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Introduction Clinical reasoning is crucial in medical practice, yet its teaching faces challenges due to varied clinical experiences, limited time, and absence from competency frameworks. Despite efforts, effective teaching methodologies remain elusive. Strategies like the One Minute Preceptor (OMP) and SNAPPS are proposed as solutions, particularly in workplace settings. SNAPPS, introduced in 2003, offers a structured approach but lacks comprehensive evidence of its effectiveness. Methodological shortcomings hinder discerning its specific effects. Therefore, a systematic review is proposed to evaluate SNAPPS' impact on clinical reasoning teaching. Content We searched PubMed, EMBASE, and CINAHL for randomized controlled trials (RCTs) comparing SNAPPS against other methods. Data selection and extraction were performed in duplicate. Bias and certainty of evidence were evaluated using Cochrane RoB-2 and GRADE approach. Summary We identified five RCTs performed on medical students and residents. Two compared SNAPPS with an active control such as One Minute Preceptor or training with feedback. None reported the effects of SNAPPS in workplace settings (Kirkpatrick Level 3) or patients (Kirkpatrick Level 4). Low to moderate certainty of evidence suggests that SNAPPS increases the total presentation length by increasing discussion length. Low to moderate certainty of evidence may increase the number of differential diagnoses and the expression of uncertainties. Low certainty of evidence suggests that SNAPPS may increase the odds of trainees initiating a management plan and seeking clarification. Outlook Evidence from this systematic review suggests that SNAPPS has some advantages in terms of clinical reasoning, self-directed learning outcomes, and cost-effectiveness. Furthermore, it appears more beneficial when used by residents than medical students. However, future research should explore outcomes outside SNAPPS-related outcomes, such as workplace or patient-related outcomes.

Coronavirus disease 2019 (COVID-19) is frequently associated with thrombo inflammation, which can predispose to developing of life-threatening conditions in children such as the multisystem inflammatory syndrome (MIS-C) and Kawasaki disease. Because of the consistent overlap in pathogenesis and symptoms, identifying laboratory tests that may aid in the differential diagnosis of these pathologies becomes crucial. We performed an electronic search in PubMed, Web of Science and Scopus, without date or language restrictions, to identify all possible studies reporting D-dimer values in separate cohorts of children with MIS-C or Kawasaki disease. Three multicenter cohort studies were included in our analysis, totaling 487 patients (270 with MIS-C and 217 with Kawasaki disease). In this meta-analysis, significantly higher D-dimer values were found in MIS-C compared to Kawasaki disease in all three studies, yielding an SMD of 1.5 (95 % CI, 1.3–1.7) mg/L. Thus, very high D-dimer values early in the course of disease should raise the clinical suspicion of MIS-C rather than Kawasaki disease. Further studies should be planned to identify harmonized D-dimer diagnostic thresholds that may help discriminate these conditions.

At the moment, the academic world is faced with various challenges that negatively impact science integrity. One is hijacked journals, a second, inauthentic website for indexed legitimate journals, managed by cybercriminals. These journals publish any manuscript by charging authors and pose a risk to scientific integrity. This piece compares a journal’s original and hijacked versions regarding authority in search engines. A list of 16 medical journals, along with their hijacked versions, has been collected. The MOZ Domain Authority has been used to check the authority of both original and hijacked journals, and the results have been discussed. It indicates that hijacked journals are gaining more credibility than original ones. This should alarm academia and highlights a need for serious action against hijacked journals. The related policies should be planned, and tools should be developed to support easy detection of hijacked journals. On the publishers’ side, the visibility of journals’ websites must be enhanced to address this issue.

Insight has been studied as an element of problem solving in the field of cognitive psychology and may play an important role in clinical reasoning. We propose a new strategy based on theories that promote insight that may help generate further diagnostic hypotheses by reviewing the interpretation of a case and an individual’s list of differential diagnoses from multiple perspectives: formation (F), re-encoding (R), analogy (A), modification (M), elaboration (E), and deliberation (D) (FRAMED). The FRAMED strategy may help clinicians overcome misinterpretations and cognitive bias by systematically reflecting on previous clinical reasoning processes from multiple perspectives.

Algorithms are a ubiquitous part of modern life. Despite being a component of medicine since early efforts to deploy computers in medicine, clinicians’ resistance to using decision support and use algorithms to address cognitive biases has been limited. This resistance is not just limited to the use of algorithmic clinical decision support, but also evidence and stochastic reasoning and the implications of the forcing function of the electronic medical record. Physician resistance to algorithmic support in clinical decision making is in stark contrast to their general acceptance of algorithmic support in other aspects of life.

Objectives Validate the diagnostic accuracy of the Artificial Intelligence Large Language Model ChatGPT4 by comparing diagnosis lists produced by ChatGPT4 to Isabel Pro. Methods This study used 201 cases, comparing ChatGPT4 to Isabel Pro. Systems inputs were identical. Mean Reciprocal Rank (MRR) compares the correct diagnosis’s rank between systems. Isabel Pro ranks by the frequency with which the symptoms appear in the reference dataset. The mechanism ChatGPT4 uses to rank the diagnoses is unknown. A Wilcoxon Signed Rank Sum test failed to reject the null hypothesis. Results Both systems produced comprehensive differential diagnosis lists. Isabel Pro’s list appears immediately upon submission, while ChatGPT4 takes several minutes. Isabel Pro produced 175 (87.1 %) correct diagnoses and ChatGPT4 165 (82.1 %). The MRR for ChatGPT4 was 0.428 (rank 2.31), and Isabel Pro was 0.389 (rank 2.57), an average rank of three for each. ChatGPT4 outperformed on Recall at Rank 1, 5, and 10, with Isabel Pro outperforming at 20, 30, and 40. The Wilcoxon Signed Rank Sum Test confirmed that the sample size was inadequate to conclude that the systems are equivalent. ChatGPT4 fabricated citations and DOIs, producing 145 correct references (87.9 %) but only 52 correct DOIs (31.5 %). Conclusions This study validates the promise of Clinical Diagnostic Decision Support Systems, including the Large Language Model form of artificial intelligence (AI). Until the issue of hallucination of references and, perhaps diagnoses, is resolved in favor of absolute accuracy, clinicians will make cautious use of Large Language Model systems in diagnosis, if at all.

Objectives This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education. Methods Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT’s across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests. Results ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts. Conclusions The study reveals AI’s capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as a reference tool that students could utilize to enhance their understanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.

Objectives We sought within an ambulatory safety study to understand if the Revised Safer Dx instrument may be helpful in identification of diagnostic missed opportunities in care of children with type 1 diabetes (T1D) and autism spectrum disorder (ASD). Methods We reviewed two months of emergency department (ED) encounters for all patients at our tertiary care site with T1D and a sample of such encounters for patients with ASD over a 15-month period, and their pre-visit communication methods to better understand opportunities to improve diagnosis. We applied the Revised Safer Dx instrument to each diagnostic journey. We chose potentially preventable ED visits for hyperglycemia, diabetic ketoacidosis, and behavioral crises, and reviewed electronic health record data over the prior three months related to the illness that resulted in the ED visit. Results We identified 63 T1D and 27 ASD ED visits. Using the Revised Safer Dx instrument, we did not identify any potentially missed opportunities to improve diagnosis in T1D. We found two potential missed opportunities (Safer Dx overall score of 5) in ASD, related to potential for ambulatory medical management to be improved. Over this period, 40 % of T1D and 52 % of ASD patients used communication prior to the ED visit. Conclusions Using the Revised Safer Dx instrument, we uncommonly identified missed opportunities to improve diagnosis in patients who presented to the ED with potentially preventable complications of their chronic diseases. Future researchers should consider prospectively collected data as well as development or adaptation of tools like the Safer Dx.

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.

Objectives Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions. Methods This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features. Results Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases. Conclusions This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes.

Objectives Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts. Methods We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility. Results We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms. Conclusions Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

Objectives Low-value care is associated with increased healthcare costs and direct harm to patients. We sought to develop and validate a simple diagnostic intensity index (DII) to quantify hospital-level diagnostic intensity, defined by the prevalence of advanced imaging among patients with selected clinical diagnoses that may not require imaging, and to describe hospital characteristics associated with high diagnostic intensity. Methods We utilized State Inpatient Database data for inpatient hospitalizations with one or more pre-defined discharge diagnoses at acute care hospitals. We measured receipt of advanced imaging for an associated diagnosis. Candidate metrics were defined by the proportion of inpatients at a hospital with a given diagnosis who underwent associated imaging. Candidate metrics exhibiting temporal stability and internal consistency were included in the final DII. Hospitals were stratified according to the DII, and the relationship between hospital characteristics and DII score was described. Multilevel regression was used to externally validate the index using pre-specified Medicare county-level cost measures, a Dartmouth Atlas measure, and a previously developed hospital-level utilization index. Results This novel DII, comprised of eight metrics, correlated in a dose-dependent fashion with four of these five measures. The strongest relationship was with imaging costs (odds ratio of 3.41 of being in a higher DII tertile when comparing tertiles three and one of imaging costs (95 % CI 2.02–5.75)). Conclusions A small set of medical conditions and related imaging can be used to draw meaningful inferences more broadly on hospital diagnostic intensity. This could be used to better understand hospital characteristics associated with low-value care.

Objectives Type 2 diabetes (T2DM) is associated with increased risk for cardiovascular disease (CVD). Whether screen-detected T2DM, based on fasting plasma glucose (FPG) or on HbA 1c , are associated with different risks of incident CVD in high-risk populations and which one is preferable for diabetes screening in these populations, remains unclear. Methods A total of 8,274 high-risk CVD participants were included from the UCC-SMART cohort. Participants were divided into groups based on prior T2DM diagnosis, and combinations of elevated/non-elevated FPG and HbA 1c (cut-offs at 7 mmol/L and 48 mmol/mol, respectively): Group 0: known T2DM; group 1: elevated FPG/HbA 1c ; group 2: elevated FPG, non-elevated HbA 1c ; group 3: non-elevated FPG, elevated HbA 1c ; group 1 + 2: elevated FPG, regardless of HbA 1c ; group 1 + 3: elevated HbA 1c , regardless of FPG; and group 4 (reference), non-elevated FPG/HbA 1c . Results During a median follow-up of 6.3 years (IQR 3.3–9.8), 712 cardiovascular events occurred. Compared to the reference (group 4), group 0 was at increased risk (HR 1.40; 95 % CI 1.16–1.68), but group 1 (HR 1.16; 95 % CI 0.62–2.18), 2 (HR 1.18; 95 % CI 0.84–1.67), 3 (HR 0.61; 95 % CI 0.15–2.44), 1 + 2 (HR 1.17; 95 % CI 0.86–1.59) and 1 + 3 (HR 1.01; 95 % CI 0.57–1.79) were not. However, spline interpolation showed a linearly increasing risk with increasing HbA 1c /FPG, but did not allow for identification of other cut-off points. Conclusions Based on current cut-offs, FPG and HbA 1c at screening were equally related to incident CVD in high-risk populations without known T2DM. Hence, neither FPG, nor HbA 1c , is preferential for diabetes screening in this population with respect to risk of incident CVD.

Objectives The potential of artificial intelligence (AI) chatbots, particularly the fourth-generation chat generative pretrained transformer (ChatGPT-4), in assisting with medical diagnosis is an emerging research area. While there has been significant emphasis on creating lists of differential diagnoses, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in these lists. This short communication aimed to assess the accuracy of ChatGPT-4 in evaluating lists of differential diagnosis compared to medical professionals’ assessments. Methods We used ChatGPT-4 to evaluate whether the final diagnosis was included in the top 10 differential diagnosis lists created by physicians, ChatGPT-3, and ChatGPT-4, using clinical vignettes. Eighty-two clinical vignettes were used, comprising 52 complex case reports published by the authors from the department and 30 mock cases of common diseases created by physicians from the same department. We compared the agreement between ChatGPT-4 and the physicians on whether the final diagnosis was included in the top 10 differential diagnosis lists using the kappa coefficient. Results Three sets of differential diagnoses were evaluated for each of the 82 cases, resulting in a total of 246 lists. The agreement rate between ChatGPT-4 and physicians was 236 out of 246 (95.9 %), with a kappa coefficient of 0.86, indicating very good agreement. Conclusions ChatGPT-4 demonstrated very good agreement with physicians in evaluating whether the final diagnosis should be included in the differential diagnosis lists.

Objectives The preanalytical phase in clinical laboratory diagnostics is currently receiving more and more attention. This term describes one part of actions and aspects of the “brain-to-brain cycle” of the medical laboratory diagnostic procedure that take place before the analytical phase. However, the preanalytical activities, the handling of unsuitable samples and the reporting procedures are neither fully standardized nor harmonized worldwide. The influence of the properties of the blood collection needle must be acknowledged. In this work, we focused on the investigation of the internal structure and size of standardized 21G blood collection needles. Methods All parameters were measured with a scanning electron microscope using a Jeol model JSM-6000PLUS. Our. Results The obtained data shows that the internal surfaces of the needles vary greatly from manufacturer to manufacturer (by around 35 %), and this may play an important role in influencing blood flow and even the risk of blood cell injury (especially hemolysis) during blood drawing. Conclusions The differential actual needle diameters can vary greatly between needle manufactures and this variety may have a significant impact on laboratory values and may also lead to specimen rejection.

Objectives Intraneural ganglionic cysts are non-neoplastic cysts that can cause signs and symptoms of peripheral neuropathy. However, the scarcity of such cases can lead to cognitive biases. Early surgical exploration of space occupying lesions plays an important role in identification and improving the outcomes for intraneural ganglionic cysts. Case presentation This patient presented with loss of sensation on the right sole with tingling numbness for six months. A diagnosis of tarsal tunnel syndrome was made. Nerve conduction study revealed that the mixed nerve action potential (NAP) was absent in the right medial and lateral plantar nerves. The magnetic resonance imaging (MRI) found a cystic lesion measuring 1.4×1.8×3.8 cm as the presumed cause of the neuropathy. Surgical exploration revealed a ganglionic cyst traversing towards the flexor retinaculum with baby cysts. The latter finding came as a surprise to the treating surgeon and was confirmed to be an intraneural ganglionic cyst based on the histopathology report. Conclusions Through integrated commentary by a case discussant and reflection by an orthopedician, this case highlights the significance of the availability heuristic, confirmation bias, and anchoring bias in a case of rare disease. Despite diagnostic delays, a medically knowledgeable patient’s involvement in their own care lead to a more positive outcome. A fish-bone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic delay. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl related to availability heuristic and the sunk cost fallacy.