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DECLARE: A Comprehensive, Multifaceted Cognitive Forcing Strategy to Confront Complex Cases

Diagnostic excellence is an important goal in medicine. The enhancement of clinical reasoning skills of physicians, which is at the core of this concept, is a significant challenge. To achieve this improvement, it is necessary to enhance the ability to collect patient history information and to inte...

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Detalles Bibliográficos
Autor principal: Shimizu, Taro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10149772/
https://www.ncbi.nlm.nih.gov/pubmed/37139260
http://dx.doi.org/10.2147/IJGM.S406165
Descripción
Sumario:Diagnostic excellence is an important goal in medicine. The enhancement of clinical reasoning skills of physicians, which is at the core of this concept, is a significant challenge. To achieve this improvement, it is necessary to enhance the ability to collect patient history information and to integrate the information. Additionally, the complexity of diagnosis is confounded by biases, noise, uncertainty, and contextual factors, and the impact of these factors is particularly prominent in complex cases. In such cases, the dual process theory, which is a classical reasoning measure, alone is insufficient to cope with these challenges, and a multifaceted and comprehensive approach is required to supplement its limitations. Therefore, the author presents six concrete steps, represented by the acronym DECLARE (Decomposition, Extraction, Causation Link, Assessing Accountability, Recomposition, Explanation and Exploration), that implement the concept of cognitive forcing strategy that has been shown to be effective in bias control, and include reflection, meta-cognition, and the recently popularized decision hygiene procedure. DECLARE is a strategy that should be deployed when faced with more complex diagnostic scenarios. By examining each of the six steps that comprise DECLARE individually, cognitive load can be reduced. Furthermore, by verifying causation and accountability when constructing diagnostic hypotheses, biases can be mitigated, which can also help to address noise and uncertainty, leading to an improvement in the quality of diagnosis and effectiveness in medical education.