scholarly journals Heuristics and medical errors. Part 2: How to make better medical decisions

2021 ◽  
Vol 25 (1) ◽  
pp. 45-52
Author(s):  
Mark A. Graber
Keyword(s):  
Medical Errors ◽  
Patient Management ◽  
Family Doctor ◽  
Confirmation Bias ◽  
Diagnostic Error ◽  
Frame Of Reference ◽  
Diagnostic Process ◽  
Medical Decisions ◽  
Diagnostic Uncertainty ◽  
Availability Bias

This publication is a continuation of the article published in the 4th issue of the journal Russian family doctor for 2020 Heuristics, language and medical errors, which described the ways of making medical decisions that can lead to errors in patient management tactics, in particular affect of heuristics / visceral bias, attribution error, frame of reference, availability bias, one-word-one-meaning-fallacy. This article discusses additional sources of diagnostic error, including diagnosis momentum, confirmation bias, representativeness, and premature closure also the conflict that arises from diagnostic uncertainty is discussed. All errors in the tactics and the diagnostic process are illustrated by clinical cases from the personal practice of the author of the article.

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.

The impact of electronic health records on diagnosis

Diagnosis ◽  
2017 ◽  
Vol 4 (4) ◽  
pp. 211-223 ◽  
Author(s):  
Mark L. Graber ◽  
Colene Byrne ◽  
Doug Johnston
Keyword(s):  
Safety Concern ◽  
Diagnostic Error ◽  
The United States ◽  
Unintended Consequences ◽  
Diagnostic Process ◽  
Health It ◽  
Health Care Information ◽  
Health Care Information Technology ◽  
Electronic Health ◽  
The Impact

AbstractDiagnostic error may be the largest unaddressed patient safety concern in the United States, responsible for an estimated 40,000–80,000 deaths annually. With the electronic health record (EHR) now in near universal use, the goal of this narrative review is to synthesize evidence and opinion regarding the impact of the EHR and health care information technology (health IT) on the diagnostic process and its outcomes. We consider the many ways in which the EHR and health IT facilitate diagnosis and improve the diagnostic process, and conversely the major ways in which it is problematic, including the unintended consequences that contribute to diagnostic error and sometimes patient deaths. We conclude with a summary of suggestions for improving the safety and safe use of these resources for diagnosis in the future.

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.

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.

scholarly journals Use of Heuristics during the clinical decision process in from family physicians in real conditions.

2021 ◽  
Author(s):  
Carmen Fernández Aguilar ◽  
José-Jesús Martín-Martín ◽  
Sergio Minué-Lorenzo ◽  
Alberto Fernández Ajuria
Keyword(s):  
Primary Care ◽  
Decision Process ◽  
Primary Care Physicians ◽  
Clinical Decision ◽  
Diagnostic Error ◽  
Operational Definition ◽  
Statistical Correlation ◽  
Diagnostic Process ◽  
Representativeness Heuristic ◽  
Definition Of

Rationale, aims and objectives: The available evidence on the existence and consequences of the use of heuristics in the clinical decision process is very scarce. The purpose of this study is to measure the use of the Representativeness, Availability and Overconfidence heuristics in real conditions with Primary Care physicians in cases of dyspnea and to study the possible correlation with diagnostic error. Methods: A prospective cohort study was carried out in 4 Primary Care centers in which 371 new cases or dyspnea were registered. The use of the three heuristics in the diagnostic process is measured through an operational definition of the same. Subsequently, the statistical correlation with the identified clinical errors is analyzed. Results: In 9.97% of the registered cases a diagnostic error was identified. In 49.59% of the cases, the physicians used the representativeness heuristic in the diagnostic decision process. The availability heuristic was used by 82.38% of the doctors and finally, in more than 50% of the cases the doctors showed excess confidence. None of the heuristics showed a statistically significant correlation with diagnostic error. Conclusion: The three heuristics have been used as mental shortcuts by Primary Care physicians in the clinical decision process in cases of dyspnea, but their influence on the diagnostic error is not significant. New studies based on the proposed methodology will allow confirming both its importance and its association with diagnostic error.

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.

Detecting diagnostic error in psychiatry

Diagnosis ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 75-78 ◽  
Author(s):  
James Phillips
Keyword(s):  
Error Detection ◽  
Gold Standard ◽  
Laboratory Tests ◽  
General Medicine ◽  
Diagnostic Error ◽  
Diagnostic Process ◽  
Psychiatric Diagnoses ◽  
Tissue Analysis ◽  
Laboratory Findings ◽  
Diagnostic Categories

AbstractThe question of diagnostic error in psychiatry involves two intertwined issues, diagnosis and error detection. You cannot detect diagnostic error unless you have a reliable, valid method of making diagnoses. Since the diagnostic process is less certain in psychiatry than in general medicine, that will make the detection of error less confidant. Psychiatric diagnostic categories are developed without laboratory tests and other biomarkers. These limitations dramatically weaken the validity of psychiatric diagnoses and render error detection an uncertain undertaking, with go gold standard such as laboratory findings and tissue analysis, as in most of general medicine. With these limitations in mind, I review the methods that are available for error detection in psychiatry.

scholarly journals Mind the overlap: how system problems contribute to cognitive failure and diagnostic errors

Diagnosis ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 151-156 ◽  
Author(s):  
Ashwin Gupta ◽  
Molly Harrod ◽  
Martha Quinn ◽  
Milisa Manojlovich ◽  
Karen E. Fowler ◽  
...  
Keyword(s):  
Information Processing ◽  
Data Gathering ◽  
Diagnostic Errors ◽  
Organizational Structures ◽  
Diagnostic Error ◽  
Diagnostic Process ◽  
Cognitive Errors ◽  
Data Verification ◽  
Consulting Services ◽  
Cognitive Failure

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.

Taxonomy and Epidemiology of Error in Radiology

2019 ◽  
pp. 29-74
Author(s):  
Michael A. Bruno
Keyword(s):  
Working Memory ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Diagnostic Error ◽  
Attentional Focus ◽  
Relative Contribution ◽  
Anchoring Bias ◽  
Classification Of Error

This chapter provides an overview of the prevalence and classification of error types in radiology, including the frequency and types of errors made by radiologists. We will review the relative contribution of perceptual error—in which findings are simply not seen—as compared to other common types of error. This error epidemiology will be considered in the light of the underlying variability and uncertainties present in the radiological process. The role of key cognitive biases will also be reviewed, including anchoring bias, confirmation bias, and availability bias. The role of attentional focus, working memory, and problems caused by fatigue and interruption will also be explored. Finally, the problem of radiologist error will be considered in the context of the overall problem of diagnostic error in medicine.

scholarly journals P085: Remember that patient you saw last week – Characteristics of patients experiencing unanticipated death following emergency department discharge

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S95-S95
Author(s):  
R. Hoang ◽  
K. Sampsel ◽  
A. Willmore ◽  
K. Yelle-Labre ◽  
V. Thiruganasambandamoorthy ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Medical Errors ◽  
Medical Error ◽  
Tertiary Care ◽  
Vital Signs ◽  
Diagnostic Error ◽  
Sufficient Information ◽  
Risk Area ◽  
Ed Visits

Background: The emergency department (ED) is an at-risk area for medical error. We measured the frequency and characteristics of patients with unanticipated death within 7 days of ED discharge and whether medical error contributed. Aim Statement: This study aimed to calculate the frequency of patients experiencing death within 7 days after ED discharge and determine whether these deaths were related to their index ED visit, were unanticipated, and whether possible medical error occurred. Measures & Design: We performed a single-centre health records review of 200 consecutive cases from an eligible 458,634 ED visits from 2014-2017 in two urban, academic, tertiary care EDs. We included patients evaluated by an emergency physician who were discharged and died within 7 days. Three trained and blinded reviewers determined if deaths were related to the index visit, anticipated or unanticipated, or due to potential medical error. Reviewers performed content analysis to identify themes. Evaluation/Results: Of the 200 cases, 129 had sufficient information for analysis, translating to 44 deaths per 100,000 ED discharges. We found 13 cases per 100,000 ED discharges were related and unanticipated deaths and 18 of these were due to potential medical errors. Over half (52.7%) of 129 patients displayed abnormal vital signs at discharge. Patients experienced pneumonia (27.1%) as their most common cause of death. Patient characteristic themes were: difficult historian, multiple complaints, multiple comorbidities, acute progression of chronic disease, recurrent falls. Provider themes were: failure to consider infectious etiology, failure to admit high-risk elderly patient, missed diagnosis. System themes included multiple ED visits or recent admission, no repeat vital signs recorded. Discussion/Impact: Though the frequency of related and unanticipated deaths and those due to medical error was low, these results highlight opportunities to potentially enhance ED discharge decisions. These data add to the growing body of ED diagnostic error literature and emphasize the importance of identifying potentially high risk patients as well as being cognizant of the common medical errors leading to patient harm.

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