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 ◽  
...  

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.

Diagnosis ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 151-156 ◽  
Author(s):  
Ashwin Gupta ◽  
Molly Harrod ◽  
Martha Quinn ◽  
Milisa Manojlovich ◽  
Karen E. Fowler ◽  
...  

Abstract Background Traditionally, research has examined systems- and cognitive-based sources of diagnostic error as individual entities. However, half of all errors have origins in both domains. Methods We conducted a focused ethnography of inpatient physicians at two academic institutions to understand how systems-based problems contribute to cognitive errors in diagnosis. Medicine teams were observed on rounds and during post-round work after which interviews were conducted. Field notes related to the diagnostic process and the work system were recorded, and findings were organized into themes. Using deductive content analysis, themes were categorized based on a published taxonomy to link systems-based contributions and cognitive errors such as faulty data gathering, information processing, data verification and errors associated with multiple domains. Results Observations, focus groups and interviews of 10 teams were conducted between January 2016 and April 2017. The following themes were identified: (1) challenges with interdisciplinary communication and communication within the electronic medical record (EMR) contributed to faulty data gathering; (2) organizational structures such as the operation of consulting services in silos promoted faulty information processing; (3) care handoffs led to faulty data verification and (4) interruptions, time constraints and a cluttered physical environment negatively influenced multiple cognitive domains. Conclusions Systems-based factors often facilitate and promote cognitive problems in diagnosis. Linking systems-based contributions to downstream cognitive impacts and intervening on both in tandem may help prevent diagnostic errors.


2018 ◽  
Vol 28 (2) ◽  
pp. 151-159 ◽  
Author(s):  
Daniel R Murphy ◽  
Ashley ND Meyer ◽  
Dean F Sittig ◽  
Derek W Meeks ◽  
Eric J Thomas ◽  
...  

Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electronic data and maturing data warehousing techniques and infrastructure offer an unprecedented opportunity to implement new types of e-trigger tools that use algorithms to identify risks and events related to the diagnostic process. We present a knowledge discovery framework, the Safer Dx Trigger Tools Framework, that enables health systems to develop and implement e-trigger tools to identify and measure diagnostic errors using comprehensive electronic health record (EHR) data. Safer Dx e-trigger tools detect potential diagnostic events, allowing health systems to monitor event rates, study contributory factors and identify targets for improving diagnostic safety. In addition to promoting organisational learning, some e-triggers can monitor data prospectively and help identify patients at high-risk for a future adverse event, enabling clinicians, patients or safety personnel to take preventive actions proactively. Successful application of electronic algorithms requires health systems to invest in clinical informaticists, information technology professionals, patient safety professionals and clinicians, all of who work closely together to overcome development and implementation challenges. We outline key future research, including advances in natural language processing and machine learning, needed to improve effectiveness of e-triggers. Integrating diagnostic safety e-triggers in institutional patient safety strategies can accelerate progress in reducing preventable harm from diagnostic errors.


Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Taro Shimizu

Abstract Diagnostic errors are an internationally recognized patient safety concern, and leading causes are faulty data gathering and faulty information processing. Obtaining a full and accurate history from the patient is the foundation for timely and accurate diagnosis. A key concept underlying ideal history acquisition is “history clarification,” meaning that the history is clarified to be depicted as clearly as a video, with the chronology being accurately reproduced. A novel approach is presented to improve history-taking, involving six dimensions: Courtesy, Control, Compassion, Curiosity, Clear mind, and Concentration, the ‘6 C’s’. We report a case that illustrates how the 6C approach can improve diagnosis, especially in relation to artificial intelligence tools that assist with differential diagnosis.


2021 ◽  
pp. bmjqs-2020-011593
Author(s):  
Traber D Giardina ◽  
Saritha Korukonda ◽  
Umber Shahid ◽  
Viralkumar Vaghani ◽  
Divvy K Upadhyay ◽  
...  

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.


Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Vita Jaspan ◽  
Verity Schaye ◽  
Andrew S. Parsons ◽  
David Kudlowitz

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.


Diagnosis ◽  
2015 ◽  
Vol 2 (3) ◽  
pp. 163-169 ◽  
Author(s):  
John W. Ely ◽  
Mark A. Graber

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.


2015 ◽  
Vol 8 (3) ◽  
pp. 91-98
Author(s):  
L. Zwaan

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.


Diagnosis ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 107-118 ◽  
Author(s):  
Mark L. Graber ◽  
Joseph Rencic ◽  
Diana Rusz ◽  
Frank Papa ◽  
Pat Croskerry ◽  
...  

Abstract Diagnostic error is increasingly recognized as a major patient safety concern. Efforts to improve diagnosis have largely focused on safety and quality improvement initiatives that patients, providers, and health care organizations can take to improve the diagnostic process and its outcomes. This educational policy brief presents an alternative strategy for improving diagnosis, centered on future healthcare providers, to improve the education and training of clinicians in every health care profession. The hypothesis is that we can improve diagnosis by improving education. A literature search was first conducted to understand the relationship of education and training to diagnosis and diagnostic error in different health care professions. Based on the findings from this search we present the justification for focusing on education and training, recommendations for specific content that should be incorporated to improve diagnosis, and recommendations on educational approaches that should be used. Using an iterative, consensus-based process, we then developed a driver diagram that categorizes the key content into five areas. Learners should: 1) Acquire and effectively use a relevant knowledge base, 2) Optimize clinical reasoning to reduce cognitive error, 3) Understand system-related aspects of care, 4) Effectively engage patients and the diagnostic team, and 5) Acquire appropriate perspectives and attitudes about diagnosis. These domains echo recommendations in the National Academy of Medicine’s report Improving Diagnosis in Health Care. The National Academy report suggests that true interprofessional education and training, incorporating recent advances in understanding diagnostic error, and improving clinical reasoning and other aspects of education, can ultimately improve diagnosis by improving the knowledge, skills, and attitudes of all health care professionals.


Diagnosis ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Paul A. Bergl ◽  
Thilan P. Wijesekera ◽  
Najlla Nassery ◽  
Karen S. Cosby

AbstractSince the 2015 publication of the National Academy of Medicine’s (NAM) Improving Diagnosis in Health Care (Improving Diagnosis in Health Care. In: Balogh EP, Miller BT, Ball JR, editors. Improving Diagnosis in Health Care. Washington (DC): National Academies Press, 2015.), literature in diagnostic safety has grown rapidly. This update was presented at the annual international meeting of the Society to Improve Diagnosis in Medicine (SIDM). We focused our literature search on articles published between 2016 and 2018 using keywords in Pubmed and the Agency for Healthcare Research and Quality (AHRQ)’s Patient Safety Network’s running bibliography of diagnostic error literature (Diagnostic Errors Patient Safety Network: Agency for Healthcare Research and Quality; Available from: https://psnet.ahrq.gov/search?topic=Diagnostic-Errors&f_topicIDs=407). Three key topics emerged from our review of recent abstracts in diagnostic safety. First, definitions of diagnostic error and related concepts are evolving since the NAM’s report. Second, medical educators are grappling with new approaches to teaching clinical reasoning and diagnosis. Finally, the potential of artificial intelligence (AI) to advance diagnostic excellence is coming to fruition. Here we present contemporary debates around these three topics in a pro/con format.


2019 ◽  
Vol 09 (04) ◽  
pp. 324-325
Author(s):  
Shafaq Sultana ◽  
Farhat Fatima

Arriving at an accurate diagnosis is one of the competencies prime of the medical practitioner. Errors may occur in the diagnostic process anywhere from the point of patient’s initial assessment and interpretation of diagnostic tests, and even during follow-up and patient referral. Patient safety is gaining global precedence and in this context diagnostic errors are speculate as an important cause of harm to the patients.1 An awareness of the possible underlying factors leading to diagnostic errors, along with a repertoire of strategies to improve can be of great help to both junior and senior medical residents


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