HCIL-2016-02

Onukwugha, E., Plaisant, C., Shneiderman, B.
In Hesse, B., Ahern, D., and Beckjord, E. (Eds.) Oncology Informatics, Elsevier (2016 to appear)
HCIL-2016-02
The era of "big data" promises more information for health practitioners, patients, researchers, and policy makers. For big data resources to be more than larger haystacks in which to find precious needles, stakeholders will have to aim higher than increasing computing power and producing faster, nimbler machines. We will have to develop tools for visualizing information; generating insight; and creating actionable, on-demand knowledge for clinical decision making. This chapter has three objectives: 1) to review the data visualization tools that are currently available and their use in oncology; 2) to discuss implications for research, practice, and decision making in oncology; and 3) to illustrate the possibilities for generating insight and actionable evidence using targeted case studies. A few innovative applications of data visualization are available from the clinical and research settings. We highlight some of these applications and discuss the implications for evidence generation and clinical practice. In addition, we develop two case studies to illustrate the possibilities for generating insight from the strategic application of data visualization tools where the interoperability problem is solved. Using linked cancer registry and Medicare claims data available from the National Cancer Institute, we illustrate how data visualization tools unlock insights from temporal event sequences represented in large, population-based datasets. We show that the information gained from the application of visualization tools such as EventFlow can define questions, refine measures, and formulate testable hypotheses for the investigation of cancer-related clinical and process outcomes.
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