Diagnostic scope is the range of diagnoses found in a clinical setting. Although the diagnostic scope is an essential feature of training and evaluating artificial intelligence (AI) systems to promote diagnostic excellence, its impact on AI systems and the diagnostic process remains under-explored.

We define the concept of diagnostic scope, discuss its nuanced role in building safe and effective AI-based diagnostic decision support systems, review current challenges to measurement and use, and highlight knowledge gaps for future research.

The diagnostic scope parallels the differential diagnosis although the latter is at the level of an encounter and the former is at the level of a clinical setting. Therefore, diagnostic scope will vary by local characteristics including geography, population, and resources. The true, observed, and considered scope in each setting may also diverge, both posing challenges for clinicians, patients, and AI developers, while also highlighting opportunities to improve safety. Further work is needed to systematically define and measure diagnostic scope in terms that are accurate, equitable, and meaningful at the bedside. AI tools tailored to a particular setting, such as a primary care clinic or intensive care unit, will each require specifying and measuring the appropriate diagnostic scope.

AI tools will promote diagnostic excellence if they are aligned with patient and clinician needs and trained on an accurately measured diagnostic scope. A careful understanding and rigorous evaluation of the diagnostic scope in each clinical setting will promote optimal care through human-AI collaborations in the diagnostic process.

Diagnostic errors frequently represent significant adverse events that can occur in any medical setting, particularly in rushed handovers and constrained timing. Cases that result in emergency hospitalization at the time of the initial outpatient visit are more likely to have complex or serious patient conditions and more detrimental diagnostic errors. Our study investigated diagnostic errors in these under reported situations.

We conducted a retrospective study using electronic medical record data on patients who were directly admitted to a newly established outpatient clinic at a single university hospital in Japan. Diagnostic errors were assessed independently by two physicians using the Revised Safer Dx instrument. We analyzed patient demographics, symptoms, referrals, and resident doctor (postgraduate-year-1) involvement using logistic regression to compare groups with and without diagnostic error. Additionally, we employed the Diagnostic Error Evaluation and Research (DEER) taxonomy and Generic Diagnostic Pitfalls (GDP) to examine the factors associated with diagnostic errors.

The study included 321 patients, with diagnostic errors identified in 39 cases (12.1 %). Factors contributing to diagnostic errors included the involvement of young residents, male patients, the number of symptoms, and atypical presentation. The most common causes of diagnostic errors were "too much weight given to competing/coexisting diagnosis" as indicated by DEER and "atypical presentation" by GDP.

The frequency of diagnostic errors in this study was higher than those in previous studies of new outpatient visits, underscoring the imperative for heightened scrutiny in cases involving medical residents especially when patients present with multiple or atypical symptoms. This vigilance is crucial to mitigating the risk of diagnostic inaccuracies in these settings. Cases that result in emergency hospitalization at the time of the initial outpatient visit are more likely to have complex or serious patient conditions and more detrimental diagnostic errors.

To analyse endometriosis diagnostic errors made by clinicians as reported by patients with endometriosis.

This study deductively analysed qualitative data as part of a larger mixed-methods research study examining 'invalidating communication' by clinicians concerning patients' symptoms. Data analysed were responses to an open-ended prompt asking participants to describe an interaction with a clinician prior to their diagnosis in which they felt their symptoms were dismissed. We used three validated taxonomies for diagnosing diagnostic error (Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC) and generic diagnostic pitfalls taxonomies).

A total of 476 relevant interactions with clinicians were identified from 444 patients to the open-ended prompt, which identified 692 codable units using the DEER taxonomy, 286 codable units using the RDC taxonomy and 602 codable diagnostic pitfalls. Most prevalent subcategories among these three taxonomies were inaccurate/misinterpreted/overlooked critical piece of history data (from DEER Taxonomy; n=291), no specific diagnosis was ever made (from diagnostic pitfalls taxonomy; n=271), and unfamiliar with endometriosis (from RDC Taxonomy; n=144).

Examining a series of patient-described diagnostic errors reported by patients with surgically confirmed endometriosis using three validated taxonomies demonstrates numerous areas for improvement. These findings can help patients, clinicians and healthcare organisations better anticipate errors in endometriosis diagnosis and design and implement education efforts and safety to prevent or mitigate such errors.

Diagnostic errors (DEs) are a significant global patient safety issue, often associated with increased morbidity and mortality due to overlooked, delayed, or incorrect diagnoses. Our aim was to study the occurrence of DEs and adverse events (AEs), patient-related harm to identify vulnerable steps in the diagnostic process.

A retrospective analysis of data from two public, national databases-National Health Care Compensation Claims Database (2009-2018) and Danish Patient Safety Database with AEs (2015-2020). Vulnerable steps in the diagnostic process were identified using a scoring tool developed by The Controlled Risk Insurance Company.

In the analysis of patient compensation claims, 14.5% of all settled cases (n=90 000) were classified as due to a DE, with a 59% compensation rate for DEs, twice the rate compared with other compensated cases (25%). DEs constituted 29% of all compensated cases. Death due to DEs was 8.3% (n=680 cases), 1.8 times higher compared with other cases and DEs resulted in higher degrees of disability.In the overall reported AEs, 0.3% of AEs were fatal and 1.7% AEs caused severe patient harm, per year. In a representative sample of AEs with a severe or fatal consequence (n=269), 33% were due to DEs.The initial clinical assessment was a cause or contributor to the DE in 80% of the compensation cases and in 83% of the severe or fatal AEs. The follow-up and coordination phase were a cause in 33% of compensation cases and 46% of severe or fatal AEs.

Errors and AEs in the diagnostic process are prevalent and a significant patient safety issue in Danish healthcare. This study identifies vulnerable steps in the diagnostic process, with patterns correlated to different degrees of severity, and highlights steps for future improvements efforts needed to mitigate the risk of DEs.

Erroneous and malpractice claim cases reflect knowledge gaps and complex contextual factors. Incorporating such cases into clinical reasoning education (CRE) may enhance learning and diagnostic skills. However, they may also elicit anxiety among learners, potentially impacting learning. As a result, the optimal utilization of such cases in CRE remains uncertain. This study aims to investigate the effect of erroneous and malpractice claim case vignettes on anxiety and future diagnostic performance in CRE and explores possible underlying factors that may influence learning, including self-reported confidence in the final diagnosis, learners' satisfaction, and retrospective impact of the cases. In this three-phase experiment, GP residents and supervisors were randomly assigned to one of three experimental conditions: neutral (without reference to an error), erroneous (involving a diagnostic error), or malpractice claim (involving a diagnostic error along with a malpractice claim description). During the first session, participants reviewed six cases exclusively in the version of their assigned condition, with anxiety levels measured before and after. In the second session, participants solved six neutral clinical cases featuring the same diagnoses as those in the learning phase but presented in different scenarios, along with four filler cases. Diagnostic performance and self-reported confidence in the diagnosis were assessed. The third session measured learners' satisfaction and longer-term impact on the participants. Case vignettes featuring diagnostic errors or malpractice claims did not lead to increased anxiety and resulted in similar future diagnostic performance compared to neutral vignettes. Additionally, self-reported confidence, learners' satisfaction and long-term impact scores did not differ significantly between conditions. This suggests these cases can be integrated into CRE programs, offering a valuable source of diverse, context-rich examples that broaden case libraries without interfering with diagnostic performance or causing anxiety in learners.

Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.

This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance.

We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests.

The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (χ21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (χ21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (χ21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (χ21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (χ21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%.

The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems.

Adults over the age of 65 are at a higher risk for diagnostic errors due to a myriad of reasons. In primary care settings, a large contributor of diagnostic errors are breakdowns in information gathering and synthesis throughout the patient-provider encounter. Diagnostic communication interventions, such as the Agency for Healthcare Research and Quality's "Be the Expert on You" note sheet, may require adaptations to address older adults' unique needs.

We recruited and partnered with older adult patients (n = 6) in focus group sessions to understand their perspectives on diagnostic communication and the existing AHRQ note sheet. A two-page communication and clinic workflow tool was developed and implemented over a 6-month period using three Plan-Do-Check-Act cycles. Physicians, nurses, staff, and patients were surveyed.

Most older adult patients (n = 31) found the tailored diagnostic communication note sheet to be easy-to-use, helpful for provider communication, and would recommend its use to other patients. Physicians and staff members were satisfied with the note sheet and described few challenges in using it in practice.

Our findings contribute to the growing body of evidence around diagnostic safety interventions and patient engagement by demonstrating the feasibility and benefits of actively involving older adult patients in quality initiatives.

Background: Orofacial pain (OFP) encompasses a complex array of conditions affecting the face, mouth, and jaws, often leading to significant diagnostic challenges and high rates of misdiagnosis. Artificial intelligence, particularly large language models like GPT4 (OpenAI, San Francisco, CA, USA), offers potential as a diagnostic aid in healthcare settings. Objective: To evaluate the diagnostic accuracy of GPT4 in OFP cases as a clinical decision support system (CDSS) and compare its performance against treating clinicians, expert evaluators, medical students, and general practitioners. Methods: A total of 100 anonymized patient case descriptions involving diverse OFP conditions were collected. GPT4 was prompted to generate primary and differential diagnoses for each case using the International Classification of Orofacial Pain (ICOP) criteria. Diagnoses were compared to gold-standard diagnoses established by treating clinicians, and a scoring system was used to assess accuracy at three hierarchical ICOP levels. A subset of 24 cases was also evaluated by two clinical experts, two final-year medical students, and two general practitioners for comparative analysis. Diagnostic performance and interrater reliability were calculated. Results: GPT4 achieved the highest accuracy level (ICOP level 3) in 38% of cases, with an overall diagnostic performance score of 157 out of 300 points (52%). The model provided accurate differential diagnoses in 80% of cases (400 out of 500 points). In the subset of 24 cases, the model's performance was comparable to non-expert human evaluators but was surpassed by clinical experts, who correctly diagnosed 54% of cases at level 3. GPT4 demonstrated high accuracy in specific categories, correctly diagnosing 81% of trigeminal neuralgia cases at level 3. Interrater reliability between GPT4 and human evaluators was low (κ = 0.219, p < 0.001), indicating variability in diagnostic agreement. Conclusions: GPT4 shows promise as a CDSS for OFP by improving diagnostic accuracy and offering structured differential diagnoses. While not yet outperforming expert clinicians, GPT4 can augment diagnostic workflows, particularly in primary care or educational settings. Effective integration into clinical practice requires adherence to rigorous guidelines, thorough validation, and ongoing professional oversight to ensure patient safety and diagnostic reliability.

This article examines how clinical reasoning about the critical patient is currently treated and draws attention to some critical issues already often highlighted in the literature. Traditional approaches to clinical reasoning, even when applied to critical patients, prioritize identifying structured diseases. In contrast, the critical care setting demands an alternative approach that aligns with the intensivist's goal of supporting or substituting vital organ functions. In this manuscript, we emphasized the reasons that make it primary for intensivists to obtain a diagnosis of function in order to act therapeutically. Moreover, we highlighted the challenges posed by diagnostic errors, often attributed to cognitive biases and shortcomings in clinical reasoning, which can adversely affect patient outcomes and resource utilization. We also discussed the complexities of clinical decision-making in emergency medical services, where physicians must perform rapid actions in the face of incomplete information and high uncertainty. We underscore the limitations of traditional information technology tools in facilitating practical clinical reasoning, advocating for the integration of relevant data that directly informs on organ function and pathophysiological mechanisms. This discourse emphasizes a deep understanding of physiology and pathophysiology as foundational for practical clinical reasoning in critical care. Finally, we propose a structured assessment method that prioritizes pinpointing the compromised organ function, elucidating the pathophysiological mechanism responsible, hypothesizing potential causes, and testing these hypotheses to guide therapeutic interventions. This approach aligns clinical reasoning with the intensivist's goal: supporting and restoring vital functions in the critically ill patient.

Diagnostic errors are a global patient safety challenge. Over 75% of diagnostic errors in ambulatory care result from breakdowns in patient-clinician communication. Encouraging patients to speak up and ask questions has been recommended as one strategy to mitigate these failures.

The goal of the scoping review was to identify, summarize, and thematically map questions patients are recommended to ask during ambulatory encounters along the diagnostic process. This is the first step in a larger study to co-design a patient-facing question prompt list for patients to use throughout the diagnostic process.

Medline and Google Scholar were searched to identify question lists in the peer-reviewed literature. Organizational websites and a search engine were searched to identify question lists in the gray literature. Articles and resources were screened for eligibility and data were abstracted. Interrater reliability (K = 0.875) was achieved.

A total of 5509 questions from 235 resources met inclusion criteria. Most questions ( n = 4243, 77.02%) were found in the gray literature. Question lists included an average of 23.44 questions. Questions were most commonly coded along the diagnostic process categories of treatment (2434 questions from 250 resources), communication of the diagnosis (1160 questions, 204 resources), and outcomes (766 questions, 172 resources).

Despite recommendations for patients to ask questions, most question prompt lists focus on later stages of the diagnostic process such as communication of the diagnosis, treatment, and outcomes. Further research is needed to identify and prioritize diagnostic-related questions from the patient perspective and to develop simple, usable guidance on question-asking to improve patient safety across the diagnostic continuum.

Diagnostic excellence is central to healthcare quality and safety. Prior literature identified a lack of psychological safety and time as barriers to diagnostic reasoning education. We performed a needs assessment to inform the development of diagnostic safety education.

To evaluate existing educational programming and identify opportunities for content delivery, surveys were emailed to 155 interprofessional educational leaders and 627 clinicians at our hospital. Educational leaders and learners were invited to participate in focus groups to further explore beliefs, perceptions, and recommendations about diagnostic reasoning. The study team analyzed data using directed content analysis to identify themes.

Of the 57 education leaders who responded to our survey, only 2 (5%) reported having formal training on diagnostic reasoning in their respective departments. The learner survey had a response rate of 47% (293/627). Learners expressed discomfort discussing diagnostic uncertainty and preferred case-based discussions and bedside learning as avenues for learning about the topic. Focus groups, including 7 educators and 16 learners, identified the following as necessary precursors to effective teaching about diagnostic safety: (1) faculty development, (2) institutional culture change, and (3) improved reporting of missed diagnoses. Participants preferred mandatory sessions integrated into existing educational programs.

Our needs assessment identified a broad interest in education regarding medical diagnosis and potential barriers to implementation. Respondents highlighted the need to develop communication skills regarding diagnostic errors and uncertainty across professions and care areas. Study findings informed a pilot diagnostic reasoning curriculum for faculty and trainees.

Owing to the rapid progress in artificial intelligence (AI) and the widespread use of generative learning, the problem of sparse data has been solved effectively in various research fields. The application of AI technologies has resulted in important transformations in healthcare, particularly in radiology. To ensure the high quality, safety, and effectiveness of AI and machine learning (ML) medical devices, the US Food and Drug Administration (FDA) has established regulatory guidelines to support the performance evaluation of medical devices. Furthermore, the FDA has proposed continuous surveillance requirements for AI/ML medical devices. This paper presents a summary of SaMD products that have passed the FDA 510 (k) AI/ML pathway, the challenges associated with the current AI/ML software-as-a-medical-device, and solutions for promoting the development of AI technologies in medicine. We hope to provide valuable information pertaining to medical-device design, development, and monitoring to ultimately achieve safer and more effective personalized medical services.

Diagnosis improvement requires physician-nurse collaboration. This study explored nurses' concerns regarding physicians' diagnoses and how they were communicated to physicians. This cross-sectional study, employing a web-based questionnaire, included nurses registered on Japan's largest online media site from June 26, 2023, to July 31, 2023. The survey inquired whether participants felt concerned about a physician's diagnosis within a month, if they communicated their concerns once they arose, and, if not, their reasons. The reasons for not being investigated were also examined. The nurses' frequency of feeling concerned about a physician's diagnosis and the barriers to communicating these concerns to the physician were evaluated. Overall, 430 nurses answered the survey (female, 349 [81.2%]; median age, 45 [35-51] years; median years of experience, 19 [12-25]). Of the nurses, 61.2% experienced concerns about a physician's diagnosis within the past month; 52.5% felt concerned but did not communicate this to the physician. The most common reasons for not communicating included concern about the physician's pride, being ignored when communicating, and the nurse not believing that a diagnosis should be made. Our results highlight the need to foster psychologically safe workplaces for nurses and create educational programs encouraging nurse involvement in diagnosis.

Pregnancy-related morbidity and mortality remain high across the United States, with the majority of deaths being deemed preventable. Misdiagnosis and delay in diagnosis are thought to be significant contributors to preventable harm. These diagnostic errors in obstetrics are understudied. Presented here are five selected research methods to ascertain the rates of and harm associated with diagnostic errors and the pros and cons of each. These methodologies include clinicopathologic autopsy studies, retrospective chart reviews based on clinical criteria, obstetric simulations, pregnancy-related harm case reviews, and malpractice and administrative claim database research. We then present a framework for a future study of diagnostic errors and the pursuit of diagnostic excellence in obstetrics: (1) defining and capturing diagnostic errors, (2) targeting bias in diagnostic processes, (3) implementing and monitoring safety bundles, (4) leveraging electronic health record triggers for case reviews, (5) improving diagnostic skills via simulation training, and (6) publishing error rates and reduction strategies. Evaluation of the effectiveness of this framework to ascertain diagnostic error rates, as well as its impact on patient outcomes, is required.

Pyuria is nonspecific and may result in over-treatment of asymptomatic bacteriuria (ASB). The Infectious Diseases Society of America recommends against antibiotic treatment of ASB for most patients including those presenting with altered mental status (AMS). Close observation is recommended over treatment to avoid missing alternative causes of AMS and overuse of antibiotics resulting in adverse events and resistance.

The purpose of this study was to evaluate patient outcomes associated with antibiotic treatment of pyuria in patients presenting with AMS at hospital admission without specific urinary tract infection (UTI) symptoms. The primary objective was to compare 30-day readmission rates of patients with pyuria and AMS treated with antibiotics (AMS+Tx) versus those who were not treated (AMS-NoTx). Secondary outcomes included identifying risk factors for antibiotic treatment, comparing alternative diagnoses for AMS, and comparing safety outcomes.

This retrospective cohort study evaluated adult patients with AMS and pyuria (10 WBC/hpf) admitted between February 1, 2020 and October 1, 2021, in a 350-bed community teaching hospital. Patients with documented urinary symptoms were excluded. Additional exclusion criteria included admission to critical care, history of renal transplant, urological surgery, coinfections, pregnancy, and neutropenia.

Two-hundred patients were included (AMS+Tx, n = 162; AMS-NoTx, n=38). There was no difference in 30-day hospital readmission rate for AMS between groups (AMS+Tx 16.7% vs AMS-NoTx 23.7%, P = 0.311). An alternative diagnosis of AMS occurred more frequently when antibiotics were withheld (AMS+Tx 66% vs. AMS-NoTx 86.8%, P = 0.012). Urinalyses showing bacteria (odds ratio 2.52; 95% CI, 1.11-5.731) and positive urine culture (OR 3.36; 95% CI, 1.46-7.711) were associated with antibiotic prescribing.

Inappropriate antibiotic use is common among hospitalized patients presenting with AMS and pyuria; however, treatment of asymptomatic pyuria did not decrease rates of subsequent readmission for AMS or retreatment of symptomatic UTI. Patients who were monitored off antibiotics had higher rates of alternative AMS diagnosis.

Inappropriate diagnosis of infections results in antibiotic overuse and may delay diagnosis of underlying conditions. Here we describe the development and characteristics of 2 safety measures of inappropriate diagnosis of urinary tract infection (UTI) and community-acquired pneumonia (CAP), the most common inpatient infections on general medicine services.

Measures were developed from guidelines and literature and adapted based on data from patients hospitalized with UTI and CAP in 49 Michigan hospitals and feedback from end-users, a technical expert panel (TEP), and a patient focus group. Each measure was assessed for reliability, validity, feasibility, and usability.

Two measures, now endorsed by the National Quality Forum (NQF), were developed. Measure reliability (derived from 24 483 patients) was excellent (0.90 for UTI; 0.91 for CAP). Both measures had strong validity demonstrated through (a) face validity by hospital users, the TEPs, and patient focus group, (b) implicit case review (ĸ 0.72 for UTI; ĸ 0.72 for CAP), and (c) rare case misclassification (4% for UTI; 0% for CAP) due to data errors (<2% for UTI; 6.3% for CAP). Measure implementation through hospital peer comparison in Michigan hospitals (2017 to 2020) demonstrated significant decreases in inappropriate diagnosis of UTI and CAP (37% and 32%, respectively, P < .001), supporting usability.

We developed highly reliable, valid, and usable measures of inappropriate diagnosis of UTI and CAP for hospitalized patients. Hospitals seeking to improve diagnostic safety, antibiotic use, and patient care should consider using these measures to reduce inappropriate diagnosis of CAP and UTI.

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

There has been growing recognition that contextual factors influence the physician's cognitive processes. However, given that cognitive processes may depend on the physicians' specialties, the effects of contextual factors on diagnostic errors reported in previous studies could be confounded by difference in physicians.

This study aimed to clarify whether contextual factors such as location and consultation type affect diagnostic accuracy.

We reviewed the medical records of 1992 consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine in a university hospital between 1 January and 31 December 2019. Diagnostic processes were assessed using the Revised Safer Dx Instrument. Patients were categorised into three groups according to contextual factors (location and consultation type): (1) referred patients with scheduled visit to the outpatient department; (2) patients with urgent visit to the outpatient department; and (3) patients with emergency visit to the emergency room. The effect of the contextual factors on the prevalence of diagnostic errors was investigated using logistic regression analysis.

Diagnostic errors were observed in 12 of 534 referred patients with scheduled visit to the outpatient department (2.2%), 3 of 599 patients with urgent visit to the outpatient department (0.5%) and 13 of 859 patients with emergency visit to the emergency room (1.5%). Multivariable logistic regression analysis showed a significantly higher prevalence of diagnostic errors in referred patients with scheduled visit to the outpatient department than in patients with urgent visit to the outpatient department (OR 4.08, p=0.03), but no difference between patients with emergency and urgent visit to the emergency room and outpatient department, respectively.

Contextual factors such as consultation type may affect diagnostic errors; however, since the differences in the prevalence of diagnostic errors were small, the effect of contextual factors on diagnostic accuracy may be small in physicians working in different care settings.

Existing handover communication tools often lack a clear theoretical foundation, have limited psychometric evidence, and overlook effective communication strategies for enhancing diagnostic reasoning. This oversight becomes critical as communication breakdowns during handovers have been implicated in poor patient care. To address these issues, we developed a structured communication tool: Background, Responsible diagnosis, Included differential diagnosis, Excluded differential diagnosis, Follow-up, and Communication (BRIEF-C). It is informed by cognitive bias theory, shows evidence of reliability and validity of its scores, and includes strategies for actively sending and receiving information in medical handovers.

A pre-test post-test intervention study.

Inpatient internal medicine and orthopaedic surgery units at one tertiary care hospital.

The BRIEF-C tool was presented to internal medicine and orthopaedic surgery faculty and residents who participated in an in-person educational session, followed by a 2-week period where they practised using it with feedback.

Clinical handovers were audiorecorded over 1 week for the pre- and again for the post-periods, then transcribed for analysis. Two faculty raters from internal medicine and orthopaedic surgery scored the transcripts of handovers using the BRIEF-C framework. The two raters were blinded to the time periods.

A principal component analysis identified two subscales on the BRIEF-C: diagnostic clinical reasoning and communication, with high interitem consistency (Cronbach's alpha of 0.82 and 0.99, respectively). One sample t-test indicated significant improvement in diagnostic clinical reasoning (pre-test: M=0.97, SD=0.50; post-test: M=1.31, SD=0.64; t(64)=4.26, p<0.05, medium to large Cohen's d=0.63) and communication (pre-test: M=0.02, SD=0.16; post-test: M=0.48, SD=0.83); t(64)=4.52, p<0.05, large Cohen's d=0.83).

This study demonstrates evidence supporting the reliability and validity of scores on the BRIEF-C as good indicators of diagnostic clinical reasoning and communication shared during handovers.

Despite the many advancements made in patient safety over the past decade, combating diagnostic errors (DEs) remains a crucial, yet understudied initiative toward improvement. This study sought to understand the perception of dental patients who have experienced a dental diagnostic failure (DDF) and to identify patient-centered strategies to help reduce future occurrences of DDF.

Through social media recruitment, we conducted a screening survey, initial assessment, and 67 individual patient interviews to capture the effects of misdiagnosis, missed diagnosis, or delayed diagnosis on patient lives. Audio recordings of patient interviews were transcribed, and a hybrid thematic analysis approach was used to capture details about 4 main domains of interest: the patient's DDF experience, contributing factors, impact, and strategies to mitigate future occurrences.

Dental patients endured prolonged suffering, disease progression, unnecessary treatments, and the development of new symptoms as a result of experiencing DE. Poor provider communication, inadequate time with provider, and lack of patient self-advocacy and health literacy were among the top attributes patients believed contributed to the development of a DE. Patients suggested that improvements in provider chairside manners, more detailed patient diagnostic workups, and improving personal self-advocacy; along with enhanced reporting systems, could help mitigate future DE.

This study demonstrates the valuable insight the patient perspective provides in understanding DEs, therefore aiding the development of strategies to help reduce the occurrences of future DDF events. Given the challenges patients expressed, there is a significant need to create an accessible reporting system that fosters constructive clinician learning.

Diagnostic momentum refers to ruling in a particular diagnosis without adequate evidence. As the field of physical therapy continues to transition more towards autonomous practitioners with direct access, there is a need to identify the effect of a physician diagnosis on a therapist's examination and treatment. The purpose of this study was to identify if diagnostic momentum exists in physical therapy and whether this phenomenon could affect the ability of the therapist to identify clinical red flags.

An online survey with randomized case scenarios was completed by 75 licensed practicing physical therapists. Participants received one of two scenarios: a case vignette where the patient was referred to physical therapy for left shoulder pain and presented with 'red flags' indicative of myocardial infarction, or a similar vignette with additional results from an exercise stress test that ruled out myocardial infarction. The subjects were asked if they would 'treat' or 'refer' to another healthcare provider and the reason behind their decision. Independent t-tests and χ2 analyses were conducted to understand the differences between the groups. A thematic analysis was used to explore the therapists' responses regarding the reasoning for their decision.

There was no significant difference in clinical decision making based on age, gender, years of experience, advanced certification, primary caseload or primary practice setting. Among those who received the case without the stress test, 31.4% of participants indicated that they would refer, compared to 12.5% of the participants that had the additional stress test result included within their case. The presence of the negative stress test was indicated as the main reason for choosing to treat without referral by 65.7% of the subjects that received the additional stress test result.

This study suggests that practicing physical therapists may be influenced by diagnostic decisions made by other clinicians, causing them to overlook signs and symptoms of possible myocardial infarction.

Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients.

To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died.

Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023.

Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors.

Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors.

In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.

Diagnostic errors cause substantial preventable harms worldwide, but rigorous estimates for total burden are lacking. We previously estimated diagnostic error and serious harm rates for key dangerous diseases in major disease categories and validated plausible ranges using clinical experts.

We sought to estimate the annual US burden of serious misdiagnosis-related harms (permanent morbidity, mortality) by combining prior results with rigorous estimates of disease incidence.

Cross-sectional analysis of US-based nationally representative observational data. We estimated annual incident vascular events and infections from 21.5 million (M) sampled US hospital discharges (2012-2014). Annual new cancers were taken from US-based registries (2014). Years were selected for coding consistency with prior literature. Disease-specific incidences for 15 major vascular events, infections and cancers ('Big Three' categories) were multiplied by literature-based rates to derive diagnostic errors and serious harms. We calculated uncertainty estimates using Monte Carlo simulations. Validity checks included sensitivity analyses and comparison with prior published estimates.

Annual US incidence was 6.0 M vascular events, 6.2 M infections and 1.5 M cancers. Per 'Big Three' dangerous disease case, weighted mean error and serious harm rates were 11.1% and 4.4%, respectively. Extrapolating to all diseases (including non-'Big Three' dangerous disease categories), we estimated total serious harms annually in the USA to be 795 000 (plausible range 598 000-1 023 000). Sensitivity analyses using more conservative assumptions estimated 549 000 serious harms. Results were compatible with setting-specific serious harm estimates from inpatient, emergency department and ambulatory care. The 15 dangerous diseases accounted for 50.7% of total serious harms and the top 5 (stroke, sepsis, pneumonia, venous thromboembolism and lung cancer) accounted for 38.7%.

An estimated 795 000 Americans become permanently disabled or die annually across care settings because dangerous diseases are misdiagnosed. Just 15 diseases account for about half of all serious harms, so the problem may be more tractable than previously imagined.

Objective The etiologies of diagnostic errors among internal medicine physicians are unclear. To understand the causes and characteristics of diagnostic errors through reflection by those involved in them. Methods We conducted a cross-sectional study using a web-based questionnaire in Japan in January 2019. Over a 10-day period, a total of 2,220 participants agreed to participate in the study, of whom 687 internists were included in the final analysis. Participants were asked about their most memorable diagnostic error cases, in which the time course, situational factors, and psychosocial context could be most vividly recalled and where the participant provided care. We categorized diagnostic errors and identified contributing factors (i.e., situational factors, data collection/interpretation factors, and cognitive biases). Results Two-thirds of the identified diagnostic errors occurred in the clinic or emergency department. Errors were most frequently categorized as wrong diagnoses, followed by delayed and missed diagnoses. Errors most often involved diagnoses related to malignancy, circulatory system disorders, or infectious diseases. Situational factors were the most cited error cause, followed by data collection factors and cognitive bias. Common situational factors included limited consultation during office hours and weekends and barriers that prevented consultation with a supervisor or another department. Conclusion Internists reported situational factors as a significant cause of diagnostic errors. Other factors, such as cognitive biases, were also evident, although the difference in clinical settings may have influenced the proportions of the etiologies of the errors that were observed. Furthermore, wrong, delayed, and missed diagnoses may have distinctive associated cognitive biases.

Classification and analysis of existing data on medical malpractice lawsuits are useful in identifying the root causes of medical errors and considering measures to prevent recurrence. No study has shown the actual prevalence of all closed malpractice claims in Japan, including the number of cases and their trial results. In this study, we illustrated the recent trends of closed malpractice claims by medical specialty, the effects of the acceptance rates and the settlements and clarified the trends and characteristics. This was a descriptive study of all closed malpractice claims data from the Supreme Court in Japan from 2006-2021. Trends and the characteristics in closed malpractice claims by medical specialty and the outcomes of the claims, including settlements and judgments, were extracted. The total number of closed medical malpractice claims was 13,340 in 16 years, with a high percentage ending in settlement (7,062, 52.9%), and when concluding in judgment (4,734, 35.3%), the medical profession (3,589, 75.8%) was favored. When compared by medical specialty, plastic surgery and obstetrics/gynecology were more likely resolved by settlement. By contrast, psychiatry cases exhibited a lower likelihood of settlement, and the percentage of cases resulting in unfavorable outcomes for patients was notably high. Furthermore, there has been a decline in the number of closed medical malpractice claims in Japan in recent years compared to the figures observed in 2006. In particular, the number of closed medical malpractice claims in obstetrics/gynecology and the number of closed medical malpractice claims per 1,000 physicians decreased significantly compared to other specialties. In conclusion, half of the closed malpractice claims were settled, and a low percentage of patients won their cases. Closed medical malpractice claims in Japan have declined in most medical specialties since 2006. Additionally, obstetrics/gynecology revealed a significant decrease since introducing the Obstetrics/Gynecology Medical Compensation System in 2009.

Most medical students entering clerkships have limited understanding of clinical reasoning concepts. The value of teaching theories of clinical reasoning and cognitive biases to first-year medical students is unknown. This study aimed to evaluate the value of explicitly teaching clinical reasoning theory and cognitive bias to first-year medical students.

Using Kolb's experiential learning model, we introduced dual process theory, script theory, and cognitive biases in teaching clinical reasoning to first-year medical students at an academic medical center in New York City between January and June 2020. Due to the COVID-19 pandemic, instruction was transitioned to a distance learning format in March 2020. The curriculum included a series of written clinical reasoning examinations with facilitated small group discussions. Written self-assessments prompted each student to reflect on the experience, draw conclusions about their clinical reasoning, and plan for future encounters involving clinical reasoning. We evaluated the value of the curriculum using mixed-methods to analyze faculty assessments, student self-assessment questionnaires, and an end-of-curriculum anonymous questionnaire eliciting student feedback.

Among 318 total examinations of 106 students, 254 (80%) had a complete problem representation, while 199 (63%) of problem representations were considered concise. The most common cognitive biases described by students in their clinical reasoning were anchoring bias, availability bias, and premature closure. Four major themes emerged as valuable outcomes of the CREs as identified by students: (1) synthesis of medical knowledge; (2) enhanced ability to generate differential diagnoses; (3) development of self-efficacy related to clinical reasoning; (4) raised awareness of personal cognitive biases.

We found that explicitly teaching clinical reasoning theory and cognitive biases using an experiential learning model provides first-year medical students with valuable opportunities for developing knowledge, skills, and self-efficacy related to clinical reasoning.

Achieving diagnostic excellence on medical wards requires teamwork and effective team dynamics. However, the study of ward team dynamics in teaching hospitals is relatively underdeveloped. We aim to enhance understanding of how ward team members interact in the diagnostic process and of the underlying behavioral, psychological, and cognitive mechanisms driving team interactions.

We used mixed-methods to develop and refine a conceptual model of how ward team dynamics in an academic medical center influence the diagnostic process. First, we systematically searched existing literature for conceptual models and empirical studies of team dynamics. Then, we conducted field observations with thematic analysis to refine our model.

We present a conceptual model of how medical ward team dynamics influence the diagnostic process, which serves as a roadmap for future research and interventions in this area. We identified three underexplored areas of team dynamics that are relevant to diagnostic excellence and that merit future investigation (1): ward team structures (e.g., team roles, responsibilities) (2); contextual factors (e.g., time constraints, location of team members, culture, diversity); and (3) emergent states (shared mental models, psychological safety, team trust, and team emotions).

Optimizing the diagnostic process to achieve diagnostic excellence is likely to depend on addressing all of the potential barriers and facilitators to ward team dynamics presented in our model.

Because artificial intelligence (AI)-powered algorithms allow automated image analysis in a growing number of diagnostic scenarios, some healthcare stakeholders have raised doubts about the future of the entire radiologic profession. Their view disregards not only the role of radiologists in the diagnostic service chain beyond reporting, but also the many multidisciplinary and patient-related consulting tasks for which radiologists are solicited. The time commitment for these non-reporting tasks is considerable but difficult to quantify and often impossible to fulfil considering the current mismatch between workload and workforce in many countries. Nonetheless, multidisciplinary, and patient-centred consulting activities could move up on radiologists' agendas as soon as AI-based tools can save time in daily routine. Although there are many reasons why AI will assist and not replace radiologists as imaging experts in the future, it is important to position the next generation of European radiologists in view of this expected trend. To ensure radiologists' personal professional recognition and fulfilment in multidisciplinary environments, the focus of training should go beyond diagnostic reporting, concentrating on clinical backgrounds, specific communication skills with referrers and patients, and integration of imaging findings with those of other disciplines. Close collaboration between the European Society of Radiology (ESR) and European national radiologic societies can help to achieve these goals. Although each adequate treatment begins with a correct diagnosis, many health politicians see radiologic procedures mainly as a cost factor. Radiologic research should, therefore, increasingly investigate the imaging impact on treatment and outcome rather than focusing mainly on technical improvements and diagnostic accuracy alone.Critical relevance statement Strategies are presented to prepare for a successful future of the radiologic profession in Europe, if AI-powered tools can alleviate the current reporting overload: engaging in multidisciplinary activities (clinical and integrative diagnostics), enhancing the value and recognition of radiologists' role through clinical expertise, focusing radiological research on the impact on diagnosis and outcome, and promoting patient-centred radiology by enhancing communication skills.Key points • AI-powered tools will not replace radiologists but hold promise to reduce the current reporting burden, enabling them to reinvest liberated time in multidisciplinary clinical and patient-related tasks.• The skills and resources for these tasks should be considered when recruiting and teaching the next generation of radiologists, when organising departments and planning staffing.• Communication skills will play an increasing role in both multidisciplinary activities and patient-centred radiology.• The value and importance of a correct and integrative diagnosis and the cost of an incorrect imaging diagnosis should be emphasised when discussing with non-medical stakeholders in healthcare.• The radiologic community in Europe should start now to prepare for a bright future of the profession for the benefit of patients and medical colleagues alike.

Hospitalized patients are at risk for diagnostic errors. Hospitalists caring for these patients are often multitasking when overseeing patient care. We aimed to measure hospitalist workload and understand its influences on diagnostic performance in a real-world clinical setting.

We conducted a single-center, prospective, pilot observational study of hospitalists admitting new patients to the hospital. Hospitalists completed an abridged Mindful Attention Awareness Tool and a survey about diagnostic confidence at shift completion. Data on differential diagnoses and resource utilization (e.g., laboratory, imaging tests ordered, and consultations) were collected from the medical record. The number of admissions and paging volume per shift were used as separate proxies for shift busyness. Data were analyzed using linear mixed effects models (continuous outcomes) or mixed effects logistic regression (dichotomous outcomes).

Of the 53 hospitalists approached, 47 (89%) agreed to participate; complete data were available for 37 unique hospitalists who admitted 160 unique patients. Increases in admissions (odds ratio, 1.99; 95% confidence interval [CI], 1.04 to 3.82; P = 0.04) and pages (odds ratio, 1.11; 95% CI, 1.02 to 1.21; P = 0.01) were associated with increased odds of hospitalists finding it "difficult to focus on what is happening in the present." Increased pages was associated with a decrease in the number of listed differential diagnoses (coefficient, -0.02; 95% CI, -0.04 to -0.003; P = 0.02).

Evaluation of hospitalist busyness and its associations with factors that may influence diagnosis in a real-world environment was feasible and demonstrated important implications on physician focus and differential diagnosis.

Diagnostic error remains understudied and underaddressed despite causing significant morbidity and mortality. One barrier to addressing this issue remains provider discomfort. Survey studies have shown significantly more discomfort among providers in discussing diagnostic error compared with other forms of error. Whether the comfort in discussing diagnostic error differs depending on practice setting has not been previously studied. The objective of this study was to assess differences in provider willingness to discuss diagnostic error in the inpatient versus outpatient setting. A multicenter survey was sent out to 3881 providers between May and June 2018. This survey was designed to assess comfort level of discussing diagnostic error and looking at barriers to discussing diagnostic error. Forty-three percent versus 22% of inpatient versus outpatient providers (P = 0.004) were comfortable discussing short-term diagnostic error publicly. Similarly, 76% versus 60% of inpatient versus outpatient providers (P = 0.010) were comfortable discussing short-term diagnostic error privately. A higher percentage of inpatient (64%) compared with outpatient providers (46%) (P = 0.043) were comfortable discussing long-term diagnostic error privately. Forty percent versus 24% of inpatient versus outpatient providers (P = 0.018) were comfortable discussing long-term error publicly. No difference in barriers cited depending on practice setting. Inpatient providers are more comfortable discussing diagnostic error than their outpatient counterparts. More study is needed to determine the etiology of this discrepancy and to develop strategies to increase outpatient provider comfort.

Introduction Pediatric medical trainees, like other medical professionals, can be held accountable for their actions and may be included in malpractice lawsuits. The aim of this study was to investigate the sources of malpractice cases involving pediatric trainees in order to inform the development of strategies to protect against such incidents. Methods LexisNexis, an online public legal research database containing records from the United States, was retrospectively reviewed for malpractice cases involving pediatric interns, residents, or fellows from January 1, 2000, to December 31, 2021. Cases were included if malpractice occurred following the delivery of a newborn through the care of young adults up to age 21. Results A total of 56 cases were included, consisting of 10 pediatric interns, 43 second- or third-year residents, and 11 pediatric fellows as defendants. Seventeen cases (30.4%) led to patient mortality. Incorrect diagnosis or treatment was claimed in 45 cases (80.4%), delay in evaluation in 24 (42.9%), failure to supervise trainee in 22 (39.3%), trainee inexperience in 21 (37.5%), procedural error in 21 (37.5%), lack of informed consent of resident being involved in two (3.6%), prolonged operative time in one (1.8%), and lack of informed consent of procedure/complications in one (1.8%). Conclusion Malpractice cases involving pediatric trainees highlight the importance of adequate supervision by attending physicians. These concerns are not exclusive to interns and residents and necessitate action by all members of the healthcare team. Given the interplay of supervision and diagnostic accuracy, trainee education and faculty development should emphasize malpractice education and strategies to mitigate lawsuits to both improve patient outcomes and reduce the likelihood of future malpractice claims.

To identify the components of the collaborative diagnostic conversations between clinicians, patients, and their families and how deficiencies in these conversations can lead to diagnostic errors.

We purposively selected 60 video recordings of clinical encounters that included diagnosis conversations. These videos were obtained from the internal medicine, and family medicine services at Mayo Clinic's campus in Rochester, Minnesota. These clinical encounters were recorded between November 2017, and December 2021, during the conduct of studies aiming at developing or testing shared decision-making interventions. We followed a critically reflective approach model for data analysis.

We identified 3 components of diagnostic conversations as follows: (1) recognizing diagnostic situations, (2) setting priorities, and (3) creating and reconciling a diagnostic plan. Deficiencies in diagnostic conversations could lead to framing issues in a way that sets diagnostic activities off in an incorrect or undesirable direction, incorrect prioritization of diagnostic concerns, and diagnostic plans of care that are not feasible, desirable, or productive.

We identified 3 clinician-and-patient diagnostic conversation components and mapped them to potential diagnostic errors. This information may inform additional research to identify areas of intervention to decrease the frequency and harm associated with diagnostic errors in clinical practice.

Using malpractice claims cases as vignettes is a promising approach for improving clinical reasoning education (CRE), as malpractice claims can provide a variety of content- and context-rich examples. However, the effect on learning of adding information about a malpractice claim, which may evoke a deeper emotional response, is not yet clear. This study examined whether knowing that a diagnostic error resulted in a malpractice claim affects diagnostic accuracy and self-reported confidence in the diagnosis of future cases. Moreover, suitability of using erroneous cases with and without a malpractice claim for CRE, as judged by participants, was evaluated.

In the first session of this two-phased, within-subjects experiment, 81 first-year residents of general practice (GP) were exposed to both erroneous cases with (M) and erroneous cases without (NM) malpractice claim information, derived from a malpractice claims database. Participants rated suitability of the cases for CRE on a five-point Likert scale. In the second session, one week later, participants solved four different cases with the same diagnoses. Diagnostic accuracy was measured with three questions, scored on a 0-1 scale: (1) What is your next step? (2) What is your differential diagnosis? (3) What is your most probable diagnosis and what is your level of certainty on this? Both subjective suitability and diagnostic accuracy scores were compared between the versions (M and NM) using repeated measures ANOVA.

There were no differences in diagnostic accuracy parameters (M vs. NM next step: 0.79 vs. 0.77, p = 0.505; differential diagnosis 0.68 vs. 0.75, p = 0.072; most probable diagnosis 0.52 vs. 0.57, p = 0.216) and self-reported confidence (53.7% vs. 55.8% p = 0.390) of diagnoses previously seen with or without malpractice claim information. Subjective suitability- and complexity scores for the two versions were similar (suitability: 3.68 vs. 3.84, p = 0.568; complexity 3.71 vs. 3.88, p = 0.218) and significantly increased for higher education levels for both versions.

The similar diagnostic accuracy rates between cases studied with or without malpractice claim information suggests both versions are equally effective for CRE in GP training. Residents judged both case versions to be similarly suitable for CRE; both were considered more suitable for advanced than for novice learners.

Diagnostic error has recently become a crucial clinical problem and an area of intense research. However, the reality of diagnostic errors in regional hospitals remains unknown. This study aimed to clarify the reality of diagnostic errors in regional hospitals in Japan. A 10-month retrospective cohort study was conducted from January to October 2021 at the emergency room of Oda Municipal Hospital in central Shimane Prefecture, Japan. Participants were divided into groups with or without diagnostic errors, and independent variables of patient, physician, and environmental factors were analyzed using Fisher's exact test, univariate (Student's t-test and Welch's t-test), and logistic regression analyses. Diagnostic errors accounted for 13.1% of all eligible cases. Remarkably, the proportion of patients treated without oxygen support and the proportion of male patients were significantly higher in the group with diagnostic errors. Sex bias was present. Additionally, cognitive bias, a major factor in diagnostic errors, may have occurred in patients who did not require oxygen support. Numerous factors contribute to diagnostic errors; however, it is important to understand the trends in the setting of each healthcare facility and plan and implement individualized countermeasures.

Missed and delayed diagnoses have received substantial attention as a quality and patient safety priority. To the extent that electronic health records, team-based care, and other mitigation strategies have been successful in improving diagnosis since the last large-scale study, we would expect that the contributing factors to diagnostic claims may have changed.

This study sought to examine paid medical malpractice claims as a proxy to identify contributing factors that reflect a clear diagnostic error. Diagnostic error cases with indemnity payments (2009-2020) were identified using the Candello (formerly known as CRICO) proprietary taxonomy. Factors associated with indemnity payments were analyzed using a multivariable logistic regression model.

Of 5367 included claims, 2161 (40%) had indemnity payments. A majority of claims had multiple contributing factors on the diagnostic pathway. In multivariable analysis, factors independently associated with an indemnity payment included the insurer (odds ratio and 95% confidence interval, 2.8 [2.4-3.3]), high injury severity (1.9 [1.3-2.8]) or death (1.5 [0.99-2.1]), and case setting (inpatient (0.77 [0.65-0.91]) or emergency department (0.67 [0.49-0.92])). Importantly, cases with contributing factors outside of Candello's diagnostic pathway were more likely to lead to indemnity payment.

The digital transformation and acceleration of team-based care in medicine have not mitigated the malpractice risks of complex cases with severe injuries and multiple missteps.

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Despite diagnostic errors impacting an estimated 12 million people yearly in the United States, educational strategies that foster diagnostic performance among nurse practitioner (NP) students remain elusive. One possible solution is to focus explicitly on competencies fundamental for diagnostic excellence. Currently, no educational tools were found that comprehensively address individual diagnostic reasoning competencies during simulated-based learning experiences.

Our research team developed and explored psychometric properties of the "Diagnostic Competency During Simulation-based (DCDS) Learning Tool."

Items and domains were developed based on existing frameworks. Content validity was determined by a convenience sample of eight experts. Inter-rater reliability was determined by four faculty rating eight simulation scenarios.

Final individual competency domain scale content validity index (CVI) scores ranged between 0.9175 and 1.0; total scale CVI score was 0.98. The intra-class correlation coefficient (ICC) for the tool was 0.548 (p < 0.0001, 95 % confidence interval CI [0.482-0.612]).

Results suggest that the DCDS Learning Tool is relevant to diagnostic reasoning competencies and may be implemented with moderate reliability across varied simulation scenarios and performance levels. The DCDS tool expands the landscape of diagnostic reasoning assessment by providing NP educators with granular, actionable, competency-specific assessment measures to foster improvement.

This study aimed to depict the characteristics, injury outcomes, and payment of obstetric malpractice lawsuits to better understand the medicolegal burden in obstetrics and categorize the causes of obstetric malpractice lawsuits using The National Health Service Litigation Authority coding taxonomy for further quality improvement in maternity care.

We reviewed and retrieved key information on court records of legal trials from China Judgment Online between 2013 and 2021.

A total of 3441 obstetric malpractice lawsuits successfully claimed were reviewed in this study, with a total indemnity payment of $139,875,375. After peaking in 2017, the number of obstetric malpractice claims begins to decline. Of the 2424 hospitals that were sued, 8.3% (201/2424) were referred to as "repeat defendant" because they were involved in multiple lawsuits. Death and injury were the outcomes in 53.4% and 46.6% of the cases, respectively. The most common outcome type was neonatal death, which made up 29.8% of all cases. The median indemnity payment for death was higher compared with injury (P < 0.05). In terms of detailed injury outcomes, the major neonatal injury had higher median indemnity payments than neonatal death and fetal death (P < 0.05). The median indemnity payment of the major maternal injury was higher than that of maternal death (P < 0.05). The leading causes of obstetric malpractice were the management of birth complications and adverse events (23.3%), management of labor (14.4%), career decision making (13.7%), fetal surveillance (11.0%), and cesarean section management (9.5%). The cause for 8.7% of cases was high payment (≥$100, 000). As indicated by the results of the multivariate analysis, the hospitals in the midland of China (odds ratio [OR], 0.476; 95% confidence interval [CI], 0.348-0.651), the hospitals in the west of China (OR, 0.523; 95% CI, 0.357-0.767), and the secondary hospitals (OR, 0.587; 95% CI, 0.356-0.967) had lower risks of high payment. Hospitals with ultimate liability (OR, 9.695; 95% CI, 4.072-23.803), full liability (OR, 16.442; 95% CI, 6.231-43.391), major neonatal injury (OR, 12.326; 95% CI, 5.836-26.033), major maternal injury (OR, 20.885; 95% CI, 7.929-55.011), maternal death (OR, 18.783; 95% CI, 8.887-39.697), maternal death with child injury (OR, 54.682; 95% CI, 10.900-274.319), maternal injury with child death (OR, 6.935; 95% CI, 2.773-17.344), and deaths of both mother and child (OR, 12.770; 95% CI, 5.136-31.754) had higher risks of high payment. In the causative domain, only anesthetics had a higher risk of high payment (OR, 5.605; 95% CI, 1.347-23.320), but anesthetic-related lawsuits made up just 1.4% of all cases.

The healthcare systems had to pay a significant amount as a result of obstetric malpractice lawsuits. Greater efforts are required to minimize serious injury outcomes and improve obstetric quality in the risky domains.

The coronary artery bypass grafting (CABG) is one of the high-risk litigated medical specialties. Further elucidating the causes behind these malpractice claims can help physicians avoid patient injury. This study analyzed CABG litigations occurred in different level hospitals to outline the basic characteristics, as well as present a analysis on the medical malpractice that result in lawsuits.

This study utilized the "China Judgments Online" database to compile litigations from 2012 to 2021 across China. 109 cases related to the CABG were included in the study, and were analyzed for demographic, patient outcomes and verdict characteristics in different levels of hospitals.

The median age of plaintiff patient was 62 years, the median length of stay was 25 days, and the median responsibility ratio of the litigation cases was 30%. The average proportion of responsibility of national, provincial and municipal hospitals were 29.6%, 28.4% and 39.5% respectively, and the median days after surgery to death of that were 15, 9 and 5 separately. The top 5 postoperative complications in dispute cases were: low cardiac output syndrome, postoperative hemorrhage, non-surgical site infections, surgical site infections and arrhythmia.

The diagnosis and treatment capabilities of coronary artery bypass grafting in different levels of hospitals in China were inconsistent, and the treatment capabilities in prefecture-level hospitals were lower than that in national hospitals. The procedural error, failure to properly monitor the patient and diagnostic errors were common in CABG litigations. Postoperative complications related to surgical injuries and insufficient basic postoperative management lead to a higher responsibility proportion.

The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities.

This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients' hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction.

We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute care hospitals in the state of Florida, merged with the SDoH data from the American Community Survey. A case-control design was adopted to construct stroke and stroke mimic cohorts. We compared the algorithm performance and feature importance measures of the ML models (ie, gradient boosting machine and random forest) with those of the logistic regression model based on 3 sets of predictors. To provide insights into the prediction and ultimately assist care providers in decision-making, we used TreeSHAP for tree-based ML models to explain the stroke prediction.

Our analysis included 143,203 hospital visits of unique patients, and it was confirmed based on the principal diagnosis at discharge that 73% (n=104,662) of these patients had a stroke. The approach proposed in this study has high sensitivity and is particularly effective at reducing the misdiagnosis of dangerous stroke chameleons (false-negative rate <4%). ML classifiers consistently outperformed the benchmark logistic regression in all 3 input combinations. We found significant consistency across the models in the features that explain their performance. The most important features are age, the number of chronic conditions on admission, and primary payer (eg, Medicare or private insurance). Although both the individual- and community-level SDoH features helped improve the predictive performance of the models, the inclusion of the individual-level SDoH features led to a much larger improvement (area under the receiver operating characteristic curve increased from 0.694 to 0.823) than the inclusion of the community-level SDoH features (area under the receiver operating characteristic curve increased from 0.823 to 0.829).

Using data widely available at the time of patients' hospital presentations, we developed a stroke prediction model with high sensitivity and reasonable specificity. The prediction algorithm uses variables that are routinely collected by providers and payers and might be useful in underresourced hospitals with limited availability of sensitive diagnostic tools or incomplete data-gathering capabilities.

Diagnostic errors are a major source of patient harm, most of which are caused by cognitive errors and biases. Despite research showing the relationship between software systems and cognitive processes, the impact of the electronic health record (EHR) on diagnostic error remains unknown.

We conducted a scoping review of the scientific literature to (1) survey the association between aspects of the EHR and diagnostic error, and (2) through a human-systems integration lens, identify the types of EHR issues and their impact on the stages of the diagnostic process.

We analyzed 11 research articles for the relationship between EHR use and diagnostic error. These articles highlight specific technical, usability, and workflow issues with the EHR that pose risks for diagnostic error at every stage of the diagnostic process.

Although technical problems such as EHR interoperability and data integrity pose critical issues for the diagnostic process, usability and workflow issues such as poor display design, and inability to track test results also hamper clinicians' ability to track, process, and act in the diagnostic process. Current research methods have limited coverage over clinical settings, are not standardized, and rarely include measures of patient harm.

The available evidence shows that EHRs pose risks for diagnostic error throughout the diagnostic process, with most issues involving their incompatibility with providers' cognitive processing. A structured and systematic model of collecting and reporting on these errors is needed to understand how the EHR shapes the diagnostic process and improve diagnostic accuracy.

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

While the core principles of medical education remain the same, the students' socioecological backgrounds, values and learning requirements are constantly changing. Bridging the generation gap between teachers and students is a key challenge of medical didactics. To meet the demands of today's classroom, we piloted a novel three-stage peer teaching and key feature concept. First, an on-demand key feature video case was presented. Second a background video was launched, followed by a self-assessment tool. Third, a live case discussion webinar focusing on clinical reasoning was held. The contents were created by near-peers experienced in medical didactics and checked by clinical experts. The elective format resonated with 652 participating graduate students and 1250 interactions per webinar, suggesting that students' strengths and weaknesses were addressed adequately. We aim to provide educators with input for creating a flexible and integrative learning environment utilising modern technological and didactic tools that shape the healthcare workers of tomorrow.

Diagnostic reasoning is an important topic in General Practitioners' (GPs) vocational training. Interestingly, research has paid little attention to the content of the cases used in clinical reasoning education. Malpractice claims of diagnostic errors represent cases that impact patients and that reflect potential knowledge gaps and contextual factors. With this study, we aimed to identify and prioritize educational content from a malpractice claims database in order to improve clinical reasoning education in GP training. With input from various experts in clinical reasoning and diagnostic error, we defined five priority criteria that reflect educational relevance. Fifty unique medical conditions from a malpractice claims database were scored on those priority criteria by stakeholders in clinical reasoning education in 2021. Subsequently, we calculated the mean total priority score for each condition. Mean total priority score (min 5-max 25) for all fifty diagnoses was 17,11 with a range from 13,89 to 19,61. We identified and described the fifteen highest scoring diseases (with priority scores ranging from 18,17 to 19,61). The prioritized conditions involved complex common (e.g., cardiovascular diseases, renal insufficiency and cancer), complex rare (e.g., endocarditis, ectopic pregnancy, testicular torsion) and more straightforward common conditions (e.g., tendon rupture/injury, eye infection). The claim cases often demonstrated atypical presentations or complex contextual factors. Including those malpractice cases in GP vocational training could enrich the illness scripts of diseases that are at high risk of errors, which may reduce diagnostic error and related patient harm.