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Investigating the design features for cognitive support in mass casualty triage : Developing a design for a cognitive tool to support decision-making

In response to the critical need for a national mass casualty triage system in Sweden, this design study presents an iterative approach to developing a cognitive support tool tailored for medical personnel. The study integrates user feedback garnered through interviews with potential end-users and thematic analysis to understand the needs, preferences, and challenges in triage situations. The themes derived work to inform the creation of two distinct design proposals, with one of them based on existing support material to support the recognition primed decision making (RPDM) theory. Subsequently, these proposals undergo rigorous evaluation via a questionnaire designed to assess various criteria crucial to effective decision-making in such high-pressure scenarios. This questionnaire measures multiple design criteria essential for effective decision-making, including clarity, ease of use, comprehensiveness, and usability. Participants also provide qualitative feedback, offering insights into the strengths and weaknesses of each design proposal.  Analysis of the questionnaire data reveals a clear preference for specific aspects of each proposal. Specifically, participants favour the front side of Proposal 2 for its simple design of the triage algorithm. The back side of Proposal 1 received higher scores for its recognition primed design and intuitive portray of the triage system. The findings of this study underscore the importance of iterative design processes informed by user feedback in the development of decision support tools for high-stakes scenarios such as mass casualty triage. Future iterations of the design will integrate these preferences to refine the tool further, ultimately aiming to enhance the effectiveness and efficiency of medical personnel in responding to mass casualty incidents and to investigate the cognitive theories supporting design.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-205616
Date January 2024
CreatorsEhrling, Stella
PublisherLinköpings universitet, Institutionen för datavetenskap
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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