Toward the eradication of medical diagnostic errors.

The medical community does not broadcast the problem, but there are many studies that have reinforced a serious issue with diagnostic errors. A recent study concluded: "We estimate that nearly 800,000 Americans die or are permanently disabled by diagnostic errors each year." Diagnostic errors are inaccurate assessments of a patient's root cause of illness, such as missing a heart attack or infection or assigning the wrong diagnosis of pneumonia when the correct one is pulmonary embolism. Despite ever-increasing use of medical imaging and laboratory tests intended to promote diagnostic accuracy, there is nothing to suggest improvement since the report by the National Academies of Sciences, Engineering and Medicine in 2015, which provided a conservative estimate that 5% of adults experience a diagnostic error each year, and that most people will experience at least one in their lifetime.

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.

Diagnosing, assessing and managing cellulitis.

Cellulitis is an acute bacterial infection that affects the deep dermis and surrounding subcutaneous tissue. Although it is a common condition, it is often misdiagnosed because it can mimic a range of conditions that also cause inflamed, red, irritated and painful skin. Such misdiagnoses may lead to unnecessary hospital admissions and antibiotic overuse, with most alternative diagnoses being non-infectious. Undertaking a holistic patient assessment, skin assessment and thorough clinical history is important in the diagnosis of cellulitis, and it is vital to use a collaborative multidisciplinary approach in its acute management and to prevent recurrence. This article defines the term cellulitis and explores its presenting features. The author also discusses the associated risk factors, clinical assessment techniques and effective management strategies, as well as outlining the actions that nurses can take to prevent recurrence.

Insights into diagnostic errors in endocrinology: a prospective, case-based, international study.

BACKGROUND: Diagnostic errors in internal medicine are common. While cognitive errors have previously been identified to be the most common contributor to errors, very little is known about errors in specific fields of internal medicine such as endocrinology. This prospective, multicenter study focused on better understanding the causes of diagnostic errors made by general practitioners and internal specialists in the area of endocrinology. METHODS: From August 2019 until January 2020, 24 physicians completed five endocrine cases on an online platform that simulated the diagnostic process. After each case, the participants had to state and explain why they chose their assumed diagnosis. The data gathering process as well as the participants' explanations were quantitatively and qualitatively analyzed to determine the causes of the errors. The diagnostic processes in correctly and incorrectly solved cases were compared. RESULTS: Seven different causes of diagnostic error were identified, the most frequent being misidentification (mistaking one diagnosis with a related one or with more frequent and similar diseases) in 23% of the cases. Other causes were faulty context generation (21%) and premature closure (17%). The diagnostic confidence did not differ between correctly and incorrectly solved cases (median 8 out of 10, p = 0.24). However, in incorrectly solved cases, physicians spent less time on the technical findings (such as lab results, imaging) (median 250 s versus 199 s, p < 0.049). CONCLUSIONS: The causes for errors in endocrine case scenarios are similar to the causes in other fields of internal medicine. Spending more time on technical findings might prevent misdiagnoses in everyday clinical practice.

False-Positive and False-Negative Contrast-enhanced Mammograms: Pitfalls and Strategies to Improve Cancer Detection.

Contrast-enhanced mammography (CEM) is a relatively new breast imaging modality that uses intravenous contrast material to increase detection of breast cancer. CEM combines the structural information of conventional mammography with the functional information of tumor neovascularity. Initial studies have demonstrated that CEM and MRI perform with similar accuracies, with CEM having a slightly higher specificity (fewer false positives), although larger studies are needed. There are various reasons for false positives and false negatives at CEM. False positives at CEM can be caused by benign lesions with vascularity, including benign tumors, infection or inflammation, benign lesions in the skin, and imaging artifacts. False negatives at CEM can be attributed to incomplete or inadequate visualization of lesions, marked background parenchymal enhancement (BPE) obscuring cancer, lack of lesion contrast enhancement due to technical issues or less-vascular cancers, artifacts, and errors of lesion perception or characterization. When possible, real-time interpretation of CEM studies is ideal. If additional views are necessary, they may be obtained while contrast material is still in the breast parenchyma. Until recently, a limitation of CEM was the lack of CEM-guided biopsy capability. However, in 2020, the U.S. Food and Drug Administration cleared two devices to support CEM-guided biopsy using a stereotactic biopsy technique. The authors review various causes of false-positive and false-negative contrast-enhanced mammograms and discuss strategies to reduce these diagnostic errors to improve cancer detection while mitigating unnecessary additional imaging and procedures. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Diagnostic Errors: A Stronger Role for Nursing Could Help Reduce Patient Harm.

Institutional support and better nurse training are key.

The Nature, Causes, and Clinical Impact of Errors in the Clinical Laboratory Testing Process Leading to Diagnostic Error: A Voluntary Incident Report Analysis.

OBJECTIVES: Diagnostic errors, that is, missed, delayed, or wrong diagnoses, are a common type of medical errors and preventable iatrogenic harm. Errors in the laboratory testing process can lead to diagnostic errors. This retrospective analysis of voluntary incident reports aimed to investigate the nature, causes, and clinical impact of errors, including diagnostic errors, in the clinical laboratory testing process. METHODS: We used a sample of 600 voluntary incident reports concerning diagnostic testing selected from all incident reports filed at the University Medical Center Utrecht in 2017-2018. From these incident reports, we included all reports concerning the clinical laboratory testing process. For these incidents, we determined the following: nature: in which phase of the testing process the error occurred; cause: human, technical, organizational; and clinical impact: the type and severity of the harm to the patient, including diagnostic error. RESULTS: Three hundred twenty-seven reports were included in the analysis. In 77.1%, the error occurred in the preanalytical phase, 13.5% in the analytical phase and 8.0% in the postanalytical phase (1.5% undetermined). Human factors were the most frequent cause (58.7%). Severe clinical impact occurred relatively more often in the analytical and postanalytical phase, 32% and 28%, respectively, compared with the preanalytical phase (40%). In 195 cases (60%), there was a potential diagnostic error as consequence, mainly a potential delay in the diagnostic process (50.5%). CONCLUSIONS: Errors in the laboratory testing process often lead to potential diagnostic errors. Although prone to incomplete information on causes and clinical impact, voluntary incident reports are a valuable source for research on diagnostic error related to errors in the clinical laboratory testing process.

Identifying novel disease categories through divergence optimization: An approach to prevent misdiagnosis in medical imaging.

Given the significant changes in human lifestyle, the incidence of colon cancer has rapidly increased. The diagnostic process can often be complicated due to symptom similarities between colon cancer and other colon-related diseases. In an effort to minimize misdiagnosis, deep learning-based approaches for colon cancer diagnosis have notably progressed within the field of clinical medicine, offering more precise detection and improved patient outcomes. Despite these advancements, practical application of these techniques continues to encounter two major challenges: 1) due to the need for expert annotation, only a limited number of labels are utilized for diagnosis; and 2) the existence of diverse disease types can lead to misdiagnosis when the model encounters unfamiliar disease categories. To overcome these hurdles, we present a method incorporating Universal Domain Adaptation (UniDA). By optimizing the divergence of samples in the source domain, our method detects noise. Furthermore, to identify categories that are not present in the source domain, we optimize the divergence of unlabeled samples in the target domain. Experimental validation on two gastrointestinal datasets demonstrates that our method surpasses current state-of-the-art domain adaptation techniques in identifying unknown disease classes. It is worth noting that our proposed method is the first work of medical image diagnosis aimed at the identification of unknown categories of diseases.

Common ECG interpretation software mistakes Part II: Computer errors that hide diagnostic clues.

Electrocardiogram (ECG) interpretation software mistakes can lead to incorrect diagnoses and inappropriate treatments. Occasionally, however, repetitive and consistent computer errors may hide important clues for correct diagnoses that otherwise could have been missed. We present a collection of a few common and clinically important such peculiarities, and provide tools on how to prove or disprove the suspected diagnosis. In addition to the illustrations in print, an online supplement (OS) shows more examples of the discussed phenomena. In each ECG, the original computer interpretations were enlarged for legibility.

Errors in clinical diagnosis: a narrative review.

Diagnostic errors are often caused by cognitive biases and sometimes by other cognitive errors, which are driven by factors specific to clinicians, patients, diseases, and health care systems. An experienced clinician diagnoses routine cases intuitively, effortlessly, and automatically through non-analytic reasoning and uses deliberate, cognitively effortful analytic reasoning to diagnose atypical or complicated clinical cases. However, diagnostic errors can never be completely avoided. To minimize the frequency of diagnostic errors, it is advisable to rely on multiple sources of information including the clinician's personal experience, expert opinion, principals of statistics, evidence-based data, and well-designed algorithms and guidelines, if available. It is also important to frequently engage in thoughtful, reflective, and metacognitive practices that can serve to strengthen the clinician's diagnostic skills, with a consequent reduction in the risk of diagnostic error. The purpose of this narrative review was to highlight certain factors that influence the genesis of diagnostic errors. Understanding the dynamic, adaptive, and complex interactions among these factors may assist clinicians, managers of health care systems, and public health policy makers in formulating strategies and guidelines aimed at reducing the incidence and prevalence of the phenomenon of clinical diagnostic error, which poses a public health hazard.

Collaboration with Clinical Laboratory Positively Impacts Proper Test Utilization and Decreases Diagnostic Errors.

BACKGROUND: Diagnostic errors in clinical laboratory testing are extremely common and are major roadblocks in providing timely patient care. The purpose of this project was to investigate whether collaboration between the clinical laboratory, a diagnostic management team (DMT), and physicians who are ordering tests for a patient, resulted in improved test utilization by choosing wisely and better patient care in an academic medical center. METHODS: A retrospective study for a period of 24 months between 2017 and 2019 evaluated whether improvement of test ordering was achieved by timely interventions from the clinical laboratory and the coagulation DMT, resulting in fewer test selection errors. RESULTS: The results showed about 54% improvement in diagnostic errors for coagulation test selection in 634 patients evaluated for bleeding or thrombotic disorders by DMT when compared to previous studies. Furthermore, a total of approximately 2,400 coagulation test orders for patients that were done from July 2017 to July 2018 required intervention in 12% of the cases in the initial six months. When physician education was provided, intervention was needed in only approximately 4% of the cases, an improvement of 67% that was statistically significant at p-value < 0.05. Only 28% of the cases were associated with underutilization or failure to order required initial tests. The generated cost savings from prevention of over and underutilization of laboratory tests was in the order of ~ $16,000. CONCLUSIONS: The clinical laboratory and a DMT can function as an effective decision support system in decreasing errors in diagnostic test selection and facilitate knowledge among care providers regarding test results and interpretation, that may help in proper evidence-based guidelines and disease management.

Why don't we inform patients about the risk of diagnostic errors?

The principles of autonomy and informed consent dictate that patients who undergo a radiological examination should actually be informed about the risk of diagnostic errors. Implementing such a policy could potentially increase the quality of care. However, due to the vast number of radiological examinations that are performed in each hospital each day, financial constraints, and the risk of losing trust, patients, and income if the requirement for informed consent is not imposed by law on a state or national level, it may be challenging to inform patients about the risk of diagnostic errors. Future research is necessary to determine if and how an informed consent procedure for diagnostic errors can be implemented in clinical practice.

Use of nongynecologic cytologic-histologic correlation to identify patterns of error: An institutional experience.

BACKGROUND: Quality management practices empower cytology laboratories to deliver consistent, high-quality patient care. Monitoring of key performance indicators is one way by which laboratories can identify patterns of error and focus their improvement activities. Cytologic-histologic correlation (CHC) identifies error by retrospectively reviewing cytology cases when discordant surgical pathology diagnoses are reported. Analysis of CHC data can elucidate patterns of error and direct quality improvement initiatives. METHODS: CHC data of nongynecologic cytology specimens were reviewed over a 3-year period (2018-2021). Errors were separated by anatomic site and classified as either sampling or interpretive errors. RESULTS: A total of 364 discordant cases were identified out of 4422 cytologic-histologic pairs (a discordant rate of 8%). The majority (272; 75%) were sampling errors, with fewer interpretive errors (92; 25%). Sampling errors were found to occur most commonly in lower urinary tract and lung. Interpretive errors were most commonly found in lower urinary tract and thyroid. CONCLUSIONS: Nongynecologic CHC data can be a valuable resource for cytology laboratories. By studying the types of errors, quality improvement activities can be targeted toward problem areas.

Patterns of Major Frozen Section Interpretation Error: An In-Depth Analysis From a Complex Academic Surgical Pathology Practice.

OBJECTIVES: To establish baseline error rates due to misinterpretation and to identify scenarios in which major errors were most common and potentially preventable. METHODS: Our database was queried over a 3-year period for major discrepancies due to misinterpretation. These were stratified by histomorphologic setting, service, availability/type of prior material, and years of experience and subspecialization of the interpreting pathologist. RESULTS: The overall discordance rate between frozen section (FS) and final diagnoses was 2.9% (199/6,910). Seventy-two errors were due to interpretation, of which 34 (47.2%) were major. Major error rates were highest on the gastrointestinal and thoracic services. Of major discrepancies, 82.4% were rendered in subdisciplines outside those of the FS pathologist. Pathologists with fewer than 10 years' experience made more errors than those with more experience (55.9% vs 23.5%, P = .006). Major error rates were greater for cases without previous material compared to those with a prior glass slide (47.1% vs 17.6%, P = .009). Common histomorphologic scenarios in which disagreements were made involved discriminating mesothelial cells from carcinoma (20.6%) and accurately recognizing squamous carcinoma/severe dysplasia (17.6%). CONCLUSIONS: To improve performance and decrease future misdiagnoses, monitoring discordances should be a continuous component of surgical pathology quality assurance programs.

Computer-Generated ECG Interpretation Challenge.

The predictive accuracy of 12-lead electrocardiogram (ECG) machines is often challenged across all clinical settings. Emergency clinicians must beware of computer-generated ECG reports specifically during the initial medical screening process. Blindly trusting computer-generated reports may delay care for patients with an acute cardiac disorder. Cardiology consultation is always advised, and there should be no hesitation when it comes to abnormal ECGs. However, cardiologists are often consulted on patients based on incorrect ECG interpretation, misdiagnosis, or overdiagnosis by computer-generated reports. The following 12-lead ECGs should encourage emergency providers to take caution and challenge computer-generated reports. The purpose of this exercise is to carefully review a set of 12-lead ECGs and determine whether the computer-generated interpretations are accurate.

Feasibility of a rapid diagnosis discussion tool for reducing misdiagnosis of patients presenting to emergency departments with abdominal pain.

OBJECTIVES: Providing accurate and timely diagnoses is challenging in ED settings. We evaluated the feasibility and effectiveness of a short, structured rapid diagnosis discussion (RaDD) between a patient's initial doctor and a second doctor for patients presenting to ED with abdominal pain. METHODS: Controlled pre-post, mixed-methods pilot study in a metropolitan hospital network in Melbourne, Australia. Comparisons were made between an ED using RaDD for a 1-month period (n = 155) and two control EDs within the same hospital network (n = 2227) using standard practices. A short survey of 27 clinicians was also undertaken. RESULTS: Provisional diagnoses changed in 24.7% (95% confidence interval 19.0, 30.4) of all cases for which a RaDD case report sheet was completed, and clinicians' confidence in their decision-making was significantly higher when using RaDD (r = 0.27). RaDD significantly increased the likelihood that patients would be sent to the short stay unit and have a blood test ordered, and significantly reduced the likelihood that patients would be discharged home from the ED or leave at their own risk. Usage of the RaDD tool was low (25.2% of eligible cases), and qualitative feedback indicated that time limitations inhibited uptake. CONCLUSIONS: RaDD encouraged clinicians to take a more cautious, risk-averse approach to care and improved confidence in their diagnostic decisions. However, cost effectiveness of these outcomes and possible implementation barriers need to be further considered in subsequent studies.