Navigating Malpractice: Guide for U.S. Radiologists

The majority of radiologists will face litigation in their careers, yet it remains an uncommon topic for training programs and educational conferences. The authors explore the landscape of radiology malpractice litigation to equip radiologists with essential knowledge before and in the event of a lawsuit. Radiologists should understand the four components necessary to be found liable for medical negligence: patient duty, breach of standard of care, injury, and proximate causality between the breach of standard of care and the injury. The authors introduce the mechanics of a lawsuit, common causes of lawsuits, and factors affecting risk. Many current radiologic norms and standards derive from legal precedent; examining these precedents and their effects on current practice through the context of prior litigated cases can help radiologists understand their evolving roles and responsibilities. Ultimately, 63% of malpractice claims are dismissed or dropped, 28% reach settlement agreements, and the remaining claims proceed to trial, where most result in defense wins. Radiologists should be familiar with common practices that may affect their legal risk, as well as potential misunderstandings regarding the discoverability of morbidity and mortality conferences, tumor boards, and other interdisciplinary conferences. Although litigation may not always be preventable, radiologists who understand the U.S. malpractice and medicolegal environment will be better positioned to mitigate unfavorable patient and legal outcomes. ©RSNA, 2025.

Diagnosing diagnostic error of endometriosis: a secondary analysis of patient experiences from a mixed-methods survey

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Patterns of errors and weaknesses in the diagnostic process: retrospective analysis of malpractice claims and adverse events from two national databases

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement. AI can streamline the information-gathering process by automating data retrieval, reducing cognitive load, and providing clinicians with essential patient details quickly. AI-driven CDS systems enhance diagnostic decision making by offering real-time insights, reducing cognitive biases, and prioritizing differential diagnoses. Furthermore, AI-powered feedback loops can facilitate continuous learning and refinement of diagnostic processes by providing targeted education and outcome feedback to clinicians. By integrating AI into these areas, the potential for reducing diagnostic errors and improving patient safety in the ED is substantial. However, successfully implementing AI in the ED is challenging and complex. Developing, validating, and implementing AI as a safe, human-centered ED tool requires thoughtful design and meticulous attention to ethical and practical considerations. Clinicians and patients must be integrated as key stakeholders across these processes. Ultimately, AI should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.

Neurologic Specific Risk: Strokes, Lytics, and Litigation

Misdiagnosis in Emergency Medicine can be associated with patient harm, with neurologic diagnoses among the most common conditions to confound physicians. These are often complex, time-sensitive and nuanced, offering opportunity for mimics and chameleons to make assessment, diagnosis and treatment challenging. This article discusses the legal considerations pertinent to neurologic diagnoses for the emergency physician, including assessment, diagnosis, treatment, transfer and documentation in order to ensure excellent patient care as well as protection from liability.

From stable teamwork to dynamic teaming in the ambulatory care diagnostic process

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.

Framing diagnostic error: an epidemiological perspective

Diagnostic errors burden the United States healthcare system. Depending on how they are defined, between 40,000 and 4 million cases occur annually. Despite this striking statistic, and the potential benefits epidemiological approaches offer in identifying risk factors for sub-optimal diagnoses, diagnostic error remains an underprioritized epidemiolocal research topic. Magnifying the challenge are the array of forms and definitions of diagnostic errors, and limited sources of data documenting their occurrence. In this narrative review, we outline a framework for improving epidemiological applications in understanding risk factors for diagnostic error. This includes explicitly defining diagnostic error, specifying the hypothesis and research questions, consideration of systemic including social and economic factors, as well as the time-dependency of diagnosis relative to disease progression. Additional considerations for future epidemiological research on diagnostic errors include establishing standardized research databases, as well as identifying potential important sources of study bias.

Clinical characteristics and diagnostic accuracy of preliminary diagnoses in adults with infections in Danish emergency departments: a multicentre combined cross-sectional and diagnostic study

OBJECTIVE

Rapid and accurate infection diagnosis is a prerequisite for appropriate antibiotic prescriptions in an ED. Accurately diagnosing acute infections can be difficult due to nonspecific symptoms and limitations of diagnostic testing. The accuracy of preliminary diagnoses, established on the initial clinical assessment, depends on a physician's skills and knowledge. It has been scarcely studied, and knowledge of how infected patients present at EDs today is needed to improve it. Based on expert reference diagnoses and a current ED population, this study aimed to characterise adults presenting at EDs with suspected infection to distinguish between infections and non-infections and to investigate the accuracy of the preliminary infection diagnoses.

DESIGN

This study was multicentre with a design that combined a cross-sectional study and a diagnostic study with a prospective enrolment.

SETTING

Multicenter study including EDs at three Danish hospitals.

PARTICIPANTS

Adults admitted with a preliminary diagnosis of an infectious disease.

OUTCOME MEASURES

Data were collected from medical records and participant interviews. The primary outcome was the reference diagnosis made by two medical experts on chart review. Univariate logistic regression analysis was performed to identify factors associated with infectious diseases.

RESULTS

We included 954 patients initially suspected of having an infection, with 81% later having an infectious disease confirmed by experts. Parameters correlating to infection were fever, feeling unwell, male sex, high C-reactive protein, symptoms onset within 3 days, high heart rate, low oxygen saturation and abnormal values of neutrophilocytes and leucocytes. The three main conditions were community-acquired pneumonia (CAP) (34%), urinary tract infection (UTI) with systemic symptoms (21%) and cellulitis (10%). The sensitivity of the physician's preliminary infection diagnoses was 87% for CAP, 74% for UTI and 77% for other infections.

CONCLUSIONS

Four out of five patients with a preliminary infection diagnosis, established on initial clinical assessment, were ultimately confirmed to have an infectious disease. The main infections included CAP, UTI with systemic symptoms and cellulitis. Physicians' preliminary infection diagnoses were moderately in accordance with the reference diagnoses.

TRIAL REGISTRATION NUMBER

NCT04661085, NCT04681963, NCT04667195.

Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer 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 .

Root cause analysis of cases involving diagnosis

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

Interventions to improve timely cancer diagnosis: an integrative review

Cancer will affect more than one in three U.S. residents in their lifetime, and although the diagnosis will be made efficiently in most of these cases, roughly one in five patients will experience a delayed or missed diagnosis. In this integrative review, we focus on missed opportunities in the diagnosis of breast, lung, and colorectal cancer in the ambulatory care environment. From a review of 493 publications, we summarize the current evidence regarding the contributing factors to missed or delayed cancer diagnosis in ambulatory care, as well as evidence to support possible strategies for intervention. Cancer diagnoses are made after follow-up of a positive screening test or an incidental finding, or most commonly, by following up and clarifying non-specific initial presentations to primary care. Breakdowns and delays are unacceptably common in each of these pathways, representing failures to follow-up on abnormal test results, incidental findings, non-specific symptoms, or consults. Interventions aimed at 'closing the loop' represent an opportunity to improve the timeliness of cancer diagnosis and reduce the harm from diagnostic errors. Improving patient engagement, using 'safety netting,' and taking advantage of the functionality offered through health information technology are all viable options to address these problems.

Development and Psychometric Analysis of a Patient-Reported Measure of Diagnostic Excellence for Emergency and Urgent Care Settings

BACKGROUND

Emergency and urgent care settings face challenges with routinely obtaining performance feedback related to diagnostic care. Patients and their care partners provide an important perspective on the diagnostic process and outcome of care in these settings. We sought to develop and test psychometric properties of Patient-Report to IMprove Diagnostic Excellence in Emergency Department settings (PRIME-ED), a measure of patient-reported diagnostic excellence in these care settings.

METHODS

We developed PRIME-ED based on literature review, expert feedback, and cognitive testing. To assess psychometric properties, we surveyed AmeriSpeak, a probability-based panel that provides sample coverage of approximately 97% of the U.S. household population, in February 2022 to adult patients, or their care partners, who had presented to an emergency department or urgent care facility within the last 30 days. Respondents rated their agreement on a 5-point Likert scale with each of 17 statements across multiple domains of patient-reported diagnostic excellence. Demographics, visit characteristics, and a subset of the Emergency Department Consumer Assessment of Healthcare Providers & Systems were also collected. We conducted psychometric testing for reliability and validity.

RESULTS

Over a thousand (n = 1116) national panelists completed the PRIME-ED survey, of which 58.7% were patients and 40.9% were care partners; 49.6% received care at an emergency department and 49.9% at an urgent care facility. Responses had high internal consistency within 3 patient-reported diagnostic excellence domain groupings: diagnostic process (Cronbach's alpha 0.94), accuracy of diagnosis (0.93), and communication of diagnosis (0.94). Domain groupings were significantly correlated with concurrent Emergency Department Consumer Assessment of Healthcare Providers & Systems items. Factor analyses substantiated 3 domain groupings.

CONCLUSIONS

PRIME-ED has potential as a tool for capturing patient-reported diagnostic excellence in emergency and urgent care.

Reducing the Risk of Delayed Colorectal Cancer Diagnoses Through an Ambulatory Safety Net Collaborative

BACKGROUND

An estimated 12 million adults in the United States experience delayed diagnoses and other diagnostic errors annually. Ambulatory safety nets (ASNs) are an intervention to reduce delayed diagnoses by identifying patients with abnormal results overdue for follow-up using registries, workflow redesign, and patient navigation. The authors sought to co-design a collaborative and implement colorectal cancer (CRC) ASNs across various health care settings.

METHODS

A working group was convened to co-design implementation guidance, measures, and the collaborative model. Collaborative sites were recruited through a medical professional liability insurance program and chose to begin with developing an ASN for positive at-home CRC screening or overdue surveillance colonoscopy. The 18-month Breakthrough Series Collaborative ran from January 2022 to July 2023, with sites continuing to collect data while sustaining their ASNs. Data were collected from sites monthly on patients in the ASN, including the proportion that was successfully contacted, scheduled, and completed a follow-up colonoscopy.

RESULTS

Six sites participated; four had an operational ASN at the end of the Breakthrough Series, with the remaining sites launching three months later. From October 2022 through February 2024, the Collaborative ASNs collectively identified 5,165 patients from the registry as needing outreach. Among patients needing outreach, 3,555 (68.8%) were successfully contacted, 2,060 (39.9%) were scheduled for a colonoscopy, and 1,504 (29.1%) completed their colonoscopy.

CONCLUSION

The Collaborative successfully identified patients with previously abnormal CRC screening and facilitated completion of follow-up testing. The CRC ASN Implementation Guide offers a comprehensive road map for health care leaders interested in implementing CRC ASNs.

Clinical metabolomics: current state and prospects in Russia

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.

Back to Basics: A Clinical Medicine to Safeguard International Cooperation

The medical profession is currently undergoing a significant transformation. In recent decades, we have seen the emergence and implementation of new diagnostic tools, therapeutic targets, and technical procedures that have revolutionized our clinical practice. These resources have undoubtedly improved patient outcomes but have also led to excessive reliance on technology. This overreliance can limit the new generation's capacity to provide humane and comprehensive patient care and develop critical thinking skills. In this article, we reflect on the urgent impact of this trend on pediatric international cooperation and propose workable solutions to this problem. We stress the importance of maintaining a patient-centered approach in the face of these technological advancements, as it ensures that the patient's needs remain at the forefront of our practice.

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department

In emergency department (ED) settings, rapid and precise diagnostic evaluations are critical to ensure better patient outcomes and efficient healthcare delivery. This study assesses the accuracy of differential diagnosis lists generated by the third-generation ChatGPT (ChatGPT-3.5) and the fourth-generation ChatGPT (ChatGPT-4) based on electronic health record notes recorded within the first 24 h of ED admission. These models process unstructured text to formulate a ranked list of potential diagnoses. The accuracy of these models was benchmarked against actual discharge diagnoses to evaluate their utility as diagnostic aids. Results indicated that both GPT-3.5 and GPT-4 reasonably accurately predicted diagnoses at the body system level, with GPT-4 slightly outperforming its predecessor. However, their performance at the more granular category level was inconsistent, often showing decreased precision. Notably, GPT-4 demonstrated improved accuracy in several critical categories that underscores its advanced capabilities in managing complex clinical scenarios.

The Big Three diagnostic errors through reflections of Japanese internists

OBJECTIVES

To analyze the Big Three diagnostic errors (malignant neoplasms, cardiovascular diseases, and infectious diseases) through internists' self-reflection on their most memorable diagnostic errors.

METHODS

This secondary analysis study, based on a web-based cross-sectional survey, recruited participants from January 21 to 31, 2019. The participants were asked to recall the most memorable diagnostic error cases in which they were primarily involved. We gathered data on internists' demographics, time to error recognition, and error location. Factors causing diagnostic errors included environmental conditions, information processing, and cognitive bias. Participants scored the significance of each contributing factor on a Likert scale (0, unimportant; 10, extremely important).

RESULTS

The Big Three comprised 54.1 % (n=372) of the 687 cases reviewed. The median physician age was 51.5 years (interquartile range, 42-58 years); 65.6 % of physicians worked in hospital settings. Delayed diagnoses were the most common among malignancies (n=64, 46 %). Diagnostic errors related to malignancy were frequent in general outpatient settings on weekdays and in the mornings and were not identified for several months following the event. Environmental factors often contributed to cardiovascular disease-related errors, which were typically identified within days in emergency departments, during night shifts, and on holidays. Information gathering and interpretation significantly impacted infectious disease diagnoses.

CONCLUSIONS

The Big Three accounted for the majority of cases recalled by Japanese internists. The most relevant contributing factors were different for each of the three categories. Addressing these errors may require a unique approach based on the disease associations.

Development and Implementation of a Digital Quality Measure of Emergency Cancer Diagnosis

PURPOSE

Missed and delayed cancer diagnoses are common, harmful, and often preventable. Automated measures of quality of cancer diagnosis are lacking but could identify gaps and guide interventions. We developed and implemented a digital quality measure (dQM) of cancer emergency presentation (EP) using electronic health record databases of two health systems and characterized the measure's association with missed opportunities for diagnosis (MODs) and mortality.

METHODS

On the basis of literature and expert input, we defined EP as a new cancer diagnosis within 30 days after emergency department or inpatient visit. We identified EPs for lung cancer and colorectal cancer (CRC) in the Department of Veterans Affairs (VA) and Geisinger from 2016 to 2020. We validated measure accuracy and identified preceding MODs through standardized chart review of 100 records per cancer per health system. Using VA's longitudinal encounter and mortality data, we applied logistic regression to assess EP's association with 1-year mortality, adjusting for cancer stage and demographics.

RESULTS

Among 38,565 and 2,914 patients with lung cancer and 14,674 and 1,649 patients with CRCs at VA and Geisinger, respectively, our dQM identified EPs in 20.9% and 9.4% of lung cancers, and 22.4% and 7.5% of CRCs. Chart reviews revealed high positive predictive values for EPs across sites and cancer types (72%-90%), and a substantial percent represented MODs (48.8%-84.9%). EP was associated with significantly higher odds of 1-year mortality for lung cancer and CRC (adjusted odds ratio, 1.78 and 1.83, respectively, 95% CI, 1.63 to 1.86 and 1.61 to 2.07).

CONCLUSION

A dQM for cancer EP was strongly associated with both mortality and MODs. The findings suggest a promising automated approach to measuring quality of cancer diagnosis in US health systems.

Effects of interacting with a large language model compared with a human coach on the clinical diagnostic process and outcomes among fourth-year medical students: study protocol for a prospective, randomised experiment using patient vignettes

INTRODUCTION

Versatile large language models (LLMs) have the potential to augment diagnostic decision-making by assisting diagnosticians, thanks to their ability to engage in open-ended, natural conversations and their comprehensive knowledge access. Yet the novelty of LLMs in diagnostic decision-making introduces uncertainties regarding their impact. Clinicians unfamiliar with the use of LLMs in their professional context may rely on general attitudes towards LLMs more broadly, potentially hindering thoughtful use and critical evaluation of their input, leading to either over-reliance and lack of critical thinking or an unwillingness to use LLMs as diagnostic aids. To address these concerns, this study examines the influence on the diagnostic process and outcomes of interacting with an LLM compared with a human coach, and of prior training vs no training for interacting with either of these 'coaches'. Our findings aim to illuminate the potential benefits and risks of employing artificial intelligence (AI) in diagnostic decision-making.

METHODS AND ANALYSIS

We are conducting a prospective, randomised experiment with N=158 fourth-year medical students from Charité Medical School, Berlin, Germany. Participants are asked to diagnose patient vignettes after being assigned to either a human coach or ChatGPT and after either training or no training (both between-subject factors). We are specifically collecting data on the effects of using either of these 'coaches' and of additional training on information search, number of hypotheses entertained, diagnostic accuracy and confidence. Statistical methods will include linear mixed effects models. Exploratory analyses of the interaction patterns and attitudes towards AI will also generate more generalisable knowledge about the role of AI in medicine.

ETHICS AND DISSEMINATION

The Bern Cantonal Ethics Committee considered the study exempt from full ethical review (BASEC No: Req-2023-01396). All methods will be conducted in accordance with relevant guidelines and regulations. Participation is voluntary and informed consent will be obtained. Results will be published in peer-reviewed scientific medical journals. Authorship will be determined according to the International Committee of Medical Journal Editors guidelines.

Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection

Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.

Development of Patient Safety Measures to Identify Inappropriate Diagnosis of Common Infections

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparative Analysis of Large Language Models in Emergency Plastic Surgery Decision-Making: The Role of Physical Exam Data

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI's ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model's performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

Automating detection of diagnostic error of infectious diseases using machine learning

Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality. Our objective was to identify cases of emergency department infectious disease misdiagnoses by measuring the deviation between predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24h of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or timely diagnosis. The proposed approach was validated against manual reviews and compared using the Spearman rank correlation. We analyzed 6.5 million ED visits and over 700 million associated clinical features from over one hundred emergency departments. The testing set performances of the infectious disease (Macro F1 = 86.7, AUROC 90.6 to 94.7) and mortality model (Macro F1 = 97.6, AUROC 89.1 to 89.1) were in expected ranges. Human reviews and the proposed automated metric demonstrated positive correlations ranging from 0.231 to 0.358. The proposed approach for diagnostic deviation shows promise as a potential tool for clinicians to find diagnostic errors. Given the vast number of clinical features used in this analysis, further improvements likely need to either take greater account of data structure (what occurs before when) or involve natural language processing. Further work is needed to explain the potential reasons for divergence and to refine and validate the approach for implementation in real-world settings.

Retrospective Analysis of US Litigations Involving Dermatologists From 2011 to 2022

BACKGROUND

Physician malpractice lawsuits are climbing, and the reasons underlying litigation against dermatologists are unclear.

OBJECTIVE

To determine the reasons patients pursue litigation against dermatologists or dermatology practices.

MATERIALS AND METHODS

A retrospective analysis of all state and federal cases between 2011 and 2022 was performed after a query using "Dermatology" and "dermatologist" as search terms on 2 national legal data repositories.

RESULTS

The authors identified a total of 48 (37 state and 11 federal) lawsuits in which a practicing dermatologist or dermatology group practice was the defendant. The most common reason for litigation was unexpected harm (26 cases, 54.2%), followed by diagnostic error (e.g. incorrect or delayed diagnoses) (16 cases, 33.3%). Six cases resulted from the dermatologist failing to communicate important information, such as medication side effects or obtaining informed consent. Male dermatologists were sued at a rate 3.1 times higher than female dermatologists.

CONCLUSION

Although lawsuits from patients against dermatologists largely involve injury from elective procedures, clinicians should practice caution regarding missed diagnoses and ensure critical information is shared with patients to safeguard against easily avoidable litigation.

The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease

INTRODUCTION

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments.

CONTENT

Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management.

SUMMARY

The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries.

OUTLOOK

As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.

Exploring Clinical Lessons Learned by Experienced Hospitalists from Diagnostic Errors and Successes

BACKGROUND

Diagnostic errors cause significant patient harm. The clinician's ultimate goal is to achieve diagnostic excellence in order to serve patients safely. This can be accomplished by learning from both errors and successes in patient care. However, the extent to which clinicians grow and navigate diagnostic errors and successes in patient care is poorly understood. Clinically experienced hospitalists, who have cared for numerous acutely ill patients, should have great insights from their successes and mistakes to inform others striving for excellence in patient care.

OBJECTIVE

To identify and characterize clinical lessons learned by experienced hospitalists from diagnostic errors and successes.

DESIGN

A semi-structured interview guide was used to collect qualitative data from hospitalists at five independently administered hospitals in the Mid-Atlantic area from February to June 2022.

PARTICIPANTS

12 academic and 12 community-based hospitalists with ≥ 5 years of clinical experience.

APPROACH

A constructivist qualitative approach was used and "reflexive thematic analysis" of interview transcripts was conducted to identify themes and patterns of meaning across the dataset.

RESULTS

Five themes were generated from the data based on clinical lessons learned by hospitalists from diagnostic errors and successes. The ideas included appreciating excellence in clinical reasoning as a core skill, connecting with patients and other members of the health care team to be able to tap into their insights, reflecting on the diagnostic process, committing to growth, and prioritizing self-care.

CONCLUSIONS

The study identifies key lessons learned from the errors and successes encountered in patient care by clinically experienced hospitalists. These findings may prove helpful for individuals and groups that are authentically committed to moving along the continuum from diagnostic competence towards excellence.

Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care

Importance

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

Objective

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

Design, Setting, and Participants

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

Main Outcomes and Measures

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

Results

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

Conclusions and Relevance

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

Burden of serious harms from diagnostic error in the USA

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.

Japanese Internists' Most Memorable Diagnostic Error Cases: A Self-reflection Survey

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

Non-canonical function of histone methyltransferase G9a in the translational regulation of chronic inflammation

We report a novel translation-regulatory function of G9a, a histone methyltransferase and well-understood transcriptional repressor, in promoting hyperinflammation and lymphopenia; two hallmarks of endotoxin tolerance (ET)-associated chronic inflammatory complications. Using multiple approaches, we demonstrate that G9a interacts with multiple translation regulators during ET, particularly the N6-methyladenosine (m6A) RNA methyltransferase METTL3, to co-upregulate expression of certain m6A-modified mRNAs that encode immune-checkpoint and anti-inflammatory proteins. Mechanistically, G9a promotes m6A methyltransferase activity of METTL3 at translational/post-translational level by regulating its expression, its methylation, and its cytosolic localization during ET. Additionally, from a broader view extended from the G9a-METTL3-m6A translation regulatory axis, our translatome proteomics approach identified numerous "G9a-translated" proteins that unite the networks associated with inflammation dysregulation, T cell dysfunction, and systemic cytokine response. In sum, we identified a previously unrecognized function of G9a in protein-specific translation that can be leveraged to treat ET-related chronic inflammatory diseases.

Characteristics and trends of medical malpractice claims in Japan between 2006 and 2021

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

Misdiagnosis of Posterior Reversible Encephalopathy Syndrome and Reversible Cerebral Vasoconstriction Syndrome in the Emergency Department

Background Cerebrovascular dysregulation syndromes, posterior reversible encephalopathy syndrome (PRES) and reversible cerebral vasoconstriction syndrome (RCVS), are challenging to diagnose because they are rare and require advanced neuroimaging for confirmation. We sought to estimate PRES/RCVS misdiagnosis in the emergency department and its associated factors. Methods and Results We conducted a retrospective cohort study of PRES/RCVS patients using administrative claims data from 11 states (2016-2018). We defined patients with a probable PRES/RCVS misdiagnosis as those with an emergency department visit for a neurological symptom resulting in discharge to home that occurred ≤14 days before PRES/RCVS hospitalization. Proportions of patients with probable misdiagnosis were calculated, characteristics of patients with and without probable misdiagnosis were compared, and regression analyses adjusted for demographics and comorbidities were performed to identify factors affecting probable misdiagnosis. We identified 4633 patients with PRES/RCVS. A total of 210 patients (4.53% [95% CI, 3.97-5.17]) had a probable preceding emergency department misdiagnosis; these patients were younger (mean age, 48 versus 54 years; P<0.001) and more often female (80.4% versus 69.3%; P<0.001). Misdiagnosed patients had fewer vascular risk factors except prior stroke (36.3% versus 24.2%; P<0.001) and more often had comorbid headache (84% versus 21.4%; P<0.001) and substance use disorder (48.8% versus 37.9%; P<0.001). Facility-level factors associated with probable misdiagnosis included smaller facility, lacking a residency program (62.2% versus 73.7%; P<0.001), and not having on-site neurological services (75.7% versus 84.3%; P<0.001). Probable misdiagnosis was not associated with higher likelihood of stroke or subarachnoid hemorrhage during PRES/RCVS hospitalization. Conclusions Probable emergency department misdiagnosis occurred in ≈1 of every 20 patients with PRES/RCVS in a large, multistate cohort.

A rationale and framework for addressing physician cognitive impairment

Medical error is costly, in terms of the health and wellbeing of the patient, their family, and the financial burden placed on the medical system. Reducing medical error is paramount to minimizing harm and improving outcomes. One potential source of medical error is physician cognitive impairment. Determining how to effectively assess and mange physician cognitive impairment is an important, albeit difficult problem to address. There have been calls and attempts to implement age-based cognitive screening, but this approach is not optimal. Instead, we propose that neuropsychological assessment is the gold standard for fitness-for-duty evaluations and that there is a need for the development of physician-based, normative data to improve these evaluations. Here, we outline the framework of our research protocol in a large, academic medical center, in partnership with hospital leadership and legal counsel, which can be modeled by other medical centers. With high rates of physician burnout and an aging physician population, the United States is facing a looming public health crisis that requires proactive management.

Optimizing measurement of misdiagnosis-related harms using symptom-disease pair analysis of diagnostic error (SPADE): comparison groups to maximize SPADE validity

Diagnostic errors in medicine represent a significant public health problem but continue to be challenging to measure accurately, reliably, and efficiently. The recently developed Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach measures misdiagnosis related harms using electronic health records or administrative claims data. The approach is clinically valid, methodologically sound, statistically robust, and operationally viable without the requirement for manual chart review. This paper clarifies aspects of the SPADE analysis to assure that researchers apply this method to yield valid results with a particular emphasis on defining appropriate comparator groups and analytical strategies for balancing differences between these groups. We discuss four distinct types of comparators (intra-group and inter-group for both look-back and look-forward analyses), detailing the rationale for choosing one over the other and inferences that can be drawn from these comparative analyses. Our aim is that these additional analytical practices will improve the validity of SPADE and related approaches to quantify diagnostic error in medicine.

Improving decision accuracy using a clinical decision support system for medical students during history-taking: a randomized clinical trial

BACKGROUND

A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google.

METHODS

This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared.

RESULTS

The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1).

CONCLUSIONS

Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases.

TRIAL REGISTRATION

This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).

How does work environment relate to diagnostic quality? A prospective, mixed methods study in primary care

OBJECTIVES

The quest to measure and improve diagnosis has proven challenging; new approaches are needed to better understand and measure key elements of the diagnostic process in clinical encounters. The aim of this study was to develop a tool assessing key elements of the diagnostic assessment process and apply it to a series of diagnostic encounters examining clinical notes and encounters' recorded transcripts. Additionally, we aimed to correlate and contextualise these findings with measures of encounter time and physician burnout.

DESIGN

We audio-recorded encounters, reviewed their transcripts and associated them with their clinical notes and findings were correlated with concurrent Mini Z Worklife measures and physician burnout.

SETTING

Three primary urgent-care settings.

PARTICIPANTS

We conducted in-depth evaluations of 28 clinical encounters delivered by seven physicians.

RESULTS

Comparing encounter transcripts with clinical notes, in 24 of 28 (86%) there was high note/transcript concordance for the diagnostic elements on our tool. Reliably included elements were red flags (92% of notes/encounters), aetiologies (88%), likelihood/uncertainties (71%) and follow-up contingencies (71%), whereas psychosocial/contextual information (35%) and mentioning common pitfalls (7%) were often missing. In 22% of encounters, follow-up contingencies were in the note, but absent from the recorded encounter. There was a trend for higher burnout scores being associated with physicians less likely to address key diagnosis items, such as psychosocial history/context.

CONCLUSIONS

A new tool shows promise as a means of assessing key elements of diagnostic quality in clinical encounters. Work conditions and physician reactions appear to correlate with diagnostic behaviours. Future research should continue to assess relationships between time pressure and diagnostic quality.

Risk assessment of the acute stroke diagnostic process using failure modes, effects, and criticality analysis

INTRODUCTION

To date, many emergency department (ED)-based quality improvement studies and interventions for acute stroke patients have focused on expediting time-sensitive treatments, particularly reducing door-to-needle time. However, prior to treatment, a diagnosis of stroke must be reached. The ED-based stroke diagnostic process has been understudied despite its importance in assuring high-quality and safe care.

METHODS

We used a learning collaborative to conduct a failure modes, effects, and criticality analysis (FMECA) of the acute stroke diagnostic process at three health systems in Chicago, IL. Our FMECA was designed to prospectively identify, characterize, and rank order failures in the systems and processes of care that offer opportunities for redesign to improve stroke diagnostic accuracy. Multidisciplinary teams involved in stroke care at five different sites participated in moderated sessions to create an acute stroke diagnostic process map as well as identify failures and existing safeguards. For each failure, a risk priority number and criticality score were calculated. Failures were then ranked, with the highest scores representing the most critical failures to be targeted for redesign.

RESULTS

A total of 28 steps were identified in the acute stroke diagnostic process. Iterative steps in the process include information gathering, clinical examination, interpretation of diagnostic test results, and reassessment. We found that failure to use existing screening scales to identify patients with large-vessel occlusions early on in their ED course ranked highest. Failure to obtain an accurate history of the index event, failure to suspect acute stroke in triage, and failure to use established stroke screening tools at ED arrival to identify potential stroke patients were also highly ranked.

CONCLUSIONS

Our study results highlight the critical importance of upstream steps in the acute stroke diagnostic process, particularly the use of existing tools to identify stroke patients who may be eligible for time-sensitive treatments.

Misdiagnosis in the Emergency Department: Time for a System Solution

This Viewpoint offers 3 insights in response to the AHRQ report on diagnostic errors made in US emergency departments: focus on the delivery systems instead of individuals, establish ways to set definitions and assess error rates, and design safe delivery systems to prevent errors.

Medical Malpractice and Diagnostic Errors in Japanese Emergency Departments

INTRODUCTION

Emergency departments (ED) are unpredictable and prone to diagnostic errors. In addition, non-emergency specialists often provide emergency care in Japan due to a lack of certified emergency specialists, making diagnostic errors and associated medical malpractice more likely. While several studies have investigated the medical malpractice related to diagnostic errors in EDs, only a few have focused on the conditions in Japan. This study examines diagnostic error-related medical malpractice lawsuits in Japanese EDs to understand how various factors contribute to diagnostic errors.

METHODS

We retrospectively examined data on medical lawsuits from 1961-2017 to identify types of diagnostic errors and initial and final diagnoses from non-trauma and trauma cases.

RESULTS

We evaluated 108 cases, of which 74 (68.5%) were diagnostic error cases. Twenty-eight of the diagnostic errors were trauma-related (37.8%). In 86.5% of these diagnostic error cases, the relevant errors were categorized as either missed or diagnosed incorrectly; the others were attributable to diagnostic delay. Cognitive factors (including faulty perception, cognitive biases, and failed heuristics) were associated with 91.7% of errors. Intracranial hemorrhage was the most common final diagnosis of trauma-related errors (42.9%), and the most common initial diagnoses of non-trauma-related errors were upper respiratory tract infection (21.7%), non-bleeding digestive tract disease (15.2%), and primary headache (10.9%).

CONCLUSION

In this study, the first to examine medical malpractice errors in Japanese EDs, we found that such claims are often developed from initial diagnoses of common diseases, such as upper respiratory tract infection, non-hemorrhagic gastrointestinal diseases, and headaches.

Diagnostic Delays in Sepsis: Lessons Learned From a Retrospective Study of Canadian Medico-Legal Claims

Although rapid treatment improves outcomes for patients presenting with sepsis, early detection can be difficult, especially in otherwise healthy adults.

OBJECTIVES

Using medico-legal data, we aimed to identify areas of focus to assist with early recognition of sepsis.

DESIGN SETTING AND PARTICIPANTS

Retrospective descriptive design. We analyzed closed medico-legal cases involving physicians from a national database repository at the Canadian Medical Protective Association. The study included cases closed between 2011 and 2020 that had documented peer expert criticism of a diagnostic issue related to sepsis or relevant infections.

MAIN OUTCOMES AND MEASURES

We used univariate statistics to describe patients and physicians and applied published frameworks to classify contributing factors (provider, team, system) and diagnostic pitfalls based on peer expert criticisms.

RESULTS

Of 162 involved patients, the median age was 53 years (interquartile range [IQR], 34-66 yr) and mortality was 49%. Of 218 implicated physicians, 169 (78%) were from family medicine, emergency medicine, or surgical specialties. Eighty patients (49%) made multiple visits to outpatient care leading up to sepsis recognition/hospitalization (median = two visits; IQR, 2-4). Almost 40% of patients were admitted to the ICU. Deficient assessments, such as failing to consider sepsis or not reassessing the patient prior to discharge, contributed to the majority of cases (81%).

CONCLUSIONS AND RELEVANCE

Sepsis continues to be a challenging diagnosis for clinicians. Multiple visits to outpatient care may be an early warning sign requiring vigilance in the patient assessment.

A clinically-guided unsupervised clustering approach to recommend symptoms of disease associated with diagnostic opportunities

OBJECTIVES

A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis.

METHODS

We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-means clustering to recommend conditions that exhibit similar trends to the initial symptoms provided. We evaluate the clinical plausibility of the recommended conditions and the corresponding number of possible diagnostic delays based on these diseases.

RESULTS

For both diseases of interest, the clustering approach suggested a large number of clinically-plausible conditions to consider (e.g., fever, hemoptysis, and pneumonia before tuberculosis). The recommended conditions had a high degree of precision in terms of clinical plausibility: >70% for tuberculosis and >90% for appendicitis. Including these additional clinically-plausible conditions resulted in more than twice the number of possible diagnostic delays identified.

CONCLUSIONS

Our approach can mine administrative datasets to detect patterns of diagnostic delay and help investigators avoid under-identifying potential missed diagnostic opportunities. In addition, the methods we describe can be used to discover less-common presentations of diseases that are frequently misdiagnosed.

Communication of Diagnostic Uncertainty in Primary Care and Its Impact on Patient Experience: an Integrative Systematic Review

BACKGROUND

Diagnostic uncertainty is a pervasive issue in primary care where patients often present with non-specific symptoms early in the disease process. Knowledge about how clinicians communicate diagnostic uncertainty to patients is crucial to prevent associated diagnostic errors. Yet, in-depth research on the interpersonal communication of diagnostic uncertainty has been limited. We conducted an integrative systematic literature review (PROSPERO CRD42020197624, unfunded) to investigate how primary care doctors communicate diagnostic uncertainty in interactions with patients and how patients experience their care in the face of uncertainty.

METHODS

We searched MEDLINE, PsycINFO, and Linguistics and Language Behaviour Abstracts (LLBA) from inception to December 2021 for MeSH and keywords related to 'communication', 'diagnosis', 'uncertainty' and 'primary care' environments and stakeholders (patients and doctors), and conducted additional handsearching. We included empirical primary care studies published in English on spoken communication of diagnostic uncertainty by doctors to patients. We assessed risk of bias with the QATSDD quality assessment tool and conducted thematic and content analysis to synthesise the results.

RESULTS

Inclusion criteria were met for 19 out of 1281 studies. Doctors used two main communication strategies to manage diagnostic uncertainty: (1) patient-centred communication strategies (e.g. use of empathy), and (2) diagnostic reasoning strategies (e.g. excluding serious diagnoses). Linguistically, diagnostic uncertainty was either disclosed explicitly or implicitly through diverse lexical and syntactical constructions, or not communicated (omission). Patients' experiences of care in response to the diverse communicative and linguistic strategies were mixed. Patient-centred approaches were generally regarded positively by patients.

DISCUSSION

Despite a small number of included studies, this is the first review to systematically catalogue the diverse communication and linguistic strategies to express diagnostic uncertainty in primary care. Health professionals should be aware of the diverse strategies used to express diagnostic uncertainty in practice and the value of combining patient-centred approaches with diagnostic reasoning strategies.

Defining Diagnostic Error: A Scoping Review to Assess the Impact of the National Academies' Report Improving Diagnosis in Health Care

BACKGROUND

Standards for accurate and timely diagnosis are ill-defined. In 2015, the National Academies of Science, Engineering, and Medicine (NASEM) committee published a landmark report, Improving Diagnosis in Health Care , and proposed a new definition of diagnostic error, "the failure to ( a ) establish an accurate and timely explanation of the patient's health problem(s) or ( b ) communicate that explanation to the patient."

OBJECTIVE

This study aimed to explore how researchers operationalize the NASEM's definition of diagnostic error with relevance to accuracy, timeliness, and/or communication in peer-reviewed published literature.

METHODS

Using the Arskey and O'Malley's framework framework, we identified published literature from October 2015 to February 2021 using Medline and Google Scholar. We also conducted subject matter expert interviews with researchers.

RESULTS

Of 34 studies identified, 16 were analyzed and abstracted to determine how diagnostic error was operationalized and measured. Studies were grouped by theme: epidemiology, patient focus, measurement/surveillance, and clinician focus. Nine studies indicated using the NASEM definition. Of those, 5 studies also operationalized with existing definitions proposed before the NASEM report. Four studies operationalized the components of the NASEM definition and did not cite existing definitions. Three studies operationalized error using existing definitions only. Subject matter experts indicated that the NASEM definition functions as foundation for researchers to conceptualize diagnostic error.

CONCLUSIONS

The NASEM report produced a common understanding of diagnostic error that includes accuracy, timeliness, and communication. In recent peer-reviewed literature, most researchers continue to use pre-NASEM report definitions to operationalize accuracy and timeliness. The report catalyzed the use of patient-centered concepts in the definition, resulting in emerging studies focused on examining errors related to communicating diagnosis to patients.