Prevalence of harmful diagnostic errors in hospitalised adults: a systematic review and meta-analysis

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.

Equity-Driven Diagnostic Excellence framework: An upstream approach to minimize risk of diagnostic inequity

OBJECTIVES

Diagnostic errors represent the most common and costly preventable patient safety events, with historically marginalized populations disproportionately impacted due to systemic inequities in healthcare. Addressing these disparities requires embedding equity into every facet of the diagnostic process. The aim was to develop, refine, and validate a competency framework for Equity-Driven Diagnostic Excellence (DxEqEx).

METHODS

A modified Delphi method was used, involving transdisciplinary diverse healthcare system participants, including patient advocates, physicians, nurses, and other healthcare professionals. Participants were guided through multiple rounds of feedback and ratings, assessing the importance, disciplinary relevance, feasibility, skill acquisition level required, granularity, and representativeness of the DxEqEx framework.

RESULTS

Sixteen essential competencies have been identified, categorized into three domains: Intrapersonal, Team-based, and Structural. Participants rated the framework with high importance and strong relevance to their respective disciplines. However, the feasibility of implementing the framework varied, largely due to broader challenges within the healthcare system. The competencies were assessed as requiring a proficient skill level according to Dreyfus' model. The final round maintained strong ratings for granularity and representativeness, which supported the final version of the framework.

CONCLUSIONS

The DxEqEx framework holds significant potential to proactively address the needs of historically marginalized patients throughout the diagnostic process. Future research should focus on participatory, resource-efficient implementation.

Enabling diagnostic excellence in the real world: Managing complexity, uncertainty and clinical responsibility

Diagnostic error is a significant category within preventable patient harm, and it takes many years of effort to develop proficiency in diagnostic reasoning. One of the key challenges medical schools must address is preparing students for the complexity, uncertainty and clinical responsibility in going from student to doctor. Recognising the importance of both cognitive and systems-related factors in diagnostic accuracy, we designed the QUID Prompt (Questions to Use for Improving Diagnosis) for students to refer to at the bedside. This set of questions prompts careful consideration, analysis, and signposting of decision-making processes, to assist students in transitioning from medical school to the real-world of work and achieving diagnostic excellence in clinical settings.

Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship. Additionally, we highlight our most important generative AI ethics and equity considerations for these stakeholders. For clinicians, we recommend approaching generative AI similarly to other important biomedical advancements, critically appraising its evidence and utility and incorporating it thoughtfully into practice. For technologists developing generative AI for healthcare applications, we recommend a major frameshift in thinking away from the expectation that clinicians will "supervise" generative AI. Rather, these organizations and individuals should hold themselves and their technologies to the same set of high standards expected of the clinical workforce and strive to design high-performing, well-studied tools that improve care and foster the therapeutic relationship, not simply those that improve efficiency or market share. We further recommend deep and ongoing partnerships with clinicians and patients as necessary collaborators in this work. And for healthcare organizations, we recommend pursuing a combination of both incremental and transformative change with generative AI, directing resources toward both endeavors, and avoiding the urge to rapidly displace the human clinical workforce with generative AI. We affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.

Hospital commitments to address diagnostic errors: An assessment of 95 US hospitals

BACKGROUND

Diagnostic errors are a leading cause of patient harm. In 2022, the Leapfrog Group published a report containing 29 evidence-based practices that hospitals can adopt to reduce diagnostic errors.

OBJECTIVES

To understand the extent to which US hospitals have already implemented these practices, we conducted a national pilot survey of Leapfrog-participating hospitals.

METHODS

To reduce respondent burden, we divided the 29 practices across two surveys: one focused on organizational culture and structure (Domain 1), and the second focused on the diagnostic process itself (Domain 2).

RESULTS

A total of 95 hospitals from 23 states responded to one or both surveys. On average, hospitals reported implementing 9 of the 16 practices (56%) in Domain 1 and 8 of the 13 practices (62%) in Domain 2. The rate of practice implementation varied greatly, with some hospitals implementing as few as three practices in their domain. The most commonly implemented practices were ensuring access to medical interpreters, continuous access to radiologists, ensuring staff and patients can report diagnostic errors and concerns, and having a formal process to identify and notify patients when diagnostic errors occur. The least implemented practices included convening a multidisciplinary team focused on diagnostic safety and quality, a CEO commitment to diagnostic excellence, conducting diagnosis-focused risk assessments, and training clinicians to optimize clinical reasoning in the diagnostic process.

CONCLUSIONS

The findings suggest large and important implementation gaps for practices related to diagnostic excellence and can inform new initiatives to promote diagnostic excellence in US hospitals.

Adverse diagnostic events in hospitalised patients: a single-centre, retrospective cohort study

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.

"I Had No Idea This Happened": Electronic Feedback on Clinical Reasoning for Hospitalists

BACKGROUND

Feedback on the diagnostic process has been proposed as a method of improving clinical reasoning and reducing diagnostic errors. Barriers to the delivery and receipt of feedback include time constraints and negative reactions. Given the shift toward asynchronous, digital communication, it is possible that electronic feedback ("e-feedback") could overcome these barriers.

OBJECTIVES

We developed an e-feedback system for hospitalists around episodes of care escalation (transfers to ICU and rapid responses). The intervention was evaluated by measuring hospitalists' satisfaction with e-feedback and commitment to change.

DESIGN

A qualitative survey study conducted at one academic medical center from February to June 2023.

PARTICIPANTS

Hospitalists - physicians and advanced practice providers.

APPROACH

Two hospitalists, one internal medicine resident, and a nurse reviewed escalations of care on the hospitalist service each week using the Revised Safer Dx framework. Confidential feedback was emailed to the hospitalists involved in the patient's care. Hospitalists were asked to rate and explain their satisfaction with the e-feedback and whether they might modify their clinical practice based on the e-feedback. The open-ended text comments from the hospitalists were analyzed using a thematic analysis framework.

RESULTS

Forty-nine out of fifty-eight hospitalists agreed to participate. One hundred five out of one hundred twenty-four (85%) e-feedback surveys that were sent were returned by the hospitalists. Hospitalists were highly satisfied with 67% (n = 70) of the e-feedback reports, moderately satisfied with 23% (n = 24), and not satisfied with 10% (n = 11). Six themes were identified based on analysis of the comments. Themes related to satisfaction with the intervention included appreciation for learning about patient outcomes, general appreciation of feedback on clinical care, and importance of detailed and specific feedback. Themes related to changing clinical practice included reflection on clinical decision-making, value of new insights, and anticipated future behavior change.

CONCLUSIONS

E-feedback was well received by hospitalists. Their perspectives offer useful insights for enhancing electronic feedback interventions.

What is diagnostic safety? A review of safety science paradigms and rethinking paths to improving diagnosis

Diagnostic errors in health care are a global threat to patient safety. Researchers have traditionally focused diagnostic safety efforts on identifying errors and their causes with the goal of reducing diagnostic error rates. More recently, complementary approaches to diagnostic errors have focused on improving diagnostic performance drawn from the safety sciences. These approaches have been called Safety-II and Safety-III, which apply resilience engineering and system safety principles, respectively. This review explores the safety science paradigms and their implications for analyzing diagnostic errors, highlighting their distinct yet complementary perspectives. The integration of Safety-I, Safety-II, and Safety-III paradigms presents a promising pathway for improving diagnosis. Diagnostic researchers not yet familiar with the various approaches and potential paradigm shift in diagnostic safety research may use this review as a starting point for considering Safety-I, Safety-II, and Safety-III in their efforts to both reduce diagnostic errors and improve diagnostic performance.

Teaching Clinical Reasoning in the Preclinical Period

INTRODUCTION

Developing the clinical reasoning skills necessary to becoming an astute diagnostician is essential for medical students. While some medical schools offer longitudinal opportunities for students to practice clinical reasoning during the preclinical curriculum, there remains a paucity of literature fully describing what that curriculum looks like. As a result, medical educators struggle to know what an effective clinical reasoning curriculum should look like, how it should be delivered, how it should be assessed, or what faculty development is necessary to be successful. We present our Introduction to Clinical Reasoning course that is offered throughout the preclinical curriculum of the Uniformed Services University of the Health Sciences. The course introduces clinical reasoning through interactive lectures and 28 case-based small group activities over 15 months.The curriculum is grounded in script theory with a focus on diagnostic reasoning. Specific emphasis is placed on building the student's semantic competence, constructing problem lists, comparing and contrasting similar diagnoses, constructing a summary statement, and formulating a prioritized differential diagnosis the student can defend. Several complementary methods of assessment are utilized across the curriculum. These include assessments of participation, knowledge, and application. The course leverages clinical faculty, graduate medical education trainees, and senior medical students as small group facilitators. Feedback from students and faculty consistently identifies the course as a highly effective and engaging way to teach clinical reasoning.

CONCLUSION

Our Introduction to Clinical Reasoning course offers students repeated exposure to well-selected cases to promote their development of clinical reasoning. The course is an example of how clinical reasoning can be taught across the preclinical curriculum without extensive faculty training in medical education or clinical reasoning theory. The course can be adapted into different instructional formats to cover a variety of topics to provide the early learner with sequential exposure and practice in diagnostic reasoning.

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

BACKGROUND

Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay.

OBJECTIVE

This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data.

METHODS

Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors.

RESULTS

In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P=.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P=.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P=.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P=.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F1-score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients.

CONCLUSIONS

Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.

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.

Assessment of Clinical Reasoning in Undergraduate Medical Education: A Pragmatic Approach to Programmatic Assessment

PURPOSE

Clinical reasoning, a complex construct integral to the practice of medicine, has been challenging to define, teach, and assess. Programmatic assessment purports to overcome validity limitations of judgments made from individual assessments through proportionality and triangulation processes. This study explored a pragmatic approach to the programmatic assessment of clinical reasoning.

METHOD

The study analyzed data from 2 student cohorts from the University of Utah School of Medicine (UUSOM) (n = 113 in cohort 1 and 119 in cohort 2) and 1 cohort from the University of Colorado School of Medicine (CUSOM) using assessment data that spanned from 2017 to 2021 (n = 199). The study methods included the following: (1) asking faculty judges to categorize student clinical reasoning skills, (2) selecting institution-specific assessment data conceptually aligned with clinical reasoning, (3) calculating correlations between assessment data and faculty judgments, and (4) developing regression models between assessment data and faculty judgments.

RESULTS

Faculty judgments of student clinical reasoning skills were converted to a continuous variable of clinical reasoning struggles, with mean (SD) ratings of 2.93 (0.27) for the 232 UUSOM students and 2.96 (0.17) for the 199 CUSOM students. A total of 67 and 32 discrete assessment variables were included from the UUSOM and CUSOM, respectively. Pearson r correlations were moderate to strong between many individual and composite assessment variables and faculty judgments. Regression models demonstrated an overall adjusted R2 (standard error of the estimate) of 0.50 (0.19) for UUSOM cohort 1, 0.28 (0.15) for UUSOM cohort 2, and 0.30 (0.14) for CUSOM.

CONCLUSIONS

This study represents an early pragmatic exploration of regression analysis as a potential tool for operationalizing the proportionality and triangulation principles of programmatic assessment. The study found that programmatic assessment may be a useful framework for longitudinal assessment of complicated constructs, such as clinical reasoning.

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.

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

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.

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.

Effect of contextual factors on the prevalence of diagnostic errors among patients managed by physicians of the same specialty: a single-centre retrospective observational study

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Intermediate-Risk and High-Risk Pulmonary Embolism: Recognition and Management: Cardiology Clinics: Cardiac Emergencies

Pulmonary embolism (PE) is the third most common cause of cardiovascular death. Every specialty of medical practitioner will encounter PE in their patients, and should be prepared to employ contemporary strategies for diagnosis and initial risk-stratification. Treatment of PE is based on risk-stratification, with anticoagulation for all patients, and advanced modalities including systemic thrombolysis, catheter-directed therapies, and mechanical circulatory supports utilized in a manner paralleling PE severity and clinical context.

Exploring Ward Team Handoffs of Overnight Admissions: Key Lessons from Field Observations

BACKGROUND

The diagnostic process is a dynamic, team-based activity that is an important aspect of ward rounds in teaching hospitals. However, few studies have examined how academic ward teams operate in areas such as diagnosis in the handoff of overnight admissions during ward rounds. This study draws key lessons from team interactions in the handoff process during ward rounds.

OBJECTIVE

To describe how ward teams operate in the handoff of patients admitted overnight during ward rounds, and to characterize the role of the bedside patient evaluation in this context.

DESIGN

A qualitative ethnographic approach using field observations and documentary analysis.

PARTICIPANTS

Attending physicians, medical residents, and medical students on general medicine services in a single teaching hospital.

APPROACH

Thirty-five hours of observations were undertaken over a 4-month period. We purposively approached a diverse group of attendings who cover a range of clinical teaching experience, and obtained informed consent from all ward team members and observed patients. Thirty patient handoffs were observed across 5 ward teams with 45 team members. We conducted thematic analysis of researcher field notes and electronic health record documents using social cognitive theories to characterize the dynamic interactions occurring in the real clinical environment.

KEY RESULTS

Teams spent less time during ward rounds on verifying history and physical examination findings, performing bedside evaluations, and discussing differential diagnoses than other aspects (e.g., reviewing patient data in conference rooms) in the team handoff process of overnight admissions. Several team-based approaches to diagnosis and bedside patient evaluations were observed, including debriefing for learning and decision-making.

CONCLUSIONS

This study highlights potential strengths and missed opportunities for teaching, learning, and engaging directly with patients in the ward team handoff of patients admitted overnight. These findings may inform curriculum development, faculty training, and patient safety research.

The SBAR tool for communication and patient safety in gynaecology and obstetrics: a Tunisian pilot study

BACKGROUND

In healthcare, inadequate communication among providers and insufficient information transmission represent primary contributors to adverse events, particularly in medical specialties such as obstetrics and gynecology. The implementation of SBAR (Situation-Background-Assessment-Recommendation) has been proposed as a standardized communication tool to enhance patient safety. This study aims to evaluate the knowledge, attitudes, and practices related to SBAR communication through a pilot study conducted in a middle-income country.

METHODS

This prospective longitudinal study took place in the gynecology-obstetrics department of a Tunisian university hospital from May to June 2019. All medical and paramedical staff underwent comprehensive theoretical and practical training through a 4-hour SBAR simulation. To gauge participants' knowledge, anonymous multiple-choice questionnaires were administered before the training initiation, with a second assessment conducted at the end of the training to measure satisfaction levels. Two months later, the evaluation utilized questionnaires validated by the French National Authority for Health (HAS).

RESULTS

Among the 62 care staff participants in this study, a majority (89%) demonstrated a low level of knowledge regarding the SBAR tool. The majority (75.8%) expressed enjoyment with the training and indicated their intention to implement changes in their practice by incorporating the SBAR tool in the future (80.7%). Notably, over half of the participants (79%) expressed satisfaction with the training objectives, and 74% reported acquiring new information. Evaluation of the practice revealed positive feedback, particularly in terms of clarity, the relevance of communication, and the time spent on the call.

CONCLUSION

Our pilot study showed that the majority of professionals on the ward had little knowledge of the SBAR tool, a good attitude and a willingness to put it into practice. It is essential that healthcare managers and professionals from all disciplines work together to ensure that good communication practice is developed and maintained. Organisations, including universities and hospitals, need to invest in the education and training of students and health professionals to ensure good quality standardised communication.

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.

Fatigue and fallibility: the perils of prolonged shifts for neonatologists

Sleep deprivation is a major challenge for neonatologists, who face increasing demands in the complex healthcare system. Current neonatal intensive care unit (NICU) schedule models often include extended shifts and overnight call, which can lead to sleep deprivation. This lack of sufficient sleep poses adverse health risks to neonatologists and can impair cognitive function, which increases the risk of medical errors and compromises patient safety. This paper proposes reducing shift durations and implementing policies and interventions to reduce fatigue among neonatologists and improve patient safety. The paper also offers policymakers, healthcare leaders, and NICU physicians valuable insights on potential ways to promote the health of the neonatologist workforce and safety in the NICU.

Diagnostic error among vulnerable populations presenting to the emergency department with cardiovascular and cerebrovascular or neurological symptoms: a systematic review

BACKGROUND

Diagnostic error (DE) is a common problem in clinical practice, particularly in the emergency department (ED) setting. Among ED patients presenting with cardiovascular or cerebrovascular/neurological symptoms, a delay in diagnosis or failure to hospitalise may be most impactful in terms of adverse outcomes. Minorities and other vulnerable populations may be at higher risk of DE. We aimed to systematically review studies reporting the frequency and causes of DE in under-resourced patients presenting to the ED with cardiovascular or cerebrovascular/neurological symptoms.

METHODS

We searched EBM Reviews, Embase, Medline, Scopus and Web of Science from 2000 through 14 August 2022. Data were abstracted by two independent reviewers using a standardised form. The risk of bias (ROB) was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach.

RESULTS

Of the 7342 studies screened, we included 20 studies evaluating 7436,737 patients. Most studies were conducted in the USA, and one study was multicountry. 11 studies evaluated DE in patients with cerebrovascular/neurological symptoms, 8 studies with cardiovascular symptoms and 1 study examined both types of symptoms. 13 studies investigated missed diagnoses and 7 studies explored delayed diagnoses. There was significant clinical and methodological variability, including heterogeneity of DE definitions and predictor variable definitions as well as methods of DE assessment, study design and reporting.Among the studies evaluating cardiovascular symptoms, black race was significantly associated with higher odds of DE in 4/6 studies evaluating missed acute myocardial infarction (AMI)/acute coronary syndrome (ACS) diagnosis compared with white race (OR from 1.18 (1.12-1.24) to 4.5 (1.8-11.8)). The association between other analysed factors (ethnicity, insurance and limited English proficiency) and DE in this domain varied from study to study and was inconclusive.Among the studies evaluating DE in patients with cerebrovascular/neurological symptoms, no consistent association was found indicating higher or lower odds of DE. Although some studies showed significant differences, these were not consistently in the same direction.The overall ROB was low for most included studies; however, the certainty of evidence was very low, mostly due to serious inconsistency in definitions and measurement approaches across studies.

CONCLUSIONS

This systematic review demonstrated consistent increased odds of missed AMI/ACS diagnosis among black patients presenting to the ED compared with white patients in most studies. No consistent associations between demographic groups and DE related to cerebrovascular/neurological diagnoses were identified. More standardised approaches to study design, measurement of DE and outcomes assessment are needed to understand this problem among vulnerable populations.

TRIAL REGISTRATION NUMBER

The study protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO 2020 CRD42020178885 and is available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178885.

Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach

Objective

To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients.

Materials and Methods

Three interventions were prioritized for development: a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. Initial requirements were refined from analysis of test cases with elevated risk predicted by DSC logic compared to risk perceived by a clinician working group; DTO testing sessions with clinicians; PDQ responses from patients; and focus groups with clinicians and patient advisors using storyboarding to model the integrated interventions. Mixed methods analysis of participant responses was used to identify final requirements and potential implementation barriers.

Results

Final requirements from analysis of 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses included the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. Analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team's diagnosis (PDQ).

Discussion

A user-centered approach led to evolution of requirements for 3 interventions targeting key diagnostic process failures in hospitalized patients at risk for DE.

Conclusions

We identify challenges and offer lessons from our user-centered design process.

Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19

BACKGROUND

The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs).

OBJECTIVE

To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19.

DESIGN

Retrospective cohort.

SETTING

Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN).

TARGET POPULATION

Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020.

MEASUREMENTS

We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs.

RESULTS

Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error.

LIMITATIONS

Results are limited by available documentation and do not capture communication between providers and patients.

CONCLUSION

Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation.

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.

Ear, Nose and Throat (ENT) disease diagnostic error in low-resource health care: Observations from a hospital-based cross-sectional study

Although the global burden of ear, nose and throat (ENT) diseases is high, data relating to ENT disease epidemiology and diagnostic error in resource-limited settings remain scarce. We conducted a retrospective cross-sectional review of ENT patients' clinical records at a resource-limited tertiary hospital. We determined the diagnostic accuracy and appropriateness of patient referrals for ENT specialist care using descriptive statistics. Cohens kappa coefficient (κ) was calculated to determine the diagnostic agreement between non-ENT clinicians and the ENT specialist, and logistic regression applied to establish the likelihood of patient misdiagnosis by non-ENT clinicians. Of the 1543 patients studied [age 0-87 years, mean age 25(21) years (mean(SD)], non-ENT clinicians misdiagnosed 67.4% and inappropriately referred 50.4%. Compared to those aged 0-5 years, patients aged 51-87 years were 1.77 (95%CI: 1.03-3.04) fold more likely to have a referral misdiagnosis for specialist care. Patients with ear (aOR: 1.63; 95% CI: 1.14-2.33) and those with sinonasal diseases (aOR: 1.80; 95% CI: 1.14-2.45) had greater likelihood of referral misdiagnosis than those with head and neck diseases. Agreement in diagnosis between the ENT specialist and non-ENT clinicians was poor (κ = 0.0001). More effective, accelerated training of clinicians may improve diagnostic accuracy in low-resource settings.

Survey of Inpatient Consultations with General Internal Medicine Physicians in a Tertiary Hospital: A Retrospective Observational Study

Purpose

The general internal medicine (GIM) department can be an effective diagnostic coordinator for undiagnosed outpatients. We investigated the contribution of GIM consultations to the diagnosis of patients admitted to specialty departments in hospitals in Japan that have not yet adopted a hospitalist system.

Patients and Methods

This single-center, retrospective observational study was conducted at a university hospital in Japan. GIM consultations from other departments on inpatients aged ≥20 years, from April 2016 to March 2021, were included. Data were extracted from electronic medical records, and consultation purposes were categorized into diagnosis, treatment, and diagnosis and treatment. The primary outcome was new diagnosis during hospitalization for patients with consultation purpose of diagnosis or diagnosis and treatment. The secondary outcomes were the purposes of consultation with the Diagnostic and Generalist Medicine department.

Results

In total, 342 patients were included in the analysis. The purpose of the consultations was diagnosis for 253 patients (74%), treatment for 60 (17.5%), and diagnosis and treatment for 29 patients (8.5%). In 282 consultations for diagnosis and diagnosis and treatment, 179 new diagnoses were established for 162 patients (57.5%, 95% confidence interval [CI], 51.5-63.3).

Conclusion

The GIM department can function as a diagnostic consultant for inpatients with diagnostic problems admitted to other specialty departments in hospitals where hospitalist or other similar systems are not adopted.

Effects of Elaboration and Instructor Feedback on Retention of Clinical Reasoning Competence Among Undergraduate Medical Students: A Randomized Crossover Trial

This randomized crossover trial examines whether elaboration on common errors in patient treatment, combined with individualized mailed feedback, improves medium-term retention of clinical reasoning competence.

A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts

OBJECTIVES

To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care.

METHODS

We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution's mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort.

RESULTS

Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen's kappa 0.41) and substantial with the expert panel (Cohen's kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively.

CONCLUSIONS

We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

Frequency of diagnostic errors in the neonatal intensive care unit: a retrospective cohort study

OBJECTIVE

To determine the frequency and etiology of diagnostic errors during the first 7 days of admission for inborn neonatal intensive care unit (NICU) patients.

STUDY DESIGN

We conducted a retrospective cohort study of 600 consecutive inborn admissions. A physician used the "Safer Dx NICU Instrument" to review the electronic health record for the first 7 days of admission, and categorized cases as "yes," "unclear," or "no" for diagnostic error. A secondary reviewer evaluated all "yes" charts plus a random sample of charts in the other categories. Subsequently, all secondary reviewers reviewed records with discordance between primary and secondary review to arrive at consensus.

RESULTS

We identified 37 diagnostic errors (6.2% of study patients) with "substantial agreement" between reviewers (κ = 0.66). The most common diagnostic process breakdown was missed maternal history (51%).

CONCLUSION

The frequency of diagnostic error in inborn NICU patients during the first 7 days of admission is 6.2%.

Measure Dx: Implementing pathways to discover and learn from diagnostic errors

Despite the high frequency of diagnostic errors, multiple barriers, including measurement, make it difficult learn from these events. This article discusses Measure Dx, a new resource from the Agency for Healthcare Research and Quality that translates knowledge from diagnostic safety measurement research into actionable recommendations. Measure Dx guides healthcare organizations to detect, analyze, and learn from diagnostic safety events as part of a continuous learning and feedback cycle. Wider adoption of Measure Dx, along with the implementation of solutions that result, can advance new frontiers in reducing preventable diagnostic harm to patients.

Clinical Reasoning in Cardiology: Past, Present and Future

Clinical reasoning was born 2,500 years ago with Hippocrates, having evolved over the centuries, becoming a mixture of art and science. Several personalities throughout history have contributed to improving diagnostic accuracy. Nonetheless, diagnostic error is still common and causes a severe impact on healthcare systems. To face this challenge, several clinical reasoning models have emerged to systematize the clinical thinking process. This paper describes the history of clinical reasoning and current diagnostic reasoning methods, proposes a new clinical reasoning model, called Integrative Reasoning, and brings perspectives about the future of clinical reasoning.

Does a suggested diagnosis in a general practitioners' referral question impact diagnostic reasoning: an experimental study

BACKGROUND

Diagnostic errors are a major cause of preventable patient harm. Studies suggest that presenting inaccurate diagnostic suggestions can cause errors in physicians' diagnostic reasoning processes. It is common practice for general practitioners (GPs) to suggest a diagnosis when referring a patient to secondary care. However, it remains unclear via which underlying processes this practice can impact diagnostic performance. This study therefore examined the effect of a diagnostic suggestion in a GP's referral letter to the emergency department on the diagnostic performance of medical interns.

METHODS

Medical interns diagnosed six clinical cases formatted as GP referral letters in a randomized within-subjects experiment. They diagnosed two referral letters stating a main complaint without a diagnostic suggestion (control), two stating a correct suggestion, and two stating an incorrect suggestion. The referral question and case order were randomized. We analysed the effect of the referral question on interns' diagnostic accuracy, number of differential diagnoses, confidence, and time taken to diagnose.

RESULTS

Forty-four medical interns participated. Interns considered more diagnoses in their differential without a suggested diagnosis (M = 1.85, SD = 1.09) than with a suggested diagnosis, independent of whether this suggestion was correct (M = 1.52, SD = 0.96, d = 0.32) or incorrect ((M = 1.42, SD = 0.97, d = 0.41), χ2(2) =7.6, p = 0.022). The diagnostic suggestion did not influence diagnostic accuracy (χ2(2) = 1.446, p = 0.486), confidence, (χ2(2) = 0.058, p = 0.971) or time to diagnose (χ2(2) = 3.128, p = 0.209).

CONCLUSIONS

A diagnostic suggestion in a GPs referral letter did not influence subsequent diagnostic accuracy, confidence, or time to diagnose for medical interns. However, a correct or incorrect suggestion reduced the number of diagnoses considered. It is important for healthcare providers and teachers to be aware of this phenomenon, as fostering a broad differential could support learning. Future research is necessary to examine whether these findings generalize to other healthcare workers, such as more experienced specialists or triage nurses, whose decisions might affect the diagnostic process later on.

TRIAL REGISTRATION

The study protocol was preregistered and is available online at Open Science Framework ( https://osf.io/7de5g ).

Bedside Clinicians' Perceptions on the Contributing Role of Diagnostic Errors in Acutely Ill Patient Presentation: A Survey of Academic and Community Practice

OBJECTIVES

This study aimed to explore clinicians' perceptions of the occurrence of and factors associated with diagnostic errors in patients evaluated during a rapid response team (RRT) activation or unplanned admission to the intensive care unit (ICU).

METHODS

A multicenter prospective survey study was conducted among multiprofessional clinicians involved in the care of patients with RRT activations and/or unplanned ICU admissions (UIAs) at 2 academic hospitals and 1 community-based hospital between April 2019 and March 2020. A study investigator screened eligible patients every day. Within 24 hours of the event, a research coordinator administered the survey to clinicians, who were asked the following: whether diagnostic errors contributed to the reason for RRT/UIA, whether any new diagnosis was made after RRT/UIA, if there were any failures to communicate the diagnosis, and if involvement of specialists earlier would have benefited that patient. Patient clinical data were extracted from the electronic health record.

RESULTS

A total of 1815 patients experienced RRT activations, and 1024 patients experienced UIA. Clinicians reported that 18.2% (95/522) of patients experienced diagnostic errors, 8.0% (42/522) experienced a failure of communication, and 16.7% (87/522) may have benefitted from earlier involvement of specialists. Compared with academic settings, clinicians in the community hospital were less likely to report diagnostic errors (7.0% versus 22.8%, P = 0.002).

CONCLUSIONS

Clinicians report a high rate of diagnostic errors in patients they evaluate during RRT or UIAs.

Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic

INTRODUCTION

COVID-19 exposed systemic gaps with increased potential for diagnostic error. This project implemented a new approach leveraging electronic safety reporting to identify and categorize diagnostic errors during the pandemic.

METHODS

All safety event reports from March 1, 2020, to February 28, 2021, at an academic medical center were evaluated using two complementary pathways (Pathway 1: all reports with explicit mention of COVID-19; Pathway 2: all reports without explicit mention of COVID-19 where natural language processing [NLP] plus logic-based stratification was applied to identify potential cases). Cases were evaluated by manual review to identify diagnostic error/delay and categorize error type using a recently proposed classification framework of eight categories of pandemic-related diagnostic errors.

RESULTS

A total of 14,230 reports were included, with 95 (0.7%) identified as cases of diagnostic error/delay. Pathway 1 (n = 1,780 eligible reports) yielded 45 reports with diagnostic error/delay (positive predictive value [PPV] = 2.5%), of which 35.6% (16/45) were attributed to pandemic-related strain. In Pathway 2, the NLP-based algorithm flagged 110 safety reports for manual review from 12,450 eligible reports. Of these, 50 reports had diagnostic error/delay (PPV = 45.5%); 94.0% (47/50) were related to strain. Errors from all eight categories of the taxonomy were found on analysis.

CONCLUSION

An event reporting-based strategy including use of simple-NLP-identified COVID-19-related diagnostic errors/delays uncovered several safety concerns related to COVID-19. An NLP-based approach can complement traditional reporting and be used as a just-in-time monitoring system to enable early detection of emerging risks from large volumes of safety reports.

Incidence, origins and avoidable harm of missed opportunities in diagnosis: longitudinal patient record review in 21 English general practices

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.

Diagnostic Error in the Critically Ill: A Hidden Epidemic?

Epidemiologic studies of diagnostic error in the intensive care unit (ICU) consist mostly of descriptive autopsy series. In these studies, rates of diagnostic errors are approximately 5% to 10%. Recently validated methods for retrospectively measuring error have expanded our understanding of the scope of the problem. These alternative measurement strategies have yielded similar estimates for the frequency of diagnostic error in the ICU. Although there is a fair understanding of the frequency of errors, further research is needed to better define the risk factors for diagnostic error in the ICU.