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      Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient’s and practitioner’s individual cognitive biases

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          Abstract

          High stake clinical choices in psychiatry can be impacted by external irrelevant factors. A strong understanding of the cognitive and behavioural mechanisms involved in clinical reasoning and decision-making is fundamental in improving healthcare quality. Indeed, the decision in clinical practice can be influenced by errors or approximations which can affect the diagnosis and, by extension, the prognosis: human factors are responsible for a significant proportion of medical errors, often of cognitive origin. Both patient’s and clinician’s cognitive biases can affect decision-making procedures at different time points. From the patient’s point of view, the quality of explicit symptoms and data reported to the psychiatrist might be affected by cognitive biases affecting attention, perception or memory. From the clinician’s point of view, a variety of reasoning and decision-making pitfalls might affect the interpretation of information provided by the patient. As personal technology becomes increasingly embedded in human lives, a new concept called digital phenotyping is based on the idea of collecting real-time markers of human behaviour in order to determine the ‘digital signature of a pathology’. Indeed, this strategy relies on the assumption that behaviours are ‘quantifiable’ from data extracted and analysed through connected tools (smartphone, digital sensors and wearable devices) to deduce an ‘e-semiology’. In this article, we postulate that implementing digital phenotyping could improve clinical reasoning and decision-making outcomes by mitigating the influence of patient’s and practitioner’s individual cognitive biases.

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          Most cited references41

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          Implicit bias in healthcare professionals: a systematic review

          Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Results Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. Discussion The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.
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            Homo heuristicus: why biased minds make better inferences.

            Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an "adaptive toolbox;" and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people's adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. Copyright © 2009 Cognitive Science Society, Inc.
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              Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health.

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                Author and article information

                Journal
                Dialogues Clin Neurosci
                Dialogues Clin Neurosci
                Dialogues in Clinical Neuroscience
                Taylor & Francis
                1294-8322
                1958-5969
                1 June 2022
                2021
                1 June 2022
                : 23
                : 1
                : 52-61
                Affiliations
                [a ]Department of Psychiatry, Hôpital Saint-Antoine, Sorbonne Université, AP-HP , Paris, France
                [b ]Sorbonne Université, Hôpital de la Pitié Salpêtrière, iCRIN (Infrastructure for Clinical Research In Neurosciences), Brain Institute (ICM), INSERM, CNRS , Paris, France
                [c ]Department of Psychiatry, CHU Nîmes, University of Montpellier , Nîmes, France
                [d ]Inserm, Unit 1061 “Neuropsychiatry: Epidemiological and Clinical Research” , Montpellier, France
                [e ]PICNIC lab (Physiological investigation of clinically normal and impaired cognition), Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Université de Paris, Sorbonne Université , Paris, France
                [f ]University Hospital Cochin (site Tarnier) , Paris, France
                [g ]Department of Child and Adolescent Psychiatry, CHU de Nantes , Nantes, France
                [h ]Pays de la Loire Psychology Laboratory , Nantes, France
                [i ]Jeanne d'Arc Hospital, INICEA Korian , Saint-Mandé, France
                Author notes
                CONTACT Stéphane Mouchabac stephane.mouchabac@ 123456aphp.fr Department of Psychiatry, Hôpital Saint-Antoine, Sorbonne Université, AP-HP , Paris, 75184, France
                Author information
                https://orcid.org/0000-0002-4226-7956
                https://orcid.org/0000-0003-3888-7690
                https://orcid.org/0000-0001-8035-8843
                https://orcid.org/0000-0003-1438-5733
                https://orcid.org/0000-0002-1427-2096
                Article
                2042165
                10.1080/19585969.2022.2042165
                9286737
                35860175
                0b430a64-86b7-4028-9db6-ade71e43f7a4
                © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Page count
                Figures: 2, Tables: 1, Pages: 10, Words: 6726
                Categories
                Review
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                Neurosciences
                digital phenotyping,cognitive bias,medical decision making
                Neurosciences
                digital phenotyping, cognitive bias, medical decision making

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