Clinical reasoning is fundamental for effective clinical practice. Traditional consultation models for teaching clinical reasoning or conventional approaches for teaching students how to make a diagnosis or management plan that rely on learning through observation only, are increasingly recognised as insufficient. There are also many challenges to supporting learners in developing clinical reasoning over time as well as across different clinical presentations and contexts. These challenges are compounded by the differences in how experts and novices make sense of clinical information, and the different cognitive processes each use when processing and communicating this information using precise medical language. Diagnostic errors may be due to cognitive biases but also, in a majority of cases, due to a lack of clinical knowledge. Therefore, effective educational strategies to develop clinical reasoning include identifying learners\' knowledge gaps, using worked examples to prevent cognitive overload, promoting the use of key features and practising the construction of accurate problem representations. Deliberate reflection on diagnostic justification is also recommended, and overall, contributes to a growing number of evidence-based and theory-driven educational interventions for reducing diagnostic errors and improving patient care.

OBJECTIVE: Biological variation is a relevant component of diagnostic uncertainty. In addition to within-subject and between-subject variation, preanalytical variation also includes components that contribute to biological variability. Among these, daily recurring, i.e., diurnal physiological variation is of particular importance, as it contains both a random and a non-random component if the exact time of blood collection is not known.
METHODS: We introduce four time-dependent characteristics (TDC) of diurnal variations for measurands to assess the relevance and extent of time dependence on the evaluation of laboratory results.
RESULTS: TDC address (i) a threshold for considering diurnality, (ii) the expected relative changes per time unit, (iii) the permissible time interval between two blood collections at different daytimes within which the expected time dependence does not exceed a defined analytical uncertainty, and (iv) a rhythm-expanded reference change value. TDC and their importance will be exemplified by the measurands aspartate aminotransferase, creatine kinase, glucose, thyroid stimulating hormone, and total bilirubin. TDCs are calculated for four time slots that reflect known blood collection schedules, i.e., 07:00-09:00, 08:00-12:00, 06:00-18:00, and 00:00-24:00. The amplitude and the temporal location of the acrophase are major determinates impacting the diagnostic uncertainty and thus the medical interpretation, especially within the typical blood collection time from 07:00 to 09:00.
CONCLUSIONS: We propose to check measurands for the existence of diurnal variations and, if applicable, to specify their time-dependent characteristics as outlined in our concept.

Until the discovery of the gene for cystic fibrosis (CF) in 1989, diagnostic developments were limited, and treatment focused on symptom alleviation. However, following the genetic breakthrough, some 2,000 mutations of the gene have been identified. More recently CF transmembrane conductance regulator modulator triple therapy (CFTRm) has been introduced in the form of triple therapy with ivacaftor, lumacaftor and tezacaftor (ETI), in the United States from 2019, Europe from 2020 and then Australia from 2021. The new treatment option has revolutionised both the quality of life and life expectancy of many persons diagnosed with CF. This editorial reviews major developments in the clinical care that can now be provided to patients, and reflects on the legal and ethical ramifications of the improved situation for many patients in the contexts of medical negligence, damages assessment, family law and criminal law. It also considers the difficult issues of access and equity caused by the limited availability of the triple therapy in low- and middle-income countries.

BACKGROUND: Non-A non-B (NANB) aortic dissections are uncommon and frequently unrecognized diseases. However, their proper identification is crucial given the unpredictable behavior of the dissected aorta with potential mortality and increased morbidity. We investigate the accuracy of radiological computed tomography angiography (CTA) reports in the diagnosis of acute NANB and the risk related to delayed recognition or misdiagnosis.
METHODS: The pretreatment contrast CTA of all consecutive patients admitted with acute aortic dissection (AAD) in a University Hospital in London (UK) between January 2017 and May 2023 were reviewed to retrospectively verify the accuracy of CTA reports in the diagnosis of NANB AAD (B1-2D The risk related to the delayed diagnosis (morbidity, mortality, and hospital readmissions) were evaluated as secondary outcomes. The study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.
RESULTS: Overall, 588 aortic CTAs were reviewed for a total of n = 393 (66.8%) type A AADs, n = 171 (29%) type B AADs and n = 25 (4.3%) NANB AADs (n = 16, 64% men, mean age 60.56, standard deviation ± 14.6 years). While no case of misdiagnosis was identified in Type A or B AAD groups, in NANBs only about a third of cases (n = 9, 36%) were immediately indicated as \"NANB\" (n = 2, 8%) or \"B with retrograde extension into the arch\" (n = 7, 28%), n = 8 cases (32%) were described generically as \"arch dissections\" (n = 6, 24%) or \"type A and B\" AAD (n = 2, 8%). The remaining 32% of patients received a diagnosis that did not include mention of the arch, as n = 6 (24%) cases were reported to be \"type A″ and n = 2 (8%) to be \"type B″ AADs. Despite the heterogeneity of terms used to describe NANB AAD, no case of cardiac tamponade, new onset malperfusion nor neurological complications were reported, and no sudden death nor home-discharge and readmission while waiting for the proper diagnosis.
CONCLUSIONS: The heterogeneity of terms used to describe NANB aortic dissection highlights the need for increased awareness, adoption of in guideline based classification systems, and further education to better understand and correctly address this challenging entity, minimizing misdiagnosis in ambiguous or difficult cases.

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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.

OBJECTIVE: Diagnostic disparities are preventable differences in diagnostic errors or opportunities to achieve diagnostic excellence. There is a need to summarize solutions with explicit considerations for addressing diagnostic disparities. We aimed to describe potential solutions to diagnostic disparities, organize them into an action-oriented typology with illustrative examples, and characterize these solutions to identify gaps for their further development.
METHODS: During four human-centered design workshops composed of diverse expertise, participants ideated and clarified potential solutions to diagnostic disparities and were supported by environmental literature scan inputs. Nineteen individual semi-structured interviews with workshop participants validated identified solution examples and solution type characterizations, refining the typology.
RESULTS: Our typology organizes 21 various types of potential diagnostic disparities solutions into four primary expertise categories needed for implementation: healthcare systems\' internal expertise, educator-, multidisciplinary patient safety researcher-, and health IT-expertise. We provide descriptions of potential solution types ideated as focused on disparities and compare those to existing examples. Six types were characterized as having diagnostic-disparity-focused examples, five as having diagnostic-focused examples, and 10 as only having general healthcare examples. Only three solution types had widespread implementation. Twelve had implementation on limited scope, and six were mostly hypothetical. We describe gaps that inform the progress needed for each of the suggested solution types to specifically address diagnostic disparities and be suitable for the implementation in routine practice.
CONCLUSIONS: Numerous opportunities exist to tailor existing solutions and promote their implementation. Likely enablers include new perspectives, more evidence, multidisciplinary collaborations, system redesign, meaningful patient engagement, and action-oriented coalitions.

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

BACKGROUND: Anemia is a complex condition with diverse causes and poses diagnostic challenges amid the expanding landscape of laboratory testing. Implementation of an anemia diagnostic management team (DMT) can aid health care providers in navigating this complexity.
METHODS: This quasi-experimental study assessed the impact of an anemia DMT on laboratory test ordering by primary care providers for anemic patients. This study included adult patients (≥18 years) with anemia (hemoglobin <12.0 g/dL for nonpregnant women, hemoglobin <13.0 g/dL for men) presenting to a family medicine clinic. Cases reviewed by the DMT (n = 100) were compared with a control group (n = 95).
RESULTS: The DMT recommended additional testing for 76 patients. Significantly more patients in the DMT group underwent follow-up tests compared with controls (59% vs 34%; P < .001). Moreover, the DMT group underwent a higher mean number of tests per patient (1.70 ± 2.2 vs 0.95 ± 1.9; P = .01).
CONCLUSIONS: Implementation of an anemia DMT influenced follow-up testing patterns in anemic patients, potentially enhancing diagnostic thoroughness and patient care.

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.
METHODS: 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.
METHODS: 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.
CONCLUSIONS: 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.

OBJECTIVE: 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.