In the precision medicine paradigm, oncological treatment leverages complex ensemble datasets of similar patients to estimate the outcomes for a current patient. A key challenge is developing and deploying easy-to-understand AI predictive models for the outcomes of a specific patient, based on patient data from multiple institutions. We describe the lessons learned from the development and deployment of an interactive dashboard to support the analysis of individual head and neck cancer patient outcomes based on cohort data. As required by the project, the dashboard design aims to handle a large client base. The dashboard combines an AI solution with a multi-view interface featuring domain-specific plots to facilitate the visual analysis of patient outcomes and to quickly stratify new patients into risk groups. A year after the successful public deployment of the dashboard, we evaluate it with clinician domain experts. We report the feedback and we reflect on the lessons learned through this experience.

Index Terms: VA-machine intelligence for healthcare data visualization, Human-centered AI for health decision-making, Dashboard, Risk Stratification, Precision Medicine

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Nipu, N., van Dijk, L., Canahuate, G., Fuller, C.D., Marai, G.E., Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator, VAHC 2025 (16th workshop on Visual Analytics in Healthcare), in conjunction with IEEE VIS 2025, Vienna, Austria, pp. 1-7, November 2nd, 2025. https://visualanalyticshealthcare.github.io/homepage/2025/