Teaching for Reducing Diagnostic Errors

Being sick to a cancer patient: pathways of delay in help seeking and diagnosis of cancer in India

There is evidence that cancer mortality and morbidity could be reduced when the disease is diagnosed and treated at an early stage. The paper examines the pathways of delay of cancer diagnosis in an Indian setting. It draws on a qualitative study conducted among cancer survivors and family members of cancer patients in the city of Bengaluru, South India. The results show that a substantial part of the delay occurred at the stage of initial formal help seeking wherein patient and family-led, disease-related and systemic factors together played a major role. Patient-led factors included trivialisation and normalisation of symptoms as part of general fatigue and aging; unrealistic risk perceptions that linked causality of cancer merely to heredity and behavioural risk factors; fear of being diagnosed as cancer patient; gender related reasons including family's gender performance expectation, lower agency of women to seek help and lower prioratisation of women's health in the household and access related issues including financial constraints and unavailability of specialised hospitals nearby. Disease-related factors included the presence of comorbidity, cancer's mimicking of symptoms of other diseases and absence of distinguishable symptoms at the initial stage for certain types of cancers. The practitioner-led and system-led factors such as trivialisation of symptoms by general practitioners, non cancer-specific referrals, and lack of cancer screening facilities accounted for a major part of delay after the formal help seeking. The paper argues that the mere knowledge of cancer symptoms did not always lead to early diagnosis due to the interplay of these factors. The ongoing cancer prevention and control interventions in India need to be informed of these micro level factors while developing strategies to prevent avoidable delays in cancer diagnosis.

Patient Safety, Clinical Microbiology, and Collaborative Healthcare

“Right to health” is a universal right inclusive of a culture of safety. This review aims to highlight how clinical microbiology laboratories can contribute to patient safety. They can bring down medical errors through clinical collaboration and quality control. Timely and accurate inputs from microbiology laboratory help in clinical correlation and aid in safe patient care. Through internet search, using keywords such as “medical errors” and “quality assurance,” global burden of medical errors has been compiled. References have been taken from guidelines and documents of standard national and international agencies, systematic reviews, observational studies, retrospective analyses, meta-analyses, health bulletins and reports, and personal views. Safety in healthcare should lay emphasis on prevention, reporting, analysis, and correction of medical errors. If not recorded, medical errors are regarded as occasional or chance events. Global data show adverse events are as high as 10% among hospitalized patients, and approximately two-thirds of these are reported from low- to middle-income countries (LMICs). This includes errors in laboratories as well. Clinical microbiology can impact patient safety when practiced properly with an aim to detect, control, and prevent infections at the earliest. It is a science that integrates a tripartite relationship between the patient, clinician, and a microbiology specialist. Through collaborative healthcare, all stakeholders benefit by understanding common errors and mitigate them through quality management. However, errors tend to happen despite standardization and streamlining all processes. The aim should be to minimize them, have fair documentation, and learn from mistakes to avoid repetition. Local targets should be set and then extended to meet national and global benchmarks.

Death Related to a Congenital Vascular Anomaly of Pulmonary Hamartoma Type: Malpractice or Tragic Fatality?

In forensic pathology, apparently straightforward cases can often hide rarities that, if not correctly interpreted, can alter the results of the entire investigation, leading to misinterpretations. This occurs when the investigation is conducted to assess medical malpractice. An unexpected death, with no known apparent cause, is often linked to an underlying disease process of unclear etiological origin whose nature can, unfortunately, be properly investigated only post-mortem. This presentation shows a case study, in which it was possible to reconduct the death of a patient to a natural pathology and not to medical treatment. Here, the authors illustrate a case with a hamartoma developed in chronic inflammatory conditions (bronchiectasis) that was difficult to differentiate from lung cancer due to the inability to perform specific instrumental examinations. The hamartoma, usually benign and identifiable by standard instrumental investigations, in this case, led to the patient's death precisely during the execution of a bronchoscopy. However, in the absence of a certain cause of death, public opinion unanimously attributes a patient's disease to medical error. Indeed, a routine practice such as bronchoscopy should not cause death and consequently, the doctor must have made a mistake. Fortunately, the autopsy not only demonstrated the origin of the bleeding but also unveiled the reason for this, as rare congenital lung disease. Fate, one might say.

What Contributes to Diagnostic Error or Delay? A Qualitative Exploration Across Diverse Acute Care Settings in the United States

OBJECTIVES

Diagnostic error and delay is a prevalent and impactful problem. This study was part of a mixed-methods approach to understand the organizational, clinician, and patient factors contributing to diagnostic error and delay among acutely ill patients within a health system, as well as recommendations for the development of tailored, targeted, feasible, and effective interventions.

METHODS

We did a multisite qualitative study using focus group methodology to explore the perspectives of key clinician stakeholders. We used a conceptual framework that characterized diagnostic error and delay as occurring within 1 of 3 stages of the patient's diagnostic journey-critical information gathering, synthesis of key information, and decision making and communication. We developed our moderator guide based on the sociotechnical frameworks previously described by Holden and Singh for understanding noncognitive factors that lead to diagnostic error and delay. Deidentified focus group transcripts were coded in triplicate and to consensus over a series of meetings. A final coded data set was then uploaded into NVivo software. The data were then analyzed to generate overarching themes and categories.

RESULTS

We recruited a total of 64 participants across 4 sites from emergency departments, hospital floor, and intensive care unit settings into 11 focus groups. Clinicians perceive that diverse organizational, communication and coordination, individual clinician, and patient factors interact to impede the process of making timely and accurate diagnoses.

CONCLUSIONS

This study highlights the complex sociotechnical system within which individual clinicians operate and the contributions of systems, processes, and institutional factors to diagnostic error and delay.

Severity, Irritability, Nature, Stage, and Stability (SINSS): A clinical perspective

Clinical reasoning errors in health-care can be mitigated with the use of systematic strategies and tools. One of these strategies is the SINSS construct, an acronym for Severity, Irritability, Nature, Stage, and Stability. The construct of SINSS appears in several textbooks and peer-reviewed articles. However, it has been inconsistently defined and applied in clinical practice. In this clinical perspective, the terms of the SINSS construct are defined in detail and their application to clinical practice is discussed. Current research showing the application of SINSS as a whole in clinical practice and educational settings is also presented. Recommendations for future application of SINSS are provided to advance the study of clinical reasoning and help minimize diagnostic, prognostic, and interventional clinical errors. The systematic use of SINSS allows the clinician to gain a thorough understanding of the patient's condition and symptoms, which can lead to a well-tolerated and appropriately tailored physical examination and intervention. Additionally, the proper use of this construct can result in more optimal patient outcomes, as well as provide a structure for the mentor and learner in helping uncover errors in the learner's clinical reasoning process.

Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets

Background

Machine-learning or deep-learning algorithms for clinical diagnosis are inherently dependent on the availability of large-scale clinical datasets. Lack of such datasets and inherent problems such as overfitting often necessitate the development of innovative solutions. Probabilistic modeling closely mimics the rationale behind clinical diagnosis and represents a unique solution.

Objective

The aim of this study was to develop and validate a probabilistic model for differential diagnosis in different medical domains.

Methods

Numerical values of symptom-disease associations were utilized to mathematically represent medical domain knowledge. These values served as the core engine for the probabilistic model. For the given set of symptoms, the model was utilized to produce a ranked list of differential diagnoses, which was compared to the differential diagnosis constructed by a physician in a consult. Practicing medical specialists were integral in the development and validation of this model. Clinical vignettes (patient case studies) were utilized to compare the accuracy of doctors and the model against the assumed gold standard. The accuracy analysis was carried out over the following metrics: top 3 accuracy, precision, and recall.

Results

The model demonstrated a statistically significant improvement (P=.002) in diagnostic accuracy (85%) as compared to the doctors’ performance (67%). This advantage was retained across all three categories of clinical vignettes: 100% vs 82% (P<.001) for highly specific disease presentation, 83% vs 65% for moderately specific disease presentation (P=.005), and 72% vs 49% (P<.001) for nonspecific disease presentation. The model performed slightly better than the doctors’ average in precision (62% vs 60%, P=.43) but there was no improvement with respect to recall (53% vs 56%, P=.27). However, neither difference was statistically significant.

Conclusions

The present study demonstrates a drastic improvement over previously reported results that can be attributed to the development of a stable probabilistic framework utilizing symptom-disease associations to mathematically represent medical domain knowledge. The current iteration relies on static, manually curated values for calculating the degree of association. Shifting to real-world data–derived values represents the next step in model development.