scholarly journals Cognitive bias: how understanding its impact on antibiotic prescribing decisions can help advance antimicrobial stewardship

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Bradley J Langford ◽  
Nick Daneman ◽  
Valerie Leung ◽  
Dale J Langford
Keyword(s):  
Decision Making ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Healthcare Setting ◽  
Hyperbolic Discounting ◽  
Antibiotic Prescribing ◽  
Support Tools ◽  
Human Decision

Abstract The way clinicians think about decision-making is evolving. Human decision-making shifts between two modes of thinking, either fast/intuitive (Type 1) or slow/deliberate (Type 2). In the healthcare setting where thousands of decisions are made daily, Type 1 thinking can reduce cognitive load and help ensure decision making is efficient and timely, but it can come at the expense of accuracy, leading to systematic errors, also called cognitive biases. This review provides an introduction to cognitive bias and provides explanation through patient vignettes of how cognitive biases contribute to suboptimal antibiotic prescribing. We describe common cognitive biases in antibiotic prescribing both from the clinician and the patient perspective, including hyperbolic discounting (the tendency to favour small immediate benefits over larger more distant benefits) and commission bias (the tendency towards action over inaction). Management of cognitive bias includes encouraging more mindful decision making (e.g., time-outs, checklists), improving awareness of one’s own biases (i.e., meta-cognition), and designing an environment that facilitates safe and accurate decision making (e.g., decision support tools, nudges). A basic understanding of cognitive biases can help explain why certain stewardship interventions are more effective than others and may inspire more creative strategies to ensure antibiotics are used more safely and more effectively in our patients.

scholarly journals A neural network framework for cognitive bias

2017 ◽  
Author(s):  
J. E. Korteling ◽  
Anne-Marie Brouwer ◽  
Alexander Toet
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Relevant Information ◽  
Theoretical Perspectives ◽  
Human Decision ◽  
Underlying Mechanisms

Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases.To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena.

The Role of Cognitive Bias in Breast Radiology Diagnostic and Judgment Errors

2020 ◽  
Vol 2 (4) ◽  
pp. 382-389
Author(s):  
Vilert A Loving ◽  
Elizabeth M Valencia ◽  
Bhavika Patel ◽  
Brian S Johnston
Keyword(s):  
Decision Making ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Organizational Level ◽  
Individual Level ◽  
Gambler’S Fallacy ◽  
Satisfaction Of Search ◽  
Human Decision ◽  
The Individual

Abstract Cognitive bias is an unavoidable aspect of human decision-making. In breast radiology, these biases contribute to missed or erroneous diagnoses and mistaken judgments. This article introduces breast radiologists to eight cognitive biases commonly encountered in breast radiology: anchoring, availability, commission, confirmation, gambler’s fallacy, omission, satisfaction of search, and outcome. In addition to illustrative cases, this article offers suggestions for radiologists to better recognize and counteract these biases at the individual level and at the organizational level.

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

Cognitive Bias and its Relationship to Decision Making: the Case of Talented Secondary Students in Jeddah: الانحياز المعرفي وعلاقته باتخاذ القرار لدى عينة من طالبات المرحلة الثانوية الموهوبات بمدينة جدة

2020 ◽  
Vol 4 (45) ◽  
Author(s):  
Dalal Hamid Al-Dhahri, Arwa Abdullah Al-Ghamdi, Mogeda El-Sa
Keyword(s):  
Decision Making ◽  
Secondary Students ◽  
Cognitive Bias ◽  
Random Sampling ◽  
Cognitive Biases ◽  
School Administration ◽  
High Level ◽  
The Relationship ◽  
Descriptive Method

This study aims at investigating the relationship between cognitive biases and decision making from a sample of gifted secondary students. It also aims at identifying the level of students’ cognitive biases and decision making and the differences in these two areas based on different classrooms. Random sampling was used to collect data from 139 female secondary students from the gifted group. Their age ranged between (16-18) with an average of (16.6), A descriptive method was adopted in the study. The research tools used consisted of DACOBS David Assessment of Cognitive biases Scale (Vander Gaag. et al., 2000), translated and standardized by the present researchers, and Tuistra’s decision making scale for teenagers (Tuinstra, et al., 2000). The findings of the study show a negative correlation between cognitive biases and decision making. Also, there were no differences between cognitive biases and decision making scores based on different classrooms. The study also shows a low level of students’ cognitive biases and a high level of decision making. The study recommends activating the role of mentors and students' counseling, planning for the values and behaviors that need to be acquired by students by including them in the annual goals of the school administration and participating in societal awareness and education.

Recent Advancement in Fuzzy System

2014 ◽  
pp. 158-168
Author(s):  
İ. Burhan Türkşen ◽  
İbrahim Özkan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Interdisciplinary Research ◽  
Decision Process ◽  
Fuzzy System ◽  
Research Field ◽  
Decision Under Uncertainty ◽  
Recent Advancement ◽  
Human Decision

Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative that best suites our needs. This process generally includes several areas of research including but not limited to Economics, Psychology, Philosophy, Mathematics, Statistics, etc. In this chapter the authors attempt to create a framework for uncertainties which surrounds the environment where human decision making takes place. For this purpose, the authors discuss how one ought to handle uncertainties within Fuzzy Logic. Furthermore, they present recent advances in Type 2 fuzzy system studies.

scholarly journals Collaborative Metacognition in Signal Detection Decision Making

2019 ◽  
Vol 63 (1) ◽  
pp. 1704-1708
Author(s):  
Jackson Duncan-Reid ◽  
Jason S. McCarley
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Detection Task ◽  
Metacognitive Awareness ◽  
Parameter Estimates ◽  
Metacognitive Judgments ◽  
Signal Detection Task ◽  
Sensitive Individual

When individuals work together to make decisions in a signal detection task, they typically achieve greater sensitivity as a group than they could each achieve on their own. The present experiments investigate whether metacognitive, or Type 2, signal detection judgements would show a similar pattern of collaborative benefit. Thirty-two participants in Experiment 1 and sixty participants in Experiment 2 completed a signal detection task individually and in groups, and measures of Type 1 and Type 2 sensitivity were calculated from participants’ confidence judgments. Bayesian parameter estimates suggested that regardless of whether teams are given feedback on their performance (Experiment 1) or receive no feedback (Experiment 2), no credible differences were observed in metacognitive efficiency between the teams and the better members, nor between the teams and the worse members. These findings suggest that teams may self-assess their performance by deferring metacognitive judgments to the most metacognitively sensitive individual within the team, even without trial-by-trial feedback, rather than integrating their judgments and achieving increased metacognitive awareness of their own performance.

SHARED DECISION MAKING IN ONCOLOGY AND PALLIATIVE CARE

2020 ◽  
Author(s):  
Paul A Glare
Keyword(s):  
Decision Making ◽  
Palliative Care ◽  
Shared Decision Making ◽  
Cognitive Biases ◽  
Treatment Options ◽  
Care Plan ◽  
Treatment Modalities ◽  
Shared Decision ◽  
Human Decision ◽  
Based Medicine

Background: Cancer raises many questions for people afflicted by it. Do I want to have genetic testing? Will I comply with screening recommendations? If I am diagnosed with it, where will I have treatment? What treatment modalities will I have? Will I go on a clinical trial? Am I willing to bankrupt my family in the process of pursuing treatment? Will I write an advance care plan? Will I accept hospice if I have run out of available treatment options? Most of these questions have more than one correct answer, and the evidence for the superiority of one option over another is either not available or does not allow differentiation. Often the best choice between two or more valid approaches depends on how individuals value their respective risks and benefits; “preference-based medicine” may be more important than “evidence-based medicine.” There are various models for eliciting preferences, but applying them can raise a number of challenges. Objectives: To present the concepts, the value, the strategies, the quandaries, and the potential pitfalls of Shared Decision Making in Oncology and Palliative Care. Method: Narrative review. Results: Some challenges to practicing preference-based medicine in oncology and palliative care include: some patients don’t want to participate in shared decision making (SDM); the whole situation needs to be addressed, not just part of it; but are some topics out of bounds? Cognitive biases apply as much in SDM as any other human decision making, affecting the choice; how numerically equivalent data are framed can also affect the outcome; conducting SDM is also important at the end of life. Conclusions: By being aware of the potential pitfalls with SDM, clinicians are more able to facilitate the discussion so that the patients’ choices truly reflect their informed preferences, at a time when stakes and emotions are high.

scholarly journals The odd case when cognitive biases are more prevalent in researchers than in samples

2017 ◽  
Author(s):  
Jose D. Perezgonzalez
Keyword(s):  
Decision Making ◽  
Cognitive Bias ◽  
Strong Evidence ◽  
Cognitive Biases ◽  
Statistical Significance ◽  
The Impact ◽  
Result Interpretation

Walmsley and Gilbey (2016) reported on the impact of cognitive biases on pilots’ decision-making, concluding that there was strong evidence that cognitive bias impacted decision making thus putting pilots' lives in danger. However, their methodology was not free of the same biases they set to research and, more importantly, they relied far too much on statistical significance as the only standard for result interpretation. Consequently, while the results obtained may have been technically correct, their divorce from the underlying methodological context made them factually wrong. Therefore, the conclusions achieved also misrepresented the true impact of cognitive biases on pilots' decision-making.

scholarly journals Measuring individual differences in cognitive biases: The Cognitive Bias Inventory

2021 ◽  
Author(s):  
Vincent Berthet ◽  
Vincent de Gardelle
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Temporal Stability ◽  
Real Life ◽  
Future Research ◽  
Personality Factors ◽  
Wrong Answer ◽  
Outcome Bias

This article described the behavioral measurement of six classic cognitive biases (framing, availability, anchoring, overconfidence, hindsight/outcome bias, confirmation bias). Each measure showed a satisfactory level of reliability with regard both to internal consistency (mean Cronbach’s alpha = .77) and temporal stability (mean test-retest correlation = .71). Multivariate analysis supported the hypothesis that each cognitive bias captures specific decision-making processes as the six biases: (a) were virtually uncorrelated (mean correlation = .08), thus indicating no general decision-making competence factor, (b) were moderately correlated with other relevant constructs (the A-DMC components, cognitive ability, decision-making styles, and personality factors), (c) were more related to performance on a narrow domain of decision-making (the ability to overcome an intuitive wrong answer as measured by the CRT) than to the general success in real-life decision-making as measured by the Decision Outcomes Inventory (DOI). We introduce this set of behavioral tasks as the Cognitive Bias Inventory (CBI), a psychometric tool allowing for the reliable assessment of individual differences in six common, independent cognitive shortcuts. The CBI appears as a useful tool for future research on decision-making competence and how it relates to decision errors.

Sign in / Sign up

Export Citation Format

Share Document