The incidence of diagnostic error in medicine

A change of mind: Error motivation is shaped by error perceptions in different learning environments

BACKGROUND

The medical profession has traditionally had a culture of "blame and shame," despite the importance errors have for learning, motivation, and improvement of clinical skills. This study aimed to explore how medical students and newly graduated doctors perceive errors across different learning contexts and levels of expertise, and how error perceptions influence motivation and engagement in learning activities.

METHODS

Semi-structured interviews were conducted, and thematic analysis was used to identify themes. Survey questions were developed based on the themes. The final survey included 27 questions divided into three sections. The survey was distributed via RedCap to medical students in year 1, year 4, and year 6 together with newly graduated doctors in Denmark.

RESULTS

Of the 541 respondents, a majority anticipated making errors in both non-clinical (77.4%) and clinical (61.5%) learning situations, finding them motivating for participation (91.9% and 96.2%, respectively). A psychologically safe learning environment was seen to enhance the perception of errors as learning opportunities (96.9%) and increase risk willingness (96.3% non-clinical, 97.7% clinical). Respondents focused on the specific errors they made (86.0%), and their supervisors were perceived to focus more on correct handling of errors (70.7%). Respondents expected to conduct fewer errors in non-clinical learning situations in PGY-1 compared to medical students in year 1 (F(3529) = 3.0, adjusted p = 0.03). They showed an increase in risk willingness in clinical learning situations from year 6 to PGY-1 (F(3520) = 2.7, adjusted p-value 0.006) as long as the learning situations were considered psychologically safe.

CONCLUSION

The study suggests that a psychologically safe learning environment mitigates the "shame and blame culture" associated with errors. Respondents generally embraced errors as valuable learning experiences but noted a lack of specific error-related feedback. These findings underscore the nuanced relationship between errors, explicit learning activities, and supervisor support in medical education.

Analysis of the accuracy of clinical diagnosis in an internal medicine department of a regional hospital: Inter-MONF study

INTRODUCTION

There are numerous studies examining the diagnostic accuracy of various supplementary tests; however, the literature focused on diagnostic accuracy derived from clinical reasoning and data is limited. Consequently, we conducted a study to assess the diagnostic accuracy of the professionals in the Internal Medicine Department at our hospital and to examine whether there are variations in accuracy related to specific pathologies and across different time periods, particularly before and after the emergence of the SARS-CoV-2 pandemic.

METHODS

This is a retrospective, longitudinal, and observational study conducted in the Internal Medicine Department of the Regional Hospital of Monforte de Lemos from 2016 to 2022, encompassing both pre- and post-SARS-CoV-2 pandemic periods. The initial diagnosis made upon patient admission was compared with the final diagnosis at discharge through an independent peer review process.

RESULTS

The diagnostic concordance at admission and discharge was 77.4%, with statistically significant differences observed between age groups (with higher concordance in patients under 55 years of age) and according to sex, with greater concordance in female patients. No differences were found regarding pathology type or temporal cohort.

CONCLUSIONS

The diagnostic accuracy of the healthcare professionals in the Internal Medicine Department at Monforte Public Hospital during the study periods was found to be high. Diagnostic concordance was greater in female patients and those under 55 years of age, with no significant differences observed across the most prevalent pathological conditions. Furthermore, the restrictive measures implemented during the SARS-CoV-2 pandemic do not appear to have negative affected diagnostic accuracy when compared to previous periods.

Trends of diagnostic adverse events in hospital deaths: longitudinal analyses of four retrospective record review studies

OBJECTIVES

To investigate longitudinal trends in the incidence, preventability, and causes of DAEs (diagnostic adverse events) between 2008 and 2019 and compare DAEs to other AE (adverse event) types.

METHODS

This study investigated longitudinal trends of DAEs using combined data from four large Dutch AE record review studies. The original four AE studies included 100-150 randomly selected records of deceased patients from around 20 hospitals in each study, resulting in a total of 10,943 patient records. Nurse reviewers indicated cases with potential AEs using a list of triggers. Subsequently, experienced physician reviewers systematically judged the occurrence of AEs, the clinical process in which these AEs occurred, and the preventability and causes.

RESULTS

The incidences of DAEs, potentially preventable DAEs and potentially preventable DAE-related deaths initially declined between 2008 and 2012 (2.3 vs. 1.2; OR=0.52, 95 % CI: 0.32 to 0.83), after which they stabilized up to 2019. These trends were largely the same for other AE types, although compared to DAEs, the incidence of other AE types increased between 2016 (DAE: 1.0, other AE types: 8.5) and 2019 (DAE: 0.8, other AE types: 13.0; rate ratio=1.88, 95 % CI: 1.12 to 2.13). Furthermore, DAEs were more preventable (p<0.001) and were associated with more potentially preventable deaths (p=0.016) than other AE types. In addition, DAEs had more and different underlying causes than other AE types (p<0.001). The DAE causes remained stable over time, except for patient-related factors, which increased between 2016 and 2019 (29.5 and 58.6 % respectively, OR=3.40, 95 % CI: 1.20 to 9.66).

CONCLUSIONS

After initial improvements of DAE incidences in 2012, no further improvement was observed in Dutch hospitals in the last decade. Similar trends were observed for other AEs. The high rate of preventability of DAEs suggest a high potential for improvement, that should be further investigated.

Harbingers of sepsis misdiagnosis among pediatric emergency department patients

OBJECTIVES

To identify clinical presentations that acted as harbingers for future sepsis hospitalizations in pediatric patients evaluated in the emergency department (ED) using the Symptom Disease Pair Analysis of Diagnostic Error (SPADE) methodology.

METHODS

We identified patients in the Pediatric Health Information Systems (PHIS) database admitted for sepsis between January 1, 2004 and December 31, 2023 and limited the study cohort to those patients who had an ED treat-and-release visit in the 30 days prior to admission. Using the look-back approach of the SPADE methodology, we identified the most common clinical presentations at the initial ED visit and used an observed to expected (O:E) analysis to determine which presentations were overrepresented. We then employed a graphical, temporal analysis with a comparison group to identify which overrepresented presentations most likely represented harbingers for future sepsis hospitalization.

RESULTS

We identified 184,157 inpatient admissions for sepsis, of which 15,331 hospitalizations (8.3 %) were preceded by a treat-and-release ED visit in the prior 30 days. Based on the O:E and temporal analyses, the presentations of fever and dehydration were both overrepresented in the study cohort and temporally clustered close to sepsis hospitalization. ED treat-and-release visits for fever or dehydration preceded 1.2 % of all sepsis admissions.

CONCLUSIONS

In pediatric patients presenting to the ED, fever and dehydration may represent harbingers for future sepsis hospitalization. The SPADE methodology could be applied to the PHIS database to develop diagnostic performance measures across a wide range of pediatric hospitals.

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.

Service delay in schizophrenia: case-control study of pathways to care among homeless and non-homeless patients

BACKGROUND

Early detection of psychosis is paramount for reducing the duration of untreated psychosis (DUP). One key factor contributing to extended DUP is service delay - the time from initial contact with psychiatric services to diagnosis. Reducing service delay depends on prompt identification of psychosis. Patients with schizophrenia and severe social impairment have been found to have prolonged DUP. Whether service delay significantly contributes to prolonged DUP in this group is unclear.

AIM

To examine and compare the course of illness for patients with schizophrenia who are homeless or domiciled, with a focus on service delay in detecting psychosis.

METHOD

In this case-control study, we included out-patients with a schizophrenia spectrum diagnosis and who were homeless or domiciled but in need of an outreach team to secure continuous treatment. Interviews included psychosocial history and psychopathological and social functioning scales.

RESULTS

We included 85 patients with schizophrenia spectrum disorder. Mean service delay was significantly longer in the homeless group (5.5 years) compared with the domiciled group (2.5 years, P = 0.001), with a total sample mean of 3.9 years. Similarly, DUP was significantly longer in the homeless group, mean 15.5 years, versus 5.0 years in the domiciled group (P < 0.001). Furthermore, the homeless group had an earlier onset of illness than the domiciled group but were almost the same age at diagnosis.

CONCLUSIONS

Our findings point to the concerning circumstance that individuals with considerable risk of developing severe schizophrenia experience a substantial delay in diagnosis and do not receive timely treatment.

Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

Concerns about the trustworthiness, fairness, and privacy of AI systems are growing, and strategies for mitigating these concerns are still in their infancy. One approach to improve trustworthiness and fairness in AI systems is to use bias mitigation algorithms. However, most bias mitigation algorithms require data sets that contain sensitive attribute values to assess the fairness of the algorithm. A growing number of real world data sets do not make sensitive attribute information readily available to researchers. One solution is to infer the missing sensitive attribute information and apply an existing bias mitigation algorithm using this inferred knowledge. While researchers are beginning to explore this question, it is still unclear how robust existing bias mitigation algorithms are to different levels of inference accuracy. This paper explores this question by investigating the impact of different levels of accuracy of the inferred sensitive attribute on the performance of different bias mitigation strategies. We generate variation in sensitive attribute accuracy using both simulation and construction of neural models for the inference task. We then assess the quality of six bias mitigation algorithms that are deployed across different parts of our learning life cycle: pre-processing, in-processing, and post-processing. We find that the disparate impact remover is the least sensitive bias mitigation strategy and that if we apply the bias mitigation algorithms using an inferred sensitive attribute with reasonable accuracy, the fairness scores are higher than the best standard model and the balanced accuracy is similar to that of the standard model. These findings open the door for improving fairness of black box AI systems using some bias mitigation strategies.

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.

A generalist medical language model for disease diagnosis assistance

The delivery of accurate diagnoses is crucial in healthcare and represents the gateway to appropriate and timely treatment. Although recent large language models (LLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning, their effectiveness in clinical diagnosis remains unproven. Here we present MedFound, a generalist medical language model with 176 billion parameters, pre-trained on a large-scale corpus derived from diverse medical text and real-world clinical records. We further fine-tuned MedFound to learn physicians' inferential diagnosis with a self-bootstrapping strategy-based chain-of-thought approach and introduced a unified preference alignment framework to align it with standard clinical practice. Extensive experiments demonstrate that our medical LLM outperforms other baseline LLMs and specialized models in in-distribution (common diseases), out-of-distribution (external validation) and long-tailed distribution (rare diseases) scenarios across eight specialties. Further ablation studies indicate the effectiveness of key components in our medical LLM training approach. We conducted a comprehensive evaluation of the clinical applicability of LLMs for diagnosis involving artificial intelligence (AI) versus physician comparison, AI-assistance study and human evaluation framework. Our proposed framework incorporates eight clinical evaluation metrics, covering capabilities such as medical record summarization, diagnostic reasoning and risk management. Our findings demonstrate the model's feasibility in assisting physicians with disease diagnosis as part of the clinical workflow.

Diagnostic reasoning and cognitive error in emergency medicine: Implications for teaching and learning

BACKGROUND

Accurate diagnosis in emergency medicine (EM) is high stakes and challenging. Research into physicians' clinical reasoning has been ongoing since the late 1970s. The dual-process theory has established itself as a valid model, including in EM. It is based on the distinction between two information-processing systems. System 1 rapidly generates one or more diagnostic hypotheses almost instantaneously, driven by experiential knowledge, while System 2 proceeds more slowly and analytically, applying formal rules to arrive at a final diagnosis.

METHODS

We reviewed the literature on dual-process theory in the fields of cognitive science, medical education and emergency medicine.

RESULTS AND CONCLUSION

The literature reflects two prominent interpretations regarding the relationship between the fast and slow phases and these interpretations carry very different implications for the training of clinical learners. One interpretation, prominent in the EM community, presents it as a "check-and-balance" framework in which most diagnostic error is caused by cognitive biases originating within System 1. As a result, EM residents are frequently advised to deploy analytical (System 2) strategies to correct such biases. However, such teaching approaches are not supported by research into the nature of diagnostic reasoning. An alternative interpretation assumes a harmonious relationship between Systems 1 and 2 in which both fast and slow processes are driven by underlying knowledge that conditions performance and the occurrence of errors. Educational strategies corresponding to this alternative have not been explored in the EM literature. In this paper, we offer proposals for improving the teaching and learning of diagnostic reasoning by EM residents.

Rheum for Improvement? Delayed Diagnosis of Juvenile Idiopathic Arthritis: A Narrative Review

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and a disease for which we have safe and effective therapies. Early diagnosis of JIA enables timely initiation of therapy and improves long-term disease outcomes. However, many patients with JIA experience prolonged diagnostic delays and have a turbulent course to diagnosis. In this narrative review, we explore the importance of early diagnosis in JIA, what is known about time to diagnosis and diagnostic trajectory, and factors that contribute to delayed diagnosis. We also discuss next steps to improve time to diagnosis for these vulnerable patients.

Adolescent and parental proxy online record access: analysis of the empirical evidence based on four bioethical principles

BACKGROUND

During recent decades, providing patients with access to their electronic health records (EHRs) has advanced in healthcare. In the European Union (EU), the General Data Protection Regulation provides individuals with the right to check their data in registries such as EHRs. A proposal for a European Health Data Space has been launched, which will further strengthen patients' right to have online access to their EHRs throughout Europe. Against these policy changes, scant attention has been paid to the ethical question about whether adolescents and parents should access the adolescent's EHR, and if so, under what conditions.

METHODS

In this paper, we apply biomedical ethical principles to explore key questions about adolescents' and parents' access to adolescents' EHRs, with the aim of informing future discussions about the development of ethical and policy practice guidelines.

RESULTS

Drawing on current empirical research, we find preliminary evidence that in some contexts, patient online record access (ORA) could help to facilitate autonomy for adolescents and parents as well as offering support in managing appointments and medications. Notably, however, we find contrasting perspectives between adolescents' and parents' experienced benefits and healthcare professionals' (HCPs) perceived potential harm, with the latter worried about decreased documentation quality after access. Concerns about capacity to understand their health information, and increased anxiety among adolescents obstruct the support of adolescent autonomy among parents and HCPs. Still, research is limited, particularly with respect to adolescents' experiences of reading their EHRs, and differences across settings have not been closely examined.

CONCLUSIONS

To advance more comprehensive understanding of the effects of ORA, and to inspire greater attention to, and development of, evidence-informed ethical guidance in this domain of clinical practice, we outline a range of empirical questions regarding adolescents' and parents' experiences that now warrant further study.

Implementation of Electronic Triggers to Identify Diagnostic Errors in Emergency Departments

Importance

Missed diagnosis can lead to preventable patient harm.

Objective

To develop and implement a portfolio of electronic triggers (e-triggers) and examine their performance for identifying missed opportunities in diagnosis (MODs) in emergency departments (EDs).

Design, Setting, and Participants

In this retrospective medical record review study of ED visits at 1321 Veterans Affairs health care sites, rules-based e-triggers were developed and implemented using a national electronic health record repository. These e-triggers targeted 6 high-risk presentations for MODs in treat-and-release ED visits. A high-risk stroke e-trigger was applied to treat-and-release ED visits from January 1, 2016, to December 31, 2020. A symptom-disease dyad e-trigger was applied to visits from January 1, 2018, to December 31, 2019. High-risk abdominal pain, unexpected ED return, unexpected hospital return, and test result e-triggers were applied to visits from January 1, 2019, to December 31, 2019. At least 100 randomly selected flagged records were reviewed by physician reviewers for each e-trigger. Data were analyzed between January 2024 and April 2024.

Exposures

Treat-and-release ED visits involving high-risk stroke, symptom-disease dyads, high-risk abdominal pain, unexpected ED return, unexpected hospital return, and abnormal test results not followed up after initial ED visit.

Main Outcomes and Measures

Trained physician reviewers evaluated the presence/absence of MODs at ED visits and recorded data on patient and clinician characteristics, types of diagnostic process breakdowns, and potential harm from MODs.

Results

The high-risk stroke e-trigger was applied to 8 792 672 treat-and-release ED visits (4 967 283 unique patients); the symptom-disease dyad e-trigger was applied to 3 692 454 visits (2 070 979 patients); and high-risk abdominal pain, unexpected ED return, unexpected hospital return, and test result e-triggers were applied to 1 845 905 visits (1 032 969 patients), overall identifying 203, 1981, 170, 116 785, 14 879, and 2090 trigger-positive records, respectively. Review of 625 randomly selected patient records (mean [SD] age, 62.5 [15.2] years; 553 [88.5%] male) showed the following MOD counts and positive predictive values (PPVs) within each category: 47 MODs (PPV, 47.0%) for stroke, 31 MODs (PPV, 25.8%) for abdominal pain, 11 MODs (PPV, 11.0%) for ED returns, 23 MODs (PPV, 23.0%) for hospital returns, 18 MODs (PPV, 18.0%) for symptom-disease dyads, and 55 MODs (PPV, 52.4%) for test results. Patients with MODs were slightly older than those without (mean [SD] age, 65.6 [14.5] vs 61.2 [15.3] years; P < .001). Reviewer agreement was favorable (range, 72%-100%). In 108 of 130 MODs (83.1%; excluding MODs related to the test result e-trigger), the most common diagnostic process breakdown involved the patient-clinician encounter. In 185 total MODs, 20 patients experienced severe harm (10.8%), and 54 patients experienced moderate harm (29.2%).

Conclusions and Relevance

In this retrospective medical record review study, rules-based e-triggers were useful for post hoc detection of MODs in ED visits. Interventions to target ED work system factors are urgently needed to support patient-clinician encounters and minimize harm from diagnostic errors.

Great Mimics in Oncology: A Retrospective Study from a Tertiary Care Centre of Eastern India

A number of benign diseases can masquerade as malignancy leading to unnecessary treatment. Vice versa, many benign-looking tumours when operated turns out to be malignant. While the latter necessitates extra surgery for oncological clearance, the former directly harms the patient impacting their lives seriously. Data pertaining to such "misdiagnosis" is scarce and there is an urgent need to document such cases to prevent public harm. We carried out a retrospective study to identify characteristic of such cases which were actually benign but operated upon with a diagnosis of malignancy. This is a retrospective study done at the Department of Surgical Oncology, Institute of Post Graduate Medical Education & Research (I.P.G.M.E&R). Databases from January 2022 to August 2023 were searched for patients who were initially diagnosed as cases of malignancy but later turned out to be benign. Demographic and clinicopathological data were retrieved and analysed. Out of 345 major cases, 18 cases were misdiagnosed as cancer. Three cases mimicked breast lump, two cases misdiagnosed as lymphoma, and one case each diagnosed as primary peritoneal carcinoma, carcinoma ovary, carcinoma gallbladder, and soft tissue tumour. Two cases turned out to be tuberculosis (TB), and one case was rare Castleman disease, while an unusual diagnosis of Ig4 disease was made on histopathology. Although mortality was zero, one patient had perioperative morbidity in the form of bleeding, post-op infection, and prolonged hospital stay while another patient received intraoperative brachytherapy unnecessarily. Out of 18 cases, ten cases had a preoperative cytology report suggestive of neoplasm, in three cases the biopsy/fine needle aspiration cytology (FNAC) report was inconclusive, while five patients were diagnosed based solely on clinical and radiologic findings due to an inconclusive cytology report. A benign entity can mimic cancer almost anywhere in the body. Due to close clinical, radiologic, and cytological findings, such situations are not uncommon in day to day practice. High degree of suspicion, good interdisciplinary communication, and review of slides by an experienced cytopathologist can help prevent such misdiagnosis to a good extent.

Smart Imitator: Learning from Imperfect Clinical Decisions

OBJECTIVES

This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.

MATERIALS AND METHODS

Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL. Smart Imitator's effectiveness was validated on 2 datasets: a sepsis dataset with 19 711 patient trajectories and a diabetes dataset with 7234 trajectories.

RESULTS

Extensive quantitative and qualitative experiments showed that SI significantly outperformed state-of-the-art baselines in both datasets. For sepsis, SI reduced estimated mortality rates by 19.6% compared to the best baseline. For diabetes, SI reduced HbA1c-High rates by 12.2%. The learned policies aligned closely with successful clinical decisions and deviated strategically when necessary. These deviations aligned with recent clinical findings, suggesting improved outcomes.

DISCUSSION

Smart Imitator advances RL applications by addressing challenges such as imperfect data and environmental complexities, demonstrating effectiveness within the tested conditions of sepsis and diabetes. Further validation across diverse conditions and exploration of additional RL algorithms are needed to enhance precision and generalizability.

CONCLUSION

This study shows potential in advancing personalized healthcare learning from clinician behaviors to improve treatment outcomes. Its methodology offers a robust approach for adaptive, personalized strategies in various complex and uncertain environments.

Clinical reasoning in real-world practice: a primer for medical trainees and practitioners

Clinical reasoning is a crucial skill and defining characteristic of the medical profession, which relates to intricate cognitive and decision-making processes that are needed to solve real-world clinical problems. However, much of our current competency-based medical education systems have focused on imparting swathes of content knowledge and skills to our medical trainees, without an adequate emphasis on strengthening the cognitive schema and psychological processes that govern actual decision-making in clinical environments. Nonetheless, flawed clinical reasoning has serious repercussions on patient care, as it is associated with diagnostic errors, inappropriate investigations, and incongruent or suboptimal management plans that can result in significant morbidity and even mortality. In this article, we discuss the psychological constructs of clinical reasoning in the form of cognitive 'thought processing' models and real-world contextual or emotional influences on clinical decision-making. In addition, we propose practical strategies, including pedagogical development of a personal cognitive schema, mitigating strategies to combat cognitive bias and flawed reasoning, and emotional regulation and self-care techniques, which can be adopted in medical training to optimize physicians' clinical reasoning in real-world practice that effectively translates learnt knowledge and skill sets into good decisions and outcomes.

Adverse Events in Patients Transitioning From the Emergency Department to the Inpatient Setting

OBJECTIVES

The objective of this study was to determine the incidence and types of adverse events (AEs), including preventable and ameliorable AEs, in patients transitioning from the emergency department (ED) to the inpatient setting. A second objective was to examine the risk factors for patients with AEs.

METHODS

This was a prospective cohort study of patients at risk for AEs in 2 urban academic hospitals from August 2020 to January 2022. Eighty-one eligible patients who were being admitted to any internal medicine or hospitalist service were recruited from the ED of these hospitals by a trained nurse. The nurse conducted a structured interview during admission and referred possible AEs for adjudication. Two blinded trained physicians using a previously established methodology adjudicated AEs.

RESULTS

Over 22% of 81 patients experienced AEs from the ED to the inpatient setting. The most common AEs were adverse drug events (42%), followed by management (38%), and diagnostic errors (21%). Of these AEs, 75% were considered preventable. Patients who stayed in the ED longer were more likely to experience an AE (adjusted odds ratio = 1.99, 95% confidence interval = 1.19-3.32, P = 0.01).

CONCLUSIONS

AEs were common for patients transitioning from the ED to the inpatient setting. Further research is needed to understand the underlying causes of AEs that occur when patients transition from the ED to the inpatient setting. Understanding the contribution of factors such as length of stay in the ED will significantly help efforts to develop targeted interventions to improve this crucial transition of care.

Clinical vs. autopsy diagnostic discrepancies in the intensive care unit: a systematic review and meta-analysis of autopsy series

PURPOSE

The aim of this study was to assess whether there is a discrepancy between clinical and autopsy-based diagnoses in adult intensive care unit (ICU) patients.

METHODS

We conducted a systematic review of cohort studies reporting on conventional autopsy-confirmed missed diagnoses. The discrepancy rate was per study calculated by dividing the number of patients with a missed diagnosis by the number of autopsies. Missed diagnoses were classified according to the Goldman classification as 'major' and 'minor' with major missed diagnoses further differentiated into Class I missed diagnoses (i.e., diagnoses that may have altered therapy or survival) and Class II missed diagnoses (i.e., diagnoses that would not have altered therapy or survival). Class I missed diagnoses constitute the primary outcome of interest. Pooled estimates for discrepancy rates (95% confidence intervals) were calculated using a mixed-effects logistic regression model with 'study' as random effect. Meta-regression was used to assess relationships between major discrepancy rates and autopsy rates, start year of study, and ICU type.

RESULTS

Forty-two studies were identified totaling 6305 analyzed autopsies and 1759 patients with missed diagnoses. The pooled discrepancy rates for Class I and major missed diagnoses were 6.5% (5-8.5) and 19.3% (15.3-24), respectively. Meta-regression analysis revealed that autopsy rate was inversely associated with discrepancy rate. Class I discrepancy rates did not change over time. Burn and trauma ICUs had lower discrepancy rates as compared to medical ICUs, possibly because of higher autopsy rates.

CONCLUSIONS

Missed diagnoses remain common in ICUs. A higher autopsy rate does not reveal more major diagnostic errors. These data support a clinically driven autopsy policy rather than a systematic autopsy policy.

Role of communicating diagnostic uncertainty in the safety-netting process: insights from a vignette study

BACKGROUND

Safety-netting is intended to protect against harm from uncertainty in diagnosis/disease trajectory. Despite recommendations to communicate diagnostic uncertainty when safety-netting, this is not always done.

AIMS

To explore how and why doctors safety-netted in response to several clinical scenarios, within the broader context of exploring how doctors communicate diagnostic uncertainty.

METHODS

Doctors working in internal medical specialties (n=36) from five hospitals were given vignettes in a randomised order (all depicting different clinical scenarios involving diagnostic uncertainty). After reading each, they told an interviewer what they would tell a 'typical patient' in this situation. A follow-up semistructured interview explored reasons for their communication. Interviews were recorded, transcribed and coded. We examined how participants safety-netted using a content analysis approach, and why they safety-netting with thematic analysis of the semistructured follow-up interviews using thematic analysis.

RESULTS

We observed n=78 instances of safety-netting (across 108 vignette encounters). We found significant variation in how participants safety-netted. Safety-netting was common (although not universal), but clinicians differed in the detail provided about symptoms to be alert for, and the action advised. Although many viewed safety-netting as an important tool for managing diagnostic uncertainty, diagnostic uncertainty was infrequently explicitly discussed; most advised patients to return if symptoms worsened or new 'red flag' symptoms developed, but they rarely linked this directly to the possibility of diagnostic error. Some participants expressed concerns that communicating diagnostic uncertainty when safety-netting may cause anxiety for patients or could drive inappropriate reattendance/over-investigation.

CONCLUSIONS

Participants safety-netted variously, even when presented with identical clinical information. Although safety-netting was seen as important in avoiding diagnostic error, concerns about worrying patients may have limited discussion about diagnostic uncertainty. Research is needed to determine whether communicating diagnostic uncertainty makes safety-netting more effective at preventing harm associated with diagnostic error, and whether it causes significant patient anxiety.

[Clinical reasoning, the art of medicine and artificial intelligence]

"Clinical reasoning" refers to all the thought processes that physicians use to make a diagnosis and determine a treatment and care plan. Artificial intelligence (AI) will enhance, improve, and accelerate human clinical diagnostic thinking, but it is unlikely to replace it. Its application in medicine has the potential to drastically reduce medical diagnostic errors and give doctors more time to care for their patients. Here, we provide an overview of some of the key elements of clinical diagnostic reasoning and the potential impacts of AI on clinical reasoning.

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification

BACKGROUND

Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case.

METHODS

An EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed.

RESULTS

A total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32 %. There was no significant difference by clinic or provider characteristics.

CONCLUSION

Our work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them.

CLINICAL SIGNIFICANCE

This study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.

Exploring sleep duration and clinical reasoning process in resident physicians: a thematic analysis

STUDY OBJECTIVES

Connecting resident physician work hours and sleep deprivation to adverse outcomes has been difficult. Our study explores clinical reasoning rather than outcomes. Diagnostic errors are a leading cause of medical error and may result from deficits in clinical reasoning. We used simulated cases to explore relationships between sleep duration and diagnostic reasoning.

METHODS

Residents were recruited for a 2-month study (inpatient/outpatient). Each participant's sleep was tracked (sleep diary/actigraphy). At the end of each month, residents watched 2 brief simulated clinical encounters and performed "think alouds" of their clinical reasoning. In each session, 1 video was straightforward and the other video contained distracting contextual factors. Sessions were recorded, transcribed, and interpreted. We conducted a thematic analysis using a constant comparative approach. Themes were compared based on sleep duration and contextual factors.

RESULTS

Residents (n = 17) slept more during outpatient compared with inpatient months (450.5 ± 7.13 vs 425.6 ± 10.78 hours, P = .02). We found the following diagnostic reasoning themes: uncertainty, disorganized knowledge, error, semantic incompetence, emotional content, and organized knowledge. Themes reflecting suboptimal clinical reasoning (disorganized knowledge, error, semantic incompetence, uncertainty) were observed more in cases with contextual factors (distractors). "Think alouds" from cases with contextual factors following sleep restriction had a greater number of themes concerning for problematic diagnostic reasoning.

CONCLUSIONS

Residents obtained significantly more sleep during outpatient compared with inpatient months. Several negative clinical reasoning themes emerged with less sleep combined with cases containing contextual distractors. Our findings reinforce the importance of adequate sleep and supervision in house officers, particularly in cases with distracting elements.

CITATION

Collen J, Durning S, Berk J, Mang J, Alcover K, Jung E. Exploring sleep duration and clinical reasoning process in resident physicians: a thematic analysis. J Clin Sleep Med. 2024;20(8):1279-1289.

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.

Clinical reasoning-the essentials for teaching medical students, trainees and non-medical healthcare professionals

Clinical reasoning is fundamental for effective clinical practice. Traditional consultation models for teaching clinical reasoning or conventional approaches for teaching students how to make a diagnosis or management plan that rely on learning through observation only, are increasingly recognised as insufficient. There are also many challenges to supporting learners in developing clinical reasoning over time as well as across different clinical presentations and contexts. These challenges are compounded by the differences in how experts and novices make sense of clinical information, and the different cognitive processes each use when processing and communicating this information using precise medical language. Diagnostic errors may be due to cognitive biases but also, in a majority of cases, due to a lack of clinical knowledge. Therefore, effective educational strategies to develop clinical reasoning include identifying learners' knowledge gaps, using worked examples to prevent cognitive overload, promoting the use of key features and practising the construction of accurate problem representations. Deliberate reflection on diagnostic justification is also recommended, and overall, contributes to a growing number of evidence-based and theory-driven educational interventions for reducing diagnostic errors and improving patient care.

Diagnostic Errors in Obstetric Morbidity and Mortality: Methods for and Challenges in Seeking Diagnostic Excellence

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

Cognitive biases in pediatric cardiac care

Medical practitioners are entrusted with the pivotal task of making optimal decisions in healthcare delivery. Despite rigorous training, our confidence in reasoning can fail when faced with pressures, uncertainties, urgencies, difficulties, and occasional errors. Day-to-day decisions rely on swift, intuitive cognitive processes known as heuristic or type 1 decision-making, which, while efficient in most scenarios, harbor inherent vulnerabilities leading to systematic errors. Cognitive biases receive limited explicit discussion during our training as junior doctors in the domain of paediatric cardiology. As pediatric cardiologists, we frequently confront emergencies necessitating rapid decision-making, while contending with the pressures of stress, fatigue, an earnest interest in "doing the right thing" and the impact of parental involvement. This article aims to describe cognitive biases in pediatric cardiology, highlighting their influence on therapeutic interventions for congenital heart disease. Whether future pediatric cardiologists or experienced professionals, understanding and actively combating cognitive biases are essential components of our ongoing medical education. Furthermore, it is our responsibility to thoroughly examine our own practices in our unwavering commitment to providing high-quality care.

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.

Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced

BACKGROUND

Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases.

FRAMEWORK

A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis.

DESIGN

Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease.

RESULTS

When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment.

CONCLUSIONS

A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself.

HIGHLIGHTS

Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.

Frontline Providers' and Patients' Perspectives on Improving Diagnostic Safety in the Emergency Department: A Qualitative Study

BACKGROUND

Few studies have described the insights of frontline health care providers and patients on how the diagnostic process can be improved in the emergency department (ED), a setting at high risk for diagnostic errors. The authors aimed to identify the perspectives of providers and patients on the diagnostic process and identify potential interventions to improve diagnostic safety.

METHODS

Semistructured interviews were conducted with 10 ED physicians, 15 ED nurses, and 9 patients/caregivers at two separate health systems. Interview questions were guided by the ED-Adapted National Academies of Sciences, Engineering, and Medicine Diagnostic Process Framework and explored participant perspectives on the ED diagnostic process, identified vulnerabilities, and solicited interventions to improve diagnostic safety. The authors performed qualitative thematic analysis on transcribed interviews.

RESULTS

The research team categorized vulnerabilities in the diagnostic process and intervention opportunities based on the ED-Adapted Framework into five domains: (1) team dynamics and communication (for example, suboptimal communication between referring physicians and the ED team); (2) information gathering related to patient presentation (for example, obtaining the history from the patients or their caregivers; (3) ED organization, system, and processes (for example, staff schedules and handoffs); (4) patient education and self-management (for example, patient education at discharge from the ED); and (5) electronic health record and patient portal use (for example, automatic release of test results into the patient portal). The authors identified 33 potential interventions, of which 17 were provider focused and 16 were patient focused.

CONCLUSION

Frontline providers and patients identified several vulnerabilities and potential interventions to improve ED diagnostic safety. Refining, implementing, and evaluating the efficacy of these interventions are required.

"It's probably an STI because you're gay": a qualitative study of diagnostic error experiences in sexual and gender minority individuals

BACKGROUND

There is a critical need to identify specific causes of and tailored solutions to diagnostic error in sexual and gender minority (SGM) populations.

PURPOSE

To identify challenges to diagnosis in SGM adults, understand the impacts of patient-reported diagnostic errors on patients' lives and elicit solutions.

METHODS

Qualitative study using in-depth semistructured interviews. Participants were recruited using convenience and snowball sampling. Recruitment efforts targeted 22 SGM-focused organisations, academic centres and clinics across the USA. Participants were encouraged to share study details with personal contacts. Interviews were analysed using codebook thematic analysis.

RESULTS

Interviewees (n=20) ranged from 20 to 60 years of age with diverse mental and physical health symptoms. All participants identified as sexual minorities, gender minorities or both. Thematic analysis revealed challenges to diagnosis. Provider-level challenges included pathologisation of SGM identity; dismissal of symptoms due to anti-SGM bias; communication failures due to providers being distracted by SGM identity and enforcement of cis-heteronormative assumptions. Patient-level challenges included internalised shame and stigma. Intersectional challenges included biases around factors like race and age. Patient-reported diagnostic error led to worsening relationships with providers, worsened mental and physical health and increased self-advocacy and community-activism. Solutions to reduce diagnostic disparities included SGM-specific medical education and provider training, using inclusive language, asking questions, avoiding assumptions, encouraging diagnostic coproduction, upholding high care standards and ethics, involving SGM individuals in healthcare improvement and increasing research on SGM health.

CONCLUSIONS

Anti-SGM bias, queerphobia, lack of provider training and heteronormative attitudes hinder diagnostic decision-making and communication. As a result, SGM patients report significant harms. Solutions to mitigate diagnostic disparities require an intersectional approach that considers patients' gender identity, sexual orientation, race, age, economic status and system-level changes.

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.

Postmortem metabolomics as a high-throughput cause-of-death screening tool for human death investigations

Autopsy rates are declining globally, impacting cause-of-death (CoD) diagnoses and quality control. Postmortem metabolomics was evaluated for CoD screening using 4,282 human cases, encompassing CoD groups: acidosis, drug intoxication, hanging, ischemic heart disease (IHD), and pneumonia. Cases were split 3:1 into training and test sets. High-resolution mass spectrometry data from femoral blood were analyzed via orthogonal-partial least squares discriminant analysis (OPLS-DA) to discriminate CoD groups. OPLS-DA achieved an R2 = 0.52 and Q2 = 0.30, with true-positive prediction rates of 68% and 65% for training and test sets, respectively, across all groups. Specificity-optimized thresholds predicted 56% of test cases with a unique CoD, average 45% sensitivity, and average 96% specificity. Prediction accuracies varied: 98.7% for acidosis, 80.5% for drug intoxication, 81.6% for hanging, 73.1% for IHD, and 93.6% for pneumonia. This study demonstrates the potential of large-scale postmortem metabolomics for CoD screening, offering high specificity and enhancing throughput and decision-making in human death investigations.

Differential screen and treatment of sternocleidomastoid syndrome versus eagle syndrome: a case report

BACKGROUND/PURPOSE

Differential screening is a complex process in chronic pain conditions. There is significant uncertainty that surrounds the pathophysiology of many chronic pain syndromes that may lead to misdiagnosis and treatment failures. Such differential screening is even more challenging where there is regional overlapping from surrounding tissues. This case report chronicles the differential screening and treatment of a patient with sternocleidomastoid syndrome (SCMS) originally diagnosed as Eagle's syndrome (ES).

CASE DESCRIPTION

A 55-year-old woman, referred to a physical therapist (PT) by an ear, nose and throat (ENT) physician with the diagnosis of ES. The patient complained of yearlong left-sided otalgia, blurred vision, excessive lacrimation, dysphagia, hyperesthesia on the left side of the face, unilateral temporal headaches, and both left mandibular and anterior neck pain.

OUTCOMES

The PT examination revealed the patient did not exhibit hallmark findings for clinical confirmation of ES and instead demonstrated multiple signs consistent with SCMS.

DISCUSSION

Manual therapy techniques and therapeutic exercises resolved the patient's year-long chronic symptoms within 6 sessions.

Fatigue and vigilance in medical experts detecting breast cancer

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.

Collaborative clinical reasoning: a scoping review

Background

Collaborative clinical reasoning (CCR) among healthcare professionals is crucial for maximizing clinical outcomes and patient safety. This scoping review explores CCR to address the gap in understanding its definition, structure, and implications.

Methods

A scoping review was undertaken to examine CCR related studies in healthcare. Medline, PsychInfo, SciVerse Scopus, and Web of Science were searched. Inclusion criteria included full-text articles published between 2011 to 2020. Search terms included cooperative, collaborative, shared, team, collective, reasoning, problem solving, decision making, combined with clinical or medicine or medical, but excluded shared decision making.

Results

A total of 24 articles were identified in the review. The review reveals a growing interest in CCR, with 14 articles emphasizing the decision-making process, five using Multidisciplinary Team-Metric for the Observation of Decision Making (MDTs-MODe), three exploring CCR theory, and two focusing on the problem-solving process. Communication, trust, and team dynamics emerge as key influencers in healthcare decision-making. Notably, only two articles provide specific CCR definitions.

Conclusions

While decision-making processes dominate CCR studies, a notable gap exists in defining and structuring CCR. Explicit theoretical frameworks, such as those proposed by Blondon et al. and Kiesewetter et al., are crucial for advancing research and understanding CCR dynamics within collaborative teams. This scoping review provides a comprehensive overview of CCR research, revealing a growing interest and diversity in the field. The review emphasizes the need for explicit theoretical frameworks, citing Blondon et al. and Kiesewetter et al. The broader landscape of interprofessional collaboration and clinical reasoning requires exploration.

Diagnostic algorithm allows for a scientifically robust and reliable retrospective diagnosis using textual evidence from mid-19th century Basel, Switzerland

OBJECTIVE

Diagnosing disease from the past using historic textual sources can be controversial as to its accuracy. To overcome these objections, an empirical approach to the historical clinical data was developed. The approach follows a standardised, objective, and systematic evaluation, satisfying the requirements of the philosophy of science.

MATERIAL

Physician-managed medical records of mid-19th century patients reported to have suffered from tuberculosis.

METHOD

A diagnostic algorithm, quantifying clinical data into a scoring system, was developed based on criteria recorded in the medical sources. The findings were compared to the autopsy results using the Receiver Operating Characteristics method.

RESULTS

The generated scoring system correctly predicted the diagnosis of tuberculosis in 86% of patients in the study. 6% false negatives and 8% false positives were predicted.

CONCLUSIONS

It is possible to retrospectively diagnose in a reliable and scientifically robust manner under certain conditions. It is important to embed the clinical data into the historical context. A general rejection of retrospective diagnosis is unsubstantiated. Well-designed, disease-specific, and source adapted medical scoring systems are new approaches and overcome criticism raised against retrospective diagnosis.

SIGNIFICANCE

This new approach utilises diverse historic sources and potentially leads to reliable retrospective diagnosis of most common diseases of the past.

LIMITATIONS

Selection bias of the records allocated. Quality of the historic sources utilized. Restricted statistical assessment potential of historic sources.

SUGGESTIONS FOR FURTHER RESEARCH

Development of disease- and epoch-specific medical score systems.

Misdiagnosis of Acute Appendicitis Cases in the Emergency Room

Acute appendicitis (AA) is one of the most frequent surgical emergencies, especially in pediatric populations, with its misdiagnosis in emergency settings presenting significant health risks. This misdiagnosis leads to various complications, such as delayed treatment or unnecessary surgeries. Factors such as age, gender, and comorbidities contribute to diagnostic errors, leading to complications such as peritonitis and increased negative appendectomy rates. This underscores the importance of accurate clinical assessment and awareness of common pitfalls, such as cognitive biases and over-reliance on laboratory tests. This review delves into the prevalence of AA misdiagnosis, its health burden, and the challenges inherent in the diagnostic process. It scrutinizes the effectiveness of different diagnostic approaches, including clinical assessment and imaging techniques. The treatment paradigms for AA are also explored, focusing on surgical interventions and the potential of conservative treatments using antibiotics. The review underscores the criticality of precise diagnosis in preventing adverse outcomes and ensuring effective treatment.

Consensus for a primary care clinical decision-making tool for assessing, diagnosing, and managing low back pain in Alberta, Canada

BACKGROUND

Low back pain (LBP) is a common condition causing disability and high healthcare costs. Alberta faces challenges with unnecessary referrals to specialists and long wait times. A province-wide standardized clinical care pathway based on evidence-based best practices can improve efficiency, reduce wait times, and enhance patient outcomes. Implementing such pathways has shown success in other areas of healthcare in Alberta. This study developed a clinical decision-making pathway to standardize care and minimize uncertainty in assessment, diagnosis, and management.

METHODS

A systematic rapid review identified existing tools and evidence that could support a comprehensive LBP clinical decision-making tool. Forty-seven healthcare professionals participated in four rounds of a modified Delphi approach to reach consensus on the assessment, diagnosis, and management of patients presenting to primary care with LBP in Alberta, Canada. This project was a collaborative effort between Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJHSCN) and the Alberta Bone and Joint Health Institute (ABJHI).

RESULTS

A province-wide expert panel consisting of professionals from different health disciplines and regions collaborated to develop an LBP clinical decision-making tool. This tool presents clinical care pathways for acute, subacute, and chronic LBP. It also provides guidance for history-taking, physical examination, patient education, and management.

CONCLUSIONS

This clinical decision-making tool will help to standardize care, provide guidance on the diagnosis and management of LBP, and assist in clinical decision-making for primary care providers in both public and private sectors.

Diagnostic odyssey of Guillain-Barré syndrome in children

BACKGROUND AND OBJECTIVES

A gap exists between difficulty in diagnosis and importance of early recognition and intervention in pediatric Guillain-Barré syndrome (GBS). Therefore, this study aimed to establish a diagnostic odyssey plot that allows "at-a-glance" overview of the diagnostic odyssey of GBS in children, including overall diagnostic delay, physician-related and patient-related diagnostic delays, and length and frequency of diagnostic errors.

METHODS

In this single-center retrospective cohort study, standardized data were obtained from children with GBS from 2003 to 2020. Overall diagnostic delay (time between symptom onset and diagnosis), physician-related diagnostic delay (time between the first medical visit and diagnosis), and patient-related diagnostic delay (time between symptom onset and the first medical visit) were analyzed.

RESULTS

The study examined a total of 21 patients (11 men, median age 4.5 years). Overall, there were 40 misdiagnoses among 17 patients, while four were diagnosed correctly at the first visit. The overall diagnostic delay was 9 days [interquartile range (IQR), 6-17 days]. Physician-related diagnostic delay, but not patient-related diagnostic delay, was correlated with the overall diagnostic delay. Patients in the late-diagnosed group were more frequently misdiagnosed during their diagnostic odyssey than patients in the other groups. Risk factors associated with diagnostic delay included delayed onset of weakness and sensory deficits, absence of swallowing problems, and misdiagnosis as orthopedic disorders or viral infections.

DISCUSSION

A unique diagnostic odyssey exists in pedaitric GBS. Several clinical risk factors were associated with the diagnostic delay.

Prevalence of atypical presentations among outpatients and associations with diagnostic error

OBJECTIVES

This study aimed to assess the prevalence of atypical presentations and their association with diagnostic errors in various diseases.

METHODS

This retrospective observational study was conducted using cohort data between January 1 and December 31, 2019. Consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine at a university hospital in Japan were included. Patients for whom the final diagnosis was not confirmed were excluded. Primary outcomes were the prevalence of atypical presentations, and the prevalence of diagnostic errors in groups with typical and atypical presentations. Diagnostic errors and atypical presentations were assessed using the Revised Safer Dx Instrument. We performed primary analyses using a criterion; the average score of less than five to item 12 of two independent reviewers was an atypical presentation (liberal criterion). We also performed additional analyses using another criterion; the average score of three or less to item 12 was an atypical presentation (conservative criterion).

RESULTS

A total of 930 patients were included out of a total of 2022 eligible. The prevalence of atypical presentation was 21.7 and 6.7 % when using liberal and conservative criteria for atypical presentation, respectively. Diagnostic errors (2.8 %) were most commonly observed in the cases with slight to moderate atypical presentation. Atypical presentation was associated with diagnostic errors with the liberal criterion for atypical presentation; however, this diminished with the conservative criterion.

CONCLUSIONS

An atypical presentation was observed in up to 20 % of outpatients with a confirmed diagnosis, and slight to moderate atypical presentation may be the highest risk population for diagnostic errors.

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.

Development of a Soft Tissue Knee Clinical Decision-Making Tool for Patients Presenting to Primary Point-of-Care Providers in Alberta, Canada

Several barriers exist in Alberta, Canada to providing accurate and accessible diagnoses for patients presenting with acute knee injuries and chronic knee problems. In efforts to improve quality of care for these patients, an evidence-informed clinical decision-making tool was developed. Forty-five expert panelists were purposively chosen to represent stakeholder groups, various expertise, and each of Alberta Health Services' 5 geographical health regions. A systematic rapid review and modified Delphi approach were executed with the intention of developing standardized clinical decision-making processes for acute knee injuries, atraumatic/overuse conditions, knee arthritis, and degenerative meniscus. Standardized criteria for screening, history-taking, physical examination, diagnostic imaging, timelines, and treatment were developed. This tool standardizes and optimizes assessment and diagnosis of acute knee injuries and chronic knee problems in Alberta. This project was a highly collaborative, province-wide effort led by Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJH SCN) and the Alberta Bone and Joint Health Institute (ABJHI).

Perceptions of medical error among general practitioners in rural China: a qualitative interview study

BACKGROUND

Medical error (ME) is a serious public health problem and a leading cause of death. The reported adverse incidents in China were much less than western countries, and the research on patient safety in rural China's primary care institutions was scarce. This study aims to identify the factors contributing to the under-reporting of ME among general practitioners in township health centres (THCs).

METHODS

A qualitative semi-structured interview study was conducted with 31 general practitioners working in 30 THCs across 6 provinces. Thematic analysis was conducted using a grounded theory approach.

RESULTS

The understanding of ME was not unified, from only mild consequence to only almost equivalent to medical malpractice. Common coping strategies for THCs after ME occurs included concealing and punishment. None of the participants reported adverse events through the National Clinical Improvement System website since they worked in THCs. Discussions about ME always focused on physicians rather than the system.

CONCLUSIONS

The low reported incidence of ME could be explained by unclear concept, unawareness and blame culture. It is imperative to provide supportive environment, patient safety training and good examples of error-based improvements to rural primary care institutions so that ME could be fully discussed, and systemic factors of ME could be recognised and improved there in the future.

Reflection on medical errors: A thematic analysis

BACKGROUND

As there is a need to prepare doctors to minimize errors, we wanted to determine how doctors go about reflecting upon their medical errors.

METHODS

We conducted a thematic analysis of the published reflection reports of 12 Dutch doctors about the errors they had made. Three questions guided our analysis: What triggers doctors to become aware of their errors? What topics do they reflect upon to explain what happened? What lessons do doctors learn after reflecting on their error?

RESULTS

We found that the triggers which made doctors aware of their errors were mostly death and/or a complication. This suggests that the trigger to recognize that something might be wrong came too late. The 12 doctors cited 20 topics' themes that explained the error and 16 lessons-learnt themes. The majority of the topics and lessons learnt were related more to the doctors' inner worlds (personal features) than to the outer world (environment).

CONCLUSION

To minimize errors, doctors should be trained to become earlier and in time aware of distracting and misleading features that might interfere with their clinical reasoning. This training should focus on reflection in action and on discovering more about doctors' personal inner world to identify vulnerabilities.

Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs

OBJECTIVES

The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists.

MATERIALS AND METHODS

A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.

RESULTS

The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.

CLINICAL SIGNIFICANCE

We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.

Angina and Non-Obstructive Coronary Artery (ANOCA) Patients with Coronary Vasomotor Disorders

Angina and Non-Obstructive Coronary Artery (ANOCA) patients often lack a clear explanation for their symptoms, and are frequently discharged with the label of "unspecified chest pain", despite the availability of functional coronary angiography (provocative spasm and microvascular function testing) to identify potential underlying coronary vasomotor disorders. This study compared the outcomes of ANOCA patients with a coronary vasomotor disorder diagnosis post elective coronary angiography to patients discharged with unspecified chest pain. Using the CADOSA (Coronary Angiogram Database of South Australia) registry, consecutive symptomatic patients (n = 7555) from 2012 to 2018 underwent elective angiography; 30% had ANOCA (stenosis <50%). Of this cohort, 9% had documented coronary vasomotor disorders diagnosed, and 91% had unspecified chest pain. Patients with coronary vasomotor disorders were younger and had a similar female prevalence compared with those with unspecified chest pain. New prescriptions of calcium channel blockers and long-acting nitrates were more common for the coronary vasomotor cohort at discharge. In the 3 years following angiography, both groups had similar all-cause mortality rates. However, those with coronary vasomotor disorders had higher rates of emergency department visits for chest pain (39% vs. 15%, p < 0.001) and readmissions for chest pain (30% vs. 10%, p < 0.001) compared with those with unspecified chest pain. This real-world study emphasizes the importance of identifying high-risk ANOCA patients for personalized management to effectively address their symptoms.

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.