing with culture-negative endocarditis, clinicians should retain a high index of suspicion of Coxiella and obtain serologies for diagnosis

Diagnostic errors and treatment of anemia caused by vitamin B<sub>12</sub> deficiency

This work was carried out in real clinical practice. Five clinical observations of anemia caused by vitamin B12 deficiency are presented, which demonstrate the difficulties of differential diagnosis. Differential diagnosis of B12‑deficiency anemia is carried out with folate deficiency anemia, hemolytic anemia, myelodysplastic syndrome, acute leukemia – erythromyelosis, aplastic anemia, myelocarcinomatosis, anemia due to copper deficiency. The relevance of the clinical problem is determined by the fact that B12‑deficiency anemia is one of the common anemias, the frequency of its detection varies in different age groups: after 60 years, vitamin B12 deficiency is found in one in 50 people, after 70 years – in every 15th. Currently, an increasingly common cause of megaloblastic anemia is vitamin B12 deficiency, which occurs while taking certain medications. Vitamin deficiency leads to a wide range of various disorders, primarily the occurrence of neurological manifestations, while there is insufficient awareness of clinicians about causes of its occurrence, nonspecific manifestations, diagnostic methods and effective therapy.

Focal electroclinical features in generalized tonic-clonic seizures: Decision flowchart for a diagnostic challenge.

Bilateral tonic-clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal EEG or semiology. We retrospectively analyzed video-EEG recordings of 101 bilateral tonic-clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1-year follow-up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7-Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal features with an accuracy of 96.6%. Focal semiology associated with bilateral tonic-clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies.

Exploring Clinical Lessons Learned by Experienced Hospitalists from Diagnostic Errors and Successes.

Diagnostic errors cause significant patient harm. The clinician's ultimate goal is to achieve diagnostic excellence in order to serve patients safely. This can be accomplished by learning from both errors and successes in patient care. However, the extent to which clinicians grow and navigate diagnostic errors and successes in patient care is poorly understood. Clinically experienced hospitalists, who have cared for numerous acutely ill patients, should have great insights from their successes and mistakes to inform others striving for excellence in patient care. To identify and characterize clinical lessons learned by experienced hospitalists from diagnostic errors and successes. A semi-structured interview guide was used to collect qualitative data from hospitalists at five independently administered hospitals in the Mid-Atlantic area from February to June 2022. 12 academic and 12 community-based hospitalists with ≥ 5years of clinical experience. A constructivist qualitative approach was used and "reflexive thematic analysis" of interview transcripts was conducted to identify themes and patterns of meaning across the dataset. Five themes were generated from the data based on clinical lessons learned by hospitalists from diagnostic errors and successes. The ideas included appreciating excellence in clinical reasoning as a core skill, connecting with patients and other members of the health care team to be able to tap into their insights, reflecting on the diagnostic process, committing to growth, and prioritizing self-care. The study identifies key lessons learned from the errors and successes encountered in patient care by clinically experienced hospitalists. These findings may prove helpful for individuals and groups that are authentically committed to moving along the continuum from diagnostic competence towards excellence.

Healthcare Quality from the Perspective of Patients in Gulf Cooperation Council Countries: A Systematic Literature Review

With the increased focus on patient-centered care, consensus on healthcare outcomes of importance to patients becomes crucial. Based on a systematic review of the literature, this study confirms the perspectives of patients on healthcare quality in GCC countries. Online databases were searched for relevant peer-reviewed articles published from 2012 to 2023. Twenty-two articles retrieved from the search were qualitatively analyzed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Most articles (90%) reported studies conducted in Saudi Arabia. Patients in GCC countries face common problems in the care delivery process, which contribute to negative perceptions of quality. These problems include diagnostic and medication errors, provider–patient communication problems, missed appointments with physicians, problems in emergency care access due to geographical distance and transportation barriers, long waiting times, and physical environments. Notably, healthcare quality is perceived to be an outcome of multiple factors dependent on the location and category of healthcare service providers; for instance, disparities in perceptions of quality were observed between patients attending Primary Health Care (PHC) centers in rural and urban areas. Issues such as lack of equitable healthcare delivery and deficiencies in Emergency Medical Services (EMS) effectiveness were disparately recognized as quality concerns by different patient populations. The findings provide insights into healthcare quality and area of weakness needing strategies and policies to ensure patient-centered, safe, equitable, timely, and effective healthcare. Healthcare providers and policymakers in GCC countries can use the results to plan, assess, and improve care delivery. Trial registration: PROSPERO ID: CRD42022326842.

Qualification of medical (medical) errors

In the article the author investigates the qualification signs of actions (inaction) of doctors (medical staff), the consequence of which is a medical error. Their content and influence at the civil law relations that arise between the doctor and the patient are established. The author emphasizes the development of a correct, well-thought-out and unified position about the legal qualification of erroneous actions of doctors (medical staff). Also in the article it is noted that possible defects of medical care are: lack of a positive result, accident, medical error, where medical error is the basis for bringing to civil liability for doctors (medical workers). It is noted that the characteristic signs of such an error are harm to the patient`s health or his death and the presence of both intentional guilt and negligence and wrongful acts (inaction) of doctors (medical workers).
 The analysis and classification of the causes (factors) of occurrence and types of medical error is carried out. The subjective and objective reasons influencing actions (inaction of doctors, medical workers are defined). It is said that the objective reasons the doctor, the medical worker cannot predict and which exclude their responsibility. It is stated that actions under the influence of objective reasons should be considered as types of accident (or incident). Subjective causes (factors) are errors made by doctors (health professionals) that unreasonably deviate from established medical standards, act carelessly, confidently or allow unreasonable risks in the absence of experience or knowledge.
 The article analyses the classification of medical errors by the causes, in particular: diagnostic errors; medical and tactical errors; technical errors; organizational errors and deontological errors. A legal assessment of a medical error as one of the possible grounds for civil liability is provided. It is substantiated that the responsibility of a doctor, medical staff depends on the presence of guilt in his actions and the qualification of the negative result of medical care.
 The article also emphasizes that doctors (medical workers) are not responsible for a medical error, but for the damage caused to the patient as a result of this error.
 Scientifically substantiated proposals and recommendations for improving the civil legislation of Ukraine in the field of medicine are formulated.

Large-Scale assessment of ChatGPT's performance in benign and malignant bone tumors imaging report diagnosis and its potential for clinical applications

ObjectiveThis study was designed to delve into the complexities involved in diagnosing of benign and malignant bone tumors and to assess the potential of AI technologies like ChatGPT in improving diagnostic accuracy and efficiency. The study also explores the few-shot learning as a method to optimize ChatGPT's performance in specialized medical domains such as benign and malignant bone tumors diagnosis. MethodsA total of 1366 benign and malignant bone tumors-related imaging reports were collected and diagnosed by 25 experienced physicians. The gold standard of diagnosis was established by combining clinical, imaging and pathological principles.These reports were then input into the ChatGPT model which underwent a few-shot learning method to generate diagnostic results. The diagnostic results of the physicians and the AI model were compared to evaluate the performance of ChatGPT. An experiment was conducted to assess the influence of different radiologist's reporting styles on the model's diagnostic performance. Furthermore, in-depth analysis of misdiagnosed cases was carried out, categorizing diagnostic errors and exploring possible causes. ResultsThe diagnostic results generated by ChatGPT showed an accuracy of 0.73, sensitivity of 0.95, and specificity of 0.58. After few-shot learning, ChatGPT demonstrated significant improvement, achieving an accuracy of 0.87, sensitivity of 0.99, and specificity of 0.73, bringing it much closer to the level of physician diagnostics. In an experiment analyzing the influence of the radiologist's reporting style, the model demonstrated higher sensitivity when interpreting reports written by high-level radiologists. In 56 benign cases, ChatGPT misdiagnosed them as malignant. Among these, 35 benign lesions- fibrous dysplasia and osteofibrous dysplasia- were incorrectly identified as metastatic tumors or osteosarcomas; 8 cases of myositis ossificans were wrongly diagnosed as extraosseous osteosarcoma. 7 cases of giant cell tumor of bone at the end of long bone were misdiagnosed as osteosarcoma by intermediate doctors. Chondroblastoma was misdiagnosed as malignant tumor in 6 cases −2 osteosarcoma and 4 chondrosarcoma-In this study, 23 osteosarcoma cases were misdiagnosed by ChatGPT as osteomyelitis; Chondrosarcoma was misdiagnosed as fibrous dysplasia or aneurysmal bone cyst in 8 cases. Four cases of spinal chordoma were misdiagnosed as spinal tuberculosis. ConclusionOur findings highlight the potential of ChatGPT in the diagnosis of benign and malignant bone tumors, offering advantages like enhanced efficiency and a reduction in missed diagnoses. However, the necessity of collaborative interactions between physicians and ChatGPT in practical settings was underscored. With an examination into AI's capacity in benign and malignant bone tumors diagnosis, this study lays the groundwork for future AI advancements in medicine. Additionally, the benefits of few-shot learning in fine-tuning ChatGPT applications in specialized fields were also demonstrated.

Multimodal approach in radiation diagnostics to determining the intramedullary phase of strictly hematogenic osteomyelitis in children: a prospective study

INTRODUCTION: Despite the fact that acute hematogenous osteomyelitis is classified as a sufficiently studied disease, the percentage of diagnostic errors is still quite high. Timely diagnosis is crucial for the successful outcome of the disease, therefore, visualization should be aimed at early diagnosis and, as a result, successful treatment of the disease.OBJECTIVE: To identify early patterns in the intramedullary phase of the course of acute hematogenous osteomyelitis (CSO) with a multimodal approach of radiation research methods.MATERIALS AND METHODS: Twenty patients underwent a comprehensive examination, including multispiral computed tomography (MSCT) using color mapping techniques.RESULTS: In the intramedullary phase of the course of CSF, no pathological changes were detected during X-ray examination of the affected limb. Ultrasound in 75% of observations revealed a thickening of the periosteum of more than 1 millimeter (mm) in the affected bone. When using MRI in 93% of cases, and with MSCT in 95% of patients, signs of bone marrow edema were detected.DISCUSSION: The role of computed tomography in the diagnosis of acute hematogenous osteomyelitis in children has received significant recognition in pediatric surgical practice in recent years, and the use of MSCT in the diagnosis of CSO has found application in domestic healthcare. At the same time, a fairly large number of researchers reported on the high informativeness of MSCT in the diagnosis of CSOs.CONCLUSION: MSCT using the color mapping technique has a higher specificity compared to MRI and can be used as an integral method of the first level in the diagnosis of the intramedullary phase of the CSF course.

Medico-legal risk of infectious disease physicians in Canada: A retrospective review.

There is little known about the medico-legal risk for infectious disease specialists in Canada. The objective of this study was to identify the causes of these medico-legal risks with the goal of improving patient safety and outcomes. A 10-year retrospective analysis of Canadian Medical Protective Association (CMPA) closed medico-legal cases from 2012 to 2021 was performed. Peer expert criticism was used to identify factors that contributed to the medico-legal cases at the provider, team, or system level, and were contrasted with the patient complaint. During the study period there were 571 infectious disease physician members of the CMPA. There were 96 patient medico-legal cases: 45 College complaints, 40 civil legal matters, and 11 hospital complaints. Ten cases were associated with severe patient harm or death. Patients were most likely to complain about perceived deficient assessments (54%), diagnostic errors (53%), inadequate monitoring or follow-up (20%), and unprofessional manner (20%). In contrast, peer experts were most critical of the areas of diagnostic assessment (20%), deficient assessment (10%), failure to perform test/intervention (8%), and failure to refer (6%). While infectious disease physicians tend to have lower medico-legal risks compared to other health care providers, these risks still do exist. This descriptive study provides insights into the types of cases, presenting conditions, and patient allegations associated with their practice.

Japanese Internists' Most Memorable Diagnostic Error Cases: A Self-reflection Survey.

Objective The etiologies of diagnostic errors among internal medicine physicians are unclear. To understand the causes and characteristics of diagnostic errors through reflection by those involved in them. Methods We conducted a cross-sectional study using a web-based questionnaire in Japan in January 2019. Over a 10-day period, a total of 2,220 participants agreed to participate in the study, of whom 687 internists were included in the final analysis. Participants were asked about their most memorable diagnostic error cases, in which the time course, situational factors, and psychosocial context could be most vividly recalled and where the participant provided care. We categorized diagnostic errors and identified contributing factors (i.e., situational factors, data collection/interpretation factors, and cognitive biases). Results Two-thirds of the identified diagnostic errors occurred in the clinic or emergency department. Errors were most frequently categorized as wrong diagnoses, followed by delayed and missed diagnoses. Errors most often involved diagnoses related to malignancy, circulatory system disorders, or infectious diseases. Situational factors were the most cited error cause, followed by data collection factors and cognitive bias. Common situational factors included limited consultation during office hours and weekends and barriers that prevented consultation with a supervisor or another department. Conclusion Internists reported situational factors as a significant cause of diagnostic errors. Other factors, such as cognitive biases, were also evident, although the difference in clinical settings may have influenced the proportions of the etiologies of the errors that were observed. Furthermore, wrong, delayed, and missed diagnoses may have distinctive associated cognitive biases.