Author: Calderon Romero, Nadya
In the era of “big data analytics” for healthcare, the personalized medicine promise offers a shift to the provision of care enabled by our technical ability to quantify and assess large volumes of biomedical data. This message however, often seems to strengthen a notion of healthcare from a “biomedical positivism framework”, that is, that diagnosis of disease, medical image analysis, integration of devices, and ultimately, the selection of the appropriate therapy is empowered by volumes of data and algorithmic accuracy, thus improving the patient’s illness. In this research program, we approached expert biomolecular analysts, recorded their sensemaking process, and analyzed the role of data visualization technologies while they performed analysis of multi-omic data for a direct-to-consumer service of personalized health. We uncovered the nature of the analysts turning to their human-interaction skillset to address the health reality of each consumer they worked for. Assertions about the scientific validity and the amount of data, often emphasize the claims of this personalized health approach, but in practice, the analysts turned to attend goals, preferences, to find actionable evidence in the data, and to frame a relatable health summary story for the clients. The role of technology design in scenarios like this one will be fundamental in properly translating and bridging the effort from these emergent providers (the analysts) in communication with the end consumers. Our findings suggest that both parties benefit from analytic capacities to explore and understand the strength of each piece of evidence in the case, including the evidence that is provided by the clients themselves beyond their biological samples. We believe that this work, along with the research methodologies deployed in work-settings, are a contribution to the Visual Analytics community to support the tasks of bio scientists in personalized medicine, as much as an HCI initiative in support of evidence-based models of preventive healthcare with large amounts of data.
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Thesis advisor: Fisher, Brian
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