CRII: SCH: Visualization for Better Medical Decision-Making

Washington University, Saint Louis MO

Investigators

Abstract

This project develops visualization methods for communicating complex health risk data to patients. The project determines how the use of visual aids can impact decision-making and whether visual aids can cause, reinforce or mitigate biases. Additionally, the project identifies strategies people use when making medical decisions. Results of this work will have broad and varying impacts. The knowledge gained from this project will provide a better understanding of how people reason and make decisions with health statistics, and how we can improve or create novel designs for communicating such complex data beyond the medical field. The output of the project will be a database of tested visualization designs for supporting informed medical decision-making. Despite being fundamental components of evidence-based decision-making, medical statistics are notoriously unintuitive and arduous for people to understand. As a result, providing patient support for health decision-making has become a significant challenge for the medical community. This research addresses this problem by calling attention to synergies between medical risk communication and visualization theory. The project will create a theoretical framework for how visual representations impact decisions. The results will demonstrate how different designs can create, reinforce, or mitigate biases related to risk perception, and will identify individual characteristics that may mediate cognitive biases. Furthermore, the project will develop computational approaches for how people use visualization throughout the decision-making process and will determine which visual elements people tend to use or ignore. Using medical risk communication as a test bed, this research will investigate core topics in the Visualization community and simultaneously make contributions to the medical community. A core product of this project will be a web-based multi-dimensional recommendation system for health risk visualization. All publications, data, and source code will be publicly available. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.