Literature DB >> 27782919

The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking.

Geoffrey R Norman1, Sandra D Monteiro, Jonathan Sherbino, Jonathan S Ilgen, Henk G Schmidt, Silvia Mamede.   

Abstract

Contemporary theories of clinical reasoning espouse a dual processing model, which consists of a rapid, intuitive component (Type 1) and a slower, logical and analytical component (Type 2). Although the general consensus is that this dual processing model is a valid representation of clinical reasoning, the causes of diagnostic errors remain unclear. Cognitive theories about human memory propose that such errors may arise from both Type 1 and Type 2 reasoning. Errors in Type 1 reasoning may be a consequence of the associative nature of memory, which can lead to cognitive biases. However, the literature indicates that, with increasing expertise (and knowledge), the likelihood of errors decreases. Errors in Type 2 reasoning may result from the limited capacity of working memory, which constrains computational processes. In this article, the authors review the medical literature to answer two substantial questions that arise from this work: (1) To what extent do diagnostic errors originate in Type 1 (intuitive) processes versus in Type 2 (analytical) processes? (2) To what extent are errors a consequence of cognitive biases versus a consequence of knowledge deficits?The literature suggests that both Type 1 and Type 2 processes contribute to errors. Although it is possible to experimentally induce cognitive biases, particularly availability bias, the extent to which these biases actually contribute to diagnostic errors is not well established. Educational strategies directed at the recognition of biases are ineffective in reducing errors; conversely, strategies focused on the reorganization of knowledge to reduce errors have small but consistent benefits.

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Year:  2017        PMID: 27782919     DOI: 10.1097/ACM.0000000000001421

Source DB:  PubMed          Journal:  Acad Med        ISSN: 1040-2446            Impact factor:   6.893


  64 in total

1.  Implicit bias in healthcare: clinical practice, research and decision making.

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2.  Critical Thinking in Critical Care: Five Strategies to Improve Teaching and Learning in the Intensive Care Unit.

Authors:  Margaret M Hayes; Souvik Chatterjee; Richard M Schwartzstein
Journal:  Ann Am Thorac Soc       Date:  2017-04

Review 3.  [Examination procedure and description of skin lesions].

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4.  Reflection in medical education: intellectual humility, discovery, and know-how.

Authors:  Edvin Schei; Abraham Fuks; J Donald Boudreau
Journal:  Med Health Care Philos       Date:  2019-06

5.  Deconstructing the diagnostic reasoning of human versus artificial intelligence.

Authors:  Thierry Pelaccia; Germain Forestier; Cédric Wemmert
Journal:  CMAJ       Date:  2019-12-02       Impact factor: 8.262

6.  Learning to Identify Rare Disease Patients from Electronic Health Records.

Authors:  Rich Colbaugh; Kristin Glass; Christopher Rudolf; Mike Tremblay Volv Global Lausanne Switzerland
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

7.  Showing Your Thinking: Using Mind Maps to Understand the Gaps Between Experienced Emergency Physicians and Their Students.

Authors:  Kira Gossack-Keenan; Kerstin De Wit; Emily Gardiner; Michelle Turcotte; Teresa M Chan
Journal:  AEM Educ Train       Date:  2019-09-01

8.  A Meta-Analysis of the Effect of Paper Versus Digital Reading on Reading Comprehension in Health Professional Education.

Authors:  Guillaume Fontaine; Ivry Zagury-Orly; Marc-André Maheu-Cadotte; Alexandra Lapierre; Nicolas Thibodeau-Jarry; Simon de Denus; Marie Lordkipanidzé; Patrice Dupont; Patrick Lavoie
Journal:  Am J Pharm Educ       Date:  2021-07-22       Impact factor: 2.047

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

Authors:  Evan J Petersen; Stephanie M Thurmond; Gail M Jensen
Journal:  J Man Manip Ther       Date:  2021-05-17

Review 10.  The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

Authors:  Claire M Felmingham; Nikki R Adler; Zongyuan Ge; Rachael L Morton; Monika Janda; Victoria J Mar
Journal:  Am J Clin Dermatol       Date:  2021-03       Impact factor: 7.403

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