CONTEXT
A vendor requested an analysis of electronic health records (EHRs) and cancer registries using Real-World Data (RWD) in Oncology. The objective was to apply advanced visualization techniques to gain deeper insights into cancer treatment effectiveness, patient demographics, and disease progression patterns, ultimately supporting better clinical decision-making.
RESOLUTION
Our team strategically selected visualization techniques tailored to different oncology outcomes:
- Waterfall Plots: Tracked individual tumor size changes over time, distinguishing responders from non-responders to treatment.
- Forest Plots: Compared treatment efficacy across patient subgroups, displaying confidence intervals and effect sizes.
- Survival Curves (Kaplan-Meier Plots): Illustrated probability of survival over time, assessing overall survival (OS) and progression-free survival (PFS) in different treatment arms.
- Heat Maps: Visualized patient characteristics such as age, gender, and genetic markers across treatment groups.
- Scatterplots: Explored relationships between clinical parameters and treatment outcomes, such as tumor size and therapy response.
RESULT
The team successfully developed interactive dashboards to explore multiple data facets, allowing filtering based on specific criteria to identify patient subgroups that may benefit from targeted therapies. Additionally, network graphs were utilized to map complex relationships between cancer types, genetic mutations, and treatment pathways, providing broader insights into treatment effectiveness beyond controlled clinical trials.
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