The incidence of diagnostic error in medicine

Effect of contextual factors on the prevalence of diagnostic errors among patients managed by physicians of the same specialty: a single-centre retrospective observational study

BACKGROUND

There has been growing recognition that contextual factors influence the physician's cognitive processes. However, given that cognitive processes may depend on the physicians' specialties, the effects of contextual factors on diagnostic errors reported in previous studies could be confounded by difference in physicians.

OBJECTIVE

This study aimed to clarify whether contextual factors such as location and consultation type affect diagnostic accuracy.

METHODS

We reviewed the medical records of 1992 consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine in a university hospital between 1 January and 31 December 2019. Diagnostic processes were assessed using the Revised Safer Dx Instrument. Patients were categorised into three groups according to contextual factors (location and consultation type): (1) referred patients with scheduled visit to the outpatient department; (2) patients with urgent visit to the outpatient department; and (3) patients with emergency visit to the emergency room. The effect of the contextual factors on the prevalence of diagnostic errors was investigated using logistic regression analysis.

RESULTS

Diagnostic errors were observed in 12 of 534 referred patients with scheduled visit to the outpatient department (2.2%), 3 of 599 patients with urgent visit to the outpatient department (0.5%) and 13 of 859 patients with emergency visit to the emergency room (1.5%). Multivariable logistic regression analysis showed a significantly higher prevalence of diagnostic errors in referred patients with scheduled visit to the outpatient department than in patients with urgent visit to the outpatient department (OR 4.08, p=0.03), but no difference between patients with emergency and urgent visit to the emergency room and outpatient department, respectively.

CONCLUSION

Contextual factors such as consultation type may affect diagnostic errors; however, since the differences in the prevalence of diagnostic errors were small, the effect of contextual factors on diagnostic accuracy may be small in physicians working in different care settings.

Postmortem metabolomics as a high-throughput cause-of-death screening tool for human death investigations

Autopsy rates are declining globally, impacting cause-of-death (CoD) diagnoses and quality control. Postmortem metabolomics was evaluated for CoD screening using 4,282 human cases, encompassing CoD groups: acidosis, drug intoxication, hanging, ischemic heart disease (IHD), and pneumonia. Cases were split 3:1 into training and test sets. High-resolution mass spectrometry data from femoral blood were analyzed via orthogonal-partial least squares discriminant analysis (OPLS-DA) to discriminate CoD groups. OPLS-DA achieved an R2 = 0.52 and Q2 = 0.30, with true-positive prediction rates of 68% and 65% for training and test sets, respectively, across all groups. Specificity-optimized thresholds predicted 56% of test cases with a unique CoD, average 45% sensitivity, and average 96% specificity. Prediction accuracies varied: 98.7% for acidosis, 80.5% for drug intoxication, 81.6% for hanging, 73.1% for IHD, and 93.6% for pneumonia. This study demonstrates the potential of large-scale postmortem metabolomics for CoD screening, offering high specificity and enhancing throughput and decision-making in human death investigations.

Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced

BACKGROUND

Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases.

FRAMEWORK

A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis.

DESIGN

Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease.

RESULTS

When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment.

CONCLUSIONS

A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself.

HIGHLIGHTS

Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.

Differential screen and treatment of sternocleidomastoid syndrome versus eagle syndrome: a case report

BACKGROUND/PURPOSE

Differential screening is a complex process in chronic pain conditions. There is significant uncertainty that surrounds the pathophysiology of many chronic pain syndromes that may lead to misdiagnosis and treatment failures. Such differential screening is even more challenging where there is regional overlapping from surrounding tissues. This case report chronicles the differential screening and treatment of a patient with sternocleidomastoid syndrome (SCMS) originally diagnosed as Eagle's syndrome (ES).

CASE DESCRIPTION

A 55-year-old woman, referred to a physical therapist (PT) by an ear, nose and throat (ENT) physician with the diagnosis of ES. The patient complained of yearlong left-sided otalgia, blurred vision, excessive lacrimation, dysphagia, hyperesthesia on the left side of the face, unilateral temporal headaches, and both left mandibular and anterior neck pain.

OUTCOMES

The PT examination revealed the patient did not exhibit hallmark findings for clinical confirmation of ES and instead demonstrated multiple signs consistent with SCMS.

DISCUSSION

Manual therapy techniques and therapeutic exercises resolved the patient's year-long chronic symptoms within 6 sessions.

The Big Three diagnostic errors through reflections of Japanese internists

OBJECTIVES

To analyze the Big Three diagnostic errors (malignant neoplasms, cardiovascular diseases, and infectious diseases) through internists' self-reflection on their most memorable diagnostic errors.

METHODS

This secondary analysis study, based on a web-based cross-sectional survey, recruited participants from January 21 to 31, 2019. The participants were asked to recall the most memorable diagnostic error cases in which they were primarily involved. We gathered data on internists' demographics, time to error recognition, and error location. Factors causing diagnostic errors included environmental conditions, information processing, and cognitive bias. Participants scored the significance of each contributing factor on a Likert scale (0, unimportant; 10, extremely important).

RESULTS

The Big Three comprised 54.1 % (n=372) of the 687 cases reviewed. The median physician age was 51.5 years (interquartile range, 42-58 years); 65.6 % of physicians worked in hospital settings. Delayed diagnoses were the most common among malignancies (n=64, 46 %). Diagnostic errors related to malignancy were frequent in general outpatient settings on weekdays and in the mornings and were not identified for several months following the event. Environmental factors often contributed to cardiovascular disease-related errors, which were typically identified within days in emergency departments, during night shifts, and on holidays. Information gathering and interpretation significantly impacted infectious disease diagnoses.

CONCLUSIONS

The Big Three accounted for the majority of cases recalled by Japanese internists. The most relevant contributing factors were different for each of the three categories. Addressing these errors may require a unique approach based on the disease associations.

Fatigue and vigilance in medical experts detecting breast cancer

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.

Frontline Providers' and Patients' Perspectives on Improving Diagnostic Safety in the Emergency Department: A Qualitative Study

BACKGROUND

Few studies have described the insights of frontline health care providers and patients on how the diagnostic process can be improved in the emergency department (ED), a setting at high risk for diagnostic errors. The authors aimed to identify the perspectives of providers and patients on the diagnostic process and identify potential interventions to improve diagnostic safety.

METHODS

Semistructured interviews were conducted with 10 ED physicians, 15 ED nurses, and 9 patients/caregivers at two separate health systems. Interview questions were guided by the ED-Adapted National Academies of Sciences, Engineering, and Medicine Diagnostic Process Framework and explored participant perspectives on the ED diagnostic process, identified vulnerabilities, and solicited interventions to improve diagnostic safety. The authors performed qualitative thematic analysis on transcribed interviews.

RESULTS

The research team categorized vulnerabilities in the diagnostic process and intervention opportunities based on the ED-Adapted Framework into five domains: (1) team dynamics and communication (for example, suboptimal communication between referring physicians and the ED team); (2) information gathering related to patient presentation (for example, obtaining the history from the patients or their caregivers; (3) ED organization, system, and processes (for example, staff schedules and handoffs); (4) patient education and self-management (for example, patient education at discharge from the ED); and (5) electronic health record and patient portal use (for example, automatic release of test results into the patient portal). The authors identified 33 potential interventions, of which 17 were provider focused and 16 were patient focused.

CONCLUSION

Frontline providers and patients identified several vulnerabilities and potential interventions to improve ED diagnostic safety. Refining, implementing, and evaluating the efficacy of these interventions are required.

Collaborative clinical reasoning: a scoping review

Background

Collaborative clinical reasoning (CCR) among healthcare professionals is crucial for maximizing clinical outcomes and patient safety. This scoping review explores CCR to address the gap in understanding its definition, structure, and implications.

Methods

A scoping review was undertaken to examine CCR related studies in healthcare. Medline, PsychInfo, SciVerse Scopus, and Web of Science were searched. Inclusion criteria included full-text articles published between 2011 to 2020. Search terms included cooperative, collaborative, shared, team, collective, reasoning, problem solving, decision making, combined with clinical or medicine or medical, but excluded shared decision making.

Results

A total of 24 articles were identified in the review. The review reveals a growing interest in CCR, with 14 articles emphasizing the decision-making process, five using Multidisciplinary Team-Metric for the Observation of Decision Making (MDTs-MODe), three exploring CCR theory, and two focusing on the problem-solving process. Communication, trust, and team dynamics emerge as key influencers in healthcare decision-making. Notably, only two articles provide specific CCR definitions.

Conclusions

While decision-making processes dominate CCR studies, a notable gap exists in defining and structuring CCR. Explicit theoretical frameworks, such as those proposed by Blondon et al. and Kiesewetter et al., are crucial for advancing research and understanding CCR dynamics within collaborative teams. This scoping review provides a comprehensive overview of CCR research, revealing a growing interest and diversity in the field. The review emphasizes the need for explicit theoretical frameworks, citing Blondon et al. and Kiesewetter et al. The broader landscape of interprofessional collaboration and clinical reasoning requires exploration.

Diagnostic algorithm allows for a scientifically robust and reliable retrospective diagnosis using textual evidence from mid-19th century Basel, Switzerland

OBJECTIVE

Diagnosing disease from the past using historic textual sources can be controversial as to its accuracy. To overcome these objections, an empirical approach to the historical clinical data was developed. The approach follows a standardised, objective, and systematic evaluation, satisfying the requirements of the philosophy of science.

MATERIAL

Physician-managed medical records of mid-19th century patients reported to have suffered from tuberculosis.

METHOD

A diagnostic algorithm, quantifying clinical data into a scoring system, was developed based on criteria recorded in the medical sources. The findings were compared to the autopsy results using the Receiver Operating Characteristics method.

RESULTS

The generated scoring system correctly predicted the diagnosis of tuberculosis in 86% of patients in the study. 6% false negatives and 8% false positives were predicted.

CONCLUSIONS

It is possible to retrospectively diagnose in a reliable and scientifically robust manner under certain conditions. It is important to embed the clinical data into the historical context. A general rejection of retrospective diagnosis is unsubstantiated. Well-designed, disease-specific, and source adapted medical scoring systems are new approaches and overcome criticism raised against retrospective diagnosis.

SIGNIFICANCE

This new approach utilises diverse historic sources and potentially leads to reliable retrospective diagnosis of most common diseases of the past.

LIMITATIONS

Selection bias of the records allocated. Quality of the historic sources utilized. Restricted statistical assessment potential of historic sources.

SUGGESTIONS FOR FURTHER RESEARCH

Development of disease- and epoch-specific medical score systems.

Misdiagnosis of Acute Appendicitis Cases in the Emergency Room

Acute appendicitis (AA) is one of the most frequent surgical emergencies, especially in pediatric populations, with its misdiagnosis in emergency settings presenting significant health risks. This misdiagnosis leads to various complications, such as delayed treatment or unnecessary surgeries. Factors such as age, gender, and comorbidities contribute to diagnostic errors, leading to complications such as peritonitis and increased negative appendectomy rates. This underscores the importance of accurate clinical assessment and awareness of common pitfalls, such as cognitive biases and over-reliance on laboratory tests. This review delves into the prevalence of AA misdiagnosis, its health burden, and the challenges inherent in the diagnostic process. It scrutinizes the effectiveness of different diagnostic approaches, including clinical assessment and imaging techniques. The treatment paradigms for AA are also explored, focusing on surgical interventions and the potential of conservative treatments using antibiotics. The review underscores the criticality of precise diagnosis in preventing adverse outcomes and ensuring effective treatment.

Consensus for a primary care clinical decision-making tool for assessing, diagnosing, and managing low back pain in Alberta, Canada

BACKGROUND

Low back pain (LBP) is a common condition causing disability and high healthcare costs. Alberta faces challenges with unnecessary referrals to specialists and long wait times. A province-wide standardized clinical care pathway based on evidence-based best practices can improve efficiency, reduce wait times, and enhance patient outcomes. Implementing such pathways has shown success in other areas of healthcare in Alberta. This study developed a clinical decision-making pathway to standardize care and minimize uncertainty in assessment, diagnosis, and management.

METHODS

A systematic rapid review identified existing tools and evidence that could support a comprehensive LBP clinical decision-making tool. Forty-seven healthcare professionals participated in four rounds of a modified Delphi approach to reach consensus on the assessment, diagnosis, and management of patients presenting to primary care with LBP in Alberta, Canada. This project was a collaborative effort between Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJHSCN) and the Alberta Bone and Joint Health Institute (ABJHI).

RESULTS

A province-wide expert panel consisting of professionals from different health disciplines and regions collaborated to develop an LBP clinical decision-making tool. This tool presents clinical care pathways for acute, subacute, and chronic LBP. It also provides guidance for history-taking, physical examination, patient education, and management.

CONCLUSIONS

This clinical decision-making tool will help to standardize care, provide guidance on the diagnosis and management of LBP, and assist in clinical decision-making for primary care providers in both public and private sectors.

Diagnostic odyssey of Guillain-Barré syndrome in children

BACKGROUND AND OBJECTIVES

A gap exists between difficulty in diagnosis and importance of early recognition and intervention in pediatric Guillain-Barré syndrome (GBS). Therefore, this study aimed to establish a diagnostic odyssey plot that allows "at-a-glance" overview of the diagnostic odyssey of GBS in children, including overall diagnostic delay, physician-related and patient-related diagnostic delays, and length and frequency of diagnostic errors.

METHODS

In this single-center retrospective cohort study, standardized data were obtained from children with GBS from 2003 to 2020. Overall diagnostic delay (time between symptom onset and diagnosis), physician-related diagnostic delay (time between the first medical visit and diagnosis), and patient-related diagnostic delay (time between symptom onset and the first medical visit) were analyzed.

RESULTS

The study examined a total of 21 patients (11 men, median age 4.5 years). Overall, there were 40 misdiagnoses among 17 patients, while four were diagnosed correctly at the first visit. The overall diagnostic delay was 9 days [interquartile range (IQR), 6-17 days]. Physician-related diagnostic delay, but not patient-related diagnostic delay, was correlated with the overall diagnostic delay. Patients in the late-diagnosed group were more frequently misdiagnosed during their diagnostic odyssey than patients in the other groups. Risk factors associated with diagnostic delay included delayed onset of weakness and sensory deficits, absence of swallowing problems, and misdiagnosis as orthopedic disorders or viral infections.

DISCUSSION

A unique diagnostic odyssey exists in pedaitric GBS. Several clinical risk factors were associated with the diagnostic delay.

Prevalence of atypical presentations among outpatients and associations with diagnostic error

OBJECTIVES

This study aimed to assess the prevalence of atypical presentations and their association with diagnostic errors in various diseases.

METHODS

This retrospective observational study was conducted using cohort data between January 1 and December 31, 2019. Consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine at a university hospital in Japan were included. Patients for whom the final diagnosis was not confirmed were excluded. Primary outcomes were the prevalence of atypical presentations, and the prevalence of diagnostic errors in groups with typical and atypical presentations. Diagnostic errors and atypical presentations were assessed using the Revised Safer Dx Instrument. We performed primary analyses using a criterion; the average score of less than five to item 12 of two independent reviewers was an atypical presentation (liberal criterion). We also performed additional analyses using another criterion; the average score of three or less to item 12 was an atypical presentation (conservative criterion).

RESULTS

A total of 930 patients were included out of a total of 2022 eligible. The prevalence of atypical presentation was 21.7 and 6.7 % when using liberal and conservative criteria for atypical presentation, respectively. Diagnostic errors (2.8 %) were most commonly observed in the cases with slight to moderate atypical presentation. Atypical presentation was associated with diagnostic errors with the liberal criterion for atypical presentation; however, this diminished with the conservative criterion.

CONCLUSIONS

An atypical presentation was observed in up to 20 % of outpatients with a confirmed diagnosis, and slight to moderate atypical presentation may be the highest risk population for diagnostic errors.

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.

Perceptions of medical error among general practitioners in rural China: a qualitative interview study

BACKGROUND

Medical error (ME) is a serious public health problem and a leading cause of death. The reported adverse incidents in China were much less than western countries, and the research on patient safety in rural China's primary care institutions was scarce. This study aims to identify the factors contributing to the under-reporting of ME among general practitioners in township health centres (THCs).

METHODS

A qualitative semi-structured interview study was conducted with 31 general practitioners working in 30 THCs across 6 provinces. Thematic analysis was conducted using a grounded theory approach.

RESULTS

The understanding of ME was not unified, from only mild consequence to only almost equivalent to medical malpractice. Common coping strategies for THCs after ME occurs included concealing and punishment. None of the participants reported adverse events through the National Clinical Improvement System website since they worked in THCs. Discussions about ME always focused on physicians rather than the system.

CONCLUSIONS

The low reported incidence of ME could be explained by unclear concept, unawareness and blame culture. It is imperative to provide supportive environment, patient safety training and good examples of error-based improvements to rural primary care institutions so that ME could be fully discussed, and systemic factors of ME could be recognised and improved there in the future.

Reflection on medical errors: A thematic analysis

BACKGROUND

As there is a need to prepare doctors to minimize errors, we wanted to determine how doctors go about reflecting upon their medical errors.

METHODS

We conducted a thematic analysis of the published reflection reports of 12 Dutch doctors about the errors they had made. Three questions guided our analysis: What triggers doctors to become aware of their errors? What topics do they reflect upon to explain what happened? What lessons do doctors learn after reflecting on their error?

RESULTS

We found that the triggers which made doctors aware of their errors were mostly death and/or a complication. This suggests that the trigger to recognize that something might be wrong came too late. The 12 doctors cited 20 topics' themes that explained the error and 16 lessons-learnt themes. The majority of the topics and lessons learnt were related more to the doctors' inner worlds (personal features) than to the outer world (environment).

CONCLUSION

To minimize errors, doctors should be trained to become earlier and in time aware of distracting and misleading features that might interfere with their clinical reasoning. This training should focus on reflection in action and on discovering more about doctors' personal inner world to identify vulnerabilities.

Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs

OBJECTIVES

The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists.

MATERIALS AND METHODS

A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.

RESULTS

The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.

CLINICAL SIGNIFICANCE

We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.

Angina and Non-Obstructive Coronary Artery (ANOCA) Patients with Coronary Vasomotor Disorders

Angina and Non-Obstructive Coronary Artery (ANOCA) patients often lack a clear explanation for their symptoms, and are frequently discharged with the label of "unspecified chest pain", despite the availability of functional coronary angiography (provocative spasm and microvascular function testing) to identify potential underlying coronary vasomotor disorders. This study compared the outcomes of ANOCA patients with a coronary vasomotor disorder diagnosis post elective coronary angiography to patients discharged with unspecified chest pain. Using the CADOSA (Coronary Angiogram Database of South Australia) registry, consecutive symptomatic patients (n = 7555) from 2012 to 2018 underwent elective angiography; 30% had ANOCA (stenosis <50%). Of this cohort, 9% had documented coronary vasomotor disorders diagnosed, and 91% had unspecified chest pain. Patients with coronary vasomotor disorders were younger and had a similar female prevalence compared with those with unspecified chest pain. New prescriptions of calcium channel blockers and long-acting nitrates were more common for the coronary vasomotor cohort at discharge. In the 3 years following angiography, both groups had similar all-cause mortality rates. However, those with coronary vasomotor disorders had higher rates of emergency department visits for chest pain (39% vs. 15%, p < 0.001) and readmissions for chest pain (30% vs. 10%, p < 0.001) compared with those with unspecified chest pain. This real-world study emphasizes the importance of identifying high-risk ANOCA patients for personalized management to effectively address their symptoms.

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Diagnostic error among vulnerable populations presenting to the emergency department with cardiovascular and cerebrovascular or neurological symptoms: a systematic review

BACKGROUND

Diagnostic error (DE) is a common problem in clinical practice, particularly in the emergency department (ED) setting. Among ED patients presenting with cardiovascular or cerebrovascular/neurological symptoms, a delay in diagnosis or failure to hospitalise may be most impactful in terms of adverse outcomes. Minorities and other vulnerable populations may be at higher risk of DE. We aimed to systematically review studies reporting the frequency and causes of DE in under-resourced patients presenting to the ED with cardiovascular or cerebrovascular/neurological symptoms.

METHODS

We searched EBM Reviews, Embase, Medline, Scopus and Web of Science from 2000 through 14 August 2022. Data were abstracted by two independent reviewers using a standardised form. The risk of bias (ROB) was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach.

RESULTS

Of the 7342 studies screened, we included 20 studies evaluating 7436,737 patients. Most studies were conducted in the USA, and one study was multicountry. 11 studies evaluated DE in patients with cerebrovascular/neurological symptoms, 8 studies with cardiovascular symptoms and 1 study examined both types of symptoms. 13 studies investigated missed diagnoses and 7 studies explored delayed diagnoses. There was significant clinical and methodological variability, including heterogeneity of DE definitions and predictor variable definitions as well as methods of DE assessment, study design and reporting.Among the studies evaluating cardiovascular symptoms, black race was significantly associated with higher odds of DE in 4/6 studies evaluating missed acute myocardial infarction (AMI)/acute coronary syndrome (ACS) diagnosis compared with white race (OR from 1.18 (1.12-1.24) to 4.5 (1.8-11.8)). The association between other analysed factors (ethnicity, insurance and limited English proficiency) and DE in this domain varied from study to study and was inconclusive.Among the studies evaluating DE in patients with cerebrovascular/neurological symptoms, no consistent association was found indicating higher or lower odds of DE. Although some studies showed significant differences, these were not consistently in the same direction.The overall ROB was low for most included studies; however, the certainty of evidence was very low, mostly due to serious inconsistency in definitions and measurement approaches across studies.

CONCLUSIONS

This systematic review demonstrated consistent increased odds of missed AMI/ACS diagnosis among black patients presenting to the ED compared with white patients in most studies. No consistent associations between demographic groups and DE related to cerebrovascular/neurological diagnoses were identified. More standardised approaches to study design, measurement of DE and outcomes assessment are needed to understand this problem among vulnerable populations.

TRIAL REGISTRATION NUMBER

The study protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO 2020 CRD42020178885 and is available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178885.

Use of nerve conduction studies in carpal tunnel syndrome

This review article examines the use of nerve conduction studies in the management of carpal tunnel syndrome. These studies should be understood not as a test that determines the diagnosis but as a measure of impaired nerve function. They are sensitive indicators of local demyelination and axonal loss that can detect and quantify these changes before the appearance of clinical signs, providing information that cannot be obtained with the unaided senses of the physician, nor by any other investigation. They are the best available indicator of overall disease severity, correlating with symptoms and anatomical change in the median nerve. They have some prognostic value for surgical outcome and are sufficiently sensitive to change for the evaluation of treatment response. When surgery does not yield the expected improvement in symptoms, they can help to establish whether decompression has been achieved provided preoperative results are available for comparison.

Multicenter evaluation of a method to identify delayed diagnosis of diabetic ketoacidosis and sepsis in administrative data

OBJECTIVES

To derive a method of automated identification of delayed diagnosis of two serious pediatric conditions seen in the emergency department (ED): new-onset diabetic ketoacidosis (DKA) and sepsis.

METHODS

Patients under 21 years old from five pediatric EDs were included if they had two encounters within 7 days, the second resulting in a diagnosis of DKA or sepsis. The main outcome was delayed diagnosis based on detailed health record review using a validated rubric. Using logistic regression, we derived a decision rule evaluating the likelihood of delayed diagnosis using only characteristics available in administrative data. Test characteristics at a maximal accuracy threshold were determined.

RESULTS

Delayed diagnosis was present in 41/46 (89 %) of DKA patients seen twice within 7 days. Because of the high rate of delayed diagnosis, no characteristic we tested added predictive power beyond the presence of a revisit. For sepsis, 109/646 (17 %) of patients were deemed to have a delay in diagnosis. Fewer days between ED encounters was the most important characteristic associated with delayed diagnosis. In sepsis, our final model had a sensitivity for delayed diagnosis of 83.5 % (95 % confidence interval 75.2-89.9) and specificity of 61.3 % (95 % confidence interval 56.0-65.4).

CONCLUSIONS

Children with delayed diagnosis of DKA can be identified by having a revisit within 7 days. Many children with delayed diagnosis of sepsis may be identified using this approach with low specificity, indicating the need for manual case review.

Understanding diagnostic conversations in clinical practice: A systematic review

OBJECTIVE

Summarize frameworks to understand diagnostic conversations and assessments of diagnostic conversations in practice.

METHODS

We systematically searched MEDLINE, Scopus, Cochrane, and other databases from inception to July 2022 for reports of diagnostic conversations. Two authors independently reviewed studies for eligibility, assessed methodological quality with the mixed methods appraisal tool and extracted information related to study characteristics, frameworks and components evaluated in assessments of diagnostic conversations and results.

RESULTS

Eight studies were included. One study reported an empiric framework of diagnostic conversations that included the following components: identifying the problem that requires diagnosis, obtaining information, and delivering the diagnosis and treatment plan. Thematic analyses highlighted communication between patients and clinicians as central in diagnostic conversations as it allows a) patient's presentation of their symptoms which guide subsequent diagnostic steps, b) negotiation of the significance of the patient's symptoms through conversation and c) introducing and resolving diagnostic uncertainty.

CONCLUSION

Despite the importance of diagnostic conversation only one empiric framework described its components. Additionally, limited available evidence suggests patients can have an important role in the diagnostic process that expands beyond patients as an information source.

PRACTICE IMPLICATIONS

Patients should be included as active partners in co-development of diagnostic plans of care.

Addressing the ethical problem of underdiagnosis in the post-pandemic Canadian healthcare system

Proper diagnosis is essential for effective treatment, yet in Canada health conditions are commonly underdiagnosed at all levels of the health system, meaning that they go undiagnosed or are diagnosed only after a delay. Underdiagnosis leads to inadequate treatment and potentially insufficient recovery and rehabilitation, as well as costly inefficiencies, such as repeat medical visits. Moreover, disparities in underdiagnosis in which vulnerable groups, such as women and Indigenous persons, are properly diagnosed at lower rates worsen existing inequities, which threatens the overall health of the general population. As health leaders and policy-makers seek to strengthen Canada's strained healthcare system, it will be important to address underdiagnosis and its causes, including systematic bias. Providing timely and accurate diagnoses for all patients is an essential component of delivering high quality, efficient, ethical, and cost-effective healthcare. The Canadian College of Health Leaders' Code of Ethics offers a framework for addressing underdiagnosis equitably. Utilizing the framework, suggestions are made for actions that can be taken at all levels of the health system to reduce underdiagnosis.

Is language an issue? Accuracy of the German computerized diagnostic decision support system ISABEL and cross-validation with the English counterpart

OBJECTIVES

Existing computerized diagnostic decision support tools (CDDS) accurately return possible differential diagnoses (DDx) based on the clinical information provided. The German versions of the CDDS tools for clinicians (Isabel Pro) and patients (Isabel Symptom Checker) from ISABEL Healthcare have not been validated yet.

METHODS

We entered clinical features of 50 patient vignettes taken from an emergency medical text book and 50 real cases with a confirmed diagnosis derived from the electronic health record (EHR) of a large academic Swiss emergency room into the German versions of Isabel Pro and Isabel Symptom Checker. We analysed the proportion of DDx lists that included the correct diagnosis.

RESULTS

Isabel Pro and Symptom Checker provided the correct diagnosis in 82 and 71 % of the cases, respectively. Overall, the correct diagnosis was ranked in 71 , 61 and 37 % of the cases within the top 20, 10 and 3 of the provided DDx when using Isabel Pro. In general, accuracy was higher with vignettes than ED cases, i.e. listed the correct diagnosis more often (non-significant) and ranked the diagnosis significantly more often within the top 20, 10 and 3. On average, 38 ± 4.5 DDx were provided by Isabel Pro and Symptom Checker.

CONCLUSIONS

The German versions of Isabel achieved a somewhat lower accuracy compared to previous studies of the English version. The accuracy decreases substantially when the position in the suggested DDx list is taken into account. Whether Isabel Pro is accurate enough to improve diagnostic quality in clinical ED routine needs further investigation.

Associations Between Hospitalist Shift Busyness, Diagnostic Confidence, and Resource Utilization: A Pilot Study

OBJECTIVES

Hospitalized patients are at risk for diagnostic errors. Hospitalists caring for these patients are often multitasking when overseeing patient care. We aimed to measure hospitalist workload and understand its influences on diagnostic performance in a real-world clinical setting.

METHODS

We conducted a single-center, prospective, pilot observational study of hospitalists admitting new patients to the hospital. Hospitalists completed an abridged Mindful Attention Awareness Tool and a survey about diagnostic confidence at shift completion. Data on differential diagnoses and resource utilization (e.g., laboratory, imaging tests ordered, and consultations) were collected from the medical record. The number of admissions and paging volume per shift were used as separate proxies for shift busyness. Data were analyzed using linear mixed effects models (continuous outcomes) or mixed effects logistic regression (dichotomous outcomes).

RESULTS

Of the 53 hospitalists approached, 47 (89%) agreed to participate; complete data were available for 37 unique hospitalists who admitted 160 unique patients. Increases in admissions (odds ratio, 1.99; 95% confidence interval [CI], 1.04 to 3.82; P = 0.04) and pages (odds ratio, 1.11; 95% CI, 1.02 to 1.21; P = 0.01) were associated with increased odds of hospitalists finding it "difficult to focus on what is happening in the present." Increased pages was associated with a decrease in the number of listed differential diagnoses (coefficient, -0.02; 95% CI, -0.04 to -0.003; P = 0.02).

CONCLUSIONS

Evaluation of hospitalist busyness and its associations with factors that may influence diagnosis in a real-world environment was feasible and demonstrated important implications on physician focus and differential diagnosis.

The Implementation of Diagnostic Assessment in Breast Lump Cases: A Cross-Sectional Study in Sragen, Indonesia

Introduction Triple assessment, consisting of clinical breast examination, breast imaging, and fine-needle aspiration biopsy (FNAB), is the gold standard for breast lump diagnosis to avoid diagnostic errors. However, current diagnostic practices for breast lump cases in Indonesia are widely variable and evidence for triple assessment implementation is lacking. We aimed to explore the implementation of diagnostic assessments in breast lump cases, its influencing factors, and its association with diagnostic error. Methods This cross-sectional study consecutively recruited 364 females with breast lumps who underwent surgery in Soehadi Prijonegoro Public Hospital, Sragen, Indonesia. Data were retrospectively collected from patient's medical records. Diagnostic assessments were classified as single assessment with clinical breast examination, double assessment with clinical breast examination and breast ultrasonography (USG) or fine-needle aspiration biopsy (FNAB), and triple assessment. Diagnostic error was defined as a discrepancy between pre- and post-surgery diagnosis or repeated surgery without neoadjuvant chemotherapy. Factors associated with diagnostic assessment implementation, diagnostic error, and repeated surgery were analyzed using the chi-square test. Results The choice of diagnostic assessment was influenced by patients' age and health insurance (p<0.001). Triple assessment was only implemented in 21 (5.8%) breast lump cases. It was more frequently applied in patients ≥40 years (57.1%) and patients with contributory health insurance (76.2%). Diagnostic errors were observed in 84 cases (23.1%) and 47 patients out of them (47%) experienced repeated surgery. The implementation of diagnostic assessments was not associated with diagnostic error (p=0.257) but was significantly associated with repeated surgery in breast cancer (p<0.001). Repeated surgery rates were significantly lowered in cases receiving double assessment with FNAB (p<0.001). Conclusions The implementation of triple assessment in the local setting was very low. The choice of diagnostic assessment was influenced by patients' age and health insurance. Further, double assessment applying clinical breast examination and FNAB significantly decreased repeated surgery rates and thus may serve as an alternative to triple assessment in the limited resource setting.

Temporal uncertainty in disease diagnosis

There is a profound paradox in modern medical knowledge production: The more we know, the more we know that we (still) do not know. Nowhere is this more visible than in diagnostics and early detection of disease. As we identify ever more markers, predictors, precursors, and risk factors of disease ever earlier, we realize that we need knowledge about whether they develop into something experienced by the person and threatening to the person's health. This study investigates how advancements in science and technology alter one type of uncertainty, i.e., temporal uncertainty of disease diagnosis. As diagnosis is related to anamnesis and prognosis it identifies how uncertainties in all these fields are interconnected. In particular, the study finds that uncertainty in disease diagnosis has become more subject to prognostic uncertainty because diagnosis is more connected to technologically detected indicators and less closely connected to manifest and experienced disease. These temporal uncertainties pose basic epistemological and ethical challenges as they can result in overdiagnosis, overtreatment, unnecessary anxiety and fear, useless and even harmful diagnostic odysseys, as well as vast opportunity costs. The point is not to stop our quest for knowledge about disease but to encourage real diagnostic improvements that help more people in ever better manner as early as possible. To do so, we need to pay careful attention to specific types of temporal uncertainty in modern diagnostics.

Clinical Reasoning Curricula in Preclinical Undergraduate Medical Education: A Scoping Review

PURPOSE

Clinical reasoning is the process of observing, collecting, analyzing, and interpreting patient information to arrive at a diagnosis and management plan. Although clinical reasoning is foundational in undergraduate medical education (UME), the current literature lacks a clear picture of the clinical reasoning curriculum in preclinical phase of UME. This scoping review explores the mechanisms of clinical reasoning education in preclinical UME.

METHOD

A scoping review was performed in accordance with the Arksey and O'Malley framework methodology for scoping reviews and is reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews.

RESULTS

The initial database search identified 3,062 articles. Of these, 241 articles were selected for a full-text review. Twenty-one articles, each reporting a single clinical reasoning curriculum, were selected for inclusion. Six of the reports included a definition of clinical reasoning, and 7 explicitly reported the theory underlying the curriculum. Reports varied in the identification of clinical reasoning content domains and teaching strategies. Only 4 curricula reported assessment validity evidence.

CONCLUSIONS

Based on this scoping review, we recommend 5 key principles for educators to consider when reporting clinical reasoning curricula in preclinical UME: (1) explicitly define clinical reasoning within the report, (2) report clinical reasoning theory(ies) used in the development of the curriculum, (3) clearly identify which clinical reasoning domains are addressed in the curriculum, (4) report validity evidence for assessments when available, and (5) describe how the reported curriculum fits into the larger clinical reasoning education at the institution.

Optimizing measurement of misdiagnosis-related harms using symptom-disease pair analysis of diagnostic error (SPADE): comparison groups to maximize SPADE validity

Diagnostic errors in medicine represent a significant public health problem but continue to be challenging to measure accurately, reliably, and efficiently. The recently developed Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach measures misdiagnosis related harms using electronic health records or administrative claims data. The approach is clinically valid, methodologically sound, statistically robust, and operationally viable without the requirement for manual chart review. This paper clarifies aspects of the SPADE analysis to assure that researchers apply this method to yield valid results with a particular emphasis on defining appropriate comparator groups and analytical strategies for balancing differences between these groups. We discuss four distinct types of comparators (intra-group and inter-group for both look-back and look-forward analyses), detailing the rationale for choosing one over the other and inferences that can be drawn from these comparative analyses. Our aim is that these additional analytical practices will improve the validity of SPADE and related approaches to quantify diagnostic error in medicine.

Lessons in clinical reasoning - pitfalls, myths, and pearls: a case of crushing, substernal chest pain

OBJECTIVES

Diagnostic error is not uncommon and diagnostic accuracy can be improved with the use of problem representation, pre-test probability, and Bayesian analysis for improved clinical reasoning.

CASE PRESENTATION

A 48-year-old female presented as a transfer from another Emergency Department (ED) to our ED with crushing, substernal pain associated with dyspnea, diaphoresis, nausea, and a tingling sensation down both arms with radiation to the back and neck. Troponins were elevated along with an abnormal electrocardiogram. A negative myocardial perfusion scan led to the patient's discharge. The patient presented to the ED 10 days later with an anterior ST-elevation myocardial infarction.

CONCLUSIONS

An overemphasis on a single testing modality led to diagnostic error and a severe event. The use of pre-test probabilities guided by history-taking can lead to improved interpretation of test results, ultimately improving diagnostic accuracy and preventing serious medical errors.

Collaborative Diagnostic Conversations Between Clinicians, Patients, and Their Families: A Way to Avoid Diagnostic Errors

Objective

To identify the components of the collaborative diagnostic conversations between clinicians, patients, and their families and how deficiencies in these conversations can lead to diagnostic errors.

Patients and Methods

We purposively selected 60 video recordings of clinical encounters that included diagnosis conversations. These videos were obtained from the internal medicine, and family medicine services at Mayo Clinic's campus in Rochester, Minnesota. These clinical encounters were recorded between November 2017, and December 2021, during the conduct of studies aiming at developing or testing shared decision-making interventions. We followed a critically reflective approach model for data analysis.

Results

We identified 3 components of diagnostic conversations as follows: (1) recognizing diagnostic situations, (2) setting priorities, and (3) creating and reconciling a diagnostic plan. Deficiencies in diagnostic conversations could lead to framing issues in a way that sets diagnostic activities off in an incorrect or undesirable direction, incorrect prioritization of diagnostic concerns, and diagnostic plans of care that are not feasible, desirable, or productive.

Conclusion

We identified 3 clinician-and-patient diagnostic conversation components and mapped them to potential diagnostic errors. This information may inform additional research to identify areas of intervention to decrease the frequency and harm associated with diagnostic errors in clinical practice.

Comparing rates of adverse events detected in incident reporting and the Global Trigger Tool: a systematic review

Many hospitals continue to use incident reporting systems (IRSs) as their primary patient safety data source. The information IRSs collect on the frequency of harm to patients [adverse events (AEs)] is generally of poor quality, and some incident types (e.g. diagnostic errors) are under-reported. Other methods of collecting patient safety information using medical record review, such as the Global Trigger Tool (GTT), have been developed. The aim of this study was to undertake a systematic review to empirically quantify the gap between the percentage of AEs detected using the GTT to those that are also detected via IRSs. The review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies published in English, which collected AE data using the GTT and IRSs, were included. In total, 14 studies met the inclusion criteria. All studies were undertaken in hospitals and were published between 2006 and 2022. The studies were conducted in six countries, mainly in the USA (nine studies). Studies reviewed 22 589 medical records using the GTT across 107 institutions finding 7166 AEs. The percentage of AEs detected using the GTT that were also detected in corresponding IRSs ranged from 0% to 37.4% with an average of 7.0% (SD 9.1; median 3.9 and IQR 5.2). Twelve of the fourteen studies found <10% of the AEs detected using the GTT were also found in corresponding IRSs. The >10-fold gap between the detection rates of the GTT and IRSs is strong evidence that the rate of AEs collected in IRSs in hospitals should not be used to measure or as a proxy for the level of safety of a hospital. IRSs should be recognized for their strengths which are to detect rare, serious, and new incident types and to enable analysis of contributing and contextual factors to develop preventive and corrective strategies. Health systems should use multiple patient safety data sources to prioritize interventions and promote a cycle of action and improvement based on data rather than merely just collecting and analysing information.

Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map

The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 patients, and those diagnosed by the physicians on the field, showed an average of 87% accuracy for the 11 febrile diseases used in the study. The number of comorbidities of diseases with varying degrees of severity for most patients in the study also covary strongly with those found by the physicians in the field.

Diagnostic Errors in Japanese Community Hospitals and Related Factors: A Retrospective Cohort Study

Diagnostic error has recently become a crucial clinical problem and an area of intense research. However, the reality of diagnostic errors in regional hospitals remains unknown. This study aimed to clarify the reality of diagnostic errors in regional hospitals in Japan. A 10-month retrospective cohort study was conducted from January to October 2021 at the emergency room of Oda Municipal Hospital in central Shimane Prefecture, Japan. Participants were divided into groups with or without diagnostic errors, and independent variables of patient, physician, and environmental factors were analyzed using Fisher's exact test, univariate (Student's t-test and Welch's t-test), and logistic regression analyses. Diagnostic errors accounted for 13.1% of all eligible cases. Remarkably, the proportion of patients treated without oxygen support and the proportion of male patients were significantly higher in the group with diagnostic errors. Sex bias was present. Additionally, cognitive bias, a major factor in diagnostic errors, may have occurred in patients who did not require oxygen support. Numerous factors contribute to diagnostic errors; however, it is important to understand the trends in the setting of each healthcare facility and plan and implement individualized countermeasures.

"It's probably an STI because you're gay": a qualitative study of diagnostic error experiences in sexual and gender minority individuals

BACKGROUND

There is a critical need to identify specific causes of and tailored solutions to diagnostic error in sexual and gender minority (SGM) populations.

PURPOSE

To identify challenges to diagnosis in SGM adults, understand the impacts of patient-reported diagnostic errors on patients' lives and elicit solutions.

METHODS

Qualitative study using in-depth semistructured interviews. Participants were recruited using convenience and snowball sampling. Recruitment efforts targeted 22 SGM-focused organisations, academic centres and clinics across the USA. Participants were encouraged to share study details with personal contacts. Interviews were analysed using codebook thematic analysis.

RESULTS

Interviewees (n=20) ranged from 20 to 60 years of age with diverse mental and physical health symptoms. All participants identified as sexual minorities, gender minorities or both. Thematic analysis revealed challenges to diagnosis. Provider-level challenges included pathologisation of SGM identity; dismissal of symptoms due to anti-SGM bias; communication failures due to providers being distracted by SGM identity and enforcement of cis-heteronormative assumptions. Patient-level challenges included internalised shame and stigma. Intersectional challenges included biases around factors like race and age. Patient-reported diagnostic error led to worsening relationships with providers, worsened mental and physical health and increased self-advocacy and community-activism. Solutions to reduce diagnostic disparities included SGM-specific medical education and provider training, using inclusive language, asking questions, avoiding assumptions, encouraging diagnostic coproduction, upholding high care standards and ethics, involving SGM individuals in healthcare improvement and increasing research on SGM health.

CONCLUSIONS

Anti-SGM bias, queerphobia, lack of provider training and heteronormative attitudes hinder diagnostic decision-making and communication. As a result, SGM patients report significant harms. Solutions to mitigate diagnostic disparities require an intersectional approach that considers patients' gender identity, sexual orientation, race, age, economic status and system-level changes.

How does work environment relate to diagnostic quality? A prospective, mixed methods study in primary care

OBJECTIVES

The quest to measure and improve diagnosis has proven challenging; new approaches are needed to better understand and measure key elements of the diagnostic process in clinical encounters. The aim of this study was to develop a tool assessing key elements of the diagnostic assessment process and apply it to a series of diagnostic encounters examining clinical notes and encounters' recorded transcripts. Additionally, we aimed to correlate and contextualise these findings with measures of encounter time and physician burnout.

DESIGN

We audio-recorded encounters, reviewed their transcripts and associated them with their clinical notes and findings were correlated with concurrent Mini Z Worklife measures and physician burnout.

SETTING

Three primary urgent-care settings.

PARTICIPANTS

We conducted in-depth evaluations of 28 clinical encounters delivered by seven physicians.

RESULTS

Comparing encounter transcripts with clinical notes, in 24 of 28 (86%) there was high note/transcript concordance for the diagnostic elements on our tool. Reliably included elements were red flags (92% of notes/encounters), aetiologies (88%), likelihood/uncertainties (71%) and follow-up contingencies (71%), whereas psychosocial/contextual information (35%) and mentioning common pitfalls (7%) were often missing. In 22% of encounters, follow-up contingencies were in the note, but absent from the recorded encounter. There was a trend for higher burnout scores being associated with physicians less likely to address key diagnosis items, such as psychosocial history/context.

CONCLUSIONS

A new tool shows promise as a means of assessing key elements of diagnostic quality in clinical encounters. Work conditions and physician reactions appear to correlate with diagnostic behaviours. Future research should continue to assess relationships between time pressure and diagnostic quality.

Evaluating an app for digital medical history taking in urgent care practices: study protocol of the cluster-randomized interventional trial 'DASI'

BACKGROUND

In out-of-hours urgent care practices in Germany, physicians of different specialties care for a large number of patients, most of all unknown to them, resulting in a high workload and challenging diagnostic decision-making. As there is no common patient file, physicians have no information about patients' previous conditions or received treatments. In this setting, a digital tool for medical history taking could improve the quality of medical care. This study aims to implement and evaluate a software application (app) that takes a structured symptom-oriented medical history from patients in urgent care settings.

METHODS

We conduct a time-cluster-randomized trial in two out-of-hours urgent care practices in Germany for 12 consecutive months. Each week during the study defines a cluster. We will compare participants with (intervention group) and without app use (control group) prior to consultation and provision of the self-reported information for the physician. We expect the app to improve diagnostic accuracy (primary outcome), reduce physicians' perceived diagnostic uncertainty, and increase patients' satisfaction and the satisfaction with communication of both physician and patient (secondary outcomes).

DISCUSSION

While similar tools have only been subject to small-scale pilot studies surveying feasibility and usability, the present study uses a rigorous study design to measure outcomes that are directly associated with the quality of delivered care.

TRIAL REGISTRATION

The study was registered at the German Clinical Trials Register (No. DRKS00026659 registered Nov 03 2021. World Health Organization Trial Registration Data Set, https://trialsearch.who.int/Trial2.aspx? TrialID = DRKS00026659.

A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods

BACKGROUND

The incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different diseases or settings. Administrative and other real-world data sources may offer the ability to better identify and study diagnostic delays for a range of diseases.

METHODS

We propose a comprehensive framework to estimate the frequency of missed diagnostic opportunities for a given disease using real-world longitudinal data sources. We provide a conceptual model of the disease-diagnostic, data-generating process. We then propose a bootstrapping method to estimate measures of the frequency of missed diagnostic opportunities and duration of delays. This approach identifies diagnostic opportunities based on signs and symptoms occurring prior to an initial diagnosis, while accounting for expected patterns of healthcare that may appear as coincidental symptoms. Three different bootstrapping algorithms are described along with estimation procedures to implement the resampling. Finally, we apply our approach to the diseases of tuberculosis, acute myocardial infarction, and stroke to estimate the frequency and duration of diagnostic delays for these diseases.

RESULTS

Using the IBM MarketScan Research databases from 2001 to 2017, we identified 2,073 cases of tuberculosis, 359,625 cases of AMI, and 367,768 cases of stroke. Depending on the simulation approach that was used, we estimated that 6.9-8.3% of patients with stroke, 16.0-21.3% of patients with AMI and 63.9-82.3% of patients with tuberculosis experienced a missed diagnostic opportunity. Similarly, we estimated that, on average, diagnostic delays lasted 6.7-7.6 days for stroke, 6.7-8.2 days for AMI, and 34.3-44.5 days for tuberculosis. Estimates for each of these measures was consistent with prior literature; however, specific estimates varied across the different simulation algorithms considered.

CONCLUSIONS

Our approach can be easily applied to study diagnostic delays using longitudinal administrative data sources. Moreover, this general approach can be customized to fit a range of diseases to account for specific clinical characteristics of a given disease. We summarize how the choice of simulation algorithm may impact the resulting estimates and provide guidance on the statistical considerations for applying our approach to future studies.

Integrative diagnostics: the time is now-a report from the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Future Directions for Ruminant Diagnostics

Diagnostic advances such as next-generation sequencing, highly multiplexed real-time PCR tests, and MALDI-TOF mass spectrometry have provided a tremendous increase in the amount of diagnostic information to clinicians. However, interpretation and application of these results to both individual and herd-level diagnostics still require the necessary skills in critical thinking and diagnostic interpretation to maximize benefit. This article provides a summary of advancements in diagnostic medicine and interpretation, as well as identifies gaps in knowledge that can be targeted to continue to build on best practices and application of diagnostic tools to improve ruminant health.

A clinically-guided unsupervised clustering approach to recommend symptoms of disease associated with diagnostic opportunities

OBJECTIVES

A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis.

METHODS

We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-means clustering to recommend conditions that exhibit similar trends to the initial symptoms provided. We evaluate the clinical plausibility of the recommended conditions and the corresponding number of possible diagnostic delays based on these diseases.

RESULTS

For both diseases of interest, the clustering approach suggested a large number of clinically-plausible conditions to consider (e.g., fever, hemoptysis, and pneumonia before tuberculosis). The recommended conditions had a high degree of precision in terms of clinical plausibility: >70% for tuberculosis and >90% for appendicitis. Including these additional clinically-plausible conditions resulted in more than twice the number of possible diagnostic delays identified.

CONCLUSIONS

Our approach can mine administrative datasets to detect patterns of diagnostic delay and help investigators avoid under-identifying potential missed diagnostic opportunities. In addition, the methods we describe can be used to discover less-common presentations of diseases that are frequently misdiagnosed.

[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.

Prueba de concordancia de guiones para entrenar el razonamiento clínico en estudiantes de fonoaudiología

La prueba de concordancia de guiones (PCG) ha sido utilizada en el entrenamiento y evaluación del razonamiento clínico (RC) como una estrategia innovadora en la formación de profesionales. Sin embargo, no se dispone de evidencia de su aplicación en el pregrado de fonoaudiología. El objetivo de esta investigación fue analizar el desempeño y la percepción de estudiantes de fonoaudiología con respecto al uso de scripts. Se diseñó un piloto pre-experimental y multicéntrico, complementado con tres grupos focales. Las variables cuantitativas continuas fueron resumidas a través de medias y desviación estándar. La comparación entre grupos se ejecutó con Anova one way y la prueba post hoc de Bonferroni, considerando un nivel de significancia p<.05. La fase cualitativa incorporó un análisis de contenido mediante la codificación abierta de textos y la identificación e interpretación de familias de significado emergentes. El rendimiento promedio de los estudiantes fue de 4.03 (DS= 0.35), observándose un incremento en el rendimiento de RC durante el semestre (p= 0.03). La percepción de los estudiantes resulto positiva y se identificó cuatro familias de significado relacionadas con: razonamiento clínico, oportunidades de mejora implementación de la estrategia y retroalimentación docente. A modo de conclusión, la incorporación de scripts en estudiantes de pregrado de fonoaudiología es factible, incrementa el rendimiento y apoya el desarrollo del RC.

An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease

Objective

We retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs.

Methods

We applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the "University Hospital Salzburg" clinic group. Within 1 month, the AI screened 350,116 EHRs reaching back 15 years from five hospitals, and 104 patients were flagged as probable for Pompe disease. Flagged patients were manually reviewed and assessed by generalist and specialist physicians for their likelihood for Pompe disease, from which the performance of the algorithms was evaluated.

Results

Of the 104 patients flagged by the algorithms, generalist physicians found five "diagnosed," 10 "suspected," and seven patients with "reduced suspicion." After feedback from Pompe disease specialist physicians, 19 patients remained clinically plausible for Pompe disease, resulting in a specificity of 18.27% for the AI. Estimating from the remaining plausible patients, the prevalence of Pompe disease for the greater Salzburg region [incl. Bavaria (Germany), Styria (Austria), and Upper Austria (Austria)] was one in every 18,427 people. Phenotypes for patient cohorts with an approximated onset of symptoms above or below 1 year of age were established, which correspond to infantile-onset Pompe disease (IOPD) and late-onset Pompe disease (LOPD), respectively.

Conclusion

Our study shows the feasibility of Symptoma's AI-based approach for identifying rare disease patients using retrospective EHRs. Via the algorithm's screening of an entire EHR population, a physician had only to manually review 5.47 patients on average to find one suspected candidate. This efficiency is crucial as Pompe disease, while rare, is a progressively debilitating but treatable neuromuscular disease. As such, we demonstrated both the efficiency of the approach and the potential of a scalable solution to the systematic identification of rare disease patients. Thus, similar implementation of this methodology should be encouraged to improve care for all rare disease patients.

Integrative Diagnostics: The Time Is Now-A Report From the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Diagnostic errors in Dentistry, opinions of egyptian dental teaching staff, a cross-sectional study

BACKGROUND

Diagnostic errors is a known problem in healthcare practice. Data on diagnostic errors in the dental field are extremely lacking. The objective of the study is to explore the perception of dental teaching staff about the prevalence of dental diagnostic errors in Egypt, identify the most commonly misdiagnosed dental conditions and point out the contributing factors and levels of patient harm.

METHODS

A cross-sectional questionnaire-based study was conducted on 151 dental teaching staff of Egyptian governmental and private universities. The questionnaire was distributed electronically via social media and messaging apps to dental staff members with at least five years of clinical experience to assess their opinion regarding the study objectives. Results were collected and statistically analyzed.

RESULTS

94.7% of participants believed that diagnostic errors represent an urgent problem, lecturers believed by 2.703 folds more than professors that diagnostic errors are an urgent problem The percentage of diagnostic errors was estimated to be < 20% and 20-40% by more than 90% of participants. The most commonly misdiagnosed conditions were oral mucosal lesions (83.4%), followed by temporomandibular joint and periodontal conditions (58.9%) for each. More than half of the participants (60.9%) believe that medical education methodology is one of the factors that lead to dental diagnosis errors. For the impact of errors on patients, 53% of participants reported moderate impacts followed by minor impact (37.7%) while 4.6% reported no impact and the same percentage reported major impact.

CONCLUSION

This study with statistically significant results reported that dental diagnostic errors are frequent and need to be approached. Oral mucosal lesions, periodontal and temporomandibular joint diseases represent areas that include the most commonly seen errors. Further, besides the lack of resources, the dental education system and lack of proper training are the main causes of this problem.

Postmortem Metabolomics of Insulin Intoxications and the Potential Application to Find Hypoglycemia-Related Deaths

Postmortem metabolomics can assist death investigations by characterizing metabolic fingerprints differentiating causes of death. Hypoglycemia-related deaths, including insulin intoxications, are difficult to identify and, thus, presumably underdiagnosed. This investigation aims to differentiate insulin intoxication deaths by metabolomics, and identify a metabolic fingerprint to screen for unknown hypoglycemia-related deaths. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry data were obtained from 19 insulin intoxications (hypo), 19 diabetic comas (hyper), and 38 hangings (control). Screening for potentially unknown hypoglycemia-related deaths was performed using 776 random postmortem cases. Data were processed using XCMS and SIMCA. Multivariate modeling revealed group separations between hypo, hyper, and control groups. A metabolic fingerprint for the hypo group was identified, and analyses revealed significant decreases in 12 acylcarnitines, including nine hydroxylated-acylcarnitines. Screening of random postmortem cases identified 46 cases (5.9%) as potentially hypoglycemia-related, including six with unknown causes of death. Autopsy report review revealed plausible hypoglycemia-cause for five unknown cases. Additionally, two diabetic cases were found, with a metformin intoxication and a suspicious but unverified insulin intoxication, respectively. Further studies are required to expand on the potential of postmortem metabolomics as a tool in hypoglycemia-related death investigations, and the future application of screening for potential insulin intoxications.

Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma

Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.