The global progress for improving diagnosis: what we’ve learned, what comes next
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Taro Shimizu
,
Wolf E. Hautz
,
Charlotte van Sassen
Abstract
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.
Progress since 2015 and the current state
A decade ago, the ground-breaking report from the National Academies of Science, Engineering, and Medicine on “Improving Diagnosis in Health Care” (‘the NASEM report’) brought global attention to the importance of diagnostic safety [1]. While this report has been most impactful in the US, this past decade has seen important progress in advancing diagnostic safety internationally as well. The number of international researchers has grown appreciably, and over the past three years (April 2022-March 2025 issue), studies on diagnosis accounted for 10.9 % of original research articles in BMJ Quality and Safety, compared to roughly zero 10 years ago [2]. Besides the annual meetings in the US, international meetings on diagnostic safety have now been hosted regularly in Australia, Europe, and Japan.
In Australia and New Zealand, these meeting have been instrumental in raising awareness and gaining the attention of specialist societies, medical indemnity organizations and Universities. The result has been a growing number of publications including on the state of the science [3], [4], [5], critical thinking [6] and interpersonal communication and patient engagement in diagnosis [7]. Individual hospital departments have published data on their diagnostic error rates [8]. Examples of tangible progress have included the development of a specialty specific incident reporting system in emergency medicine whereby diagnostic safety events can be reported, analyzed and learnings disseminated widely to the specialty [9], [10], [11]. A number of medical indemnity organizations have used the conferences as a resource for their members to learn about diagnostic error and safety [12], [13], [14], [15]. In the pursuit of educating for diagnostic excellence, one medical school has developed a tool for students based on human factors principles to assist in clinical reasoning and avoid traps in the diagnostic process [16]. Recent studies have also highlighted key system gaps that impact diagnostic processes, such as the frequent absence of documented clinical impressions in emergency department assessments [17] and the strong predictive value of caregiver concern in identifying critical illness in children [18].
Over the past decade, Europe has made notable advances in diagnostic safety, particularly through clinical reasoning education and digital innovation. In several European countries, research groups on diagnostic safety emerged or expanded. For many years, research on diagnostic safety has been carried out in the Netherlands, with a broad range of projects focused on understanding clinical reasoning and enhancing diagnostic safety. Recent work includes studying real-world diagnostic errors, such as malpractice claims and serious incident reports [19], exploring the role of shared-decision making in the diagnostic process [20] and examining the reasoning processes behind clinical decisions, including bias [21], 22], overconfidence [23], 24], and metacognition [25], [26], [27]. These insights have been used to improve diagnostic reasoning, through approaches such as deliberate reflection [28], structured feedback [29], 30], and learning from real diagnostic error cases [31], 32]. In Sweden, attention for diagnostic safety has increased through work on malpractice claims analyses and the development of safety-netting interventions [33], 34]. Safety-netting has also been extensively studied in the UK, where there is a strong focus on the early detection of cancer [35], 36]. In addition to research on safety-netting, the UK has a particularly strong track record in epidemiological studies related to cancer diagnosis [37], 38], as well as ways to improve early cancer detection, work that continues to have international impact [39], 40]. In Switzerland, research has focused on understanding diagnostic quality in emergency departments [41], collaborative decision-making [42], 43], and, more recently, a large clinical trial investigating the impact of implementing a diagnostic decision support system in emergency medicine [44]. Technological innovations for diagnostic safety have been a central theme in European research. Besides the Swiss clinical trial, several groups started research on this topic [45], [46], [47]. Another important development over the last 10 years is the increase of international collaborations within Europe, specifically within the domain of clinical reasoning education. Two examples of international European consortia that are focused on clinical reasoning education are DID-ACT and D-CREDO [48], 49]. These consortia have developed international, structured, competency-based clinical reasoning curricula for students and educators, promoting reflective, evidence-based diagnostic reasoning across institutions. While DID-ACT focused on integrating clinical reasoning into medical education through virtual patient cases and faculty training, D-CREDO started in 2024 and builds on this by introducing teaching about the use of digital health tools in practice and education of clinical reasoning. The project emphasizes critical reflection on these technologies, helping learners and educators understand their benefits and limitations in real-world diagnostic practice. Together, advancements in collaboration and research on intervention development and testing including technological innovations, have made a strong contribution to improving diagnostic safety in Europe and beyond.
Within the global landscape of diagnostic excellence, Japan has built a strong and distinctive foundation rooted in its long tradition of clinical reasoning mastery. This alignment is rooted in a longstanding culture of pursuing mastery of the craft of clinical reasoning, as reflected in case-based conferences conducted over decades by physician groups as part of regionally structured and coordinated efforts nationwide to advance clinical reasoning [50]. Building on this tradition, the country has developed a robust body of theoretical research on clinical reasoning expertise and diagnostic cognition [51], most notably through the discipline of Diagnostic Strategy, an emerging field that systematically develops and refines strategic frameworks and practical tools to guide physicians’ diagnostic thinking and decision-making. One example of these frameworks is the Pivot and Cluster Strategy [52]. This field has also generated significant advances in closely related domains, including cognitive psychology, decision-making under uncertainty, and the study of creativity in reasoning [53], 54]. Situation-based studies have also explored how contextual factors in clinical settings influence diagnostic decisions [55], including patterns of error in atypical conditions [56]. Alongside these developments, researchers have actively advanced technological approaches to support diagnostic decision-making. These include the design of automated history-taking systems [57] and evaluations of AI tools such as ChatGPT in generating and validating differential diagnoses [58], [59], [60]. The research team has positioned itself as a proactive contributor to global innovation in AI-based diagnostic support. Cultural context has also shaped the diagnostic excellence movement. While sometimes seen as a barrier to transparency, a national tendency toward low tolerance for error has fueled academic inquiry into structural and human contributors to diagnostic failure [61]. Studies have examined malpractice claims [62], errors in acute care settings [63], 64], as well as educational innovation including gamification to foster cost-aware diagnostic decision-making [65] and national surveys examining how medical students conceptualize diagnostic error [66]. Together, these academic, technological, and cultural efforts have established a strong foundation for Japan’s leadership in diagnostic excellence. The first national diagnostic excellence conference in 2023 attracted over 1,500 participants across the nation, reflecting broad professional interest [67], followed by Diagnosis Insight Asia 2024 (DIA24) conference [68]. This leadership now plays a central role in promoting diagnostic improvement in the eastern countries.
In 2024 a new international benchmark was realized, with the World Health Organization hosting a formal international meeting on diagnostic safety, and designating this the focus of International Patient Safety Day [69]. Although this international progress is important and laudable, it has been uneven. We know very little about the incidence and types of diagnostic error in low and middle-income countries (LMIC). And not all countries are even engaged in diagnostic safety improvement: A WHO member state survey found that only 47 % of countries have initiatives addressing diagnostic safety [69]. Studies in high-income countries document challenges ensuring high-quality care throughout the diagnostic process, and it is highly likely that these same problems exist in LMIC as well, but magnified by resource limitations. LMIC face particular barriers to participating in diagnostic research, including limited access to diagnostic testing, underdeveloped research infrastructure, and insufficient funding for academic initiatives. Understanding global variations is important for the future, because they operate in distinct contexts. In particular, some regions may face fundamentally different root causes of diagnostic failure. It is therefore essential that these local challenges be studied using established research methodologies, ideally adapted to fit the specific context, to ensure inclusive progress in diagnostic safety worldwide. Diseases that are endemic in one region may be virtually nonexistent in others. Thus, while a small rate of misdiagnosis may be acceptable where a disease is rare, the same rate of misdiagnosis may burden a considerable population where the disease is endemic. Further, the differences in disease prevalence between regions become particularly important for the transferability of novel technical solutions: artificial intelligence (AI) programs that have been trained on a North American dataset may have encountered only very few cases of dengue or malaria, and may thus be less useful in regions where such diseases are more prevalent. Moreover, given the shortage of medical workforce in the latter regions, the temptation to use these flawed AI tools may be considerable.
At this ten-year anniversary since the NASEM report, we conclude that there has been some progress internationally in recognizing the importance of diagnostic quality and safety but much remains to be done. This is an appropriate time to pause and consider how progress can be sustained. What is the path forward? International diagnostic initiatives should set clear, measurable, and time-bound objectives [70]. We suggest that prioritization and planning be done in an organized way, using the five domains of progress that have been widely adopted by SIDM and CIDM: research, education, practice improvement, patient engagement, and policy. This approach provides a practical framework for planning and evaluating diagnostic improvement initiatives internationally, and also avoids overlap with the forthcoming WHO framework.
Looking to the future
Research: strengthening evidence with global collaboration
Recently, international efforts have highlighted diagnostic research priorities from both professional and patient perspectives [71], 72]. Large-scale consensus exercises have emphasized the need to strengthen systems, teams, and patient engagement in diagnosis [71]. Parallel patient-driven initiatives have prioritized care coordination, better measurement of diagnostic errors, and addressing implicit bias and vulnerable populations [72]. These complementary perspectives offer a robust foundation for future progress. A critical next step is enhancing diagnostic data infrastructure. Establishing national and regional diagnostic error databases by leveraging existing incident reporting systems will facilitate benchmarking, enable comparative analysis, and allow for more targeted and effective improvement strategies [73]. The rapid evolution of AI-driven diagnostic tools and data analytics presents unique opportunities to accelerate research, particularly in the detection and analysis of diagnostic errors at scale. In addition, the implementation and validation of these technologies require rigorous frameworks and collaboration across countries [44]. International research networks and consortia should also focus on under-researched settings and disciplines, such as surgery and psychiatry [74], and expand data sources beyond traditional chart reviews to include patient and clinician reports as well as real-time EHR triggers [75]. By integrating global leadership, networks, and robust infrastructure, diagnostic improvement can move from isolated projects to a cohesive, worldwide movement. Regional alliances and international consortia, such as CIDM [76], provide platforms for shared learning, capacity-building, enhance diagnostic databases, data infrastructure, and harmonized research [77]. DIA24, as mentioned, focused on developing clinical reasoning research and education tailored for Asian healthcare systems, bringing together policy-makers, educators, and clinical leaders. Meanwhile, in areas with cultural challenges [61] or lower-resource regions in improving diagnosis, each region should leverage its unique strengths while maintaining international collaboration to drive diagnostic improvements [78]. Efforts to develop context-sensitive research methodologies have been recognized as a global need, especially for the study of region-specific disease burdens and diagnostic challenges [79], 80]. Diagnostic error measurement in hospitals and primary care remains an important research frontier for both high- and low-resource settings.
Key recommendations:
Establish national and regional diagnostic error databases to enable benchmarking and targeted interventions.
Foster multicenter implementation research, especially in under-represented countries and disciplines.
Promote international collaboration to assess the performance, benefits, and risks of AI in diagnostic research.
Education: building capacity for improving diagnosis
Education is a cross-cutting enabler in diagnostic improvement, supporting both system-wide change and individual skill development. The international expansion of diagnostic improvement requires cultivating global leadership and implementing standardized educational systems with a holistic view of the diagnostic process, encompassing individual skills, teamwork, and system-related factors [81]. Designing a global educational curriculum to improve diagnosis can enable healthcare professionals worldwide to acquire standardized skills, ultimately reducing diagnostic errors. Universal, reproducible programs adaptable to any environment or healthcare infrastructure are needed. Structured educational platforms, online learning, and simulation-based training should be prioritized. The importance of interprofessional education in diagnostic safety has been emphasized in recent frameworks, with competencies now being developed and implemented across disciplines [82]. Additionally, accreditation and degree programs, such as a master’s in quality improvement [83], can provide a strong foundation for cultivating a workforce equipped to approach diagnosis as a distinct and professional discipline. Train-the-trainer programs and continuing professional development will build sustainable local capacity. The concept of a “Grandmaster” designation for clinical reasoning experts, as discussed in N Engl J Med, DEX, and past SIDM conferences, should be further developed to formalize expertise and establish clear career pathways.
Key recommendations:
Develop and disseminate standardized clinical reasoning curricula, including online and simulation-based platforms, with local adaptation.
Expand train-the-trainer and certification programs to accelerate global impact.
Promote ongoing continuing professional development (CPD) and lifelong learning programs in diagnostic education to build sustainable capacity and adaptability across diverse healthcare settings.
Practice improvement: embedding quality in everyday diagnosis
Practice improvement depends on systematic approaches at both the clinical and organizational level [84]. Implementing structured diagnostic review processes and feedback loops, such as audit-and-feedback programs, helps identify and address diagnostic safety issues. Standardized protocols and decision support tools at the point of care further enhance improvement efforts. In recent years, the integration of AI-powered clinical decision support tools has enabled real-time identification of patterns and potential diagnostic errors. However, these technologies must be introduced alongside robust governance and continuous evaluation to ensure safety and efficacy. In resource-limited settings, building diagnostic infrastructure and workforce capacity remains the first priority before large-scale adoption of advanced digital tools or AI. Only after these basics are in place will more complex technologies offer full benefit.
Mutual learning between countries and institutions is essential. Lessons and best practices from one region can often be adapted to another, as highlighted at international conferences such as DIA24. Shared learning platforms and benchmarking enable organizations to track their progress and accelerate improvement. Importantly, previous reviews have demonstrated that structured audit-and-feedback interventions can reduce diagnostic errors and improve patient outcomes [85]. Implementation science models, such as large-scale knowledge translation frameworks, can help scale up successful interventions across diverse healthcare settings [77]. In this context, innovations in microsystems and safety culture have demonstrated sustained improvements in diagnostic performance at the organizational level [86].
Key recommendations:
Implement structured diagnostic audit, feedback, and early warning systems to address diagnostic safety issues.
Adopt decision support tools and standardized protocols at the clinical level.
Evaluate the effectiveness and limitations of AI-based interventions within real-world clinical workflows.
Patient engagement: partnering with patients for safer diagnosis
Engaging patients as partners is increasingly recognized as essential for diagnostic safety. Integrating patient perspectives through co-design of safety interventions and systematic feedback (e.g., patient reporting systems) improves care quality and addresses gaps that may not be visible to clinicians alone. Emerging AI-powered tools, such as automated patient feedback analysis or symptom-checkers, offer opportunities to enhance patient engagement but must be developed and implemented with transparency and sensitivity to privacy concerns. Patient education and support for shared decision-making are especially important for vulnerable populations.
Regional conferences and networks increasingly prioritize patient engagement as a core theme. Regular forums and learning platforms can further accelerate the exchange of practical solutions and best practices worldwide. Systematic reviews indicate that patient involvement in safety initiatives, including diagnostic safety, can meaningfully reduce preventable harm [87]. Moreover, integrating patient outcomes and feedback in research and improvement cycles has been advocated as a core competency for improving diagnosis. International guidelines now emphasize that active patient and family engagement is fundamental to achieving diagnostic excellence in both acute and chronic care settings [88]. Furthermore, involving families in the diagnostic process has led to more effective identification of potential errors, especially in pediatric and long-term care.
Key recommendations:
Integrate patient perspectives through patient reporting systems and co-design of interventions.
Expand patient education and shared decision-making, particularly for vulnerable populations.
Ensure that AI applications in patient engagement are transparent, ethical, and aligned with patient needs and values.
Policy: enabling system-wide change for diagnostic safety
Lasting progress requires supportive policies and leadership at the national and organizational level. Countries must prioritize diagnostic safety in national strategies, embedding quality metrics in accreditation, reimbursement, and health system planning.
Simultaneously, it is essential to promote policy-level initiatives, such as those inspired by the Evidence-Informed Policy Network (EVIPNet) [89], which can embed diagnostic safety assessment and goal-setting into national strategies. Policy must also address the regulatory, ethical, and legal frameworks required for safe and effective deployment of AI in healthcare, including continuous monitoring for bias, safety, and patient impact. Policy actions should incentivize accurate and timely diagnosis, with clear indicators and mechanisms for accountability. Countries could adopt strategies such as introducing concrete diagnostic improvement indicators and establishing reimbursement models that incentivize accurate and timely diagnoses [90]. For example, national initiatives such as the Surviving Sepsis Campaign illustrate how specific, measurable goals and international collaboration can accelerate progress and benchmark outcomes [91]. Additionally, payment reforms and value-based incentives have been piloted in several countries to reward diagnostic accuracy and timeliness [92].
Key recommendations:
Introduce diagnostic safety indicators into accreditation and reimbursement systems to incentivize improvement.
Build foundational policy frameworks for diagnostic safety in LMIC and evaluate progress using measurable outcomes.
Develop and enforce guidelines for the ethical, safe, and equitable use of AI in diagnostic practice and policy.
The priorities and strategies summarized in Table 1 are designed to guide implementation across diverse settings and support collective progress toward safer, more reliable diagnosis by 2035.
Key recommendations for advancing diagnostic quality and safety across five domains.
| Domain | Key recommendation | Example/Action | Short-term indicator | Stakeholders |
|---|---|---|---|---|
| Research | Establish national and regional diagnostic error databases to enable benchmarking and targeted interventions. | Develop and maintain diagnostic error databases at country/region level. | Database established and actively used in at least 3 countries. | Ministries of Health, Academic Consortia |
| Foster multicenter implementation research, especially in under-represented countries and disciplines. | Collaborative studies across hospitals and countries. | Number of multicenter studies launched. | International research networks | |
| Promote international collaboration to assess the performance, benefits, and risks of AI in diagnostic research. | Conduct multinational studies on AI tools in diagnosis. | Peer-reviewed outcomes published. | Research teams, AI developers, regulators | |
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| Education | Develop and disseminate standardized clinical reasoning curricula, including online and simulation-based platforms, with local adaptation. | Publish/adopt global curriculum; launch e-learning platforms. | Number of sites/countries implementing curriculum. | Universities, Ministries of Health, Medical Societies |
| Expand train-the-trainer and certification programs to accelerate global impact. | Launch trainer workshops, develop certification pathways. | Number of trainers certified. | Universities, Professional Societies | |
| Promote ongoing CPD and lifelong learning in diagnostic education. | Run CPD programs, evaluate uptake | Number of participants in CPD/Lifelong learning | Universities, Medical Societies, Hospitals | |
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| Practice improvement | Implement structured diagnostic audit, feedback, and early warning systems to address diagnostic safety issues. | Roll out audit & feedback protocols. | Proportion of facilities with systems implemented. | Hospitals, Health Systems, QA Committees |
| Adopt decision support tools and standardized protocols at the clinical level. | Deploy AI/CDS tools in clinical practice. | Usage rates, clinician feedback. | Hospitals, IT Departments | |
| Evaluate the effectiveness and limitations of AI-based interventions within real-world clinical workflows. | Conduct implementation studies for AI-based CDS. | Number of published studies on real-world effectiveness. | Academic centers, hospitals | |
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| Patient engagement | Integrate patient perspectives through patient reporting systems and co-design of interventions. | Develop/implement patient safety reporting systems. | Number of reports and interventions co-designed with patients. | Hospitals, Patient Advocacy Groups |
| Expand patient education and shared decision-making, particularly for vulnerable populations. | Distribute education materials; run SDM workshops. | Number of patients/families participating. | Hospitals, Community Orgs, NGOs | |
| Ensure that AI applications in patient engagement are transparent, ethical, and aligned with patient needs and values. | Develop ethical guidelines for patient-facing AI tools. | Guidelines published/adopted. | Professional Societies, AI developers, Regulators | |
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| Policy | Introduce diagnostic safety indicators into accreditation and reimbursement systems to incentivize improvement. | Revise accreditation standards; pilot reimbursement models. | Number of systems including diagnostic indicators. | Government, Payers, Accrediting Bodies |
| Build foundational policy frameworks for diagnostic safety in LMICs and evaluate progress using measurable outcomes. | Develop national strategies; monitor progress. | Progress reports published. | Ministries of Health, WHO, NGOs | |
| Develop and enforce guidelines for the ethical, safe, and equitable use of AI in diagnostic practice and policy. | Publish and update national AI guidelines. | Number of countries adopting guidelines. | Regulators, Health Ministries, AI Committees | |
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SDM, shared decision making; CPD, continuing professional development; AI, artificial intelligence; LMIC, low- and middle-income countries; QA, quality assurance; CDS, clinical decision support; NGO, non-governmental organization; IT, information technology; WHO, World Health Organization.
Conclusions
There is now widespread agreement that enhancing the quality and safety of the diagnostic process is a global imperative, and that this will necessitate coordinated efforts among nations. Diagnostic safety must be prioritized in every country, and it is crucial to increase reporting and research efforts from regions and nations where such work is currently lacking. Although healthcare practices and resources may differ greatly from one country to another, there is a universal commonality in medical care; advances in diagnosis in one country are likely to be transferable to many others. These advances need to be communicated, and learning from each other needs to be emphasized and prioritized.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. TS, WH, CS, and LZ created the draft. TS finalized the draft.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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