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Visual Hierarchy and Communication Effectiveness in Medical Decision Tools for Surrogate-Decision-Makers of Critically Ill Traumatic Brain Injury Patients

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Augmented Cognition (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12776))

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Abstract

A major goal of decision support tools (decision aids [DAs]) is to help people make informed decisions; hence, they often provide users with important information. This is particularly true for medical shared decision-making tools (decision aids [DAs]) for surrogate-decision-makers. These tools are designed to help individuals make crucial life-or-death decisions for loved ones who, due to a severe illness, are unable to make their own decisions. Making such decisions on behalf of a loved one often requires the processing of a great deal of medical information, typically in a short period of time, while respecting the patient’s values and preferences. Hence, it is particularly important to design such DAs in a way to communicate provided information effectively. In this study, we examined the impact of visual hierarchy on the communication effectiveness of a DA designed for surrogate decision-makers of critically ill traumatic brain injury patients. We compared users’ viewing behavior between two prototypes with different visual hierarchy levels. We also examined the impact of visual hierarchy that was created through images on engagement with the content. Our results show that creating distinct visual hierarchies can have a notable impact on how effectively provided information is communicated to users. The results also show that creating visual hierarchies via images can improve user engagement with textual information in medical tools designed for surrogate-decision-makers.

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Correspondence to Javad Norouzi Nia .

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Norouzi Nia, J., Varzgani, F., Djamasbi, S., Tulu, B., Lee, C., Muehlschlegel, S. (2021). Visual Hierarchy and Communication Effectiveness in Medical Decision Tools for Surrogate-Decision-Makers of Critically Ill Traumatic Brain Injury Patients. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2021. Lecture Notes in Computer Science(), vol 12776. Springer, Cham. https://doi.org/10.1007/978-3-030-78114-9_15

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  • DOI: https://doi.org/10.1007/978-3-030-78114-9_15

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