Neonatal autopsy-is it relevant in today's era?

Autopsy of infants can provide vital information about the cause of death and contributes to the detection of diagnostic errors, especially in a low- or middle-income country. To observe the clinicopathological agreement in neonatal deaths in neonatal intensive care units (NICU) and comment on the additional information retrieved by autopsy. A retrospective observational study was conducted in the NICU from January 2020 to December 2022. Neonatal deaths were analyzed, and clinical details and autopsy findings were collected. Both clinical and pathological diagnoses were classified according to the Goldman classification. Twenty-two newborn infants were enrolled. The mean gestational age was 33.5 (±4.38) weeks, and the median birth weight was 1510 (1005-2100) g. There was complete concordance between clinical and pathological diagnosis in 11 (50%) cases. Major diagnostic errors occurred in 41% of cases. Respiratory system disorders (lung infections, airway anomalies) accounted for six (54%) cases of missed diagnosis. Our study showed that the diagnosis was revised after autopsy in about one-third of cases, and newer findings were identified in one-fifth of cases.

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification.

BACKGROUND: Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case. METHODS: An EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed. RESULTS: A total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32 %. There was no significant difference by clinic or provider characteristics. CONCLUSION: Our work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them. CLINICAL SIGNIFICANCE: This study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.

Acute kidney injury in hospitalized children in 20 hospitals of China's Hunan Province: a cross-sectional survey.

OBJECTIVE: The incidence of acute kidney injury (AKI) in pediatric patients has been increasing over the years, and AKI significantly impacts children's health and quality of life. This article reviews the current epidemiological research on pediatric AKI. METHODS: The clinical data of hospitalized children aged 0 to 14 years from 20 different hospitals in Hunan Province, China, collected from December 2017 to February 2018, were analyzed. The incidence rate, misdiagnosis rate, main causes, and medical costs of AKI in hospitalized children were examined. RESULTS: A total of 29,639 patients were included, with an AKI incidence rate of 4.34% (1286/29,639). Among the 1286 AKI patients, 863 (67.11%) were classified as AKI stage 1324 (25.19%) as AKI stage 2, and 99 (7.7%) as AKI stage 3. AKI patients had significantly longer hospital stays [6.0 (4.0, 10) days vs. 6.0 (4.0, 8.0) days, p < 0.001] and higher hospitalization costs [3375.22 (1600, 6083.83) yuan vs. 2729.4 (1659.45, 8216.65) yuan, p = 0.003] than non-AKI patients. The mortality rate (1.2% vs. 0.1%, p < 0.001), intensive care unit (ICU) transfer rate (8.7% vs. 5.97%, p < 0.001), and use of invasive mechanical ventilation (3.6% vs. 1%, p < 0.001) were significantly greater in patients with AKI than in those without AKI patients. The etiology of AKI varied among different age groups, and dehydration, diarrhea, and shock were the main causes of pre-renal AKI. CONCLUSION: The incidence and missed diagnosis rates of AKI in hospitalized children were high. AKI prolongs hospital stays, increases hospitalization costs, and increases the risk of mortality in children.

Clinicopathological discrepancies in the diagnoses of childhood causes of death in the CHAMPS network: An analysis of antemortem diagnostic inaccuracies.

INTRODUCTION: Determining aetiology of severe illness can be difficult, especially in settings with limited diagnostic resources, yet critical for providing life-saving care. Our objective was to describe the accuracy of antemortem clinical diagnoses in young children in high-mortality settings, compared with results of specific postmortem diagnoses obtained from Child Health and Mortality Prevention Surveillance (CHAMPS). METHODS: We analysed data collected during 2016-2022 from seven sites in Africa and South Asia. We compared antemortem clinical diagnoses from clinical records to a reference standard of postmortem diagnoses determined by expert panels at each site who reviewed the results of histopathological and microbiological testing of tissue, blood, and cerebrospinal fluid. We calculated test characteristics and 95% CIs of antemortem clinical diagnostic accuracy for the 10 most common causes of death. We classified diagnostic discrepancies as major and minor, per Goldman criteria later modified by Battle. RESULTS: CHAMPS enrolled 1454 deceased young children aged 1-59 months during the study period; 881 had available clinical records and were analysed. The median age at death was 11 months (IQR 4-21 months) and 47.3% (n=417) were female. We identified a clinicopathological discrepancy in 39.5% (n=348) of deaths; 82.3% of diagnostic errors were major. The sensitivity of clinician antemortem diagnosis ranged from 26% (95% CI 14.6% to 40.3%) for non-infectious respiratory diseases (eg, aspiration pneumonia, interstitial lung disease, etc) to 82.2% (95% CI 72.7% to 89.5%) for diarrhoeal diseases. Antemortem clinical diagnostic specificity ranged from 75.2% (95% CI 72.1% to 78.2%) for diarrhoeal diseases to 99.0% (95% CI 98.1% to 99.6%) for HIV. CONCLUSIONS: Antemortem clinical diagnostic errors were common for young children who died in areas with high childhood mortality rates. To further reduce childhood mortality in resource-limited settings, there is an urgent need to improve antemortem diagnostic capability through advances in the availability of diagnostic testing and clinical skills.

Laboratory specimen rejection rate and associated factors among referred specimens at Debre Markos Referral Hospital, Ethiopia: prospective cross-sectional study.

Introduction: laboratory errors mostly emerge from the pre-analytical phase, mainly those related to collection, handling, transportation, and storage of diagnostic specimens. Specimen rejection due to improper sample collection, may lead to poor patient outcomes, such as incorrect diagnosis, inappropriate treatment, and death. This study aimed to assess the specimen rejection rate and associated factors among referred specimens at Debre Markos Referral Hospital. Methods: a prospective cross-sectional study design was applied from January 2020 to April 2020 to investigate specimen rejection rate and associated factors among referred specimens. The study population was all laboratory specimens referred for viral load, CD4 count, gene expert, and early infant diagnosis to the Debre Markos Referral Hospital laboratory. The statistical analysis was done with Statistical Package for Social Sciences version 20.0 software. Results: of the total of 2750 specimens submitted to the laboratory from January 2020 to April 2020, 37 (1.34%) specimens were rejected due to different reasons like insufficient volume, hemolysis, and an inappropriate specimen container. Specimen collector training status and experience had a significant association with the specimen rejection rate. Conclusion: the results of our study show that the specimen rejection rate among referred specimens was high, indicating that more interventions are required to decrease the specimen rejection rate.

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

Performance evaluation of ChatGPT in detecting diagnostic errors and their contributing factors: an analysis of 545 case reports of diagnostic errors.

BACKGROUND: Manual chart review using validated assessment tools is a standardised methodology for detecting diagnostic errors. However, this requires considerable human resources and time. ChatGPT, a recently developed artificial intelligence chatbot based on a large language model, can effectively classify text based on suitable prompts. Therefore, ChatGPT can assist manual chart reviews in detecting diagnostic errors. OBJECTIVE: This study aimed to clarify whether ChatGPT could correctly detect diagnostic errors and possible factors contributing to them based on case presentations. METHODS: We analysed 545 published case reports that included diagnostic errors. We imputed the texts of case presentations and the final diagnoses with some original prompts into ChatGPT (GPT-4) to generate responses, including the judgement of diagnostic errors and contributing factors of diagnostic errors. Factors contributing to diagnostic errors were coded according to the following three taxonomies: Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC) and Generic Diagnostic Pitfalls (GDP). The responses on the contributing factors from ChatGPT were compared with those from physicians. RESULTS: ChatGPT correctly detected diagnostic errors in 519/545 cases (95%) and coded statistically larger numbers of factors contributing to diagnostic errors per case than physicians: DEER (median 5 vs 1, p<0.001), RDC (median 4 vs 2, p<0.001) and GDP (median 4 vs 1, p<0.001). The most important contributing factors of diagnostic errors coded by ChatGPT were 'failure/delay in considering the diagnosis' (315, 57.8%) in DEER, 'atypical presentation' (365, 67.0%) in RDC, and 'atypical presentation' (264, 48.4%) in GDP. CONCLUSION: ChatGPT accurately detects diagnostic errors from case presentations. ChatGPT may be more sensitive than manual reviewing in detecting factors contributing to diagnostic errors, especially for 'atypical presentation'.

[Possibilities of using results of head computed tomography in victims with traumatic brain injuries]. / Vozmozhnosti ispol'zovaniya rezul'tatov komp'yuternoi tomografii golovy u poterpevshikh s cherepno-mozgovoi travmoi.

The constant increase in the number of neurotraumas in the country leads to an increase in forensic examinations of a persons. In Russia, about 600 thousand people receive craniocerebral injuries annually, of which 50 thousand die, others are potentially will be in forensic examination during or after treatment. With an increase in the total number of such examinations, the number of erroneous conclusions is expected to increase. If it is impossible for the radiologist included in the commission to review the results of computed tomography of the head performed in the hospital, the experts are forced to use the data that are recorded in the medical documents. The present study revealed the percentage of erroneous interpretations in such descriptions, systematized typical errors, calculated the sensitivity, specificity and accuracy of computed tomography in craniocerebral injury.

Clinical distinctions in symptomatology and psychiatric comorbidities between misdiagnosed bipolar I and bipolar II disorder versus major depressive disorder.

BACKGROUND: To explore the demographic and clinical features of current depressive episode that discriminate patients diagnosed with major depressive disorder (MDD) from those with bipolar I (BP-I) and bipolar II (BP-II) disorder who were misdiagnosed as having MDD . METHODS: The Mini-International Neuropsychiatric Interview (MINI) assessment was performed to establish DSM-IV diagnoses of MDD, and BP-I and BP-II, previously being misdiagnosed as MDD. Demographics, depressive symptoms and psychiatric comorbidities were compared between 1463 patients with BP-I, BP-II and MDD from 8 psychiatric settings in mainland China. A multinomial logistic regression model was performed to assess clinical correlates of diagnoses. RESULTS: A total of 14.5% of the enrolled patients initially diagnosed with MDD were eventually diagnosed with BP. Broad illness characteristics including younger age, higher prevalence of recurrence, concurrent dysthymia, suicidal attempts, agitation, psychotic features and psychiatric comorbidities, as well as lower prevalence of insomnia, weight loss and somatic symptoms were featured by patients with BP-I and/or BP-I, compared to those with MDD. Comparisons between BP-I and BP-II versus MDD indicated distinct symptom profiles and comorbidity patterns with more differences being observed between BP-II and MDD, than between BP-I and MDD . CONCLUSION: The results provide evidence of clinically distinguishing characteristics between misdiagnosed BP-I and BP- II versus MDD. The findings have implications for guiding more accurate diagnoses of bipolar disorders.

The discrepancy of antemortem clinical diagnosis and postmortem autopsy diagnosis of lung pathologies in under-five deaths and the reasons for discrepancies: a case series analysis.

BACKGROUND: Diagnostic autopsy is the most reliable approach to definitively ascertain the cause of death and evaluate the accuracy of antemortem clinical diagnoses. Identifying diagnostic discrepancies is vital to understanding common gaps in antemortem clinical diagnoses and modifying antemortem diagnostic approaches to increase the accuracy of clinical diagnosis. The objective of this study was to determine the frequency of diagnostic discrepancies between antemortem clinical diagnoses and postmortem autopsies on lung pathologies and to understand the reasons for diagnostic discrepancies among cases included in Child Health and Mortality Prevention Surveillance (CHAMPS) in Ethiopia. METHODS: A clinical case series study of deaths among children under-five in the CHAMPS study at three sites in Ethiopia between October 2019 and April 2022 was conducted. The antemortem clinical diagnoses and postmortem pathological diagnoses of the lung were compared for each case. Two senior physicians assessed the findings for both agreement and disagreement. McNemar's test was used to assess for statistically significant differences between antemortem and postmortem diagnoses. RESULTS: Seventy-five cases were included (73.3% male). Over half (54.7%) died between the 1st and 7th day of life. Sepsis (66.7%), pneumonia (6.7%), and meconium aspiration syndrome (5.0%) were the most common immediate causes of death. Half (52%) of cases were correctly diagnosed antemortem. The magnitude of diagnostic discrepancy was 35% (95% CI: 20-47%). The most common contributing factors to diagnostic discrepancy were gaps in knowledge (22/75, 35.5%) and problems in consultation and teamwork (22/75, 35.5%). CONCLUSIONS: Misdiagnoses were common among young children who died with positive lung pathology findings. In-service education initiatives and multidisciplinary collaboration are needed to mitigate high rates of diagnostic discrepancies among young children to potentially prevent future deaths.

Paediatric-onset lymphomatoid papulosis: results of a multicentre retrospective cohort study on behalf of the EORTC Cutaneous Lymphoma Tumours Group (CLTG).

BACKGROUND: Lymphomatoid papulosis (LyP) is a rare cutaneous T-cell lymphoproliferative disorder. Comprehensive data on LyP in the paediatric population are scarce. OBJECTIVES: To characterize the epidemiological, clinical, histopathological and prognostic features of paediatric LyP. METHODS: This was a retrospective multicentre international cohort study that included 87 children and adolescents with LyP diagnosed between 1998 and 2022. Patients aged ≤ 18 years at disease onset were included. LyP diagnosis was made in each centre, based on clinicopathological correlation. RESULTS: Eighty-seven patients from 12 centres were included. Mean age at disease onset was 7.0 years (range 3 months-18 years) with a male to female ratio of 2 : 1. Mean time between the onset of the first cutaneous lesions and diagnosis was 1.3 years (range 0-14). Initial misdiagnosis concerned 26% of patients. LyP was most often misdiagnosed as pityriasis lichenoides et varioliformis acuta, insect bites or mollusca contagiosa. Erythematous papules or papulonodules were the most frequent clinical presentation. Pruritus was specifically mentioned in 21% of patients. The main histological subtype was type A in 55% of cases. When analysed, monoclonal T-cell receptor rearrangement was found in 77% of skin biopsies. The overall survival rate was 100%, with follow-up at 5 years available for 33 patients and at 15 years for 8 patients. Associated haematological malignancy (HM) occurred in 10% of cases (n = 7/73), including four patients with mycosis fungoides, one with primary cutaneous anaplastic large cell lymphoma (ALCL), one with systemic ALCL and one with acute myeloid leukaemia. If we compared incidence rates of cancer with the world population aged 0-19 years from 2001 to 2010, we estimated a significantly higher risk of associated malignancy in general, occurring before the age of 19 years (incidence rate ratio 87.49, 95% confidence interval 86.01-88.99). CONCLUSIONS: We report epidemiological data from a large international cohort of children and adolescents with LyP. Overall, the disease prognosis is good, with excellent survival rates for all patients. Owing to an increased risk of associated HM, long-term follow-up should be recommended for patients with LyP.

Lymphomatoid papulosis is a very rare skin condition caused by an abnormal increase in white blood cells (called 'lymphocytes') in the skin. The condition rarely affects children, so most of the scientific data published about this disease focuses on adults. This study involved 12 academic dermatology centres in Europe, the Middle East and North America, and gathered data from about 87 children who presented with symptoms of lymphomatoid papulosis before the age of 19 years. The aim of this study was to better describe this disease in the paediatric population and discuss its treatment options and evolution. We found that the presentation of the disease in children is roughly the same as in adults. Safe and effective treatment options exist. The disease is not life threatening, but it requires investigation by a dermatologist, both to make a careful diagnosis and to monitor it as sometimes associated cancers that originate from blood cells can occur, mostly on the skin.

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.

BACKGROUND: General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS). METHODS: We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure. RESULTS: Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance. DISCUSSION: Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice. HIGHLIGHTS: We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes.

Is Myocardial Infarction Overdiagnosed?

This Viewpoint examines whether overdiagnosis rather than underdiagnosis may now be the dominant form of myocardial infarction misdiagnosis.

Enhanced accuracy and reduced delay in diagnosing bone tumors within an expert sarcoma network: A nationwide study.

INTRODUCTION: Primary bone tumors encompass a range of rare and diverse lesions. Pathological diagnosis poses significant challenges, with histological discrepancies extensively studied in soft tissue sarcomas but lacking specific investigation in bone lesions. This study aimed to determine the rate of major diagnostic discrepancies in primary bone tumors, assessing whether initial histological analysis within an expert referral center network reduces this rate and final diagnostic delay. Additionally, we examined the impact of mandatory systematic re-reading by expert pathologists on diagnostic variation and readjustment. METHODS: Our study cohort comprised patients with primary bone tumors, drawn from the national prospective French sarcoma network database. A total of 1075 patients were included from 2018 to 2019. RESULTS: The cohort exhibited a major discrepancy rate of 24%. Within the expert referral centers network, 49 cases (7%) showed major diagnostic discrepancies in the initial analysis, compared to 207 cases (57%) outside the network (p < 0.001). Regarding the final diagnostic delay, a mean of 2.8 weeks (±4.9) was observed within the network, contrasting with 6.5 weeks (±9.1) outside the network (p < 0.001). Systematic re-reading by an expert pathologist facilitated diagnosis readjustment in 75% of the 256 cases, with 68% of all diagnostic variations occurring preoperatively. CONCLUSION: Early management within the expert network significantly reduced major diagnostic discrepancies and shortened the diagnosis delay by approximately a month. Expert pathologist systematic re-readings were responsible for diagnosis readjustments in three-quarters of cases, with two-thirds of all diagnostic variations occurring preoperatively, thereby mitigating the consequences of mistreatment.

Retrospective Analysis of US Litigations Involving Dermatologists From 2011 to 2022.

BACKGROUND: Physician malpractice lawsuits are climbing, and the reasons underlying litigation against dermatologists are unclear. OBJECTIVE: To determine the reasons patients pursue litigation against dermatologists or dermatology practices. MATERIALS AND METHODS: A retrospective analysis of all state and federal cases between 2011 and 2022 was performed after a query using "Dermatology" and "dermatologist" as search terms on 2 national legal data repositories. RESULTS: The authors identified a total of 48 (37 state and 11 federal) lawsuits in which a practicing dermatologist or dermatology group practice was the defendant. The most common reason for litigation was unexpected harm (26 cases, 54.2%), followed by diagnostic error (e.g. incorrect or delayed diagnoses) (16 cases, 33.3%). Six cases resulted from the dermatologist failing to communicate important information, such as medication side effects or obtaining informed consent. Male dermatologists were sued at a rate 3.1 times higher than female dermatologists. CONCLUSION: Although lawsuits from patients against dermatologists largely involve injury from elective procedures, clinicians should practice caution regarding missed diagnoses and ensure critical information is shared with patients to safeguard against easily avoidable litigation.

Development of Patient Safety Measures to Identify Inappropriate Diagnosis of Common Infections.

BACKGROUND: Inappropriate diagnosis of infections results in antibiotic overuse and may delay diagnosis of underlying conditions. Here we describe the development and characteristics of 2 safety measures of inappropriate diagnosis of urinary tract infection (UTI) and community-acquired pneumonia (CAP), the most common inpatient infections on general medicine services. METHODS: Measures were developed from guidelines and literature and adapted based on data from patients hospitalized with UTI and CAP in 49 Michigan hospitals and feedback from end-users, a technical expert panel (TEP), and a patient focus group. Each measure was assessed for reliability, validity, feasibility, and usability. RESULTS: Two measures, now endorsed by the National Quality Forum (NQF), were developed. Measure reliability (derived from 24 483 patients) was excellent (0.90 for UTI; 0.91 for CAP). Both measures had strong validity demonstrated through (a) face validity by hospital users, the TEPs, and patient focus group, (b) implicit case review (ĸ 0.72 for UTI; ĸ 0.72 for CAP), and (c) rare case misclassification (4% for UTI; 0% for CAP) due to data errors (<2% for UTI; 6.3% for CAP). Measure implementation through hospital peer comparison in Michigan hospitals (2017 to 2020) demonstrated significant decreases in inappropriate diagnosis of UTI and CAP (37% and 32%, respectively, P < .001), supporting usability. CONCLUSIONS: We developed highly reliable, valid, and usable measures of inappropriate diagnosis of UTI and CAP for hospitalized patients. Hospitals seeking to improve diagnostic safety, antibiotic use, and patient care should consider using these measures to reduce inappropriate diagnosis of CAP and UTI.

Diagnostic errors during perceptual learning in dermatology: a prospective cohort study of Finnish undergraduate students.

BACKGROUND: Perceptual learning modules (PLMs) have been shown to significantly improve learning outcomes in teaching dermatology. OBJECTIVES: To investigate the quantity and quality of diagnostic errors made during undergraduate PLMs and their potential implications. METHODS: The study data were acquired from 8 successive dermatology courses (2021-23) from 142 undergraduate medical students. Digital PLMs were held before, during and after the courses. We investigated the number and distribution of diagnostic errors, differences between specific skin conditions and classified the errors based on type. RESULTS: Diagnostic errors were not randomly distributed. Some skin conditions were almost always correctly identified, whereas a significant number of errors were made for other diagnoses. Errors were classified into one of three groups: mostly systematic errors of relevant differential diagnoses ('similarity' errors); partly systematic errors ('mixed' errors); and 'random' errors. While a significant learning effect during the repeated measures was found in accuracy (P < 0.001, η²P = 0.64), confidence (P < 0.001, η²P = 0.60) and fluency (P < 0.001, η²P = 0.16), the three categories differed in all outcome measures (all P < 0.001, all η²P > 0.47). Visual learning was more difficult for diagnoses in the similarity category (all P < 0.001, all η²P > 0.12) than for those in the mixed and random categories. CONCLUSIONS: Error analysis of PLMs provided relevant information about learning efficacy and progression, and systematic errors in tasks and more difficult-to-learn conditions. This information could be used in the development of adaptive, individual error-based PLMs to improve learning outcomes, both in dermatology and medical education in general.

Histopathologic evaluation of gastric intestinal metaplasia in non-neoplastic biopsy specimens: Accuracy and interobserver reliability among general pathologists and pathology residents.

OBJECTIVES: This study aimed to evaluate the accuracy and interobserver reliability of diagnosing and subtyping gastric intestinal metaplasia (IM) among general pathologists and pathology residents at a university hospital in Thailand, focusing on the challenges in the histopathologic evaluation of gastric IM for less experienced practitioners. METHODS: The study analyzed 44 non-neoplastic gastric biopsies, using a consensus diagnosis of gastrointestinal pathologists as the reference standard. Participants included 6 general pathologists and 9 pathology residents who assessed gastric IM and categorized its subtype (complete, incomplete, or mixed) on digital slides. After initial evaluations and receiving feedback, participants reviewed specific images of gastric IM, as agreed by experts. Following a one-month washout period, a reevaluation of the slides was conducted. RESULTS: Diagnostic accuracy, interobserver reliability, and time taken for diagnosis improved following training, with general pathologists showing higher accuracies than residents (median accuracy of gastric IM detection: 100 % vs. 97.7 %). Increased years of experience were associated with more IM detection accuracy (p-value<0.05). However, the overall median accuracy for diagnosing incomplete IM remained lower than for complete IM (86.4 % vs. 97.7 %). After training, diagnostic errors occurred in 6 out of 44 specimens (13.6 %), reported by over 40 % of participants. Errors involved omitting 5 slides with incomplete IM and 1 with complete IM, all showing a subtle presence of IM. CONCLUSIONS: The study highlights the diagnostic challenges in identifying incomplete gastric IM, showing notable discrepancies in accuracy and interobserver agreement. It underscores the need for better diagnostic protocols and training to enhance detection and management outcomes.