Diagnosis最新文献

In the decade before and 10 years since the National Academies of Sciences, Engineering, and Medicine (NASEM) Improving Diagnosis in Health Care report, the U.S. Agency for Health Research and Quality (AHRQ) has played a major role in convening, coordinating and funding research and quality improvement efforts to learn from and prevent diagnostic errors. As part of a 10th Anniversary reflection of progress since the 2016 NASEM report, we review the historic diagnostic safety contributions of AHRQ and contemplate AHRQ's future at a critical time given recent staffing reductions and budget cuts. AHRQ contributions have included funding annual Diagnostic Error in Medicine conferences, studies on error epidemiology, projects to improve timeliness and accuracy of specific diagnoses (e.g. chest pain, dizziness), diagnosis improvement in various settings (ED, inpatient, primary care,) and disciplines (laboratory, radiology). In the past decade AHRQ has funded two major diagnosis improvement initiatives via a) its Patient Safety Learning Laboratories (PSLL) projects which take a systems engineering approach to improve clinical care processes, and b) 10 Diagnostic Centers of Excellence (DCE) working to develop systems, measures and new technologies to improve diagnostic safety and quality. Support for patient engagement has been a major strategic emphasis for AHRQ's projects, tools, and diagnosis safety information disseminated. While facing an uncertain future, federal funding and leadership is needed now more than ever given the extent of the problems that have been documented and need to build on progress to date. We project a bold vision for a bigger, better future AHRQ.

Objectives: We will explore the diagnostic similarities of spindle cell neoplasms and the attributed heuristics that lead to misdiagnosis biases. The biases explored will include availability bias and anchoring bias, with a discussion on the events leading to their formation.

Case presentation: A 58-year-old African American male with a past medical history of well-controlled HIV presented to the dermatology clinic for a two-year history of several persistent skin nodules on his lower legs. One lesion on his left lateral calf, a 1.5 cm dome-shaped nodule with a centralized keratinous plug, was suspicious for squamous cell carcinoma (SCC), prompting a shave biopsy. The dermatopathology report identified the lesion as dermatofibrosarcoma protuberans (DFSP) with CD34 positivity and the patient was referred for Mohs Micrographic Surgery. Frozen sections during Mohs surgery revealed concern for an alternative diagnosis, which was then confirmed as Kaposi Sarcoma.

Conclusions: This case highlights the susceptibility of dermatology to misdiagnosis. Availability bias in the clinical setting led to an inadequate biopsy method. Further anchoring bias then potentially influenced histologic interpretation and management decisions. Insufficient appreciation of Kaposi Sarcoma development in the setting of well-controlled HIV also further influenced the diagnosis rendered. Mohs Surgery evaluation allowed for de-biased clinical and histologic assessment, correcting diagnosis. Several overlying factors, such as time pressures, knowledge gaps, and technique limitations, create a reliance on cognitive heuristics. Recognizing these external pressures can help clinicians enhance diagnostic accuracy by systematically considering alternative diagnoses.

The 2015 National Academy of Sciences, Engineering and Medicine report, Improving Diagnosis in Medicine, is known for its inclusive approach to patients. This paper explores the evolution of research in patient engagement in diagnosis over the past decade, drawing from peer-reviewed literature, policy initiatives, and institutional programs. Major themes include expansion from practical patient aids to co-designed patient reporting systems and patient-reported measures; a focus on diagnostic equity across all populations and conditions; and the emergence of comprehensive multidisciplinary theories framing a "diagnostic ecosystem." Drivers of change include long-standing frameworks for patient engagement, advances in health information technology, open access to medical records, and regulatory initiatives designed to enhance patient autonomy and enable systematic capture of patient perspectives. Future research in this area should improve patient-reported measures and reporting systems, identify and address diagnostic disparities, and co-create pathways to fully embrace and value the emerging patient voice.

In America, medical research is largely driven by large investments of federal funding administered by government agencies. However, the problem of diagnostic error falls outside the usual funding categories for government-sponsored biomedical research. While federal resources are vital to support academic institutions and research teams, private funders and philanthropic organizations often contribute a significant source of support for medical research, particularly for critical gaps in funding and for high risk or innovative ideas. In 2017, the Gordon and Betty Moore Foundation launched an initial exploration into work that addressed diagnostic error and subsequently committed $85 million to their Diagnostic Excellence Initiative. Their model of strategic philanthropy proposed a pathway to improved diagnostic outcomes. Their three-pronged strategy and a summary of their portfolio of work for Diagnostic Excellence is described in this article. Lessons from their experience are worth reflection: real-world problems with diagnosis and reliable delivery of diagnostic care are complex and solutions require coordinated efforts across many disciplines; and efforts are more effective when done in partnership with like-minded organizations. We celebrate the contributions of the Moore Foundation and acknowledge their contribution to helping build a community committed to diagnostic excellence, develop infrastructure for quality improvement, and advance ideas for the use of technology to improve care.

The oldest medical school of modern civilization, in Salerno, Italy, prioritized the study of philosophy, logic, and reasoning. We first retrace the history of how clinical reasoning and its perceived importance has evolved, culminating ultimately in the 2015 National Academies report on diagnostic error in healthcare. The report clearly emphasized the fundamental role of clinical reasoning in diagnosis, and the critical need to optimize the cognitive elements of diagnosis to prevent diagnostic errors in the future. The dual processing paradigm, envisioning both intuitive and rational pathways, is central to current understandings of clinical reasoning. The importance of knowledge, the impact of cognitive biases, the influence of context, and many other 'adjacent' factors also impact the likelihood of arriving at the correct diagnosis. Medical education needs to re-prioritize cognition over content, and teach clinical reasoning interprofessionally. Emphasizing rationality and recognizing cognitive and affective bias are key. A host of interventions have been proposed: patient engagement, second opinions, reflection, improving teamwork, and using AI are all well justified and worthy of trials.

Since the 2015 National Academies of Sciences, Engineering, and Medicine report on Improving Diagnosis in Health Care, global awareness of diagnostic safety has grown substantially. Progress has been most visible in high-income countries, with emerging international research networks, conferences, and educational programs. Australia and New Zealand have advanced incident reporting systems, specialty-specific diagnostic safety tools, and educational resources. European initiatives have expanded research on clinical reasoning, bias, and safety-netting, developed competency-based curricula, and investigated digital innovations including decision support systems. Japan has built on a strong tradition of clinical reasoning mastery, advancing theoretical frameworks, cultural analysis, and AI-based diagnostic support, and hosting major regional conferences. Despite these gains, engagement remains uneven, with limited data from low- and middle-income countries (LMICs). Barriers include resource constraints, underdeveloped infrastructure, and differing disease burdens that challenge the transferability of AI and other innovations. Future progress requires clear, measurable objectives across five domains: research, education, practice improvement, patient engagement, and policy. Recommendations include establishing national diagnostic error databases, promoting multicenter research in underrepresented settings, expanding standardized curricula, implementing structured audit-and-feedback systems, integrating patient perspectives, and embedding diagnostic safety indicators in policy and reimbursement frameworks. International collaboration, context-sensitive methodologies, and robust governance for emerging technologies are critical to ensure equitable improvements. By leveraging shared learning, strengthening capacity in LMICs, and aligning efforts with global policy frameworks, the diagnostic safety movement can evolve from fragmented initiatives to a cohesive, sustainable worldwide strategy, aiming for safer, more reliable diagnosis by 2035.

The Society to Improve Diagnosis in Medicine (SIDM) played a pivotal role in elevating diagnostic error from an overlooked aspect of patient safety to a recognized healthcare priority during its thirteen-year history (2011-2024). Through strategic advocacy, coalition building, and engagement with policymakers, SIDM secured dedicated federal funding for diagnostic safety research and promoted diagnostic excellence as a critical healthcare imperative. This article examines the organization's establishment, evolution and lasting impact on the field of diagnostic safety across research, education, practice improvement, and patient engagement. A crowning achievement was SIDM's success in stimulating the Institute of Medicine to study the problem, resulting in the landmark 2015 report Improving Diagnosis in Health Care (1). Despite the transformative impact of this report, substantial challenges remain in reducing harm from diagnostic error. We conclude with a call to address gaps in three critical areas: awareness, measurement, and implementation.

Objectives: Diagnostic errors are a significant source of patient harm and occur more frequently in older adults due to comorbidities, symptom ambiguity, and communication barriers. However, how these errors differ between older and younger patients remains unclear. The aim of this study was to examine the characteristics of diagnostic errors in older patients using published case reports.

Methods: We performed a secondary analysis of 534 case reports from a systematic review. Cases were divided into older (≥65 years, n=115) and younger (<65 years, n=419) groups. Data were extracted and coded using the diagnostic error evaluation and research (DEER), reliable diagnosis challenges (RDC), and generic diagnostic pitfalls (GDP) frameworks.

Results: Older patients had significantly more DEER codes per case than younger patients (2.5 vs. 2.0; p=0.01). Key DEER codes were more frequent in older adults, including "Physical examination: Failure in weighing" (7.8 vs. 2.9 %), "Assessment: Failure/delay in considering the diagnosis" (74.8 vs. 64.0 %), and "Assessment: Failure/delay to recognise/weigh urgency" (7.8 vs. 2.9 %). In RDC, "Diagnosis of complications" was also more common in older patients (11.3 vs. 5.3 %). No significant differences were found in GDP coding.

Conclusions: Diagnostic errors involving failure to consider the correct diagnosis, recognize urgency, and identify complications were more common in older patients. Understanding these mechanisms is essential to develop diagnostic strategies specific to older patients.

Objectives: To critically examine the phenomenon of threshold overfitting in diagnostic accuracy research and evaluate its methodological implications through a structured review of relevant literature.

Methods: This article presents a narrative and critical review of methodological studies and reporting guidelines related to threshold selection in diagnostic test accuracy. It focuses on the misuse of post hoc thresholds, the misapplication of bias assessment tools such as QUADAS-2, and the frequent absence of independent validation. In addition to identifying these structural flaws, the article proposes a set of five concrete safeguards - ranging from transparent reporting to rigorous risk of bias classification - designed to mitigate threshold-related bias in future diagnostic studies.

Results: Thresholds are frequently derived and evaluated within the same dataset, inflating sensitivity and specificity estimates. This overfitting is seldom acknowledged and is often misclassified as low risk of bias. QUADAS-2 is frequently misapplied, with reviewers mistaking the mere presence of a threshold for proper pre-specification. The article identifies five key safeguards to mitigate this bias: (1) clear declaration of pre-specification, (2) justification of threshold choice, (3) independent validation, (4) full performance reporting across thresholds, and (5) rigorous application of bias assessment tools.

Conclusions: Threshold overfitting remains an underrecognized but methodologically critical source of bias in diagnostic accuracy studies. Addressing it requires more than awareness - it demands transparent reporting, proper validation, and stricter adherence to methodological standards.

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.