Grant: Discovery Grants Program - Individual

A high-priority area of R&D for Canada is development of software solutions that deal with the explosive growth of data and escalating needs for novel ways to use computational technologies to make decisions and discover new knowledge. With the accelerating growth of health data, new areas and activities in health research, innovation, and investigation are emerging, such as in neuroscience, pharmacogenomics, precision medicine, and global disease monitoring and forecasting. For instance, precision medicine is an innovative approach to tailoring disease prevention and treatment to the needs of different people based on their genes, environments, and lifestyles. Pharmacogenomics, also related to precision medicine, looks at how people's DNA affects the way they respond to drugs. Activities in these areas are complex, knowledge intensive, cumbersome, and error prone. They need precise analysis and problem solving and use of sophisticated computational tools. Older technological and design solutions are not effective at supporting such emerging activities. Electronic health records (EHRs) are being rapidly deployed worldwide, including Canada. They provide a vast body of patient data that can be analyzed. This holds great potential to advance health research and to mitigate the cost of healthcare delivery in Canada. However, EHR data are very complex to analyze. Despite the vast potential of EHRs, due to the complexity and challenges of generating actionable knowledge from EHR data, analysts, such as health and drug researchers, pandemic managers, policy makers, economists, and social scientists, use EHR data in a limited capacity. This is why we need novel computer systems that can support analysts. Visual analytics tools are emerging computational technologies that offer great promise for supporting complex tasks involving EHRs. These computational tools can combine novel interactive data visualizations and automated data analytics to help analysts with their reasoning and decision-making tasks. While data size and computational powers of visual analytics tools are constantly increasing, human cognitive and mental capabilities are constant, and analysts are the main bottleneck in the analytic workflow. Using cognitive-science-informed theories, this proposal aims to develop novel techniques for how humans interact with visual analytics tools to enable performance of complex analytical and research tasks involving large bodies of electronic health record data. These techniques and strategies will improve human cognitive performance and facilitate working with large datasets and difficult-to-understand automated data analysis models. The proposed research is innovative and unique. This research will enable knowledge workers to perform emerging, hitherto impossible R&D activities, impacting the healthcare sector of the economy, particularly in an age when healthcare costs are skyrocketing.