Heuristics in managing complex clinical decision tasks in experts decision making

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Publication Type pre-print
School or College School of Medicine
Department Biomedical Informatics
Creator Del Fiol, Guilherme
Other Author Islam, Roosan; Weir, Charlene
Title Heuristics in managing complex clinical decision tasks in experts decision making
Date 2014-01-01
Description Background: Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. Objective: The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. Method: After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. Results: We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trad e-offs, managing uncertainty and generating rule of thumb. Conclusion: Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Application: Understanding comp lex decision making processes can help design allocation based on the complexity of task for clinical decision support design.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 186
Last Page 193
Language eng
Bibliographic Citation Islam, R., Weir, C., & Del Fiol, G. (2014). Heuristics in managing complex clinical decision tasks in experts decision making. Proceedings - 2014 IEEE International Conference on Healthcare Informatics, 186-93.
Rights Management (c) 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 188,682 bytes
Identifier uspace,19419
ARK ark:/87278/s6323527
Setname ir_uspace
Date Created 2016-02-02
Date Modified 2016-02-02
ID 713353
Reference URL https://collections.lib.utah.edu/ark:/87278/s6323527
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