Phenomenological analysis of diagnostic radiology: description and relevance to diagnostic errors

Diagnosis ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 215-225 ◽  
Author(s):  
Mindaugas Briedis ◽  
Ruta Briediene
Keyword(s):  
Cognitive Processes ◽  
Imaging Modality ◽  
Image Interpretation ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Phenomenological Analysis ◽  
Diagnostic Radiology ◽  
Personal Factors ◽  
Deep Appreciation ◽  
Comprehensive Knowledge

AbstractThis paper uses novel qualitative research methods (phenomenology, ethnography and enactivism) to understand the cognitive processes through which radiologists interpret medical images to arrive at a diagnosis. From this perspective, diagnosis is not simply a matching of findings to retrieved mental images, but more properly an act of embodied or situated cognition, one that involves perception along with the actualization of professional memory and imagination and an expert-level understanding of the involved technology. Image interpretation involves a diverse set of factors, each of which is critical to arriving at the correct diagnostic interpretations, and conversely, may be the source of mis-interpretations and diagnostic error. Interpretation depends on the radiologist’s understanding of the imaging modality that was used, a deep appreciation of anatomy and comprehensive knowledge of relevant diseases and how they manifest in medical imaging. A range of personal and inter-personal factors may also come into play, including understanding the actions, values and goals of the patient, the imaging technicians and the clinicians and other medical professionals involved in the patient’s care. This multi-dimensional perspective provides novel insights regarding the cognitive aspects of diagnostic radiology and a novel framework for understanding how diagnostic errors arise in this process. Some of the findings of this research may have applications for diagnostic praxis in general, that is, beyond radiology diagnostics.

256 Shades of gray: uncertainty and diagnostic error in radiology

Diagnosis ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 149-157 ◽  
Author(s):  
Michael A. Bruno
Keyword(s):  
Cognitive Processes ◽  
Image Interpretation ◽  
Diagnostic Error ◽  
High Variability ◽  
Radiological Imaging ◽  
Reduction Strategies ◽  
Diagnostic Power ◽  
High Level ◽  
High Uncertainty ◽  
Degree Of Overlap

Abstract Radiologists practice in an environment of extraordinarily high uncertainty, which results partly from the high variability of the physical and technical aspects of imaging, partly from the inherent limitations in the diagnostic power of the various imaging modalities, and partly from the complex visual-perceptual and cognitive processes involved in image interpretation. This paper reviews the high level of uncertainty inherent to the process of radiological imaging and image interpretation vis-à-vis the issue of radiological interpretive error, in order to highlight the considerable degree of overlap that exists between these. The scope of radiological error, its many potential causes and various error-reduction strategies in radiology are also reviewed.

Handshake antimicrobial stewardship as a model to recognize and prevent diagnostic errors

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Justin B. Searns ◽  
Manon C. Williams ◽  
Christine E. MacBrayne ◽  
Ann L. Wirtz ◽  
Jan E. Leonard ◽  
...  
Keyword(s):  
Antimicrobial Stewardship ◽  
Interrater Reliability ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Primary Treatment ◽  
Treatment Team ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
The Impact ◽  
Made In

AbstractObjectivesFew studies describe the impact of antimicrobial stewardship programs (ASPs) on recognizing and preventing diagnostic errors. Handshake stewardship (HS-ASP) is a novel ASP model that prospectively reviews hospital-wide antimicrobial usage with recommendations made in person to treatment teams. The purpose of this study was to determine if HS-ASP could identify and intervene on potential diagnostic errors for children hospitalized at a quaternary care children’s hospital.MethodsPreviously self-identified “Great Catch” (GC) interventions by the Children’s Hospital Colorado HS-ASP team from 10/2014 through 5/2018 were retrospectively reviewed. Each GC was categorized based on the types of recommendations from HS-ASP, including if any diagnostic recommendations were made to the treatment team. Each GC was independently scored using the “Safer Dx Instrument” to determine presence of diagnostic error based on a previously determined cut-off score of ≤1.50. Interrater reliability for the instrument was measured using a randomized subset of one third of GCs.ResultsDuring the study period, there were 162 GC interventions. Of these, 65 (40%) included diagnostic recommendations by HS-ASP and 19 (12%) had a Safer Dx Score of ≤1.50, (Κ=0.44; moderate agreement). Of those GCs associated with diagnostic errors, the HS-ASP team made a diagnostic recommendation to the primary treatment team 95% of the time.ConclusionsHandshake stewardship has the potential to identify and intervene on diagnostic errors for hospitalized children.

scholarly journals Use of patient complaints to identify diagnosis-related safety concerns: a mixed-method evaluation

2021 ◽  
pp. bmjqs-2020-011593
Author(s):  
Traber D Giardina ◽  
Saritha Korukonda ◽  
Umber Shahid ◽  
Viralkumar Vaghani ◽  
Divvy K Upadhyay ◽  
...  
Keyword(s):  
Medical Record ◽  
Clinical Care ◽  
Diagnostic Testing ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Diagnostic Process ◽  
Healthcare Organisation ◽  
Safety Concerns ◽  
Patient Complaints ◽  
Malpractice Claims

BackgroundPatient complaints are associated with adverse events and malpractice claims but underused in patient safety improvement.ObjectiveTo systematically evaluate the use of patient complaint data to identify safety concerns related to diagnosis as an initial step to using this information to facilitate learning and improvement.MethodsWe reviewed patient complaints submitted to Geisinger, a large healthcare organisation in the USA, from August to December 2017 (cohort 1) and January to June 2018 (cohort 2). We selected complaints more likely to be associated with diagnostic concerns in Geisinger’s existing complaint taxonomy. Investigators reviewed all complaint summaries and identified cases as ‘concerning’ for diagnostic error using the National Academy of Medicine’s definition of diagnostic error. For all ‘concerning’ cases, a clinician-reviewer evaluated the associated investigation report and the patient’s medical record to identify any missed opportunities in making a correct or timely diagnosis. In cohort 2, we selected a 10% sample of ‘concerning’ cases to test this smaller pragmatic sample as a proof of concept for future organisational monitoring.ResultsIn cohort 1, we reviewed 1865 complaint summaries and identified 177 (9.5%) concerning reports. Review and analysis identified 39 diagnostic errors. Most were categorised as ‘Clinical Care issues’ (27, 69.2%), defined as concerns/questions related to the care that is provided by clinicians in any setting. In cohort 2, we reviewed 2423 patient complaint summaries and identified 310 (12.8%) concerning reports. The 10% sample (n=31 cases) contained five diagnostic errors. Qualitative analysis of cohort 1 cases identified concerns about return visits for persistent and/or worsening symptoms, interpersonal issues and diagnostic testing.ConclusionsAnalysis of patient complaint data and corresponding medical record review identifies patterns of failures in the diagnostic process reported by patients and families. Health systems could systematically analyse available data on patient complaints to monitor diagnostic safety concerns and identify opportunities for learning and improvement.

Lessons in clinical reasoning – pitfalls, myths, and pearls: the contribution of faulty data gathering and synthesis to diagnostic error

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Martin A. Schaller-Paule ◽  
Helmuth Steinmetz ◽  
Friederike S. Vollmer ◽  
Melissa Plesac ◽  
Felix Wicke ◽  
...  
Keyword(s):  
Patient Safety ◽  
Clinical Reasoning ◽  
Data Gathering ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Care Physician ◽  
Diagnostic Process ◽  
Myasthenic Crisis ◽  
Hospital Organization ◽  
Substantial Impact

Abstract Objectives Errors in clinical reasoning are a major factor for delayed or flawed diagnoses and put patient safety at risk. The diagnostic process is highly dependent on dynamic team factors, local hospital organization structure and culture, and cognitive factors. In everyday decision-making, physicians engage that challenge partly by relying on heuristics – subconscious mental short-cuts that are based on intuition and experience. Without structural corrective mechanisms, clinical judgement under time pressure creates space for harms resulting from systems and cognitive errors. Based on a case-example, we outline different pitfalls and provide strategies aimed at reducing diagnostic errors in health care. Case presentation A 67-year-old male patient was referred to the neurology department by his primary-care physician with the diagnosis of exacerbation of known myasthenia gravis. He reported shortness of breath and generalized weakness, but no other symptoms. Diagnosis of respiratory distress due to a myasthenic crisis was made and immunosuppressive therapy and pyridostigmine were given and plasmapheresis was performed without clinical improvement. Two weeks into the hospital stay, the patient’s dyspnea worsened. A CT scan revealed extensive segmental and subsegmental pulmonary emboli. Conclusions Faulty data gathering and flawed data synthesis are major drivers of diagnostic errors. While there is limited evidence for individual debiasing strategies, improving team factors and structural conditions can have substantial impact on the extent of diagnostic errors. Healthcare organizations should provide the structural supports to address errors and promote a constructive culture of patient safety.

Lessons in clinical reasoning ‒ pitfalls, myths and pearls: a case of recurrent pancreatitis

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Vita Jaspan ◽  
Verity Schaye ◽  
Andrew S. Parsons ◽  
David Kudlowitz
Keyword(s):  
Acute Pancreatitis ◽  
Clinical Reasoning ◽  
Cognitive Biases ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
First Episode ◽  
Gut Flora ◽  
Case Presentation ◽  
Early Hypothesis ◽  
Elevated Transaminases

Abstract Objectives Cognitive biases can result in clinical reasoning failures that can lead to diagnostic errors. Autobrewery syndrome is a rare, but likely underdiagnosed, condition in which gut flora ferment glucose, producing ethanol. It most frequently presents with unexplained episodes of inebriation, though more case studies are necessary to better characterize the syndrome. Case presentation This is a case of a 41-year old male with a past medical history notable only for frequent sinus infections, who presented with recurrent episodes of acute pancreatitis. In the week prior to his first episode of pancreatitis, he consumed four beers, an increase from his baseline of 1–2 drinks per month. At home, he had several episodes of confusion, which he attributed to fatigue. He underwent laparoscopic cholecystectomy and testing for genetic and autoimmune causes of pancreatitis, which were non-revealing. He was hospitalized 10 more times during that 9-month period for acute pancreatitis with elevated transaminases. During these admissions, he had elevated triglycerides requiring an insulin drip and elevated alcohol level despite abstaining from alcohol for the prior eight months. His alcohol level increased after consumption of complex carbohydrates, confirming the diagnosis of autobrewery syndrome. Conclusions Through integrated commentary on the diagnostic reasoning process, this case underscores how overconfidence can lead to premature closure and anchoring resulting in diagnostic error. Using a metacognitive overview, case discussants describe the importance of structured reflection and a standardized approach to early hypothesis generation to navigate these cognitive biases.

Checklists to prevent diagnostic errors: a pilot randomized controlled trial

Diagnosis ◽  
2015 ◽  
Vol 2 (3) ◽  
pp. 163-169 ◽  
Author(s):  
John W. Ely ◽  
Mark A. Graber
Keyword(s):  
Primary Care ◽  
Usual Care ◽  
Error Rate ◽  
Primary Care Physicians ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Diagnostic Process ◽  
Emergency Physicians ◽  
Mean Error ◽  
Premature Closure

AbstractMany diagnostic errors are caused by premature closure of the diagnostic process. To help prevent premature closure, we developed checklists that prompt physicians to consider all reasonable diagnoses for symptoms that commonly present in primary care.We enrolled 14 primary care physicians and 100 patients in a randomized clinical trial. The study took place in an emergency department (5 physicians) and a same-day access clinic (9 physicians). The physicians were randomized to usual care vs. diagnostic checklist. After completing the history and physical exam, checklist physicians read aloud a differential diagnosis checklist for the chief complaint. The primary outcome was diagnostic error, which was defined as a discrepancy between the diagnosis documented at the acute visit and the diagnosis based on a 1-month follow-up phone call and record review.There were 17 diagnostic errors. The mean error rate among the seven checklist physicians was not significantly different from the rate among the seven usual-care physicians (11.2% vs. 17.8%; p=0.46). In a post-hoc subgroup analysis, emergency physicians in the checklist group had a lower mean error rate than emergency physicians in the usual-care group (19.1% vs. 45.0%; p=0.04). Checklist physicians considered more diagnoses than usual-care physicians during the patient encounters (6.5 diagnoses [SD 4.2] vs. 3.4 diagnoses [SD 2.0], p<0.001).Checklists did not improve the diagnostic error rate in this study. However further development and testing of checklists in larger studies may be warranted.

Classification of Error in Abdominal Imaging: Pearls and Pitfalls for Radiologists

2018 ◽  
Vol 69 (4) ◽  
pp. 409-416 ◽  
Author(s):  
Csilla Egri ◽  
Kathryn E. Darras ◽  
Elena P. Scali ◽  
Alison C. Harris
Keyword(s):  
Peer Review ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Abdominal Imaging ◽  
Patient Harm ◽  
Contributing Factors ◽  
Abdominal Radiology ◽  
Pictorial Review ◽  
Classification Of Error

Peer review for radiologists plays an important role in identifying contributing factors that can lead to diagnostic errors and patient harm. It is essential that all radiologists be aware of the multifactorial causes of diagnostic error in radiology and the methods available to reduce it. This pictorial review provides readers with an overview of common errors that occur in abdominal radiology and strategies to reduce them. This review aims to make readers more aware of pitfalls in abdominal imaging so that these errors can be avoided in the future. This essay also provides a systematic approach to classifying abdominal imaging errors that will be of value to all radiologists participating in peer review.

A Literature Survey of Ultrasound and Computed Tomography-Based Cardiac Image Registration

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Chieng Thion Ming ◽  
Zaid Omar ◽  
Nasrul Humaimi Mahmood ◽  
Suhaini Kadiman
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Imaging Modality ◽  
User Involvement ◽  
Medical Practitioner ◽  
Literature Survey ◽  
Cardiac Image ◽  
Comprehensive Knowledge ◽  
Visualization Systems ◽  
Imaging Sensors

A literature survey of Ultrasound and Computed Tomography (CT) -based cardiac image registration is presented in this article. We aim to provide the reader with a preliminary discussion into the area of cardiac image registration, as well as to briefly describe the major contributions in the field and present collective and comprehensive knowledge as guidelines for beginners in this field to initiate their research. We also highlight the major challenges where CT and Ultrasound are the modalities concerned in fusion and registration tasks. Further, we found that a majority of research in medical image registration are suitably categorized based on these factors: anatomy, imaging modality and image registration methods. Our focus in the article is on Ultrasound-CT image registration of the heart, where numerous algorithms under this scope have been elaborated. Overall, multimodal cardiac image registration offers great benefit for image visualization systems during surgery. It facilitates accurate alignment of the patient’s heart imagery acquired via different imaging sensors, without extensive user involvement and interception. Through registration, the combined anatomical and functional information from multiple modalities may be derived by the medical practitioner to aid in physiological understanding, disease monitoring, clinical treatment and diagnostic purposes.

scholarly journals The psychology of diagnostic error

2015 ◽  
Vol 8 (3) ◽  
pp. 91-98
Author(s):  
L. Zwaan
Keyword(s):  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Dual Process ◽  
Dual Process Theory ◽  
Process Theory ◽  
Diagnostic Process ◽  
Cognitive Errors ◽  
System 2 ◽  
System 1 ◽  
Analytic Reasoning

Diagnostic errors in medicine occur frequently and the consequences for the patient can be severe. Cognitive errors as well as system related errors contribute to the occurrence of diagnostic error, but it is generally accepted that cognitive errors are the main contributor. The diagnostic reasoning process in medicine, is an understudied area of research. One reason is because of the complexity of the diagnostic process and therefore the difficulty to measure diagnostic errors and the causes of diagnostic error. In this paper, I discuss some of the complexities of the diagnostic process. I describe the dual-process theory, which defines two reasoning modes, 1. a fast, automatic and unconscious reasoning mode called system 1, and a slow and analytic reasoning mode called system 2. Furthermore, the main cognitive causes of diagnostic error are described.

scholarly journals Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

10.2196/16047 ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. e16047 ◽  
Author(s):  
Don Roosan ◽  
Anandi V Law ◽  
Mazharul Karim ◽  
Moom Roosan
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Correct Diagnosis ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Positive Direction ◽  
Team Members ◽  
Diagnostic Decision ◽  
Diagnostic Decision Making ◽  
Collaborative Decision

Background According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis. Objective The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management. Methods To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3). Results Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021. Conclusions The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics. International Registered Report Identifier (IRRID) DERR1-10.2196/16047

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