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.

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.

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.

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.

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

Institutional support and better nurse training are key.

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.

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.

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.

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.

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.

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.

Identifying Electronic Health Record Contributions to Diagnostic Error in Ambulatory Settings Through Legal Claims Analysis

This qualitative study analyzes closed legal claims data to determine whether problems with electronic health records are associated with diagnostic errors, in which part of the diagnostic process errors occur, and the specific types of errors that occur.

Misdiagnosis in the Emergency Department: Time for a System Solution

This Viewpoint offers 3 insights in response to the AHRQ report on diagnostic errors made in US emergency departments: focus on the delivery systems instead of individuals, establish ways to set definitions and assess error rates, and design safe delivery systems to prevent errors.

Discriminative Accuracy of the CAPTURE Tool for Identifying Chronic Obstructive Pulmonary Disease in US Primary Care Settings

Importance

Chronic obstructive pulmonary disease (COPD) is underdiagnosed in primary care.

Objective

To evaluate the operating characteristics of the CAPTURE (COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk) screening tool for identifying US primary care patients with undiagnosed, clinically significant COPD.

Design, Setting, and Participants

In this cross-sectional study, 4679 primary care patients aged 45 years to 80 years without a prior COPD diagnosis were enrolled by 7 primary care practice-based research networks across the US between October 12, 2018, and April 1, 2022. The CAPTURE questionnaire responses, peak expiratory flow rate, COPD Assessment Test scores, history of acute respiratory illnesses, demographics, and spirometry results were collected.

Exposure

Undiagnosed COPD.

Main Outcomes and Measures

The primary outcome was the CAPTURE tool's sensitivity and specificity for identifying patients with undiagnosed, clinically significant COPD. The secondary outcomes included the analyses of varying thresholds for defining a positive screening result for clinically significant COPD. A positive screening result was defined as (1) a CAPTURE questionnaire score of 5 or 6 or (2) a questionnaire score of 2, 3, or 4 together with a peak expiratory flow rate of less than 250 L/min for females or less than 350 L/min for males. Clinically significant COPD was defined as spirometry-defined COPD (postbronchodilator ratio of forced expiratory volume in the first second of expiration [FEV1] to forced vital capacity [FEV1:FVC] <0.70 or prebronchodilator FEV1:FVC <0.65 if postbronchodilator spirometry was not completed) combined with either an FEV1 less than 60% of the predicted value or a self-reported history of an acute respiratory illness within the past 12 months.

Results

Of the 4325 patients who had adequate data for analysis (63.0% were women; the mean age was 61.6 years [SD, 9.1 years]), 44.6% had ever smoked cigarettes, 18.3% reported a prior asthma diagnosis or use of inhaled respiratory medications, 13.2% currently smoked cigarettes, and 10.0% reported at least 1 cardiovascular comorbidity. Among the 110 patients (2.5% of 4325) with undiagnosed, clinically significant COPD, 53 had a positive screening result with a sensitivity of 48.2% (95% CI, 38.6%-57.9%) and a specificity of 88.6% (95% CI, 87.6%-89.6%). The area under the receiver operating curve for varying positive screening thresholds was 0.81 (95% CI, 0.77-0.85).

Conclusions and Relevance

Within this US primary care population, the CAPTURE screening tool had a low sensitivity but a high specificity for identifying clinically significant COPD defined by presence of airflow obstruction that is of moderate severity or accompanied by a history of acute respiratory illness. Further research is needed to optimize performance of the screening tool and to understand whether its use affects clinical outcomes.

[Diagnostic work-up, findings, and documentation of multiple sclerosis]

BACKGROUND

Although multiple sclerosis is the most common chronic inflammatory demyelinating disease of the central nervous system, the rate of misdiagnosis in clinical practice is high. This is usually due to the inadequate application of the McDonald criteria and misinterpretation of images.

OBJECTIVE

This review focuses on typical clinical symptoms, choice of magnetic resonance imaging (MRI) sequences, correct application of the McDonald criteria, and finally interpretation of the images.

Providers' and Patients' Perspectives on Diagnostic Errors in the Acute Care Setting

BACKGROUND

Diagnostic errors (DEs) have been studied extensively in ambulatory care, but less work has been done in the acute care setting. In this study, the authors examined health care providers' and patients' perspectives about the classification of DEs, the main causes and scope of DEs in acute care, the main gaps in current systems, and the need for innovative solutions.

METHODS

A qualitative mixed methods study was conducted, including semistructured interviews with health care providers and focus groups with patient advisors. Using grounded theory approach, thematic categories were derived from the interviews and focus groups.

RESULTS

The research team conducted interviews with 17 providers and two focus groups with seven patient advisors. Both providers and patient advisors struggled to define and describe DEs in acute care settings. Although participants agreed that DEs pose a significant risk to patient safety, their perception of the frequency of DEs was mixed. Most participants identified communication failures, lack of comfort with diagnostic uncertainty, incorrect clinical evaluation, and cognitive load as key causes of DEs. Most respondents believed that non-information technology (IT) tools and processes (for example, communication improvement strategies) could significantly reduce DEs.

CONCLUSION

The study findings represent an important supplement to our understanding of DEs in acute care settings and the advancement of a culture of patient safety in the context of patient-centered care and patient engagement. Health care organizations should consider the key factors identified in this study when trying to create a culture that engages clinicians and patients in reducing DEs.

Electronic Health Record Use Issues and Diagnostic Error: A Scoping Review and Framework

BACKGROUND

Diagnostic errors are a major source of patient harm, most of which are caused by cognitive errors and biases. Despite research showing the relationship between software systems and cognitive processes, the impact of the electronic health record (EHR) on diagnostic error remains unknown.

METHODS

We conducted a scoping review of the scientific literature to (1) survey the association between aspects of the EHR and diagnostic error, and (2) through a human-systems integration lens, identify the types of EHR issues and their impact on the stages of the diagnostic process.

RESULTS

We analyzed 11 research articles for the relationship between EHR use and diagnostic error. These articles highlight specific technical, usability, and workflow issues with the EHR that pose risks for diagnostic error at every stage of the diagnostic process.

DISCUSSION

Although technical problems such as EHR interoperability and data integrity pose critical issues for the diagnostic process, usability and workflow issues such as poor display design, and inability to track test results also hamper clinicians' ability to track, process, and act in the diagnostic process. Current research methods have limited coverage over clinical settings, are not standardized, and rarely include measures of patient harm.

CONCLUSIONS

The available evidence shows that EHRs pose risks for diagnostic error throughout the diagnostic process, with most issues involving their incompatibility with providers' cognitive processing. A structured and systematic model of collecting and reporting on these errors is needed to understand how the EHR shapes the diagnostic process and improve diagnostic accuracy.

Defining Diagnostic Error: A Scoping Review to Assess the Impact of the National Academies' Report Improving Diagnosis in Health Care

BACKGROUND

Standards for accurate and timely diagnosis are ill-defined. In 2015, the National Academies of Science, Engineering, and Medicine (NASEM) committee published a landmark report, Improving Diagnosis in Health Care , and proposed a new definition of diagnostic error, "the failure to ( a ) establish an accurate and timely explanation of the patient's health problem(s) or ( b ) communicate that explanation to the patient."

OBJECTIVE

This study aimed to explore how researchers operationalize the NASEM's definition of diagnostic error with relevance to accuracy, timeliness, and/or communication in peer-reviewed published literature.

METHODS

Using the Arskey and O'Malley's framework framework, we identified published literature from October 2015 to February 2021 using Medline and Google Scholar. We also conducted subject matter expert interviews with researchers.

RESULTS

Of 34 studies identified, 16 were analyzed and abstracted to determine how diagnostic error was operationalized and measured. Studies were grouped by theme: epidemiology, patient focus, measurement/surveillance, and clinician focus. Nine studies indicated using the NASEM definition. Of those, 5 studies also operationalized with existing definitions proposed before the NASEM report. Four studies operationalized the components of the NASEM definition and did not cite existing definitions. Three studies operationalized error using existing definitions only. Subject matter experts indicated that the NASEM definition functions as foundation for researchers to conceptualize diagnostic error.

CONCLUSIONS

The NASEM report produced a common understanding of diagnostic error that includes accuracy, timeliness, and communication. In recent peer-reviewed literature, most researchers continue to use pre-NASEM report definitions to operationalize accuracy and timeliness. The report catalyzed the use of patient-centered concepts in the definition, resulting in emerging studies focused on examining errors related to communicating diagnosis to patients.

A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts

OBJECTIVES

To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care.

METHODS

We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution's mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort.

RESULTS

Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen's kappa 0.41) and substantial with the expert panel (Cohen's kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively.

CONCLUSIONS

We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

Pursuit of "endpoint diagnoses" as a cognitive forcing strategy to avoid premature diagnostic closure

Premature closure is often described as a significant contributor to diagnostic error. Therefore, developing strategies to mitigate premature closure could reduce diagnostic errors and improve patient care. Here we propose the novel concept of pursuit of an "endpoint diagnosis" as a cognitive forcing strategy (CFS) for avoiding premature diagnostic closure. We define an "endpoint diagnosis" as an underlying causative explanation for a patient's signs, symptoms, and laboratory and radiographic data that exhausts additional relevant diagnostic evaluation. We have observed four contexts in which the error of not pursuing an endpoint diagnosis most often occurs: (1) diagnoses that appear to result in the same treatment regardless of etiology, (2) cases that are particularly complex, (3) clinical scenarios that are vulnerable to systems errors, and (4) situations in which patients' problems are attributed to uncontrolled underlying risk factors or an exacerbation of a known condition. Additionally, we address why we believe endpoint diagnoses are not universally pursued, delineate when this approach might be particularly useful, attempt to reconcile the potential conflict between accepting diagnostic ambiguity in certain instances and pursuing endpoint diagnoses, and outline possible concerns that might arise with using this CFS, including the possibility of lengthy evaluations resulting in overdiagnosis and overtreatment. Our overarching goal is for this CFS to help clinicians in their daily clinical practice as they seek to optimize their diagnostic skill and patient care.

Use of e-triggers to identify diagnostic errors in the paediatric ED

BACKGROUND

Diagnostic errors (DxEs) are an understudied source of patient harm in children rarely captured in current adverse event reporting systems. Applying electronic triggers (e-triggers) to electronic health records shows promise in identifying DxEs but has not been used in the emergency department (ED) setting.

OBJECTIVES

To assess the performance of an e-trigger and subsequent manual screening for identifying probable DxEs among children with unplanned admission following a prior ED visit and to compare performance to existing incident reporting systems.

DESIGN/METHODS

Retrospective single-centre cohort study of children ages 0-22 admitted within 14 days of a previous ED visit between 1 January 2018 and 31 December 2019. Subjects were identified by e-trigger, screened to identify cases where index visit and hospital discharge diagnoses were potentially related but pathophysiologically distinct, and then these screened-in cases were reviewed for DxE using the SaferDx Instrument. Cases of DxE identified by e-trigger were cross-referenced against existing institutional incident reporting systems.

RESULTS

An e-trigger identified 1915 unplanned admissions (7.7% of 24 849 total admissions) with a preceding index visit. 453 (23.7%) were screened in and underwent review using SaferDx. 92 cases were classified as likely DxEs, representing 0.4% of all hospital admissions, 4.8% among those selected by e-trigger and 20.3% among those screened in for review. Half of cases were reviewed by two reviewers using SaferDx with substantial inter-rater reliability (Cohen's κ=0.65 (95% CI 0.54 to 0.75)). Six (6.5%) cases had been reported elsewhere: two to the hospital's incident reporting system and five to the ED case review team (one reported to both).

CONCLUSION

An e-trigger coupled with manual screening enriched a cohort of patients at risk for DxEs. Fewer than 10% of DxEs were identified through existing surveillance systems, suggesting that they miss a large proportion of DxEs. Further study is required to identify specific clinical presentations at risk of DxEs.

Pancreatic Adenocarcinoma: Diagnostic Errors, Contributing Factors and Solutions

The purpose of this article is to review diagnostic errors in preoperative and post-operative imaging for pancreatic ductal adenocarcinoma (PDAC), discuss contributing factors, and provide solutions that minimize errors. Accurate radiological staging and restaging of PDAC dictates surgical management and errors can have significant negative effects on patient care, such as missed vessel involvement or metastatic disease that would preclude surgery. Familiarity with these errors and their contributing factors improves diagnostic accuracy and ultimately leads to improved patient outcomes.

Checklists to reduce diagnostic error: a systematic review of the literature using a human factors framework

OBJECTIVES

To apply a human factors framework to understand whether checklists reduce clinical diagnostic error have (1) gaps in composition; and (2) components that may be more likely to reduce errors.

DESIGN

Systematic review.

DATA SOURCES

PubMed, EMBASE, Scopus and Web of Science were searched through 15 February 2022.

ELIGIBILITY CRITERIA

Any article that included a clinical checklist aimed at improving the diagnostic process. Checklists were defined as any structured guide intended to elicit additional thinking regarding diagnosis.

DATA EXTRACTION AND SYNTHESIS

Two authors independently reviewed and selected articles based on eligibility criteria. Each extracted unique checklist was independently characterised according to the well-established human factors framework: Systems Engineering Initiative for Patient Safety 2.0 (SEIPS 2.0). If reported, checklist efficacy in reducing diagnostic error (eg, diagnostic accuracy, number of errors or any patient-related outcomes) was outlined. Risk of study bias was independently evaluated using standardised quality assessment tools in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

RESULTS

A total of 30 articles containing 25 unique checklists were included. Checklists were characterised within the SEIPS 2.0 framework as follows: Work Systems subcomponents of Tasks (n=13), Persons (n=2) and Internal Environment (n=3); Processes subcomponents of Cognitive (n=20) and Social and Behavioural (n=2); and Outcomes subcomponents of Professional (n=2). Other subcomponents, such as External Environment or Patient outcomes, were not addressed. Fourteen checklists examined effect on diagnostic outcomes: seven demonstrated improvement, six were without improvement and one demonstrated mixed results. Importantly, Tasks-oriented studies more often demonstrated error reduction (n=5/7) than those addressing the Cognitive process (n=4/10).

CONCLUSIONS

Most diagnostic checklists incorporated few human factors components. Checklists addressing the SEIPS 2.0 Tasks subcomponent were more often associated with a reduction in diagnostic errors. Studies examining less explored subcomponents and emphasis on Tasks, rather than the Cognitive subcomponents, may be warranted to prevent diagnostic errors.

Diagnostic challenges in primary care: Identifying and avoiding cognitive bias

These 4 cases demonstrate how cognitive bias can impede the diagnostic process.

Improving medical residents' self-assessment of their diagnostic accuracy: does feedback help?

When physicians do not estimate their diagnostic accuracy correctly, i.e. show inaccurate diagnostic calibration, diagnostic errors or overtesting can occur. A previous study showed that physicians' diagnostic calibration for easy cases improved, after they received feedback on their previous diagnoses. We investigated whether diagnostic calibration would also improve from this feedback when cases were more difficult. Sixty-nine general-practice residents were randomly assigned to one of two conditions. In the feedback condition, they diagnosed a case, rated their confidence in their diagnosis, their invested mental effort, and case complexity, and then were shown the correct diagnosis (feedback). This was repeated for 12 cases. Participants in the control condition did the same without receiving feedback. We analysed calibration in terms of (1) absolute accuracy (absolute difference between diagnostic accuracy and confidence), and (2) bias (confidence minus diagnostic calibration). There was no difference between the conditions in the measurements of calibration (absolute accuracy, p = .204; bias, p = .176). Post-hoc analyses showed that on correctly diagnosed cases (on which participants are either accurate or underconfident), calibration in the feedback condition was less accurate than in the control condition, p = .013. This study shows that feedback on diagnostic performance did not improve physicians' calibration for more difficult cases. One explanation could be that participants were confronted with their mistakes and thereafter lowered their confidence ratings even if cases were diagnosed correctly. This shows how difficult it is to improve diagnostic calibration, which is important to prevent diagnostic errors or maltreatment.

Pharmacists can improve diagnosis and help prevent diagnostic errors

We present two cases that highlight the role of pharmacists in the diagnostic process and illustrate how a culture of safety and teamwork between pharmacists and physicians can help prevent diagnostic errors.

[Ontwikkelingsstoornissen bij volwassenen: het nut van de holistische theorie]

During the diagnostic process of developmental disorders in adults, diagnosticians often encounter diagnostic uncertainty. In this article, I describe how the holistic theory can be employed in this context in order to prevent diagnostic reductionism. A fictitious vignette illustrates the method.

Histologically benign PI-RADS 4 and 5 lesions contain cancer-associated epigenetic alterations

BACKGROUND

The detection rate of clinically significant prostate cancer has improved with the use of multiparametric magnetic resonance imaging (mpMRI). Yet, even with MRI-guided biopsy 15%-35% of high-risk lesions (Prostate Imaging-Reporting and Data System [PI-RADS] 4 and 5) are histologically benign. It is unclear if these false positives are due to diagnostic/sampling errors or pathophysiological alterations. To better understand this, we tested histologically benign PI-RAD 4 and 5 lesions for common malignant epigenetic alterations.

MATERIALS AND METHODS

MRI-guided in-bore biopsy samples were collected from 45 patients with PI-RADS 4 (n = 31) or 5 (n = 14) lesions. Patients had a median clinical follow-up of 3.8 years. High-risk mpMRI patients were grouped based on their histology into biopsy positive for tumor (BPT; n = 28) or biopsy negative for tumor (BNT; n = 17). From these biopsy samples, DNA methylation of well-known tumor suppressor genes (APC, GSTP1, and RARβ2) was quantified.

RESULTS

Similar to previous work we observed high rates of promoter methylation at GSTP1 (92.7%), RARβ2 (57.3%), and APC (37.8%) in malignant BPT samples but no methylation in benign TURP chips. Interestingly, similar to the malignant samples the BNT biopsies also had increased methylation at the promoter of GSTP1 (78.8%) and RARβ2 (34.6%). However, despite these epigenetic alterations none of these BNT patients developed prostate cancer, and those who underwent repeat mpMRI (n = 8) demonstrated either radiological regression or stability.

CONCLUSIONS

Histologically benign PI-RADS 4 and 5 lesions harbor prostate cancer-associated epigenetic alterations.

Not so FAST-Chest ultrasound underdiagnoses traumatic pneumothorax

BACKGROUND

Ultrasonography for trauma is a widely used tool in the initial evaluation of trauma patients with complete ultrasonography of trauma (CUST) demonstrating equivalence to computed tomography (CT) for detecting clinically significant abdominal hemorrhage. Initial reports demonstrated high sensitivity of CUST for the bedside diagnosis of pneumothorax. We hypothesized that the sensitivity of CUST would be greater than initial supine chest radiograph (CXR) for detecting pneumothorax.

METHODS

A retrospective analysis of patients diagnosed with pneumothorax from 2018 through 2020 at a Level I trauma center was performed. Patients included had routine supine CXR and CUST performed prior to intervention as well as confirmatory CT imaging. All CUST were performed during the initial evaluation in the trauma bay by a registered sonographer. All imaging was evaluated by an attending radiologist. Subgroup analysis was performed after excluding occult pneumothorax. Immediate tube thoracostomy was defined as tube placement with confirmatory CXR within 8 hours of admission.

RESULTS

There were 568 patients screened with a diagnosis of pneumothorax, identifying 362 patients with a confirmed pneumothorax in addition to CXR, CUST, and confirmatory CT imaging. The population was 83% male, had a mean age of 45 years, with 85% presenting due to blunt trauma. Sensitivity of CXR for detecting pneumothorax was 43%, while the sensitivity of CUST was 35%. After removal of occult pneumothorax (n = 171), CXR was 78% sensitive, while CUST was 65% sensitive (p < 0.01). In this subgroup, CUST had a false-negative rate of 36% (n = 62). Of those patients with a false-negative CUST, 50% (n = 31) underwent tube thoracostomy, with 85% requiring immediate placement.

CONCLUSION

Complete ultrasonography of trauma performed on initial trauma evaluation had lower sensitivity than CXR for identification of pneumothorax including clinically significant pneumothorax requiring tube thoracostomy. Using CUST as the primary imaging modality in the initial evaluation of chest trauma should be considered with caution.

LEVEL OF EVIDENCE

Diagnostic Test study, Level IV.

'More than words' - Interpersonal communication, cognitive bias and diagnostic errors

During the diagnostic process, clinicians may make assumptions, prematurely judge or diagnose patients based on their appearance, their speech or how they are portrayed by other clinicians. Such judgements can be a major source of diagnostic error and are often linked to unconscious cognitive biases - faulty quick-fire thinking patterns that impact clinical reasoning. Patient safety is profoundly influenced by cognitive bias and language, i.e. how information is presented or gathered, and then synthesised by clinicians to form and communicate diagnostic decisions. Here, we discuss the intricate links between interpersonal communication, cognitive bias, and diagnostic error from a patient's, a linguist's and clinician's perspective. We propose that through patient engagement and applied health communication research, we can enhance our understanding of how the interplay of communication behaviours, biases and errors can impact upon the patient experience and diagnostic error. In doing so, we provide new avenues for collaborative diagnostic error research striving towards healthcare improvements and safer diagnosis.

Diagnostic statements: a linguistic analysis of how clinicians communicate diagnosis

OBJECTIVES

To investigate from a linguistic perspective how clinicians deliver diagnosis to patients, and how these statements relate to diagnostic accuracy.

METHODS

To identify temporal and discursive features in diagnostic statements, we analysed 16 video-recorded interactions collected during a practice high-stakes exam for internationally trained clinicians (25% female, n=4) to gain accreditation to practice in Australia. We recorded time spent on history-taking, examination, diagnosis and management. We extracted and deductively analysed types of diagnostic statements informed by literature.

RESULTS

Half of the participants arrived at the correct diagnosis, while the other half misdiagnosed the patient. On average, clinicians who made a diagnostic error took 30 s less in history-taking and 30 s more in providing diagnosis than clinicians with correct diagnosis. The majority of diagnostic statements were evidentialised (describing specific observations (n=24) or alluding to diagnostic processes (n=7)), personal knowledge or judgement (n=8), generalisations (n=6) and assertions (n=4). Clinicians who misdiagnosed provided more specific observations (n=14) than those who diagnosed correctly (n=9).

CONCLUSIONS

Interactions where there is a diagnostic error, had shorter history-taking periods, longer diagnostic statements and featured more evidence. Time spent on history-taking and diagnosis, and use of evidentialised diagnostic statements may be indicators for diagnostic accuracy.

Some common fallacies in arguments from M/EEG data

Like all humans, M/EEG researchers commit certain fallacies or mistakes in reasoning. This article surveys seven well-known but still common fallacies, including reverse inference, hasty generalization, hasty exclusion, inferring from group to individual, inferring from correlation to causation, affirming a disjunct, and false dichotomy. These fallacies are illustrated with classic EEG research by Libet and collaborators, but many researchers (not just Libet) continue to commit them in all areas of research (not just M/EEG). This article gives practical suggestions about how to spot and avoid each fallacy.

Blastic plasmacytoid dendritic-cell neoplasia: a challenging case report

Blastic plasmacytoid dendritic-cell neoplasm (BPDCN) is an extremely rare disease that originates from dendritic cells and is associated with a poor overall survival (OS). Diagnostic and therapeutic standards are less well-established in comparison to other leukemic conditions and standards of care are lacking. Morphologic and molecular similarities to acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML) are hard to distinguish. We here report a BPDCN patient with a long, challenging diagnostic period. While bone marrow biopsies initially failed to prove the correct diagnosis, a cutaneous biopsy finally identified a CD45⁺/CD56⁺/CD4⁺/CD123⁺/CD33⁺/MPO⁻ population suggestive of BPDCN which was confirmed by flow cytometry. Molecular analysis revealed an ASXL-1, TET2 and SRSF2-mutation, cytogenetic analysis showed a normal karyotype. Treatment with the recently approved CD123-cytotoxin Tagraxofusp showed initially a very good response. This case reflects diagnostic and therapeutic difficulties in BPDCN as very rare, easily misdiagnosed neoplasia and the need for precise diagnostic care.

Gastric Emptying Scans: Poor Adherence to National Guidelines

BACKGROUND

Accurately diagnosing gastroparesis relies upon gastric emptying scintigraphy (GES) being performed correctly. Jointly published protocol guidelines have long been available; however, the extent to which practitioners adhere to these guidelines is unknown.

AIMS

This study aimed to assess national compliance with established GES protocol guidelines.

METHODS

We developed a questionnaire addressing the key protocol measures outlined in the Consensus Recommendations for Gastric Emptying Scintigraphy. Survey questions addressed patient information collection (15), patient preparation and procedure protocol (16), meal content and preparation (7), imaging (3), interpretation (4), reporting (7), and institutional demographic data (7). The anonymous questionnaire was distributed electronically to members of the Society of Nuclear Medicine and Medical Imaging (SNMMI) and non-member recipients of the SNMMI daily email newsletter. One response per medical institution was permitted.

RESULTS

A total of 121 out of 872 potential medical institutions (MI) responded (13.9%); 49 (40.4%) were academic/teaching medical centers. The annual number (mean) of GES procedures was 199.9 (range 5-2000 GES/year). On average, MI performed 33.5/52 (64%) of protocol measures according to guidelines while academic medical centers performed 31.5/52 (61%) of protocol measures according to guidelines. Only 4 out of 88 MI (4.5%) performed GES while adhering to three critical measures: validated study duration; controlled blood glucose levels; and proper restriction of medications.

CONCLUSIONS

Low compliance with GES protocol guidelines, even among academic medical centers, raises the likely possibility of misdiagnosis and improper management of upper gastrointestinal symptoms. These results highlight a need for increased awareness of protocol guidelines for gastric scintigraphy.

Error and cognitive bias in diagnostic radiology

The above article was posted prematurely on 31 August 2021. The article will be made fully available at a later date.