Research over the past decades has established diagnostic errors to be the foremost patient safety challenge in health care today; the harm related to diagnostic errors is unacceptably high. We now understand where, when, and why these errors arise, and a wide range of interventions to improve diagnostic safety and quality are now being proposed, focusing on both the system-related, and personal, cognitive aspects of the diagnostic process. Stanford University's Clinical Excellence Research Center (CERC) has done a great service to the field by convening an expert panel to provide consensus recommendations on economically-beneficial interventions that would have the greatest clinical impact. The CERC report identified these 3 areas as the top priorities: 1 - Optimizing patient navigation and care coordination; 2 - Providing clinicians with decision support resources; and 3 - Creating diagnostic 'safety nets' to close the loop on abnormal, critical test results. Although the CERC recommendations represent state-of-the-art, authoritative advice, we believe there are 3 other interventions that have comparable financial and clinical impact profiles: 1 - Finding and learning from diagnostic errors; 2 - Improving clinical reasoning; and 3 - Promoting patient engagement. Healthcare organizations have an obligation to begin improving diagnostic safety and quality, and both the CERC recommendations and our own represent excellent options that should be considered for immediate adoption.

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 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.

BACKGROUND:Diagnostic errors are a leading cause of patient harm. In 2022, the Leapfrog Group published a report containing 29 evidence-based practices that hospitals can adopt to reduce diagnostic errors. OBJECTIVES:To understand the extent to which US hospitals have already implemented these practices, we conducted a national pilot survey of Leapfrog-participating hospitals. METHODS:To reduce respondent burden, we divided the 29 practices across two surveys: one focused on organizational culture and structure (Domain 1), and the second focused on the diagnostic process itself (Domain 2). RESULTS:A total of 95 hospitals from 23 states responded to one or both surveys. On average, hospitals reported implementing 9 of the 16 practices (56%) in Domain 1 and 8 of the 13 practices (62%) in Domain 2. The rate of practice implementation varied greatly, with some hospitals implementing as few as three practices in their domain. The most commonly implemented practices were ensuring access to medical interpreters, continuous access to radiologists, ensuring staff and patients can report diagnostic errors and concerns, and having a formal process to identify and notify patients when diagnostic errors occur. The least implemented practices included convening a multidisciplinary team focused on diagnostic safety and quality, a CEO commitment to diagnostic excellence, conducting diagnosis-focused risk assessments, and training clinicians to optimize clinical reasoning in the diagnostic process. CONCLUSIONS:The findings suggest large and important implementation gaps for practices related to diagnostic excellence and can inform new initiatives to promote diagnostic excellence in US hospitals.

BACKGROUND:Errors in reasoning are a common cause of diagnostic error. However, it is difficult to improve performance partly because providers receive little feedback on diagnostic performance. Examining means of providing consistent feedback and enabling continuous improvement may provide novel insights for diagnostic performance.METHODS:We developed a model for improving diagnostic performance through feedback using a six-step qualitative research process, including a review of existing models from within and outside of medicine, a survey, semistructured interviews with individuals working in and outside of medicine, the development of the new model, an interdisciplinary consensus meeting, and a refinement of the model.RESULTS:We applied theory and knowledge from other fields to help us conceptualise learning and comparison and translate that knowledge into an applied diagnostic context. This helped us develop a model, the Diagnosis Learning Cycle, which illustrates the need for clinicians to be given feedback about both their confidence and reasoning in a diagnosis and to be able to seamlessly compare diagnostic hypotheses and outcomes. This information would be stored in a repository to allow accessibility. Such a process would standardise diagnostic feedback and help providers learn from their practice and improve diagnostic performance. This model adds to existing models in diagnosis by including a detailed picture of diagnostic reasoning and the elements required to improve outcomes and calibration.CONCLUSION:A consistent, standard programme of feedback that includes representations of clinicians' confidence and reasoning is a common element in non-medical fields that could be applied to medicine. Adapting this approach to diagnosis in healthcare is a promising next step. This information must be stored reliably and accessed consistently. The next steps include testing the Diagnosis Learning Cycle in clinical settings.

Diagnostic errors comprise the leading threat to patient safety in healthcare today. Learning how to extract the lessons from cases where diagnosis succeeds or fails is a promising approach to improve diagnostic safety going forward. We present up-to-date and authoritative guidance on how the existing approaches to conducting root cause analyses (RCA's) can be modified to study cases involving diagnosis. There are several diffierences: In cases involving diagnosis, the investigation should begin immediately after the incident, and clinicians involved in the case should be members of the RCA team. The review must include consideration of how the clinical reasoning process went astray (or succeeded), and use a human-factors perspective to consider the system-related contextual factors in the diagnostic process. We present detailed instructions for conducting RCA's of cases involving diagnosis, with advice on how to identify root causes and contributing factors and select appropriate interventions.

Safety & Security Science, Delft University of Technology, Faculty of Technology, Policy & Management, Delft, The Netherlands Centre for Safety in Healthcare, Delft University of Technology, Delft, The Netherlands Internal Medicine, University of Texas John P and Katherine G McGovern Medical School, Houston, Texas, USA The UTHealthMemorial Hermann Center for Healthcare Quality and Safety, UTHealth, Houston, Texas, USA Stony Brook University, Stony Brook, New York, USA

Introduction The prevalence of type 2 diabetes mellitus (T2DM) continues to increase worldwide. Indonesia is no exception. The diagnoses of prediabetes and diabetes are increasing and seen in ever younger patients. Primary Health Care (PHC) plays a pivotal role in managing this disease in the community. This study aimed to determine the achievement therapy of T2DM patients in a PHC center in Surabaya, the second most populous city in Indonesia. Methods The design of this study was cross-sectional. T2DM patients who made regular visits to a PHC center in 2018 were included in this study after providing informed consent. Respondent characteristics (age, education level, family history of T2DM, physical activity, nutrient intake), as well as BMI, blood pressure, and laboratory data, including fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), serum creatinine, and albumin to creatinine ratio (ACR), were collected. Simple correlation and multiple regression analyses were performed by SPSS 17.0.0 Results The mean age of the patients was 60.9 ± 10.0 years, and participants had been diagnosed with T2DM for a mean of 6.9 ± 8.3 years. The therapeutic goal (FPG ≤130 mg/dL and HbA1c <7%) was achieved in 30.9% and 23.8% of patients, respectively. Early microvascular abnormality screening (ACR) showed that 70.9% of the patients had increased ACR, while serum creatinine level was found to be high in 22.8% of the patients. FPG correlated to HbA1c, serum creatinine and ACR (r=0.64, p<0.001; r=0.39, p=0.001; and r=0.25, p=0.03, respectively). Conclusions The majority of T2DM patients in our sample did not achieve their therapeutic goals in terms of FPG and HbA1c. Those who did not achieve FPG goals were less likely to meet ACR goals and were less likely to have normal serum creatinine, most probably reflecting a pattern of poor longterm follow-up.

Patient safety education is a mandated Common Program Requirement of the Accreditation Council for Graduate Medical Education and for the Royal College of Physicians and Surgeons of Canada in all medical residency and fellowship programs. Although many hospitals and healthcare environments have general patient safety education tools for trainees, few to none focus on the unique training milieu of pathologists, including a mix of highly automated and manual error-prone processes, frequent multiplicity of events, and lack of direct patient relationships for error disclosure. We established a national Association of Pathology Chairs -Program Directors Section Workgroup focused on patient safety education for pathology trainees entitled Training Residents in Patient Safety (TRIPS). TRIPS included diverse representatives from across the United States, as well as representatives from pathology organizations including the American Board of Pathology, the American Society for Clinical Pathology, the United States and Canadian Academy of Pathology, the College of American Pathologists, and the Society to Improve Diagnosis in Medicine. Objectives of the workgroup included developing a standardized patient safety curriculum, designing teaching and assessment tools, and refining them with pilot sites. Here we report the establishment of TRIPS as well as data from national needs assessment of Program Directors across the country, who confirmed the need for a standardized patient safety curriculum.

Learning Objectives: 1) Understand real world presentation of Giant Cell Arteritis (GCA).2) Identify types of bias leading to a delayed GCA diagnosis.Case Report: A 72-year-old male presented with multiple complaints, including scrotal pain, transient vision loss, fatigue, weight loss and new headaches.ED work-up showed leukocytosis and aortic dilation on imaging.Clinicians recommended further work-up for possible infection but did not raise concern for GCA.The patient declined admission but re-presented 2 days later endorsing similar symptoms.Ultrasound showed epididymitis for which he received Bactrim.He presented to clinic several days later with "confusion" which was attributed to Bactrim.The following week, he represented with scrotal pain and confusion.Ultrasound showed improving epididymitis, but his symptoms were attributed to epididymitis, and he was prescribed cefpodoxime.Three days later, he re-presented with worsening headaches, temporal swelling, and elevated CRP, prompting admission.He underwent temporal artery biopsy which confirmed GCA.Discussion: GCA is the most common systemic vasculitis and should be considered for patients over 50 who have new headaches, visual disturbances, and vascular abnormalities.Patients may also present with confusion.Prompt identification and treatment with corticosteroids is imperative as untreated GCA can progress to blindness.Despite his age and multiple complaints consistent with GCA, this diagnosis was not initially considered.His presentation was attributed to epididymitis, which interestingly has been associated with GCA.This patient is emblematic of broader issues in prompt GCA diagnosis, which takes a mean of 9 weeks from symptom onset.Heuristics are a helpful tool for physicians to make diagnoses.However, using mental shortcuts may have played a role in this delay, as GCA is more common in females.Anchoring bias may also have contributed.This patient endorsed persistent scrotal pain and was diagnosed with epididymitis, which may have clouded his additional complaints.

Abstract Objectives Patients with mental illness are less likely to receive the same physical healthcare as those without mental illness and are less likely to be treated in accordance with established guidelines. This study employed a randomized experiment to investigate the influence of comorbid depression on diagnostic accuracy. Methods Physicians were presented with an interactive vignette describing a patient with a complex presentation of pernicious anemia. They were randomized to diagnose either a patient with or without (control) comorbid depression and related behaviors. All other clinical information was identical. Physicians recorded a differential diagnosis, ordered tests, and rated patient likeability. Results Fifty-nine physicians completed the study. The patient with comorbid depression was less likeable than the control patient (p=0.03, 95 % CI [0.09, 1.53]). Diagnostic accuracy was lower in the depression compared to control condition (59.4 % vs. 40.7 %), however this difference was not statistically significant χ2(1)=2.035, p=0.15. Exploratory analyses revealed that patient condition (depression vs. control) interacted with the number of diagnostic tests ordered to predict diagnostic accuracy (OR=2.401, p=0.038). Accuracy was lower in the depression condition (vs. control) when physicians ordered fewer tests (1 SD below mean; OR=0.103, p=0.028), but there was no difference for physicians who ordered more tests (1 SD above mean; OR=2.042, p=0.396). Conclusions Comorbid depression and related behaviors lowered diagnostic accuracy when physicians ordered fewer tests – a time when more possibilities should have been considered. These findings underscore the critical need to develop interventions to reduce diagnostic error when treating vulnerable populations such as those with depression.

Background Clinician notes are structured in a variety of ways. This research pilot tested an innovative study design and explored the impact of note formats on diagnostic accuracy and documentation review time. Objective To compare two formats for clinical documentation (narrative format vs. list of findings) on clinician diagnostic accuracy and documentation review time. Method Participants diagnosed written clinical cases, half in narrative format, and half in list format. Diagnostic accuracy (defined as including correct case diagnosis among top three diagnoses) and time spent processing the case scenario were measured for each format. Generalised linear mixed regression models and bias-corrected bootstrap percentile confidence intervals for mean paired differences were used to analyse the primary research questions. Results Odds of correctly diagnosing list format notes were 26% greater than with narrative notes. However, there is insufficient evidence that this difference is significant (75% CI 0.8-1.99). On average the list format notes required 85.6 more seconds to process and arrive at a diagnosis compared to narrative notes (95% CI -162.3, -2.77). Of cases where participants included the correct diagnosis, on average the list format notes required 94.17 more seconds compared to narrative notes (75% CI -195.9, -8.83). Conclusion This study offers note format considerations for those interested in improving clinical documentation and suggests directions for future research. Balancing the priority of clinician preference with value of structured data may be necessary. Implications This study provides a method and suggestive results for further investigation in usability of electronic documentation formats.

Objectives Improving diagnosis-related education in the health professions has great potential to improve the quality and safety of diagnosis in practice. Twelve key diagnostic competencies have been delineated through a previous initiative. The objective of this project was to identify the next steps necessary for these to be incorporated broadly in education and training across the health professions. Methods We focused on medicine, nursing, and pharmacy as examples. A literature review was conducted to survey the state of diagnosis education in these fields, and a consensus group was convened to specify next steps, using formal approaches to rank suggestions. Results The literature review confirmed initial but insufficient progress towards addressing diagnosis-related education. By consensus, we identified the next steps necessary to advance diagnosis education, and five required elements relevant to every profession: 1) Developing a shared, common language for diagnosis, 2) developing the necessary content, 3) developing assessment tools, 4) promoting faculty development, and 5) spreading awareness of the need to improve education in regard to diagnosis. Conclusions The primary stakeholders, representing education, certification, accreditation, and licensure, in each profession must now take action in their own areas to encourage, promote, and enable improved diagnosis, and move these recommendations forward.

In the quest to improve diagnosis, a great deal of attention has already been focused on how to optimize clinical reasoning, and the importance of System 1 and System 2 processing. In this essay we consider the role of 'insight', a relatively overlooked pathway for arriving at the correct diagnosis. Insight refers to spontaneous emergence of the correct answer at some later point in time. We discuss factors that might facilitate insight, and how these could be incorporated into the diagnostic process.