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Burden of serious harms from diagnostic error in the USA
Abstract
Background
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.
Objective
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.
Methods
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.
Results
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%.
Conclusion
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.
... Systematic reviews of retrospective studies estimate that adverse diagnostic events occur in 0.7% of inpatients, but these are largely based on cohorts with severe outcomes only 3 ; thus, these are likely underestimates. 4 In a recent study which relied on the Institute for Healthcare Improvement (IHI) global trigger tool, 5 Bates et al estimated that one in four hospitalisations were associated with an AE. Of nearly 1000 AEs detected across 11 hospitals, just 10 DEs (1%) were identified as culprits. ...
... 8 10 20 Unlike prior studies that screened for DEs, our estimate reflects harmful DE rates related to exposure to hospital care received on the general medicine service, not limited to specific or enriched cohorts. 4 13 20 35 36 For example, the recent multicentre study by Auerbach et al (which was based on our approach) observed that harmful DEs occurred in 26% of patients who transferred to the ICU 24 hours or more after admission. 13 14 While our weighted sample included patients who transferred to the ICU and observed a similar rate (28.5%), we also sampled cases without these high-risk events to obtain a population estimate for hospitalised patients who received care on the general medicine service. ...
... Regarding top disease categories implicated in serious harms from DEs, while we frequently observed harmful DEs related to infection (sepsis, pneumonia), we infrequently identified harmful DEs related to vascular or cancer diagnoses. 4 Such cases may be detected in patients who receive specialised care delivered on cardiovascular and oncology services, not a general medicine service. Second, for reasons indicated earlier, we excluded cases during the initial waves of the pandemic including patients hospitalised on COVID-19 teams. ...
Background
Adverse event surveillance approaches underestimate the prevalence of harmful diagnostic errors (DEs) related to hospital care.
Methods
We conducted a single-centre, retrospective cohort study of a stratified sample of patients hospitalised on general medicine using four criteria: transfer to intensive care unit (ICU), death within 90 days, complex clinical events, and none of the aforementioned high-risk criteria. Cases in higher-risk subgroups were over-sampled in predefined percentages. Each case was reviewed by two adjudicators trained to judge the likelihood of DE using the Safer Dx instrument; characterise harm, preventability and severity; and identify associated process failures using the Diagnostic Error Evaluation and Research Taxonomy modified for acute care. Cases with discrepancies or uncertainty about DE or impact were reviewed by an expert panel. We used descriptive statistics to report population estimates of harmful, preventable and severely harmful DEs by demographic variables based on the weighted sample, and characteristics of harmful DEs. Multivariable models were used to adjust association of process failures with harmful DEs.
Results
Of 9147 eligible cases, 675 were randomly sampled within each subgroup: 100% of ICU transfers, 38.5% of deaths within 90 days, 7% of cases with complex clinical events and 2.4% of cases without high-risk criteria. Based on the weighted sample, the population estimates of harmful, preventable and severely harmful DEs were 7.2% (95% CI 4.66 to 9.80), 6.1% (95% CI 3.79 to 8.50) and 1.1% (95% CI 0.55 to 1.68), respectively. Harmful DEs were frequently characterised as delays (61.9%). Severely harmful DEs were frequent in high-risk cases (55.1%). In multivariable models, process failures in assessment, diagnostic testing, subspecialty consultation, patient experience, and history were significantly associated with harmful DEs.
Conclusions
We estimate that a harmful DE occurred in 1 of every 14 patients hospitalised on general medicine, the majority of which were preventable. Our findings underscore the need for novel approaches for adverse DE surveillance.
... Unfortunately, SEA is frequently misdiagnosed, with about 62% of cases initial-ly overlooked. 1 The thoracic and lumbosacral regions are the most common sites for SEA development, with their comparatively expansive and infection-prone adipose tissue. ...
... Newman-Toker et al. reported that about 62.1% of SEA cases are initially misdiagnosed or diagnosed late, resulting in serious harm in 22.1% of these cases. 1 The need for accurate and timely diagnosis is highlighted by the risk of permanent neurological damage or death if treatment is delayed. Additionally, the extent of neurological deficits at diagnosis closely correlates with long-term neurological recovery. ...
... Legal claims against health care providers for delayed or incorrect diagnosis and treatment, particularly in cases resulting in paralysis, highlight the importance of maintaining a high level of suspicion. 1 Prompt actions, including MRI imaging, systemic antibiotics, and neurosurgical consultation, are imperative when patients present with the triad of back pain, fever, and neurological symptoms. ...
... Failures in the diagnostic process are thought to affect at least 15% of patient encounters, cause 34% of adverse events in hospitals, are a leading cause in major malpractice claims and payouts and are recognised as a top priority in patient safety research. [1][2][3] The National Academies of Science, Engineering and Medicine defines diagnostic error as a failure to establish an accurate and timely explanation of a patient's medical problem and has been shown to contribute to the morbidity and mortality of an estimated 795 000 patients each year in the USA. 1 Although diagnostic error has received significant research attention across multiple clinical settings over the last several decades, it continues to pose consequential challenges and requires improvement in systematic investigation and operational intervention. 3 4 Additionally, few effective mitigation strategies have been designed for widespread prevention of diagnostic error. ...
... [1][2][3] The National Academies of Science, Engineering and Medicine defines diagnostic error as a failure to establish an accurate and timely explanation of a patient's medical problem and has been shown to contribute to the morbidity and mortality of an estimated 795 000 patients each year in the USA. 1 Although diagnostic error has received significant research attention across multiple clinical settings over the last several decades, it continues to pose consequential challenges and requires improvement in systematic investigation and operational intervention. 3 4 Additionally, few effective mitigation strategies have been designed for widespread prevention of diagnostic error. ...
... By nature, both sources are subject to outcomes and hindsight biases, which give little insight into contributing and interacting factors that lead to diagnostic error. 1 4 Furthermore, these data do not represent the true frequency of occurrence in clinical practice, as they likely underestimate the prevalence of diagnostic errors as they are only recognised when they lead to poor outcomes. 1 The unintentional problem with focusing on diagnostic outcomes (ie, whether the diagnosis is correct or incorrect) and then retrospectively reviewing cases in which the diagnosis went awry neglects the vital factor of how physicians arrive at a diagnosis. ...
... Diagnostic error is a public health concern. It is estimated that nearly 800,000 Americans die or are permanently disabled by diagnostic error in various clinical settings each year (Newman-Toker et al., 2024). Globally, the incidence of diagnostic error is likely even higher as access to basic diagnostic testing resources can be limited in low-resource contexts, resulting in diagnostic delays for life-threatening diseases (Newman-Toker et al., 2024). ...
... It is estimated that nearly 800,000 Americans die or are permanently disabled by diagnostic error in various clinical settings each year (Newman-Toker et al., 2024). Globally, the incidence of diagnostic error is likely even higher as access to basic diagnostic testing resources can be limited in low-resource contexts, resulting in diagnostic delays for life-threatening diseases (Newman-Toker et al., 2024). Central goals of the initial clinical reasoning process are to reduce diagnostic uncertainty and communicate a plausible differential diagnosis for safe and effective patient care. ...
Diagnostic errors pose a significant public health challenge, affecting nearly 800,000 Americans annually, with even higher rates globally. In the ICU, these errors are particularly prevalent, leading to substantial morbidity and mortality. The clinical reasoning process aims to reduce diagnostic uncertainty and establish a plausible differential diagnosis but is often hindered by cognitive load, patient complexity, and clinician burnout. These factors contribute to cognitive biases that compromise diagnostic accuracy. Emerging technologies like large language models (LLMs) offer potential solutions to enhance clinical reasoning and improve diagnostic precision. In this perspective article, we explore the roles of LLMs, such as GPT-4, in addressing diagnostic challenges in critical care settings through a case study of a critically ill patient managed with LLM assistance.
... [37][38][39] Each year in the United States alone, an estimated 800,000 people are seriously harmed or die due to diagnostic errors. 40 In studies of malpractice claims, vascular events, infections, and cancers ("Big Three" categories) account for approximately 75% of all serious misdiagnosis-related harms -highlighting these disorders for key opportunities in improvement. 39,40 Further, it has been stated that "most wrong diagnoses represent underdiagnosis of true conditions as well as excess diagnosis of erroneous ones." ...
... 40 In studies of malpractice claims, vascular events, infections, and cancers ("Big Three" categories) account for approximately 75% of all serious misdiagnosis-related harms -highlighting these disorders for key opportunities in improvement. 39,40 Further, it has been stated that "most wrong diagnoses represent underdiagnosis of true conditions as well as excess diagnosis of erroneous ones." Most of the focus on misdiagnosis has been on diseases that cause death, such as The Big 3. ...
Background
Nearly half of the global population has some form of oral health-related disease primarily associated with caries or tooth decay, periodontal disease, tooth loss or oral cancer. Oral and face pains are a primary motivator for healthcare consultation. While not specifically included in global estimates, orofacial pain associated with oral diseases and orofacial pain disorders pose significant global public health and financial burdens. Thus, it is paramount to act on opportunities to improve the practice of medicine in dentistry.
Examined Literature and Conceptual Advancement
This article centers on areas for improvement in the fields of oral health and dentistry in defining, evaluating, and treating musculoskeletal orofacial pains, a category of pain disorders within orofacial pain leading to physical pain and both mental and physical disability. We discuss how features of dentistry, current modes of specialist training, and cultural norms have inadvertently created barriers to improvements in care quality and may even promote harms to patient groups with musculoskeletal pain. We provide critical insights and solutions in patient safety and quality improvement efforts from other areas of medicine, such as in emergency or acute care settings.
Practical Implications
Because musculoskeletal orofacial pains are a wide-spread, common, and costly constellation of oral health disorders, they represent a keystone treatment area for the fields of dentistry and oral health to bridge with existing patient safety and quality improvement efforts. We conclude by providing several recommendations from lessons learned in patient safety and quality improvement applied to musculoskeletal pain care. Collectively, these lessons stand to: (1) promote sharing of information, (2) encourage collaboration and transdisciplinary problem solving as a medical community, and (3) improve diagnostic accuracy and optimal delivery of the highest quality treatment as safely as possible for both patients and providers.
... 1 Diagnostic errors including incorrect, missed or delayed diagnosis have been described as the most deadly medical error, leading to up to 371 000 annual deaths in the USA. 2 For US ...
... Open access emergency departments (EDs), a potential 7.4 million diagnostic errors have been estimated to result in up to 250 000 deaths. 3 In Australia, an estimated 140 000 patients are exposed to diagnostic errors annually, leading to 21 000 cases of serious harm and up to 2000-4000 deaths. 4 By definition, diagnostic error occurs when there is a 'failure to (1) establish an accurate and timely explanation of the patient's health problem(s) or (2) communicate that explanation to the patient' (emphasis added). 1 The US-based ECRI (formerly Emergency Care Research Institute) has listed diagnostic errors and related concepts of delayed diagnosis and cognitive bias among the top 10 patient safety concerns multiple times since 2019. [5][6][7] In 2024, the WHO's patient safety day is running under the banner of 'Improving diagnosis for patient safety'. ...
Introduction
Communication failings may compromise the diagnostic process and pose a risk to quality of care and patient safety. With a focus on emergency care settings, this project aims to examine the critical role and impact of communication in the diagnostic process, including in diagnosis-related health and research policy, and diagnostic patient–clinician interactions in emergency departments (EDs).
Methods and analysis
This project uses a qualitatively driven multimethod design integrating findings from two research studies to gain a comprehensive understanding of the impact of context and communication on diagnostic excellence from diverse perspectives. Study 1 will map the diagnostic policy and practice landscape in Australia, New Zealand and the USA through qualitative expert interviews and policy analysis. Study 2 will investigate the communication of uncertainty in diagnostic interactions through a qualitative ethnography of two metropolitan Australian ED sites incorporating observations, field notes, video-recorded interactions, semistructured interviews and written medical documentation, including linguistic analysis of recorded diagnostic interactions and written documentation. This study will also feature a description of clinician, patient and carer perspectives on, and involvement in, interpersonal diagnostic interactions and will provide crucial new insights into the impact of communicating diagnostic uncertainty for these groups. Project-spanning patient and stakeholder involvement strategies will build research capacity among healthcare consumers via educational workshops, engage with community stakeholders in analysis and build consensus among stakeholders.
Ethics and dissemination
The project has received ethical approvals from the Human Research Ethics Committee at ACT Health, Northern Sydney Local Health District and the Australian National University. Findings will be disseminated to academic peers, clinicians and healthcare consumers, health policy-makers and the general public, using local and international academic and consumer channels (journals, evidence briefs and conferences) and outreach activities (workshops and seminars).
... Diagnosis is a crucial step in clinical medicine, where a significant proportion of medical errors and harms are related to diagnostic errors [1]. The formation of a diagnostic team has been proposed as an effective strategy to mitigate the risks associated with misdiagnosis [2,3]. ...
... The consistent identification of sepsis, particularly its second-place ranking by Bard, underscores the potential of these AI systems to enhance diagnostic accuracy and reduce errors in the identification of life-threatening conditions [28]. Importantly, the top 3 differentials by Gemini Advanced and Gemini-sepsis, pneumonia, and pulmonary embolism-are among the most harmful diseases where reducing diagnostic errors is crucial [1]. This suggests a potential for GAI systems to alert medical professionals about the inclusion of these important diseases during diagnosis. ...
Background
Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user’s login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown.
Objective
This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series.
Methods
We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02.
Results
In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002).
Conclusions
The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence–enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.
... harms annually (Newman-Toker et al., 2023). Furthermore, many diagnostic errors result from information transfer problems (Zwaan et al., 2010). ...
... The ICU is one of the hospital settings (along with, e.g., the ER and Radiology) where misdiagnosis or delayed diagnosis are often caused by incomplete information, since clinicians typically do not have enough time to fully examine a patient's EHR. In healthcare, cancer, infection, and vascular dysfunction (termed the "big three") account for about 75% of all mis-diagnosis-related harms (Newman-Toker et al., 2023). Within the ICU, the latter two categories mostly manifest as pneumonia, and pulmonary edema (which in this paper we treat as interchangeable with congestive heart failure). ...
... Inaccurate diagnoses in medical imaging reports are a burden to the patient and the healthcare system (1). Reading MRI scans of patients with eye and orbit diseases poses a particular diagnostic challenge due to the rarity of these lesions. ...
... Other studies found that diagnostic errors occur at an average rate of 3%-4%, with a 32% retrospective error rate for interpretation of abnormal studies (3). These challenges may delay diagnosis and treatment or expose patients to potentially unnecessary biopsies and treatments, which can cause harm and be costly (1). Content-based image retrieval (CBIR) allows radiologists to retrieve relevant cases from a curated database with clinical or histopathological validation, based on visual similarity with supplied patient query images. ...
Background:
Diagnoses of eye and orbit pathologies by radiological imaging is challenging due to their low prevalence and the relative high number of possible pathologies and variability in presentation, thus requiring substantial domain-specific experience.
Purpose:
This study investigates whether a content-based image retrieval (CBIR) tool paired with a curated database of orbital MRI cases with verified diagnoses can enhance diagnostic accuracy and reduce reading time for radiologists across different experience levels.
Material and Methods:
We tested these two hypotheses in a multi-reader, multi-case study, with 36 readers and 48 retrospective eye and orbit MRI cases. We asked each reader to diagnose eight orbital MRI cases, four while having only status quo reference tools available (e.g. Radiopaedia.org, StatDx, etc.), and four while having a CBIR reference tool additionally available. Then, we analyzed and compared the results with linear mixed effects models, controlling for the cases and participants.
Results:
Overall, we found a strong positive effect on diagnostic accuracy when using the CBIR tool only as compared to using status quo tools only (status quo only 55.88%, CBIR only 70.59%, 26.32% relative improvement, p=.03, odds ratio=2.07), and an even stronger effect when using the CBIR tool in conjunction with status quo tools (status quo only 55.88%, CBIR + status quo 83.33%, 49% relative improvement, p=.02, odds ratio=3.65). Reading time in seconds (s) decreased when using only the CBIR tool (status quo only 334s, CBIR only 236s, 29% decrease, p<.001), but increased when used in conjunction with status quo tools (status quo only 334s, CBIR + status quo 396s, 19% increase, p<.001).
Conclusion:
We found significant positive effects on diagnostic accuracy and mixed effects on reading times when using the CBIR reference tool, indicating the potential benefits when using CBIR reference tools in diagnosing eye and orbit mass lesions by radiological imaging.
... Diagnostic disparities are preventable differences in deadly, dangerous, and costly diagnostic errors or in opportunities to achieve optimal diagnostic experiences and outcomes, or diagnostic excellence [1][2][3]. If diagnostic excellence is considered to occupy a multidimensional space, where the full space represents the sum of all health benefits that could accrue from achieving diagnostic excellence for every person in every situation, then diagnostic inequity or disparities represent the lack of a fair and just chance to have these benefits [4,5]. ...
... To address this gap in understanding applicability of existing solutions to diagnostic disparities, we undertook this review. Specifically, we aimed to (1): map the realm of potential solutions to diagnostic disparities and organize them into a typology based on a selected category (2); illustrate these solutions with existing examples and characterize solutions by their focus on diagnostic disparities and estimated state of implementation; and (3) describe gaps that prevent these solutions from being diagnostic disparitiesfocused and widely implemented. ...
Objectives
Diagnostic disparities are preventable differences in diagnostic errors or opportunities to achieve diagnostic excellence. There is a need to summarize solutions with explicit considerations for addressing diagnostic disparities. We aimed to describe potential solutions to diagnostic disparities, organize them into an action-oriented typology with illustrative examples, and characterize these solutions to identify gaps for their further development.
Methods
During four human-centered design workshops composed of diverse expertise, participants ideated and clarified potential solutions to diagnostic disparities and were supported by environmental literature scan inputs. Nineteen individual semi-structured interviews with workshop participants validated identified solution examples and solution type characterizations, refining the typology.
Results
Our typology organizes 21 various types of potential diagnostic disparities solutions into four primary expertise categories needed for implementation: healthcare systems’ internal expertise, educator-, multidisciplinary patient safety researcher-, and health IT-expertise. We provide descriptions of potential solution types ideated as focused on disparities and compare those to existing examples. Six types were characterized as having diagnostic-disparity-focused examples, five as having diagnostic-focused examples, and 10 as only having general healthcare examples. Only three solution types had widespread implementation. Twelve had implementation on limited scope, and six were mostly hypothetical. We describe gaps that inform the progress needed for each of the suggested solution types to specifically address diagnostic disparities and be suitable for the implementation in routine practice.
Conclusions
Numerous opportunities exist to tailor existing solutions and promote their implementation. Likely enablers include new perspectives, more evidence, multidisciplinary collaborations, system redesign, meaningful patient engagement, and action-oriented coalitions.
... Misdiagnoses can have severe consequences, including delayed or incorrect interventions, worsened patient conditions, and significant emotional and financial strain [5]. A comprehensive study by John Hopkins [27] revealed an alarming 11.1% misdiagnosis rate in vascular, infection, and cancer diseases, with 4.4% of those resulting in serious harm. In the United States, this translates to an estimated 549,000 annual misdiagnoses. ...
... Restricting the diseases to those in the training dataset of 40 is unrealistic and inflates diagnostic accuracy. Moreover, most ML diagnostic experiments assume the labels in the training dataset to be the "ground truth," but this is flawed due to high misdiagnosis rates [27]. 3. Scoring function: Diagnosis accuracy is quantified with scores of 100%, 50%, and 25% for hits among the top-3 choices, respectively. ...
Misdiagnosis is a significant issue in healthcare, leading to harmful consequences for patients. The propagation of mislabeled data through machine learning models into clinical practice is unacceptable. This paper proposes EVINCE, a system designed to 1) improve diagnosis accuracy and 2) rectify misdiagnoses and minimize training data errors. EVINCE stands for Entropy Variation through Information Duality with Equal Competence, leveraging this novel theory to optimize the diagnostic process using multiple Large Language Models (LLMs) in a structured debate framework. Our empirical study verifies EVINCE to be effective in achieving its design goals.
... In recent decades, much has been written about the importance of clinical reasoning in medical practice, primarily stemming from a recognition of the urgent need to combat a "diagnostic error crisis" that has plagued the medical practice, leading to patient morbidity and mortality, with downstream socioeconomic repercussions [1,2]. It has been suggested that errors in clinical reasoning are predominantly caused by "frailty of human thinking under conditions of complexity, uncertainty, and pressure of time" [3]. ...
Clinical reasoning is a quintessential aspect of medical training and practice, and is a topic that has been studied and written about extensively over the past few decades. However, the predominant conceptualisation of clinical reasoning has insofar been extrapolated from cognitive psychological theories that have been developed in other areas of human decision-making. Till date, the prevailing model of understanding clinical reasoning has remained as the dual process theory which views cognition as a dichotomous two-system construct, where intuitive thinking is fast, efficient, automatic but error-prone, and analytical thinking is slow, effortful, logical, deliberate and likely more accurate. Nonetheless, we find that the dual process model has significant flaws, not only in its fundamental construct validity, but also in its lack of practicality and applicability in naturistic clinical decision-making. Instead, we herein offer an alternative Bayesian-centric, intuitionist approach to clinical reasoning that we believe is more representative of real-world clinical decision-making, and suggest pedagogical and practice-based strategies to optimise and strengthen clinical thinking in this model to improve its accuracy in actual practice.
... In addition, unbalanced datasets for antioxidant protein identification lead to low MCC values, which indicates poor model performance in predicting a small number of classes [21][22][23][24]. Notably, in clinical applications, misdiagnoses caused by targeting only few classes may have fatal consequences [25][26][27][28][29]. To address this problem, we propose the Rore model, which achieves superior MCC values based on a feature dimensionality reduction algorithm and also preserves the original information and relationships between the features. ...
In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore .
... In today's increasingly intricate and high-pressure healthcare environment, the consequences of missed diagnoses and medication errors are more severe and pervasive than ever before. These incidents are not just unfortunate occurrences; they represent critical failures within the healthcare system that can lead to significant and often irreversible patient harm, extended hospital stays, and, in the most tragic scenarios, loss of life (Newman-Toker et al., 2024, Newman-Toker, 2023Ringer et al, 2023). According to the World Health Organization (WHO) (WHO, 2024), approximately 1 in 10 patients globally suffers harm during the course of their healthcare, resulting in over 3 million deaths each year due to unsafe healthcare practices. ...
Missed diagnoses and medication errors are significant risks in healthcare, leading to increased patient morbidity and mortality. Traditional Clinical Decision Support Systems (CDSS) rely on static, predefined rules, limiting their adaptability to personalized patient care. This chapter explores how integrating Artificial Intelligence (AI) and Machine Learning (ML) can revolutionize CDSS, driving next-generation systems. By analyzing clinical datasets in real time, AI and ML enable personalized insights that enhance diagnostic accuracy, optimize treatment recommendations, improve risk stratification, and streamline workflows. These advancements promise better patient outcomes, informed clinical decisions, and reduced costs. The chapter also addresses challenges like data quality, explainability, regulatory compliance, and ethics, proposing strategies for overcoming these. Through collaboration and research, AI and ML can transform CDSS into foundational healthcare elements, fostering personalized, data-driven, and efficient patient care.
... 2,3 Despite efforts to prevent medical errors, studies show health care-related errors continue to occur. [4][5][6] These errors, which can range in severity from near-misses and minor mistakes to serious, potentially life-threatening events, can produce powerful emotions for the health care staff involved, some of which have the potential to permanently alter the course of their personal and professional lives. ...
... Diagnostic errors are a common cause of medical claims in the USA [20][21][22]. We believe that manufacturers should not only focus on the technical precision of the product during the development of AI medical devices but also prioritize collaboration with healthcare professionals for comprehensive clinical evaluation. ...
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.
... While diagnostic teams are not uniform, we suggest the Gomez team profile is not atypical in complex diagnostic processes. Data suggest the "Big Three" diseasescancer, cardiovascular disease, and infectious diseaseaccount for about three-fourths of serious misdiagnosis-related harms in the U.S [9,10]. and often involve numerous individuals interacting in similar dynamic teaming conditions. ...
Dynamic teaming is required whenever people must coordinate with one another in a fluid context, particularly when the fundamental structures of a team, such as membership, priorities, tasks, modes of communication, and location are in near-constant flux. This is certainly the case in the contemporary ambulatory care diagnostic process, where circumstances and conditions require a shifting cast of individuals to coordinate dynamically to ensure patient safety. This article offers an updated perspective on dynamic teaming commonly required during the ambulatory diagnostic process. Drawing upon team science, it clarifies the characteristics of dynamic diagnostic teams, identifies common risk points in the teaming process and the practical implications of these risks, considers the role of providers and patients in averting adverse outcomes, and provides a case example of the challenges of dynamic teaming during the diagnostic process. Based on this, future research needs are offered as well as clinical practice recommendations related to team characteristics and breakdowns, team member knowledge/cognitions, teaming dynamics, and the patient as a team member.
... In the United States alone, medical errors are the third largest cause of death [1], and within that, diagnostic errors kill or permanently disable 800,000 people each year [2]. Research by The National Academy of Medicine as well as Newman-Toker et al. estimated that diagnostic errors are responsible for approximately 10% of patient deaths [3,4] and 6-17% of hospital complications [3]. ...
On average, one in ten patients die because of a diagnostic error and medical errors are the third largest cause of death in the US. While LLMs have been proposed to help doctors with diagnoses, no research results have been published on comparing the diagnostic ability of many popular LLMs on an openly accessible real-patient cohort. In thus study, we compare LLMs from Google, OpenAI, Meta, Mistral, Cohere and Anthropic using our previously published evaluation methodology and explore improving their accuracy with RAG.
... In the United States alone, medical errors are the third largest cause of death [1], and within that, diagnostic errors kill or permanently disable 800,000 people each year [2]. Research by The National Academy of Medicine as well as Newman-Toker et al. estimated that diagnostic errors are responsible for approximately 10% of patient deaths [3,4] and 6-17% of hospital complications [3]. ...
On average, one in ten patients die because of a diagnostic error and medical errors are the third largest cause of death in the word. While LLMs have been proposed to help doctors with diagnoses, no research results have been published on comparing the diagnostic ability of many popular LLMs on an openly accessible real-patient cohort. In thus study, we compare LLMs from Google, OpenAI, Meta, Mistral, Cohere and Anthropic using our previously published evaluation methodology and explore improving their accuracy with RAG.
... More recently, Newman-Toker et al. 3 published the first national estimate of morbidity and mortality in the USA, in which they estimated that 795,000 Americans become permanently disabled or die annually from diagnostic errors across all clinical settings, making it the single largest source of serious harm from medical mistakes. Thus, it remains imperative that we continue to look for ways to efficiently analyze our data to focus limited resources on targeted solutions. ...
Introduction
Patient safety incidents and adverse events are reported voluntarily by healthcare staff. The Global Trigger Tool, developed by the Institute for Healthcare Improvement, is used to capture unreported events. The objective of the new model is to use process mining in the context of adverse event detection to increase reporting efficiency.
Methods
In the first phase, the process mining model is used to discover the actual process map by querying inpatient processes using the provided log data. Transfusions of blood or blood products within 24 h after surgery, a subset of The Transfusion of Blood or Use of Blood Products trigger of Institute for Healthcare Improvement (GTT-C1) was deployed as a process pattern within the actual process map. Only detected patients were reviewed in the last phase.
Results
The model ran on a single hospital data set, which included 2870 patients, 1048 of which included surgeries. The model reduced the analyzed data to 57 patients. The expert group analyzed these 57 patients and defined 12 adverse events. The calculated adverse event ratio was 1.1%, comparable with other manual Global Trigger Tool studies. In addition, the reviewers’ time required decreased by 94.6%, from 138.28 to 7.52 h. Two reviewers validated the model by reviewing a sample of patients.
Conclusions
Process mining can be used in healthcare institutions to conduct routine surveillance and detect unreported safety events. This allows healthcare providers to allocate financial and human resources to more urgent needs and increase efficiency. Further research is needed to evaluate the model with other triggers.
... Данная статья, приуроченная к 80-летнему юбилею ИБМХ, является обзором этих достижений с обсуждением их внедрения в клиническую практику. тысяч американцев становятся инвалидами или ежегодно умирают в медицинских учреждениях из-за неправильной диагностики заболеваний [4]. Отсутствие подобных данных по другим странам не позволяет надеяться на лучшую ситуацию в них, учитывая высокий уровень медицины в США. ...
Using analytical technologies it is possible now to measure the entire diversity of molecules even in a small amount of biological samples. Metabolomic technologies simultaneously analyze thousands of low-molecular substances in a single drop of blood. Such analytical performance opens new possibilities for clinical laboratory diagnostics, still relying on the measurement of only a limited number of clinically significant substances. However, there are objective difficulties hampering introduction of metabolomics into clinical practice. The Institute of Biomedical Chemistry (IBMC), consolidating the efforts of leading scientific and medical organizations, has achieved success in this area by developing a clinical blood metabogram (CBM). CBM opens opportunities to obtain overview on the state of the body with the detailed individual metabolic characteristics of the patient. A number of scientific studies have shown that the CBM is an effective tool for monitoring the state of the body, and based on the CBM patterns (signatures), it is possible to diagnose and monitor the treatment of many diseases. Today, the CBM creation determines the current state and prospects of clinical metabolomics in Russia. This article, dedicated to the 80th anniversary of IBMC, is a review of these achievements focused on a discussion of their implementation in clinical practice.
... [1][2][3] Evidence suggests diagnostic error accounts for more serious harm than any other type of medical error 4 and leads to approximately 795,000 serious harm incidents in the United States each year. 5 In the ambulatory care setting, 1 in 20 patients experiences a diagnostic error. 6 Communication breakdowns between patients and clinicians have consistently been shown to be a point of failure in the diagnostic process 2,7-10 and are a significant threat to patient safety in ambulatory care. ...
Background
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.
Objectives
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.
Methods
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.
Results
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).
Conclusions
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.
... Failures to obtain the right test and appropriately interpret results probably are the major source of harm related to laboratory testing. The failure to make a timely and correct diagnosis, which includes many components-clinician decision making, radiological studies, clinical findings, and laboratory testing-is increasingly being recognized as a major cause of patient morbidity and mortality [26][27][28]. Improving the complete testing process from test ordering to the interpretation of results offers the greatest potential for improving patient care [29][30][31]. The implementation of diagnostic management teams is one recommended means of achieving this [27,31]. ...
Laboratory testing is a complex process with a significant error rate. Studies of laboratory errors have found that the major causes are preanalytical factors, interferences, and process errors. Efforts by regulatory agencies to improve quality via more stringent premarket evaluations of laboratory tests therefore have poor prospects of reducing laboratory errors and improving test quality. Efforts toward increasing the regulation of laboratory tests are analogous to preventing traffic accidents by increasing the premarket evaluation of automobiles. This analogy illustrates how increased premarket evaluation has limited prospects for quality improvement and, in some cases, actually contributes to errors and lower quality. Tools that are used by laboratories to detect, prevent, and address analytical errors are discussed, and the increased implementation of such tools offers approaches that can be used to improve laboratory quality.
... These delays that occur in the diagnosis not only impact patient outcomes but also incur substantial cost to both the health care provider and health care giver. In a study by Newman et al. [4] in the journal of Quality and Safety, it was estimated that diagnostic errors account for up to USD 100 billion in annual healthcare spending in the United States of America alone. Furthermore, delays in diagnosis have been shown as a leading cause for malpractice claims as well. ...
In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this conventional storage approach often incurs high infrastructure costs and results in sluggish information retrieval, ultimately leading to delays in diagnosis and consequential wastage of valuable time for patients. The methodology proposed in this paper offers a pioneering solution to expedite the diagnosis of medical conditions while simultaneously reducing infrastructure costs associated with data storage. Through this study, a high-speed biomedical image processing approach is designed to facilitate rapid prognosis and diagnosis. The proposed framework includes Deep learning QR code technique using an optimized database design aimed at alleviating the burden of intensive on-premises database requirements. The work includes medical dataset from Crawford Image and Data Archive and Duke CIVM for evaluating the proposed work suing different performance metrics, The work has also been compared from the previous research further enhancing the system's efficiency. By providing healthcare providers with high-speed access to medical records, this system enables swift retrieval of comprehensive patient details, thereby improving accuracy in diagnosis and supporting informed decision-making.
... Among malpractice claims, diagnostic errors are the most common, most costly, and most dangerous medical mistakes [11]. With regards to the impact of diagnostic errors on patients, the first nationwide estimate of morbidity and mortality 2 due to diagnostic errors was published in 2024 and estimates 795,000 annual serious harms or deaths related to diagnostic errors [12]. In addition, diagnostic errors are costly, estimated to total more than $100 billion per year [13]. ...
Pregnancy-related morbidity and mortality remains high across the United States and the majority of deaths are preventable. Misdiagnosis and delay in diagnosis are thought to be contributors to preventable harm. These diagnostic errors in obstetrics are understudied. We present selected research methods to ascertain rates of and harm associated with diagnostic errors, the challenges in investigating them, and present future steps toward achieving diagnostic excellence in obstetrics.
... Through a physical examination, it is easier for a nurse or doctor to gather detailed information about a person so that they can determine the diagnosis and severity of a patient's illness (2). Errors in conducting physical examinations can be fatal to patient safety, thus reducing patient and family trust in medical personnel (3,4). ...
Aims: This study aims to review the scientific literature that has been conducted and published regarding the development of one of the medical devices, namely digital stethoscopes and look for novelty as a basis for conducting research. Methods: This research is a descriptive analysis with a bibliometric analysis approach. Searches for published articles were conducted in August 2023 on the Scopus and Pubmed databases through the Publish or Perish application from 2000-2023. Results: Of all the literature and publications that have been analyzed and mapped using VosViewer, the majority of studies found focus on discussing the use of digital stethoscopes in listening to and interpreting heart sounds compared to interpreting lung sounds. Conclusion: Based on the articles collected and processed in VosViewer, the results of this study, which can be used as a reference or basis for researchers, are about digital stethoscope innovations that focus on recording and interpreting lung sounds.
... Among malpractice claims, diagnostic errors are the most common, most costly, and most dangerous medical mistakes [11]. With regard to the impact of diagnostic errors on patients, the first nationwide estimate of morbidity and mortality due to diagnostic errors was published in 2024, estimating 795,000 annual serious harms or deaths related to diagnostic errors [12]. In addition, diagnostic errors are costly, estimated to total more than USD 100 billion per year [13]. ...
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.
... Women face unique risk factors for cerebrovascular disease, and some traditional stroke risk factors exert a stronger influence in women. Furthermore, disparities persist in research representation, as women continue to be understudied, under recognized, underdiagnosed, and undertreated [4,9,10]. ...
(1) Background: Previous studies have identified disparities in stroke care and outcomes by sex. Therefore, the main objective of this study was to evaluate the average cost of stroke care and the existence of differences in care provision by biological sex. (2) Methods: This observational study adhered to the recommendations of the STROBE statement. The calculation of costs was performed based on the production cost of the service or the rate paid for a set of services, depending on the availability of the corresponding information. (3) Results: A total of 336 patients were included, of which 47.9% were women, with a mean age of 73.3 ± 11.6 years. Women were typically older, had a higher prevalence of hypertension (p = 0.005), lower pre-stroke proportion of mRS 0-2 (p = 0.014), greater stroke severity (p < 0.001), and longer hospital stays (p = 0.017), and more were referred to residential services (p = 0.001) at 90 days. Women also required higher healthcare costs related to cardiovascular risk factors, transient ischemic strokes, institutionalization, and support needs; in contrast, they necessitated lower healthcare costs when undergoing endovascular therapy and receiving rehabilitation services. The unadjusted averaged cost of stroke care was EUR 22,605.66 (CI95% 20,442.8–24,768.4), being higher in women [p = 0.027]. The primary cost concept was hospital treatment (38.8%), followed by the costs associated with dependence and support needs (36.3%). At one year post-stroke, the percentage of women not evaluated for a degree of dependency was lower (p = 0.008). (4) Conclusions: The total unadjusted costs averaged EUR 22,605.66 (CI95% EUR 20,442.8–24,768.4), being higher in women compared to men. The primary cost concept was hospital treatment (38.8%), followed by the costs associated with dependence and support needs (36.3%).
... Diagnostic errors are among the most pressing issues in medical practice [9][10][11], causing an estimated 795,000 deaths and permanent disabilities in the United States alone each year [12]. Reducing diagnostic errors-without incurring substantially higher costs-is essential to improve patient outcomes worldwide. ...
Artificial intelligence systems, particularly large language models (LLMs), are increasingly being employed in high-stakes decisions that impact both individuals and society at large, often without adequate safeguards to ensure safety, quality, and equity. Yet LLMs hallucinate, lack common sense, and are biased - shortcomings that may reflect LLMs' inherent limitations and thus may not be remedied by more sophisticated architectures, more data, or more human feedback. Relying solely on LLMs for complex, high-stakes decisions is therefore problematic. Here we present a hybrid collective intelligence system that mitigates these risks by leveraging the complementary strengths of human experience and the vast information processed by LLMs. We apply our method to open-ended medical diagnostics, combining 40,762 differential diagnoses made by physicians with the diagnoses of five state-of-the art LLMs across 2,133 medical cases. We show that hybrid collectives of physicians and LLMs outperform both single physicians and physician collectives, as well as single LLMs and LLM ensembles. This result holds across a range of medical specialties and professional experience, and can be attributed to humans' and LLMs' complementary contributions that lead to different kinds of errors. Our approach highlights the potential for collective human and machine intelligence to improve accuracy in complex, open-ended domains like medical diagnostics.
Objectives
To identify clinical presentations that acted as harbingers for future sepsis hospitalizations in pediatric patients evaluated in the emergency department (ED) using the Symptom Disease Pair Analysis of Diagnostic Error (SPADE) methodology.
Methods
We identified patients in the Pediatric Health Information Systems (PHIS) database admitted for sepsis between January 1, 2004 and December 31, 2023 and limited the study cohort to those patients who had an ED treat-and-release visit in the 30 days prior to admission. Using the look-back approach of the SPADE methodology, we identified the most common clinical presentations at the initial ED visit and used an observed to expected (O:E) analysis to determine which presentations were overrepresented. We then employed a graphical, temporal analysis with a comparison group to identify which overrepresented presentations most likely represented harbingers for future sepsis hospitalization.
Results
We identified 184,157 inpatient admissions for sepsis, of which 15,331 hospitalizations (8.3 %) were preceded by a treat-and-release ED visit in the prior 30 days. Based on the O:E and temporal analyses, the presentations of fever and dehydration were both overrepresented in the study cohort and temporally clustered close to sepsis hospitalization. ED treat-and-release visits for fever or dehydration preceded 1.2 % of all sepsis admissions.
Conclusions
In pediatric patients presenting to the ED, fever and dehydration may represent harbingers for future sepsis hospitalization. The SPADE methodology could be applied to the PHIS database to develop diagnostic performance measures across a wide range of pediatric hospitals.
Over the past two decades, oncological treatment approaches have evolved from a generalized treatment paradigm to an individualized treatment concept. Despite substantial progress, major challenges persist particularly in managing rare tumor entities (24% of all cancers) and heavily pretreated patients with complex resistance mechanisms. Increasingly more patients benefit from extended molecular pathological diagnostics. These data are interpreted by molecular tumor boards (MTB) and individually tailored treatment plans are developed. The process of translating genomic data into clinical treatment plans is complicated. The implementation necessitates substantial effort: approximately 80% are implemented in off-label use. Fragmented data and manual curation of data collectively hinder the broader implementation of MTBs in clinical practice. Artificial intelligence (AI)-assisted decision support systems can analyze large datasets and identify clinically relevant patterns. While AI is able to access annotated medical images in dermatology, pathology and radiology, unstructured clinical text data from personalized medicine are difficult to process. Moreover, the lack of standardized precision oncological recommendations has further constrained the integration of AI technologies by machine learning into the MTB workflow. The domain-specific AI system MEREDITH, which employs a retrieval-augmented generation architecture, seeks to address these limitations. A proof of concept study showed that MEREDITH exhibits strong concordance with expert clinical recommendations but further evaluation studies are needed to validate the clinical utility of MEREDITH in real-world MTB practice. The Model Project Genome Sequencing is anticipated to further increase the complexity of oncological care, emphasizing the need for the integration of innovative technologies.
„Clinical reasoning” refers to all the thought processes that physicians use to make a diagnosis and determine a treatment and care plan. Artificial intelligence (AI) will enhance, improve, and accelerate human clinical diagnostic thinking, but it is unlikely to replace it. Its application in medicine has the potential to drastically reduce medical diagnostic errors and give doctors more time to care for their patients. Here, we provide an overview of some of the key elements of clinical diagnostic reasoning and the potential impacts of AI on clinical reasoning.
Cancer remains one of the most challenging health issues globally, demanding innovative therapeutic approaches for effective treatment. Nanoparticles, particularly those composed of gold, silver, and iron oxide, have emerged as promising candidates for changing cancer therapy. This comprehensive review demonstrates the landscape of nanoparticle‐based oncological interventions, focusing on the remarkable advancements and therapeutic potentials of gold, silver, and iron oxide nanoparticles. Gold nanoparticles have garnered significant attention for their exceptional biocompatibility, tunable surface chemistry, and distinctive optical properties, rendering them ideal candidates for various cancer diagnostic and therapeutic strategies. Silver nanoparticles, renowned for their antimicrobial properties, exhibit remarkable potential in cancer therapy through multiple mechanisms, including apoptosis induction, angiogenesis inhibition, and drug delivery enhancement. With their magnetic properties and biocompatibility, iron oxide nanoparticles offer unique cancer diagnosis and targeted therapy opportunities. This review critically examines the recent advancements in the synthesis, functionalization, and biomedical applications of these nanoparticles in cancer therapy. Moreover, the challenges are discussed, including toxicity concerns, immunogenicity, and translational barriers, and ongoing efforts to overcome these hurdles are highlighted. Finally, insights into the future directions of nanoparticle‐based cancer therapy and regulatory considerations, are provided aiming to accelerate the translation of these promising technologies from bench to bedside.
This paper introduces EVINCE (Entropy and Variation IN Conditional Exchanges), a dialogue framework advancing Artificial General Intelligence (AGI) by enhancing versatility, adaptivity, and reasoning in large language models (LLMs). Leveraging adversarial debate and a novel dual entropy theory, EVINCE improves prediction accuracy, robustness, and stability in LLMs by integrating statistical modeling, information theory, and machine learning to balance diverse perspective exploration with strong prior exploitation. The framework's effectiveness is demonstrated through consistent convergence of information-theoretic metrics, particularly improved mutual information, fostering productive LLM collaboration. We apply EVINCE to healthcare, showing improved disease diagnosis, and discuss its broader implications for decision-making across domains. This work provides theoretical foundations and empirical validation for EVINCE, paving the way for advancements in LLM collaboration and AGI development.
This paper introduces EVINCE (Entropy and Variation IN Conditional Exchanges), a dialogue framework advancing Artificial General Intelligence (AGI) by enhancing versatility, adaptivity, and reasoning in large language models (LLMs). Leveraging adversarial debate and a novel dual entropy theory, EVINCE improves prediction accuracy, robustness, and stability in LLMs by integrating statistical modeling, information theory, and machine learning to balance diverse perspective exploration with strong prior exploitation. The framework's effectiveness is demonstrated through consistent convergence of information-theoretic metrics, particularly improved mutual information, fostering productive LLM collaboration. We apply EVINCE to healthcare, showing improved disease diagnosis, and discuss its broader implications for decision-making across domains. This work provides theoretical foundations and empirical validation for EVINCE, paving the way for advancements in LLM collaboration and AGI development.
Background
Disclosure of patient safety incidents (DPSIs) is a strategic measure to reduce the problems of patient safety incidents (PSIs). However, there are currently limited studies on the effects of DPSIs on resolving diagnosis-related PSIs. Therefore, this study aimed to estimate the effects of DPSIs using hypothetical cases, particularly in diagnosis-related PSIs.
Methods
A survey using 2 hypothetical cases of diagnosis-related PSIs was conducted in 5 districts of Ulsan Metropolitan City, Korea, from March 18 to 21, 2021. The survey used a multistage stratified quota sampling method to recruit participants. Multiple logistic regression and linear regression analyses were performed to determine the effectiveness of DPSIs in hypothetical cases. The outcomes were the judgment of a situation as a medical error, willingness to revisit and recommend the hypothetical physician, intention to file a medical lawsuit and commence criminal proceedings against the physicians, trust score of the involved physicians, and expected amount of compensation.
Results
In total, 620 respondents, recruited based on age, sex, and region, completed the survey. The mean age was 47.6 (standard deviation, ±15.1) years. Multiple logistic regression showed that DPSIs significantly decreased the judgment of a situation as a medical error (odds ratio [OR], 0.44; 95% confidence interval [CI], 0.24–0.79), intention to file a lawsuit (OR, 0.53; 95% CI, 0.42–0.66), and commence criminal proceedings (OR, 0.43; 95% CI, 0.34–0.55). It also increased the willingness to revisit (OR, 3.28; 95% CI, 2.37–4.55) and recommend the physician (OR, 8.21; 95% CI, 4.05–16.66). Meanwhile, the multiple linear regression demonstrated that DPSIs had a significantly positive association with the trust score of the physician (unstandardized coefficient, 1.22; 95% CI, 1.03–1.41) and a significantly negative association with the expected amount of compensation (unstandardized coefficient, −0.18; 95% CI, −0.29 to −0.06).
Conclusions
DPSIs reduces the possibility of judging the hypothetical case as a medical error, increases the willingness to revisit and recommend the physician involved in the case, and decreases the intent to file a lawsuit and commence a criminal proceeding. Although this study implemented hypothetical cases, the results are expected to serve as empirical evidence to apply DPSIs extensively in the clinical field.
This policy brief summarizes current U.S. regulatory considerations for ensuring
patient safety and health care quality of genetic/genomic test information for
precision medicine in the era of artificial intelligence/machine learning (AI/ML).
The critical role of innovative and efficient laboratory developed tests (LDTs) in
providing accurate diagnostic genetic/genomic information for U.S. patient- and
family-centered healthcare decision-making is significant. However, many LDTs
are not fully vetted for sufficient analytic and clinical validity via current FDA and
CMS regulatory oversight pathways. The U.S. Centers for Disease Control and
Prevention’s Policy Analytical Framework Tool was used to identify the issue,
perform a high-level policy analysis, and develop overview recommendations for
a bipartisan healthcare policy reform strategy acceptable to diverse precision and
systems medicine stakeholders.
Objectives
Diagnostic errors contribute substantially to preventable medical errors. Especially, the emergency department (ED) is a high-risk environment. Previous research showed that in 15%–30% of the ED patients, there is a difference between the primary diagnosis assigned by the emergency physician and the discharge diagnosis. This study aimed to determine the number and types of diagnostic discrepancies and to explore factors predicting discrepancies.
Methods
A retrospective record review was conducted in an academic medical center. The primary diagnosis assigned in the ED was compared with the discharge diagnosis after hospital admission. For each patient, we gathered additional information about the diagnostic process to identify possible predictors of diagnostic discrepancies.
Results
The electronic health records of 200 patients were reviewed. The primary diagnosis assigned in the ED was substantially different from the discharge diagnosis in 16.0%. These diagnostic discrepancies were associated with a higher number of additional diagnostics applied for (2.4 versus 2.0 diagnostics; P = 0.002) and longer stay in the ED (5.9 versus 4.7 hours; P = 0.008).
Conclusions
A difference between the diagnosis assigned by the emergency physician and the discharge diagnosis was found in almost 1 in 6 patients. The increased number of additional diagnostics and the longer stay at the ED in the group of patients with a diagnostic discrepancy suggests that these cases reflect the more difficult cases. More research should be done on predictive factors of diagnostic discrepancies.
Background
The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists.
Objective
This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series.
Methods
We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4’s evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician.
Results
The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4’s evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians’ evaluations.
Conclusions
GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.
Chemotherapy is one of the most employed strategies in clinical treatment of cancer. However, reducing medication adverse effects and improving the biological activity remains a significant issue for chemotherapy. We developed a pH and Ca²⁺‐responsive pillar[5]arene‐based supramolecular nanodrug delivery system (NDDS) WP5⊃EV@DOX to address the aforementioned challenges. The formation of this NDDS began with the spontaneous formation of supramolecular nanodrug carrier WP5⊃EV in water from PEG‐modified pillar[5]arene and the bipyridilium salt derivative EV through simple host‐guest interaction. Then the antitumor drug doxorubicin DOX was efficiently loaded with a high encapsulation rate of 84.6 %. Cytotoxicity results indicated that the constructed nanoplatform not only reduced DOX toxicity and side effects on normal cell (293T), but also significantly enhanced the antitumor activity on cancer cell (HepG2). Moreover, in vivo experiments showed that WP5⊃EV@DOX had a longer half‐life and higher bioavailability in the blood of mice compared to the nake drug DOX, with increases to 212 % and 179 %, respectively. Therefore, WP5⊃EV@DOX has great potential in tumor therapy and provides a new idea for host‐guest drug delivery system.
Background
Pathology and laboratory medicine diagnostics and diagnostic imaging are crucial to achieving universal health coverage. We analysed Service Provision Assessments (SPAs) from ten low-income and middle-income countries to benchmark diagnostic availability.
Methods
Diagnostic availabilities were determined for Bangladesh, Haiti, Malawi, Namibia, Nepal, Kenya, Rwanda, Senegal, Tanzania, and Uganda, with multiple timepoints for Haiti, Kenya, Senegal, and Tanzania. A smaller set of diagnostics were included in the analysis for primary care facilities compared with those expected at hospitals, with 16 evaluated in total. Surveys spanned 2004–18, including 8512 surveyed facilities. Country-specific facility types were mapped to basic primary care, advanced primary care, or hospital tiers. We calculated percentages of facilities offering each diagnostic, accounting for facility weights, stratifying by tier, and for some analyses, region. The tier-level estimate of diagnostic availability was defined as the median of all diagnostic-specific availabilities at each tier, and country-level estimates were the median of all diagnostic-specific availabilities of each of the tiers. Associations of country-level diagnostic availability with country income as well as (within-country) region-level availability with region-specific population densities were determined by multivariable linear regression, controlling for appropriate covariates including tier.
Findings
Median availability of diagnostics was 19·1% in basic primary care facilities, 49·2% in advanced primary care facilities, and 68·4% in hospitals. Availability varied considerably between diagnostics, ranging from 1·2% (ultrasound) to 76·7% (malaria) in primary care (basic and advanced) and from 6·1% (CT scan) to 91·6% (malaria) in hospitals. Availability also varied between countries, from 14·9% (Bangladesh) to 89·6% (Namibia). Availability correlated positively with log(income) at both primary care tiers but not the hospital tier, and positively with region-specific population density at the basic primary care tier only.
Interpretation
Major gaps in diagnostic availability exist in many low-income and middle-income countries, particularly in primary care facilities. These results can serve as a benchmark to gauge progress towards implementing guidelines such as the WHO Essential Diagnostics List and Priority Medical Devices initiatives.
Funding
Bill & Melinda Gates Foundation.
Background
Diagnostic error is a global patient safety priority.
Objectives
To estimate the incidence, origins and avoidable harm of diagnostic errors in English general practice. Diagnostic errors were defined as missed opportunities to make a correct or timely diagnosis based on the evidence available (missed diagnostic opportunities, MDOs).
Method
Retrospective medical record reviews identified MDOs in 21 general practices. In each practice, two trained general practitioner reviewers independently conducted case note reviews on 100 randomly selected adult consultations performed during 2013–2014. Consultations where either reviewer identified an MDO were jointly reviewed.
Results
Across 2057 unique consultations, reviewers agreed that an MDO was possible, likely or certain in 89 cases or 4.3% (95% CI 3.6% to 5.2%) of reviewed consultations. Inter-reviewer agreement was higher than most comparable studies (Fleiss’ kappa=0.63). Sixty-four MDOs (72%) had two or more contributing process breakdowns. Breakdowns involved problems in the patient–practitioner encounter such as history taking, examination or ordering tests (main or secondary factor in 61 (68%) cases), performance and interpretation of diagnostic tests (31; 35%) and follow-up and tracking of diagnostic information (43; 48%). 37% of MDOs were rated as resulting in moderate to severe avoidable patient harm.
Conclusions
Although MDOs occurred in fewer than 5% of the investigated consultations, the high numbers of primary care contacts nationally suggest that several million patients are potentially at risk of avoidable harm from MDOs each year. Causes of MDOs were frequently multifactorial, suggesting the need for development and evaluation of multipronged interventions, along with policy changes to support them.
Objective To estimate the incidence of avoidable significant harm in primary care in England; describe and classify the associated patient safety incidents and generate suggestions to mitigate risks of ameliorable factors contributing to the incidents.
Design Retrospective case note review. Patients with significant health problems were identified and clinical judgements were made on avoidability and severity of harm. Factors contributing to avoidable harm were identified and recorded.
Setting Primary care.
Participants Thirteen general practitioners (GPs) undertook a retrospective case note review of a sample of 14 407 primary care patients registered with 12 randomly selected general practices from three regions in England (total list size: 92 255 patients).
Main outcome measures The incidence of significant harm considered at least ‘probably avoidable’ and the nature of the safety incidents.
Results The rate of significant harm considered at least probably avoidable was 35.6 (95% CI 23.3 to 48.0) per 100 000 patient-years (57.9, 95% CI 42.2 to 73.7, per 100 000 based on a sensitivity analysis). Overall, 74 cases of avoidable harm were detected, involving 72 patients. Three types of incident accounted for more than 90% of the problems: problems with diagnosis accounted for 45/74 (60.8%) primary incidents, followed by medication-related problems (n=19, 25.7%) and delayed referrals (n=8, 10.8%). In 59 (79.7%) cases, the significant harm could have been identified sooner (n=48) or prevented (n=11) if the GP had taken actions aligned with evidence-based guidelines.
Conclusion There is likely to be a substantial burden of avoidable significant harm attributable to primary care in England with diagnostic error accounting for most harms. Based on the contributory factors we found, improvements could be made through more effective implementation of existing information technology, enhanced team coordination and communication, and greater personal and informational continuity of care.
Background:
Stroke is a major public health concern, affecting millions of people worldwide. Care of the condition however, remain inconsistent in developing countries. The purpose of this scoping review was to document evidence of stroke care and service delivery in low and middle-income countries to better inform development of a context-fit stroke model of care.
Methods:
An interpretative scoping literature review based on Arksey and O'Malley's five-stage-process was executed. The following databases searched for literature published between 2010 and 2017; Cochrane Library, Credo Reference, Health Source: Nursing/Academic Edition, Science Direct, BioMed Central, Cumulative Index to Nursing and Allied Health Literature (CINNAHL), Academic Search Complete, and Google Scholar. Single combined search terms included acute stroke, stroke care, stroke rehabilitation, developing countries, low and middle-income countries.
Results:
A total of 177 references were identified. Twenty of them, published between 2010 and 2017, were included in the review. Applying the Donebedian Model of quality of care, seven dimensions of stroke-care structure, six dimensions of stroke care processes, and six dimensions of stroke care outcomes were identified. Structure of stroke care included availability of a stroke unit, an accident and emergency department, a multidisciplinary team, stroke specialists, neuroimaging, medication, and health care policies. Stroke care processes that emerged were assessment and diagnosis, referrals, intravenous thrombolysis, rehabilitation, and primary and secondary prevention strategies. Stroke-care outcomes included quality of stroke-care practice, functional independence level, length of stay, mortality, living at home, and institutionalization.
Conclusions:
There is lack of uniformity in the way stroke care is advanced in low and middle-income countries. This is reflected in the unsatisfactory stroke care structure, processes, and outcomes. There is a need for stroke care settings to adopt quality improvement strategies. Health ministry and governments need to decisively face stroke burden by setting policies that advance improved care of patients with stroke. Stroke Units and Recombinant Tissue Plasminogen Activator (rtPA) administration could be considered as both a structural and process necessity towards improvement of outcomes of patients with stroke in the LMICs.
BACKGROUND:
Advanced stage presentation of patients with is common in low- and middle-income countries (LMICs). A comprehensive analysis of existing delays and barriers in LMICs has not been previously reported. We conducted a systematic literature review to comprehensively outline delays and barriers to identify targets for future interventions and provide recommendations for future research in this field.
MATERIALS AND METHODS:
Multiple electronic databases were searched using a standardized search strategy. Eligible articles were of any language, from LMICs, and published between January 1, 2002, and November 27, 2017. Included studies reported cancer care intervals or barriers encountered. Intervals and associated barriers were summarized by cancer type and geographical region.
RESULTS:
This review included 316 study populations from 57 LMICs: 142 (44.9%) studies addressed time intervals, whereas 214 (67.7%) studies described barriers to cancer diagnosis. The median intervals were similar in the following three stages of early diagnosis: (a) access (1.2 months), (b) diagnostic (0.9 months), and (c) treatment (0.8 months). Studies from low-income countries had significantly longer access intervals (median, 6.5 months) compared with other country income groups. Patients with breast cancer had longer delay intervals than patients with childhood cancer. No significant variation existed between geographic regions. Low health literacy was reported most frequently in studies describing barriers to cancer diagnosis and was associated with lower education level, no formal employment, lower income, and rural residence.
CONCLUSION:
Early diagnosis strategies should address barriers during all three intervals contributing to late presentation in LMICs. Standardization in studying and reporting delay intervals in LMICs is needed to monitor progress and facilitate comparisons across settings.
IMPLICATIONS FOR PRACTICE:
This review draws the attention of cancer implementation scientists globally. The findings highlight the significant delays that occur throughout the cancer care continuum in low- and middle-income countries and describe common barriers that cause them. This review will help shape the global research agenda by proposing metrics and implementation studies. By demonstrating the importance of standardized reporting metrics, this report sets forth additional research and evidence needed to inform cancer control policies.
Progress in diagnostic error research has been hampered by a lack of unified terminology and definitions. This article proposes a novel framework for considering diagnostic errors, offering a unified conceptual model for underdiagnosis, overdiagnosis, and misdiagnosis. The model clarifies the critical separation between ‘diagnostic process failures’ (incorrect workups) and ‘diagnosis label failures’ (incorrect diagnoses). By dividing processes into those that are substandard, suboptimal, or optimal, important distinctions are drawn between ‘preventable’, ‘reducible,’ and ‘unavoidable’ diagnostic errors. The new model emphasizes the importance of mitigating diagnosis-related harms, regardless of whether the solutions require traditional safety strategies (preventable errors), more effective evidence dissemination (reducible errors; harms from overtesting and overdiagnosis), or new scientific discovery (currently unavoidable errors). Doing so maximizes our ability to prioritize solving various diagnosis-related problems from a societal value perspective. This model should serve as a foundation for developing consensus terminology and operationalized definitions for relevant diagnostic-error categories.
Though administrative databases are increasingly being used for research related to myocardial infarction (MI), the validity of MI diagnoses in these databases has never been synthesized on a large scale.
To conduct the first systematic review of studies reporting on the validity of diagnostic codes for identifying MI in administrative data.
MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify MI; or (b) Evaluating the validity of MI codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value (PPV), negative predictive value, or Kappa scores) for MI, or data sufficient for their calculation. Additonal articles were located by handsearch (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.
Thirty studies published from 1984-2010 were included; most assessed codes from the International Classification of Diseases (ICD)-9th revision. Sensitivity and specificity of hospitalization data for identifying MI in most [≥50%] studies was ≥86%, and PPV in most studies was ≥93%. The PPV was higher in the more-recent studies, and lower when criteria that do not incorporate cardiac troponin levels (such as the MONICA) were employed as the gold standard. MI as a cause-of-death on death certificates also demonstrated lower accuracy, with maximum PPV of 60% (for definite MI).
Hospitalization data has higher validity and hence can be used to identify MI, but the accuracy of MI as a cause-of-death on death certificates is suboptimal, and more studies are needed on the validity of ICD-10 codes. When using administrative data for research purposes, authors should recognize these factors and avoid using vital statistics data if hospitalization data is not available to confirm deaths from MI.
Diagnostic errors lead to death or disability for an estimated 150 000 patients in the United States each year.¹ The emergency department is a known high-risk location for misdiagnosis.² Missed ischemic stroke and brain hemorrhage are recognized sources of diagnostic error, with approximately 9% of cerebrovascular events missed at first emergency department contact,³ including an estimated 20% of subarachnoid hemorrhages in patients presenting with normal mental status.⁴ Because effective treatments are available, diagnostic delays increase morbidity and mortality 3- to 8-fold,⁴,5 so accurate early diagnosis is important.
Context
Studies using physician implicit review have suggested that the number
of deaths due to medical errors in US hospitals is extremely high. However,
some have questioned the validity of these estimates.Objective
To examine the reliability of reviewer ratings of medical error and
the implications of a death described as "preventable by better care" in terms
of the probability of immediate and short-term survival if care had been optimal.Design
Retrospective implicit review of medical records from 1995-1996.Setting and Participants
Fourteen board-certified, trained internists used a previously tested
structured implicit review instrument to conduct 383 reviews of 111 hospital
deaths at 7 Department of Veterans Affairs medical centers, oversampling for
markers previously found to be associated with high rates of preventable deaths.
Patients considered terminally ill who received comfort care only were excluded.Main Outcome Measures
Reviewer estimates of whether deaths could have been prevented by optimal
care (rated on a 5-point scale) and of the probability that patients would
have lived to discharge or for 3 months or more if care had been optimal (rated
from 0%-100%).Results
Similar to previous studies, almost a quarter (22.7%) of active-care
patient deaths were rated as at least possibly preventable by optimal care,
with 6.0% rated as probably or definitely preventable. Interrater reliability
for these ratings was also similar to previous studies (0.34 for 2 reviewers).
The reviewers' estimates of the percentage of patients who would have left
the hospital alive had optimal care been provided was 6.0% (95% confidence
interval [CI], 3.4%-8.6%). However, after considering 3-month prognosis and
adjusting for the variability and skewness of reviewers' ratings, clinicians
estimated that only 0.5% (95% CI, 0.3%-0.7%) of patients who died would have
lived 3 months or more in good cognitive health if care had been optimal,
representing roughly 1 patient per 10 000 admissions to the study hospitals.Conclusions
Medical errors are a major concern regardless of patients' life expectancies,
but our study suggests that previous interpretations of medical error statistics
are probably misleading. Our data place the estimates of preventable deaths
in context, pointing out the limitations of this means of identifying medical
errors and assessing their potential implications for patient outcomes.
Figures in this Article
The number of deaths in US hospitals that are reportedly due to medical
errors is disturbingly high. A recent Institute of Medicine report quoted
rates estimating that medical errors kill between 44 000 and 98 000
people a year in US hospitals.1 These widely
quoted statistics have helped create initiatives directed at patient safety
throughout the United States. The numbers are undeniably startling; they suggest
that more Americans are killed in US hospitals every 6 months than died in
the entire Vietnam War, and some have compared the alleged rate to 3 fully
loaded jumbo jets crashing every other day.2
Widely disseminated quotes include, "medical mistakes kill 180 000 people
a year in US hospitals"3 and "medical errors
may be the 5th leading cause of death."4 If
these inferences are correct, the health care system is a public health menace
of epidemic proportions.
These statistics are generally based on peer review using structured
implicit review instruments. Physicians are trained to review hospital medical
records and give their opinion on the occurrence of adverse events and the
quality of hospital care and its impact on patient outcomes. Although the
wording of the question used to assess hospital deaths has differed somewhat
among studies, the studies have produced very similar conclusions. Perhaps
the most often quoted study is the Harvard Medical Practice Study, which assessed
negligence related to adverse events, including deaths, in New York.5 However, several other studies have asked whether
deaths would have been preventable by optimal quality of care1,6- 9
and have found similar results.
In an exchange about the validity of these estimates,10- 11
McDonald et al argued on theoretical grounds that these statistics are likely
overestimates. They were particularly concerned about the lack of consideration
of the expected risk of death in the absence of the medical error. Indeed,
these statistics have often been quoted without regard to cautions by the
authors of the original reports, who note that physician reviewers do not
believe necessarily that 100% of these deaths would be prevented if care were
optimal.12 So, the questions remain: when a
reviewer classifies a death as definitely or probably preventable or due to
medical errors, is there a 90% chance or a 10% chance that a death would have
actually been prevented if care had been optimal? How long would patients
have lived if care had been optimal? How does the interrater reliability of
reviewers' ratings affect these estimates? To examine these questions, we
trained physician reviewers to assess medical records and identify medical
errors documented in the care of patients who died at 7 Department of Veterans
Affairs (VA) medical centers and asked reviewers to estimate the probability
that these deaths could have been prevented by optimal medical care.
Importance Diagnostic errors are an understudied aspect of ambulatory patient safety.
Objectives To determine the types of diseases missed and the diagnostic processes involved in cases of confirmed diagnostic errors in primary care settings and to determine whether record reviews could shed light on potential contributory factors to inform future interventions.
Design We reviewed medical records of diagnostic errors detected at 2 sites through electronic health record–based triggers. Triggers were based on patterns of patients' unexpected return visits after an initial primary care index visit.
Setting A large urban Veterans Affairs facility and a large integrated private health care system.
Participants Our study focused on 190 unique instances of diagnostic errors detected in primary care visits between October 1, 2006, and September 30, 2007.
Main Outcome Measures Through medical record reviews, we collected data on presenting symptoms at the index visit, types of diagnoses missed, process breakdowns, potential contributory factors, and potential for harm from errors.
Results In 190 cases, a total of 68 unique diagnoses were missed. Most missed diagnoses were common conditions in primary care, with pneumonia (6.7%), decompensated congestive heart failure (5.7%), acute renal failure (5.3%), cancer (primary) (5.3%), and urinary tract infection or pyelonephritis (4.8%) being most common. Process breakdowns most frequently involved the patient-practitioner clinical encounter (78.9%) but were also related to referrals (19.5%), patient-related factors (16.3%), follow-up and tracking of diagnostic information (14.7%), and performance and interpretation of diagnostic tests (13.7%). A total of 43.7% of cases involved more than one of these processes. Patient-practitioner encounter breakdowns were primarily related to problems with history-taking (56.3%), examination (47.4%), and/or ordering diagnostic tests for further workup (57.4%). Most errors were associated with potential for moderate to severe harm.
Conclusions and Relevance Diagnostic errors identified in our study involved a large variety of common diseases and had significant potential for harm. Most errors were related to process breakdowns in the patient-practitioner clinical encounter. Preventive interventions should target common contributory factors across diagnoses, especially those that involve data gathering and synthesis in the patient-practitioner encounter.
To assess the frequency and nature of adverse events to patients in selected hospitals in developing or transitional economies.
Retrospective medical record review of hospital admissions during 2005 in eight countries.
Ministries of Health of Egypt, Jordan, Kenya, Morocco, Tunisia, Sudan, South Africa and Yemen; the World Health Organisation (WHO) Eastern Mediterranean and African Regions (EMRO and AFRO), and WHO Patient Safety.
Convenience sample of 26 hospitals from which 15,548 patient records were randomly sampled.
Two stage screening. Initial screening based on 18 explicit criteria. Records that screened positive were then reviewed by a senior physician for determination of adverse event, its preventability, and the resulting disability.
Of the 15,548 records reviewed, 8.2% showed at least one adverse event, with a range of 2.5% to 18.4% per country. Of these events, 83% were judged to be preventable, while about 30% were associated with death of the patient. About 34% adverse events were from therapeutic errors in relatively non-complex clinical situations. Inadequate training and supervision of clinical staff or the failure to follow policies or protocols contributed to most events.
Unsafe patient care represents a serious and considerable danger to patients in the hospitals that were studied, and hence should be a high priority public health problem. Many other developing and transitional economies will probably share similar rates of harm and similar contributory factors. The convenience sampling of hospitals might limit the interpretation of results, but the identified adverse event rates show an estimate that should stimulate and facilitate the urgent institution of appropriate remedial action and also to trigger more research. Prevention of these adverse events will be complex and involves improving basic clinical processes and does not simply depend on the provision of more resources.
Diagnostic errors often result in patient harm. Previous studies have shown that there is large variability in results in different medical specialties. The present study explored diagnostic adverse events (DAEs) across all medical specialties to determine their incidence and to gain insight into their causes and consequences by comparing them with other AE types.
A structured review study of 7926 patient records was conducted. Randomly selected records were reviewed by trained physicians in 21 hospitals across the Netherlands. The method used in this study was based on the well-known protocol developed by the Harvard Medical Practice Study. All AEs with diagnostic error as the main category were selected for analysis and were compared with other AE types.
Diagnostic AEs occurred in 0.4% of hospital admissions and represented 6.4% of all AEs. Of the DAEs, 83.3% were judged to be preventable. Human failure was identified as the main cause (96.3%), although organizational- and patient-related factors also contributed (25.0% and 30.0%, respectively). The consequences of DAEs were more severe (higher mortality rate) than for other AEs (29.1% vs 7.4%).
Diagnostic AEs represent an important error type, and the consequences of DAEs are severe. The causes of DAEs were mostly human, with the main causes being knowledge-based mistakes and information transfer problems. Prevention strategies should focus on training physicians and on the organization of knowledge and information transfer.
Early diagnosis and immediate initiation of treatment are essential for an effective tuberculosis (TB) control program. Delay in diagnosis is significant to both disease prognosis at the individual level and transmission within the community. Most transmissions occur between the onset of cough and initiation of treatment.
A systematic review of 58 studies addressing delay in diagnosis and treatment of TB was performed. We found different definitions of, for example, debut of symptoms, first appropriate health care provider, time to diagnosis, and start of treatment. Rather than excluding studies that failed to meet strict scientific criteria (like in a meta-analysis), we tried to extract the "solid findings" from all of them to arrive on a more global understanding of diagnostic delay in TB.
The main factors associated with diagnostic delay included human immunodeficiency virus; coexistence of chronic cough and/or other lung diseases; negative sputum smear; extrapulmonary TB; rural residence; low access (geographical or sociopsychological barriers); initial visitation of a government low-level healthcare facility, private practitioner, or traditional healer; old age; poverty; female sex; alcoholism and substance abuse; history of immigration; low educational level; low awareness of TB; incomprehensive beliefs; self-treatment; and stigma.
The core problem in delay of diagnosis and treatment seemed to be a vicious cycle of repeated visits at the same healthcare level, resulting in nonspecific antibiotic treatment and failure to access specialized TB services. Once generation of a specific diagnosis was in reach, TB treatment was initiated within a reasonable period of time.
Mortality and morbidity can be reduced if aneurysmal subarachnoid hemorrhage (SAH) is treated urgently.
To determine the association of initial misdiagnosis and outcome after SAH.
Inception cohort of 482 SAH patients admitted to a tertiary care urban hospital between August 1996 and August 2001.
Misdiagnosis was defined as failure to correctly diagnose SAH at a patient's initial contact with a medical professional. Functional outcome was assessed at 3 and 12 months with the modified Rankin Scale; quality of life (QOL), with the Sickness Impact Profile.
Fifty-six patients (12%) were initially misdiagnosed, including 42 of 221 (19%) of those with normal mental status at first contact. Migraine or tension headache (36%) was the most common incorrect diagnosis, and failure to obtain a computed tomography (CT) scan was the most common diagnostic error (73%). Neurologic complications occurred in 22 patients (39%) before they were correctly diagnosed, including 12 patients (21%) who experienced rebleeding. Normal mental status, small SAH volume, and right-sided aneurysm location were independently associated with misdiagnosis. Among patients with normal mental status at first contact, misdiagnosis was associated with worse QOL at 3 months and an increased risk of death or severe disability at 12 months.
In this study, misdiagnosis of SAH occurred in 12% of patients and was associated with a smaller hemorrhage and normal mental status. Among individuals who initially present in good condition, misdiagnosis is associated with increased mortality and morbidity. A low threshold for CT scanning of patients with mild symptoms that are suggestive of SAH may reduce the frequency of misdiagnosis.
Objectives
Isolated dizziness is a challenging stroke presentation in the emergency department, but little is known about this problem in other clinical settings. We sought to compare stroke hospitalizations after treat-and-release clinic visits for purportedly “benign dizziness” between general and specialty care settings.
Methods
This was a population-based retrospective cohort study from a national database. We included clinic patients with a first incident treat-and-release visit diagnosis of non-specific dizziness/vertigo or a peripheral vestibular disorder (ICD-9-CM 780.4 or 386.x [not 386.2]). We compared general care (internal medicine, family medicine) vs. specialty care (neurology, otolaryngology) providers. We used propensity scores to control for baseline stroke risk differences unrelated to dizziness diagnosis. We measured excess (observed>expected) stroke hospitalizations in the first 30 d (i.e., missed strokes associated with an adverse event).
Results
We analyzed 144,355 patients discharged with “benign dizziness” (n=117,117 diagnosed in general care; n=27,238 in specialty care). After propensity score matching, patients in both groups were at higher risk of stroke in the first 30 d (rate difference per 10,000 treat-and-release visits for “benign dizziness” 24.9 [95% CI 18.6–31.2] in general care and 10.6 [95% CI 6.3–14.9] in specialty care). Short-term stroke risk was higher in general care than specialty care (relative risk, RR 2.2, 95% CI 1.5–3.2) while the long-term risk was not significantly different (RR 1.3, 95% CI 0.9–1.9), indicating higher misdiagnosis-related harms among dizzy patients who initially presented to generalists after adequate propensity matching.
Conclusions
Missed stroke-related harms in general care were roughly twice that in specialty care. Solutions are needed to address this care gap.
Objective
To investigate the rate of misdiagnosis in the emergency department (ED) in patients with ruptured abdominal aortic aneurysms (rAAAs), and to investigate how misdiagnosis affects rAAA mortality.
Method
Data were extracted from the Swedish Cause of Death Registry and the Swedish National Registry for Vascular Surgery (Swedvasc), 2010‒2015. All rAAA patients registered in the healthcare system in the west of Sweden were identified. Medical charts for rAAA patients were reviewed, and patients who were correctly diagnosed at the first assessment in the ED were compared to patients who were misdiagnosed.
Results
Altogether, 455 patients with rAAA were identified, including both patients who underwent surgery and those who did not. One hundred seventy-seven (38.9%) were initially misdiagnosed. The mortality rate was 74.6% in patients who were misdiagnosed, as compared to 62.9% in correctly diagnosed patients (p=0.01). The adjusted odds ratio for mortality in misdiagnosed patients relative to correctly diagnosed patients was 1.83 (95% CI 1.13‒2.96) (p=0.01). When excluding patients offered palliative care (n=134) after detection of the rAAA, the mortality in initially misdiagnosed patients was 65.1% as compared to 46.4% in correctly diagnosed patients (p=0.001). In patients reaching surgical intervention, 37 (45.1%) of the primarily misdiagnosed patients died (30-day or in hospital mortality) as compared to 63 (38.0%) of the correctly diagnosed (p=0.34).
Conclusion
Misdiagnosis is common in patients with rAAA, and it is associated with a substantially higher risk of dying from the ruptured aneurysm.
Background
Missed vascular events, infections, and cancers account for ~75% of serious harms from diagnostic errors. Just 15 diseases from these “Big Three” categories account for nearly half of all serious misdiagnosis-related harms in malpractice claims. As part of a larger project estimating total US burden of serious misdiagnosis-related harms, we performed a focused literature review to measure diagnostic error and harm rates for these 15 conditions.
Methods
We searched PubMed, Google, and cited references. For errors, we selected high-quality, modern, US-based studies, if available, and best available evidence otherwise. For harms, we used literature-based estimates of the generic (disease-agnostic) rate of serious harms (morbidity/mortality) per diagnostic error and applied claims-based severity weights to construct disease-specific rates. Results were validated via expert review and comparison to prior literature that used different methods. We used Monte Carlo analysis to construct probabilistic plausible ranges (PPRs) around estimates.
Results
Rates for the 15 diseases were drawn from 28 published studies representing 91,755 patients. Diagnostic error (false negative) rates ranged from 2.2% (myocardial infarction) to 62.1% (spinal abscess), with a median of 13.6% [interquartile range (IQR) 9.2–24.7] and an aggregate mean of 9.7% (PPR 8.2–12.3). Serious misdiagnosis-related harm rates per incident disease case ranged from 1.2% (myocardial infarction) to 35.6% (spinal abscess), with a median of 5.5% (IQR 4.6–13.6) and an aggregate mean of 5.2% (PPR 4.5–6.7). Rates were considered face valid by domain experts and consistent with prior literature reports.
Conclusions
Diagnostic improvement initiatives should focus on dangerous conditions with higher diagnostic error and misdiagnosis-related harm rates.
Background
Diagnostic error is commonly defined as a missed, delayed or wrong diagnosis and has been described as among the most important patient safety hazards. Diagnostic errors also account for the largest category of medical malpractice high severity claims and total payouts. Despite a large literature on the incidence of inpatient adverse events, no systematic review has attempted to estimate the prevalence and nature of harmful diagnostic errors in hospitalised patients.
Methods
A systematic literature search was conducted using Medline, Embase, Web of Science and the Cochrane library from database inception through 9 July 2019. We included all studies of hospitalised adult patients that used physician review of case series of admissions and reported the frequency of diagnostic adverse events. Two reviewers independently screened studies for inclusion, extracted study characteristics and assessed risk of bias. Harmful diagnostic error rates were pooled using random-effects meta-analysis.
Results
Twenty-two studies including 80 026 patients and 760 harmful diagnostic errors from consecutive or randomly selected cohorts were pooled. The pooled rate was 0.7% (95% CI 0.5% to 1.1%). Of the 136 diagnostic errors that were described in detail, a wide range of diseases were missed, the most common being malignancy (n=15, 11%) and pulmonary embolism (n=13, 9.6%). In the USA, these estimates correspond to approximately 249 900 harmful diagnostic errors yearly.
Conclusion
Based on physician review, at least 0.7% of adult admissions involve a harmful diagnostic error. A wide range of diseases are missed, including many common diseases. Fourteen diagnoses account for more than half of all diagnostic errors. The finding that a wide range of common diagnoses are missed implies that efforts to improve diagnosis must target the basic processes of diagnosis, including both cognitive and system-related factors.
PROSPERO registration number
CRD42018115186.
Background
The number of preventable inpatient deaths in the USA is commonly estimated as between 44,000 and 98,000 deaths annually. Because many inpatient deaths are believed to be preventable, mortality rates are used for quality measures and reimbursement. We aimed to estimate the proportion of inpatient deaths that are preventable.MethodsA systematic literature search of Medline, Embase, Web of Science, and the Cochrane Library through April 8, 2019, was conducted. We included case series of adult patients who died in the hospital and were reviewed by physicians to determine if the death was preventable. Two reviewers independently performed data extraction and study quality assessment. The proportion of preventable deaths from individual studies was pooled using a random-effects model.ResultsSixteen studies met inclusion criteria. Eight studies of consecutive or randomly selected cohorts including 12,503 deaths were pooled. The pooled rate of preventable mortality was 3.1% (95% CI 2.2–4.1%). Two studies also reported rates of preventable mortality limited to patients expected to live longer than 3 months, ranging from 0.5 to 1.0%. In the USA, these estimates correspond to approximately 22,165 preventable deaths annually and 7150 deaths for patients with greater than 3-month life expectancy.DiscussionThe number of deaths due to medical error is lower than previously reported and the majority occur in patients with less than 3-month life expectancy. The vast majority of hospital deaths are due to underlying disease. Our results have implications for the use of hospital mortality rates for quality reporting and reimbursement.Study RegistrationPROSPERO registration number CRD42018095140.
Background
Diagnostic errors cause substantial preventable harm, but national estimates vary widely from 40,000 to 4 million annually. This cross-sectional analysis of a large medical malpractice claims database was the first phase of a three-phase project to estimate the US burden of serious misdiagnosis-related harms.
Methods
We sought to identify diseases accounting for the majority of serious misdiagnosis-related harms (morbidity/mortality). Diagnostic error cases were identified from Controlled Risk Insurance Company (CRICO)’s Comparative Benchmarking System (CBS) database (2006–2015), representing 28.7% of all US malpractice claims. Diseases were grouped according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS) that aggregates the International Classification of Diseases diagnostic codes into clinically sensible groupings. We analyzed vascular events, infections, and cancers (the “Big Three”), including frequency, severity, and settings. High-severity (serious) harms were defined by scores of 6–9 (serious, permanent disability, or death) on the National Association of Insurance Commissioners (NAIC) Severity of Injury Scale.
Results
From 55,377 closed claims, we analyzed 11,592 diagnostic error cases [median age 49, interquartile range (IQR) 36–60; 51.7% female]. These included 7379 with high-severity harms (53.0% death). The Big Three diseases accounted for 74.1% of high-severity cases (vascular events 22.8%, infections 13.5%, and cancers 37.8%). In aggregate, the top five from each category (n = 15 diseases) accounted for 47.1% of high-severity cases. The most frequent disease in each category, respectively, was stroke, sepsis, and lung cancer. Causes were disproportionately clinical judgment factors (85.7%) across categories (range 82.0–88.8%).
Conclusions
The Big Three diseases account for about three-fourths of serious misdiagnosis-related harms. Initial efforts to improve diagnosis should focus on vascular events, infections, and cancers.
Importance
The Institute of Medicine described diagnostic error as the next frontier in patient safety and highlighted a critical need for better measurement tools.
Objectives
To estimate the proportions of emergency department (ED) visits attributable to symptoms of imminent ruptured abdominal aortic aneurysm (AAA), acute myocardial infarction (AMI), stroke, aortic dissection, and subarachnoid hemorrhage (SAH) that end in discharge without diagnosis; to evaluate longitudinal trends; and to identify patient characteristics independently associated with missed diagnostic opportunities.
Design, Setting, and Participants
This was a retrospective cohort study of all Medicare claims for 2006 to 2014. The setting was hospital EDs in the United States. Participants included all fee-for-service Medicare patients admitted to the hospital during 2007 to 2014 for the conditions of interest. Hospice enrollees and patients with recent skilled nursing facility stays were excluded.
Main Outcomes and Measures
The proportion of potential diagnostic opportunities missed in the ED was estimated using the difference between observed and expected ED discharges within 45 days of the index hospital admissions as the numerator, basing expected discharges on ED use by the same patients in earlier months. The denominator was estimated as the number of recognized emergencies (index hospital admissions) plus unrecognized emergencies (excess discharges).
Results
There were 1 561 940 patients, including 17 963 hospitalized for ruptured AAA, 304 980 for AMI, 1 181 648 for stroke, 19 675 for aortic dissection, and 37 674 for SAH. The mean (SD) age was 77.9 (10.3) years; 8.9% were younger than 65 years, and 54.1% were female. The proportions of diagnostic opportunities missed in the ED were as follows: ruptured AAA (3.4%; 95% CI, 2.9%-4.0%), AMI (2.3%; 95% CI, 2.1%-2.4%), stroke (4.1%; 95% CI, 4.0%-4.2%), aortic dissection (4.5%; 95% CI, 3.9%-5.1%), and SAH (3.5%; 95% CI, 3.1%-3.9%). Longitudinal trends were either nonsignificant (AMI and aortic dissection) or increasing (ruptured AAA, stroke, and SAH). Patient characteristics associated with unrecognized emergencies included age younger than 65 years, dual eligibility for Medicare and Medicaid coverage, female sex, and each of the following chronic conditions: end-stage renal disease, dementia, depression, diabetes, cerebrovascular disease, hypertension, coronary artery disease, and chronic obstructive pulmonary disease.
Conclusions and Relevance
Among Medicare patients, opportunities to diagnose ruptured AAA, AMI, stroke, aortic dissection, and SAH are missed in less than 1 in 20 ED presentations. Further improvement may prove difficult.
Use of advanced imaging tests is high. For every 100 Medicare beneficiaries 65 years or older, more than 50 computed tomography (CT) scans, 50 ultrasonography scans, 15 magnetic resonance imaging scans, and 10 positron emission tomography scans are performed annually.¹,2 These numbers have more than tripled since 1997.
The scientific process of analysis and deduction is frequently, often subconsciously, used by physicians to develop a differential diagnosis based on patients' symptoms. Common disorders are most frequently diagnosed in general practice. Rare diseases are uncommon and frequently remain undiagnosed for many years. Cognitive errors in clinical judgment delay definitive diagnosis. Whole-exome sequencing has helped identify the cause of undiagnosed or rare diseases in up to 40% of children. This article provides experiences with an undiagnosed or rare disease program, where detailed data accumulation and a multifaceted analytical approach assisted in diagnosing atypical presentations of common disorders.
Objective:
To characterize patients misdiagnosed with multiple sclerosis (MS).
Methods:
Neurologists at 4 academic MS centers submitted data on patients determined to have been misdiagnosed with MS.
Results:
Of 110 misdiagnosed patients, 51 (46%) were classified as "definite" and 59 (54%) "probable" misdiagnoses according to study definitions. Alternate diagnoses included migraine alone or in combination with other diagnoses 24 (22%), fibromyalgia 16 (15%), nonspecific or nonlocalizing neurologic symptoms with abnormal MRI 13 (12%), conversion or psychogenic disorders 12 (11%), and neuromyelitis optica spectrum disorder 7 (6%). Duration of misdiagnosis was 10 years or longer in 36 (33%) and an earlier opportunity to make a correct diagnosis was identified for 79 patients (72%). Seventy-seven (70%) received disease-modifying therapy and 34 (31%) experienced unnecessary morbidity because of misdiagnosis. Four (4%) participated in a research study of an MS therapy. Leading factors contributing to misdiagnosis were consideration of symptoms atypical for demyelinating disease, lack of corroborative objective evidence of a CNS lesion as satisfying criteria for MS attacks, and overreliance on MRI abnormalities in patients with nonspecific neurologic symptoms.
Conclusions:
Misdiagnosis of MS leads to unnecessary and potentially harmful risks to patients. Misinterpretation and misapplication of MS clinical and radiographic diagnostic criteria are important contemporary contributors to misdiagnosis.
Introduction: We evaluated emergency physicians’ (EP) current perceptions, practice, and attitudes
towards evaluating stroke as a cause of dizziness among emergency department patients.
Methods: We administered a survey to all EPs in a large integrated healthcare delivery system.
The survey included clinical vignettes, perceived utility of historical and exam elements, attitudes
about the value of and requisite post-test probability of a clinical prediction rule for dizziness. We
calculated descriptive statistics and post-test probabilities for such a clinical prediction rule.
Results: The response rate was 68% (366/535). Respondents’ median practice tenure was
eight years (37% female, 92% emergency medicine board certified). Symptom quality and typical
vascular risk factors increased suspicion for stroke as a cause of dizziness. Most respondents
reported obtaining head computed tomography (CT) (74%). Nearly all respondents used and
felt confident using cranial nerve and limb strength testing. A substantial minority of EPs used
the Epley maneuver (49%) and HINTS (head-thrust test, gaze-evoked nystagmus, and skew
deviation) testing (30%); however, few EPs reported confidence in these tests’ bedside application
(35% and 16%, respectively). Respondents favorably viewed applying a properly validated clinical
prediction rule for assessment of immediate and 30-day stroke risk, but indicated it would have to
reduce stroke risk to
Populationwide mammography screening has been associated with a substantial rise in false-positive mammography findings and breast cancer overdiagnosis. However, there is a lack of current data on the associated costs in the United States. We present costs due to false-positive mammograms and breast cancer overdiagnoses among women ages 40-59, based on expenditure data from a major US health care insurance plan for 702,154 women in the years 2011-13. The average expenditures for each false-positive mammogram, invasive breast cancer, and ductal carcinoma in situ in the twelve months following diagnosis were 852, 51,837 and 4 billion each year. The costs associated with false-positive mammograms and breast cancer overdiagnoses appear to be much higher than previously documented. Screening has the potential to save lives. However, the economic impact of false-positive mammography results and breast cancer overdiagnoses must be considered in the debate about the appropriate populations for screening.
Project HOPE—The People-to-People Health Foundation, Inc.
Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover 3 main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors, to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all 3 study designs and 4 are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available at www.annals.org and on the Web sites of PLoS Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
Overuse can be defined as use of a service when the risk of harm exceeds its likely benefit. Yet, there has been little work with composite measures of overuse.
Our goal was to create a composite measure of overuse with claims data.
Observational study using 5% of Medicare claims from 2008.
All inpatient and outpatient settings of care, excluding nursing homes.
Older Americans receiving health care services in hospitals or outpatient settings.
We applied algorithms to identify specific cases of overuse across 20 previously identified procedures and used multilevel modeling techniques to examine variation in overuse across all procedures. Included in the model were patient-level factors and both procedure and regional fixed effects for the 306 hospital referral regions (HRR). These estimated regional fixed effects, representing the systematic, region variation in overuse across all measures, was then normalized compared with the overall average to generate a Z score for each HRR. The resulting "Overuse Index" was then compared with total costs, 30-day postdischarge mortality, and total mortality at the HRR level, graphically, and associations were tested using Spearman ρ.
The Overuse Index varied markedly across regions, but 23 were higher than the average (P<0.05). The Index was positively associated with total costs (ρ=0.28, P<0.0001). It was positively correlated with 30-day postdischarge mortality (ρ=0.18 P≤0.005), and neither positively or negatively correlated with total mortality.
This study confirms previous research hypothesizing that systematic regional variation in overuse exists and is measurable. Addition research is needed to validate index and to test its predictive and concurrent validity in panel data.
Background:
We sought to characterise the frequency, health outcomes and economic consequences of diagnostic errors in the USA through analysis of closed, paid malpractice claims.
Methods:
We analysed diagnosis-related claims from the National Practitioner Data Bank (1986-2010). We describe error type, outcome severity and payments (in 2011 US dollars), comparing diagnostic errors to other malpractice allegation groups and inpatient to outpatient within diagnostic errors.
Results:
We analysed 350 706 paid claims. Diagnostic errors (n=100 249) were the leading type (28.6%) and accounted for the highest proportion of total payments (35.2%). The most frequent outcomes were death, significant permanent injury, major permanent injury and minor permanent injury. Diagnostic errors more often resulted in death than other allegation groups (40.9% vs 23.9%, p<0.001) and were the leading cause of claims-associated death and disability. More diagnostic error claims were outpatient than inpatient (68.8% vs 31.2%, p<0.001), but inpatient diagnostic errors were more likely to be lethal (48.4% vs 36.9%, p<0.001). The inflation-adjusted, 25-year sum of diagnosis-related payments was US386 849; median US74 545-484 500). Per-claim payments for permanent, serious morbidity that was 'quadriplegic, brain damage, lifelong care' (4.5%; mean US564 300), 'major' (13.3%; mean US355 350), or 'significant' (16.9%; mean US269 255) exceeded those where the outcome was death (40.9%; mean US251 745).
Conclusions:
Among malpractice claims, diagnostic errors appear to be the most common, most costly and most dangerous of medical mistakes. We found roughly equal numbers of lethal and non-lethal errors in our analysis, suggesting that the public health burden of diagnostic errors could be twice that previously estimated. Healthcare stakeholders should consider diagnostic safety a critical health policy issue.
This article summarizes the phenomenon of cancer overdiagnosis—the diagnosis of a “cancer” that would otherwise not go on
to cause symptoms or death. We describe the two prerequisites for cancer overdiagnosis to occur: the existence of a silent
disease reservoir and activities leading to its detection (particularly cancer screening). We estimated the magnitude of overdiagnosis
from randomized trials: about 25% of mammographically detected breast cancers, 50% of chest x-ray and/or sputum-detected lung
cancers, and 60% of prostate-specific antigen–detected prostate cancers. We also review data from observational studies and
population-based cancer statistics suggesting overdiagnosis in computed tomography–detected lung cancer, neuroblastoma, thyroid
cancer, melanoma, and kidney cancer. To address the problem, patients must be adequately informed of the nature and the magnitude
of the trade-off involved with early cancer detection. Equally important, researchers need to work to develop better estimates
of the magnitude of overdiagnosis and develop clinical strategies to help minimize it.
SUMMARY A generalization of the sampling method introduced by Metropolis et al. (1953) is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties
of assessing the error in Monte Carlo estimates. Examples of the methods, including the generation of random orthogonal matrices
and potential applications of the methods to numerical problems arising in statistics, are discussed.
The purpose of this study was to systematically review and summarize prehospital and in-hospital stroke evaluation and treatment delay times. We identified 123 unique peer-reviewed studies published from 1981 to 2007 of prehospital and in-hospital delay time for evaluation and treatment of patients with stroke, transient ischemic attack, or stroke-like symptoms. Based on studies of 65 different population groups, the weighted Poisson regression indicated a 6.0% annual decline (P<0.001) in hours/year for prehospital delay, defined from symptom onset to emergency department arrival. For in-hospital delay, the weighted Poisson regression models indicated no meaningful changes in delay time from emergency department arrival to emergency department evaluation (3.1%, P=0.49 based on 12 population groups). There was a 10.2% annual decline in hours/year from emergency department arrival to neurology evaluation or notification (P=0.23 based on 16 population groups) and a 10.7% annual decline in hours/year for delay time from emergency department arrival to initiation of computed tomography (P=0.11 based on 23 population groups). Only one study reported on times from arrival to computed tomography scan interpretation, two studies on arrival to drug administration, and no studies on arrival to transfer to an in-patient setting, precluding generalizations. Prehospital delay continues to contribute the largest proportion of delay time. The next decade provides opportunities to establish more effective community-based interventions worldwide. It will be crucial to have effective stroke surveillance systems in place to better understand and improve both prehospital and in-hospital delays for acute stroke care.
Research based on administrative data has advantages, including large numbers, consistent data, and low cost. This study was designed to compare different methods of stroke classification using administrative data.
Administrative hospital discharge data and medical record review of 206 patients were used to evaluate 3 algorithms for classifying stroke patients. These algorithms were based on all (algorithm 1), the first 2 (algorithm 2), or the primary (algorithm 3) administrative discharge diagnosis code(s). The diagnoses after review of medical record data were considered the gold standard. Then, using a large administrative data set, we compared patients with a primary discharge diagnosis of stroke with patients with their stroke discharge diagnosis code in a nonprimary position.
Compared with the gold standard, algorithm 1 had the highest kappa for classifying ischemic stroke, with a sensitivity of 86%, specificity of 95%, positive predictive value of 90%, and kappa=0.82. Algorithm 3 had the highest kappa values for intracerebral hemorrhage and subarachnoid hemorrhage. For intracerebral hemorrhage, the sensitivity was 85%, specificity was 96%, positive predictive value was 89%, and kappa=0.82. For subarachnoid hemorrhage, those values were 90%, 97%, 94%, and 0.88, respectively. Nonprimary position ischemic stroke patients had significantly greater comorbidity and 30-day mortality (odds ratio, 3.2) than primary position ischemic stroke patients.
Stroke classification in these administrative data were optimal using all discharge diagnoses for ischemic stroke and primary discharge diagnosis only for intracerebral and subarachnoid hemorrhage. Selecting ischemic stroke patients on the basis of primary discharge diagnosis may bias administrative samples toward more benign, unrepresentative outcomes and should be avoided.
Given the same pretest probability (10%) for subarachnoid hemorrhage (SAH), pulmonary embolism (PE), and acute coronary syndrome (ACS), we determined if differences exist in the risk tolerance for disease exclusion according to published guidelines given a negative test result.
Published guidelines that make practice recommendations on the evaluation of ACS, PE, and SAH were sought using the National Guideline Clearinghouse in low-risk settings. Second-order Monte Carlo simulation was performed to determine point estimates and confidence intervals (CIs) for posttest probabilities assuming a pretest probability of 10%.
Guidelines recommend that patients with low-risk suspected ACS should undergo stress testing. For SAH, computed tomography (CT) followed by lumbar puncture (LP) is recommended without mention of pretest probability; and D-dimer testing is recommended to exclude PE in low-risk patients. Test sensitivity for thallium-201 single photon emission computed tomography (SPECT) was 89%, exercise echocardiogram was 85%, D-dimer testing was 95%, and CT/LP for SAH was 100% (as a gold standard) and CT only was 97.5%. Given a negative test result, for PE, posttest probability was 0.5% (95% CI 0.1%-0.9%); for SPECT, 1.1% (SD 0.5%-1.6%); and for exercise echocardiogram, 1.5% (95% CI 0.5%-2.5%) compared with a posttest probability of 0% for CT followed by LP for SAH. Using a CT-only approach gives a posttest probability of 0.2% (95% CI 0.2%-0.4%).
Guidelines for suspected PE and ACS allow small but nonzero calculated risk end points in low-risk settings, whereas SAH guidelines afford no misses. Because many gold standard tests are more invasive and can have adverse effects, guideline authors should consider adopting a standard acceptable miss rate as an end point for workups with low clinical suspicion to avoid the overuse of invasive testing.
Diagnostic errors in the emergency Department: a systematic review. comparative effectiveness review No.258. (prepared by the Johns Hopkins University evidence-based practice center under contract No.75Q80120D00003.) AHRQ publication No.22(23)-Ehc043
- DE Newman-Toker
- SM Peterson
- S Badihian
The Economics of patient safety in primary and ambulatory care: flying blind
- L Slawomirski
- A Auraaen
- N Klazinga
Slawomirski L, Auraaen A, Klazinga N. The Economics of
patient safety in primary and ambulatory care: flying blind.
Paris, 2018.
North American Association of central cancer registries (NAACCR)
- Naaccr Fast
- Stats
NAACCR Fast Stats. North American Association of central
cancer registries (NAACCR). Available: https://faststats.naaccr.
org [Accessed 21 Sep 2018].
on behalf of the Society to Improve Diagnosis in Medicine Policy Committee . Roadmap for research to improve diagnosis, part 1: converting National Academy of Medicine recommendations into policy action: Society to Improve Diagnosis in Medicine
- D E Newman-Toker
- L Tucker
How doctors think: common diagnostic errors in clinical judgment-lessons from an undiagnosed and rare disease program
- RM Kliegman
- BJ Bordini
- D Basel
My turn: repeal of Obamacare means repeal of protections for patients like me
- J Campisano
Campisano J. My turn: repeal of Obamacare means repeal
of protections for patients like me. Available: https://www.
azcentral.com/story/opinion/op-ed/2017/02/19/repealobamacare-pre-existing-condition/97746562 [Accessed 20 May
2023].
Diagnostic impact of a device-enabled remote "TELE-dizzy" consultation service
- D Gold
- S Peterson
- A Mcclenney
Gold D, Peterson S, McClenney A, et al. Diagnostic impact
of a device-enabled remote "TELE-dizzy" consultation
service [abstract].
Roadmap for research to improve diagnosis, part 1: converting National Academy of Medicine recommendations into policy action: Society to Improve Diagnosis in Medicine
- D E Newman-Toker
- L Tucker
Newman-Toker DE, Tucker L, on behalf of the Society to
Improve Diagnosis in Medicine Policy Committee. Roadmap
for research to improve diagnosis, part 1: converting National
Academy of Medicine recommendations into policy action:
Society to Improve Diagnosis in Medicine; 2018. Available:
https://www.improvediagnosis.org/roadmap [Accessed 20 May
2023].
Diagnostic errors in the emergency Department: a systematic review
- D E Newman-Toker
- S M Peterson
- S Badihian
Newman-Toker DE, Peterson SM, Badihian S, et al. Diagnostic
errors in the emergency Department: a systematic review.
comparative effectiveness review No.258. (prepared by the
Johns Hopkins University evidence-based practice center
under contract No.75Q80120D00003.) AHRQ publication
No.22(23)-Ehc043. Rockville, MD Agency for Healthcare
Research and Q