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      Uncertainty in Decision Making in Medicine : A Scoping Review and Thematic Analysis of Conceptual Models

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          Abstract

          The practice of medicine is rarely straightforward. Data used to facilitate medical decision-making may be conflicting, ambiguous, or scarce, and providing optimal care requires balancing clinicians’ expertise and available evidence with patients’ preferences. To explore uncertainty in decision-making across disciplines, the authors performed a scoping review and thematic analysis of the literature to formulate a model describing the decision-making process in medicine under uncertain conditions. In 2016, the authors performed a comprehensive search of key databases using a combination of keywords and controlled vocabulary. They identified and reviewed 3,398 records. After applying their inclusion and exclusion criteria to the titles and abstracts then full texts, 19 articles were selected. The authors applied a qualitative thematic analysis to these articles, using codes to extract themes related to uncertainty in decision-making. The 19 articles spanned 6 fields of study and 5 disciplines within the health sciences. The thematic analysis revealed 6 main themes: recognition of uncertainty, classification of uncertainty, stakeholder perspectives, knowledge acquisition, decision-making approach, and evaluation of the decision-making process. Based on the themes that emerged from their thematic analysis of the literature characterizing the effects of uncertainty and ambiguity on the decision-making process, the authors developed a framework depicting the interplay between these themes with a visual representation of the decision-making process under uncertain conditions. Future research includes further development and validation of this framework to inform medical school curricula.

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          Author and article information

          Journal
          Academic Medicine
          Academic Medicine
          Ovid Technologies (Wolters Kluwer Health)
          1040-2446
          2020
          January 2020
          : 95
          : 1
          : 157-165
          Article
          10.1097/ACM.0000000000002902
          6925325
          31348062
          7f8ee014-06b3-440e-aa36-d0a3d58c3cbc
          © 2020
          History

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