Volume 12, Issue 4 - Special Issue: 10 Years After “Improving Diagnosis in Health Care”; Where Have We Been and Where Are We Going?; Issue Editors: Mark L. Graber, Edward P. Hoffer, Hardeep Singh

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.

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 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.

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.

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 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.

Since the National Academies of Sciences, Engineering, and Medicine (NASEM) report Improving Diagnosis in Health Care , various research efforts have accelerated progress to understand and improve diagnostic safety. In this opinion piece, we summarize two decades of progress in methods for identifying and learning from diagnostic errors and provide recommendations for future research. Multiple methods have been used to quantify diagnostic errors in various clinical settings, thereby facilitating a deeper understanding of the nature and magnitude of the problem and enabling studies of contributing factors. However, the use of standardized definitions of a diagnostic error and/or diagnostic safety event, a shared mental model for measurement, and more universal application of tools to measure these events across the research enterprise are still needed. We highlight progress in selected research methods and applications, such as co-development with patients, inclusion of multidisciplinary perspectives (such as those from informatics, human factors, and social and cognitive sciences), and the use of sociotechnical approaches. Specific areas where research should be prioritized include the application of cognitive science to the real-world study of diagnostic errors, understanding the costs associated with diagnostic safety, developing and implementing interventions related to patient engagement, evaluating and integrating artificial intelligence, and implementing system-related interventions to improve diagnosis. To promote broad-scale improvement in diagnostic safety over the next decade, we provide several actionable steps and recommendations for various audiences, including researchers, research funders, safety professionals, and policymakers, involved in research and implementation activities for reducing preventable diagnostic harm.

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.

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.

Accurate and timely diagnosis remains one of the most complex and challenging processes in medicine. Diagnostic errors pose a significant burden on patients and healthcare systems, with laboratory-related errors playing a substantial role, especially in the pre- and post-analytical phases of the testing process. However, recent innovations have mitigated some key challenges by optimizing workflows and reducing human errors. Notable advancements include automated systems for specimen check-in, preparation, aliquoting and storage for downstream analysis. Technologies such as automated interference detection, alongside sensors monitoring specimen volume and integrity, have enhanced standardization and reliability. Automated sample storage and retrieval systems have improved traceability and retrospective analyses while preserving specimen integrity. In the analytical phase, automation has facilitated real-time anomaly detection, enabling reflex or repeat testing to ensure result accuracy. The multiple integration of different analytical platforms, coupled with automated quality control features, has reduced inter-system variability, minimized manual errors and enhanced efficiency. Advancements in molecular and genetic diagnostics have enabled more precise and personalized treatments, reducing ineffective therapies and side effects. The ongoing deployment of lab-on-a-chip technology, integration of artificial intelligence, and reinforced patient safety culture highlight the vital role of continuous innovation in laboratory medicine to enhance patient safety. However, several challenges remain, including diagnostic errors from test result misinterpretation, poor sample quality, regulatory and compliance constraints, limited data sharing among laboratories, high cost of advanced diagnostic tools and shortage of trained laboratory professionals and pathologists. Addressing these barriers is essential for further safeguarding patient safety.

Diagnostic radiologists are uniquely positioned to make a difference in the reduction of diagnostic errors in medicine, which was identified as a national priority in a special report of the National Academies of Medicine in 2015. Interest in diagnostic error reduction within the specialty of diagnostic radiology has been accelerated in recent years by the adoption of rapidly evolving technological and process-based solutions for previously identified vulnerabilities (i.e., potential failure modes) in the radiology workflow that are known to increase the risk of errors. Here we describe a range of such potential failure modes contributing to diagnostic error in the practice of diagnostic radiology and summarize evolving efforts to mitigate them. These include fostering the adoption of peer learning and other feedback and education programs, a range of measures under development for ongoing performance evaluation of radiologists and their work-processes, and the deployment of new technologies, most notably artificial intelligence tools, to augment human performance and thereby improve diagnostic accuracy in Radiology.

In the decade since the National Academies of Sciences, Engineering, and Medicine (NASEM) report Improving Diagnosis in Health Care , substantial progress has been made in understanding and teaching diagnostic reasoning. This manuscript reviews key advancements in the science and theory of clinical reasoning, including the shift from exclusive focus on individual cognitive models to those that embrace context and team-based approaches. Recent innovations in diagnosis education, such as development of formal competencies, use of structured reflection, and approaches to assessment are discussed. Despite these gains, challenges remain in translating theory into practice, particularly in curricular innovation and implementation, faculty development, and assessment. The emergence of generative artificial intelligence presents both opportunities and imperatives for reimagining diagnosis education. The authors call for sustained efforts to embed diagnostic excellence across health professions education, emphasizing interprofessional collaboration, patient engagement, and system-level reform to reduce diagnostic error and improve outcomes.