Visual analytics in precision medicine: Using mixed methods to support stakeholder data needs

Date created: 
2020-06-26
Identifier: 
etd20921
Keywords: 
Visual Analytics
Precision Medicine
Interactive Visualization
HCI
Design Thinking
Cognitive Mediators
Abstract: 

Precision medicine solutions require health consumers to increasingly interact with digital interfaces to report their medical history and conditions. This is challenging since the symptoms of an illness can often be located to an activity or a body part and untrained health consumers struggle to communicate them clearly. To address this problem, I designed a mixed methods study, where I first conducted short-term ethnography in a precision medicine company to understand the data requirements of a set of health data analysts. This exploration led to methodological and design guidelines that translated into an interactive data-capture system that visually mapped a controlled vocabulary of human disease phenotypes to a graphical depiction of the body. Results showed that describing the experience of illness in a somatic representation gave health consumers a more accurate and descriptive understanding of their illness, and by doing so captured more reliable data for analysts. The representation can support health care workers to provide more accurate analyses, aid caregivers in managing health risks, and empower health consumers to take action to better their health.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Brian Fisher
Department: 
Communication, Art & Technology: School of Interactive Arts and Technology
Thesis type: 
(Thesis) M.Sc.
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