The Independent Studies program closed in 2016. This thesis was one of 25 accepted by Library for long-term preservation and presentation in UWSpace.Contemporary technologies and user interface design enable people
to routinely interact with data in their everyday lives. While consumer
applications for shopping and travel often feature data-driven user interfaces,
health resources rarely do. These resources rely on manual translation of
medical evidence into prose instead of providing users the capacity to interact
with underlying data. The abstraction away from details about treatment
options, including data about efficacy, harms, and patient-reported outcomes,
stands in the way of people who may wish to become fully informed when
taking on important medical decisions. In spite of barriers that restrict access
to and potential to apply medical evidence, this project explored whether
contemporary open-source Web technologies could be adapted to create datadriven
resources for the exploration of such evidence.
A prototype platform and example applications were developed using
JavaScri+I3pt and React.js, with Google Spreadsheets as a data store for medical
evidence related about twelve disease-modifying antirheumatic drugs
(DMARDs) commonly used to treat rheumatoid arthritis. Research findings
were manually encoded from diverse sources, and a controlled vocabulary
and data visualization components built to bridge the gap between outcomes
and data publishing formats favored in research, and issues important to
patients with rheumatoid arthritis. The volume and heterogeneity of source
evidence revealed no straightforward parallel to consumer data-driven online
applications, especially where evidence conflicts or is uncertain. Nevertheless,
this thesis demonstrates that extant and ready-made technologies can be
combined to create an extensible, data-driven platform and user interface
elements to investigate and visualize certain kinds of evidence about chronic
disease treatment options. Future research might investigate how such
platforms might be incorporated into patient-facing decision aids, automated
synthesis of research findings, and collaborative tools to encode evidence
