Diagnosis最新文献

The field of diagnostic excellence has advanced considerably in the past decade, reframing diagnosis as a patient safety priority and highlighting the prevalence and harms of diagnostic error. Foundational evidence now supports the development of Diagnostic Excellence Programs; organizational initiatives designed to reduce diagnostic errors and improve system-level and individual performance. While early studies established the epidemiology of diagnostic error across inpatient, emergency, and ambulatory care, newer approaches emphasize continuous, systematic surveillance to inform targeted improvements. Emerging frameworks, such as the DEER Taxonomy and root cause or success cause analyses, help classify drivers of both failures and successes in diagnostic processes. Effective programs must address system factors, including electronic health record design, workload, team structures, and communication, while also enhancing individual clinician performance through feedback, diagnostic reflection, cross-checks, and coaching. Patient engagement represents a critical but underdeveloped dimension; strategies such as structured communication frameworks, patient-family advisory councils, and electronic tools co-designed with patients aim to foster shared diagnostic decision-making and improve transparency. Artificial intelligence (AI) holds promise to accelerate measurement, streamline clinical workflows, reduce cognitive load, and support communication, though careful implementation and oversight are required to ensure safety. Ultimately, Diagnostic Excellence Programs will succeed by embedding diagnostic safety into institutional standards of care, providing clinicians with ongoing, psychologically safe opportunities for recalibration, and leveraging AI to scale surveillance and improvement activities.

Objectives: Internists do not feel competent in diagnosing and treating common dermatologic conditions. Teaching clinical reasoning principles in graduate medical education can improve trainees' diagnostic accuracy, but previously published dermatology curricula did not emphasize these skills. We developed a novel curriculum applying clinical reasoning concepts to teach internal medicine (IM) residents how to describe dermatologic lesions, develop differential diagnoses, and use deliberate reflection to improve diagnostic accuracy for four common dermatologic complaints.

Methods: Five asynchronous, interactive 10-min online modules were developed and administered to all 152 IM residents at a large academic residency program in 2023. Residents were evaluated for their ability to describe dermatologic lesions, their diagnostic accuracy, and their deliberate reflection skills. Residents completed this novel assessment before, immediately after, and four months after the curriculum. Linear mixed effects regression models were used to assess changes in assessment scores over time.

Results: One hundred eleven of 152 residents (73 %) participated in the study. Total assessment scores improved between pre-test and post-test (mean difference 0.98, 95 % CI [0.32, 1.64], p=0.004), but not between pre-test and delayed post-test. Residents who completed 4 or 5 modules improved from pre-test to post-test in the description component (mean difference 0.46, 95 % CI [0.01, 0.91], p=0.043) and the final diagnosis/treatment component (mean difference 0.69, 95 % CI [0.22, 1.17] p=0.004), but not the deliberate reflection component.

Conclusions: An interactive, asynchronous clinical reasoning-based dermatology curriculum can improve IM resident knowledge of common dermatologic complaints, particularly immediately after participation and if most modules are completed.

Objectives: AL amyloidosis is a rare disorder caused by deposition of misfolded immunoglobulin light chains as amyloid fibrils in vital body organs. The diagnosis requires a triad: identification of a monoclonal protein (via serum/urine studies), histological confirmation of amyloid deposits and clinical evidence of organ dysfunction. Serum protein electrophoresis (SPEP) and immunofixation (SIFE) are first-line tests but fail to detect monoclonal proteins in 10-20 % of cases, particularly those with low plasma cell burden or rapid renal excretion of FLCs. Serum free light chain (FLC) assays and urine immunofixation (UIFE) are indispensable in such scenarios but tissue biopsy remains the diagnostic cornerstone.

Case presentation: We discuss a 67-year-old man who presented with a 4-month history of progressive bilateral lower limb edema, fatigue and frothy urine. Initial evaluation revealed nephrotic-range proteinuria. SPEP and SIFE showed no monoclonal bands. X-ray skull revealed multiple punched-out lytic lesions, raising suspicion of an underlying plasma cell dyscrasia. UIFE identified a monoclonal lambda light chain. Renal biopsy confirmed amyloid deposition. The patient was initiated on bortezomib-dexamethasone chemotherapy, targeting the plasma cell clone to halt amyloid production.

Conclusions: This case underscores the diagnostic challenges of AL amyloidosis, particularly in serum-negative presentations with low tumor burden. Role of UIFE was pivotal in detecting monoclonal lambda light chains excreted via the kidneys, overcoming the limitations of serum-based assays. The absence of serum monoclonal proteins in early-stage disease mandates a multimodal approach: integrating clinical suspicion (e.g., nephrotic syndrome, cardiomyopathy, or neuropathy in older adults), urine studies, serum FLC assays, and targeted biopsies.

Objectives: Among all of the swollen joints undergoing an aspiration in primary care, approximately 92 % are of nonseptic cause. This study therefore sought to develop a predictive model, based on simple clinical and paraclinical data, with the aim of predicting the aseptic nature of joint effusion.

Methods: This is a cohort, prospective, monocentric study. Some explanatory variables were predetermined on the basis of the literature review. A predictive model has been established based on these variables. In order to prioritise the negative predictive value, a cut-off point considering the best specificity for an observed sensitivity greater than or equal to 98 % was retained.

Results: A total of 328 participants, 49.1 % of whom were women, were included in this study, with a median age of 69 years. The median duration of evolution of joint effusion before the puncture was 30 days. Joint fluid had inflammatory characteristics in 46.0 % of cases and 8 septic arthritis were identified. The area under the receiver operating characteristic (ROC) curve of the predictive model was evaluated at 0.93. The model includes the maximum temperature, the polyarticular nature of the clinical picture and the macroscopic appearance of the joint fluid.

Conclusions: This study made it possible to develop a simple and easily accessible predictive model in a primary care setting. This tool could make it possible to exclude a priori the septic aetiology of one out of four native joint effusions. Its performances remain to be determined on an independent population in a subsequent study (confirmation cohort in progress).

The NASEM report suggested that health information technology could reduce diagnostic error if carefully implemented. Computer-based diagnostic decision support systems have a long history, but to date have not had major impact on clinical practice. Current research suggests that AI-enabled decision support systems, properly integrated into clinical workflows, will have a growing role in reducing diagnostic error. The history, current landscape and anticipated future of AI in diagnosis are discussed in this paper.

Objectives: To examine the impact of haematological and biochemical abnormalities on the failure of point-of-care troponin T (POCT) testing in patients with suspected acute coronary syndrome (ACS).

Case presentation: Five patients underwent Roche Troponin T Card Test (lateral flow immunoassay) in an emergency setting. Despite correct sampling and procedural adherence, no valid results were obtained resulting in an aborted test. Laboratory analysis revealed severe anaemia, polycythaemia, leucocytosis, thrombocytopenia, and hepatic and renal dysfunction across cases. After stabilization, repeat POCT yielded valid results in all survivors, correlating with normalized haematological parameters.

Conclusions: Pre-analytical factors such as extreme haematocrit, leucocytosis, and biochemical derangements can cause POCT failure. Pre-testing screening and guideline updates are essential to optimize POCT reliability in acute care.

Objectives: Diagnostic reasoning in clinical medicine is permeated by uncertainty. This study aims to analyze how errors in the estimation of pre-test probability affect the application of Bayesian inference in diagnostic reasoning.

Methods: We examined the propagation of pre-test probability misestimation through Bayes' Theorem, focusing on its interaction with different likelihood ratios and pre-test probabilities. The analysis explored the mathematical consequences of prior misestimation on post-test probability estimation.

Results: We demonstrate that misestimation of prior probabilities has a nonlinear impact on posterior probabilities, with errors propagating differently depending on the likelihood ratio of the diagnostic test and the real pre-test probability. Misestimated priors can produce substantial distortions in posterior probabilities, leading to misplaced confidence in diagnostic test results.

Conclusions: Accurate estimation of pre-test probability is essential for the validity of Bayesian diagnostic reasoning. Objective and evidence-based approaches to pre-test probability estimation are necessary to minimize diagnostic errors and to enhance the reliability of clinical decision-making.

Objectives: Referral documentation may either contribute to diagnostic excellence or play a role in diagnostic errors (DEs), but its exact impact remains unclear. This study investigates the association between referral documentation and DEs among patients initially evaluated by another hospital or department and subsequently referred to the general internal medicine (GIM) outpatient clinic of an acute care tertiary hospital in Japan.

Methods: This cross-sectional study analyzed outpatients who visited the GIM outpatient clinic between April 1, 2017 and March 31, 2023. Patients initially evaluated at another medical facility or department, who then visited the GIM outpatient clinic, and were subsequently readmitted unexpectedly within 14 days after GIM outpatient clinic visit were included. DEs were identified using the Revised Safer Dx Instrument. Errors were analyzed using the Diagnostic Error Evaluation and Research (DEER) taxonomy. Logistic regression analysis was performed to assess the relationship between referral letters and DEs.

Results: Of 80 patients, 29 (36.3 %) experienced DEs. Referral letters were present for 52 (65.0 %) patients. The proportion of DEs was lower in the referred patients compared to non-referred patients (25.0 vs. 57.1 %; p-value=0.004). After adjusting for age, sex, race, multimorbidity, type of previous physicians, and post-graduate year of the GIM physician, the presence of a referral letter was associated with a substantially likelihood of DEs (OR=0.20, 95 % CI: 0.06-0.62, p-value=0.005).

Conclusions: The presence of a referral letter facilitates accurate diagnoses while markedly reducing DEs. Healthcare systems should consider promoting the proper use of referral systems.

The coronavirus disease 2019 (COVID-19) pandemic has placed laboratory medicine at the forefront of public health and clinical care. Larger use of social media and official communication platforms raised public awareness of laboratory science, driving demand for rapid, accurate diagnostic information and shifting expectations around access and interpretation of testing. Laboratory medicine, rooted in accuracy, precision, reproducibility and clinical relevance, has advanced from basic diagnostics to sophisticated molecular and data-driven platforms. Yet, literature and policy on coordinated international laboratory networks, especially for surveillance and emergency response, remain limited. This opinion paper introduces the concept of "global-of-care testing", encompassing globally connected diagnostic infrastructures with regional adaptability, robust governance, and sustained investment in technology and workforce. Laboratory network design must account for geography and population density in allocating facilities. Integrated systems require automation capable of interfacing across multiple platforms (preanalytical processing, clinical chemistry, immunochemistry, hematology, coagulation, urinalysis and even molecular diagnostics and mass spectrometry) to optimize workflows, support real-time decision-making, facilitate remote collaboration and maintain rigorous quality assurance. A decentralized yet interconnected model allows peripheral laboratories to actively participate in clinical decision-making through shared protocols, telemedicine and integrated data, ultimately reducing turnaround times, improving responsiveness and enhancing patient-centred care. Embedding Value-Based Laboratory Medicine (VBLM) within this framework ensures that diagnostics are aligned with health outcomes in a multidisciplinary ecosystem organized around patient needs. The future of laboratory medicine will hence depend on evidence-based reforms that integrate technology, reorganize systems and reinforce governance for promoting quality, equitable access and sustainable precision healthcare.