Computer models of cardiac electrophysiology (EP) can help to better understand the mechanisms of arrhythmias and to guide interventions. However, model adjustment to patient data (personalization) is a required step that is still challenging from clinical data. The progress in the fusion of multimodal data opens up new possibilities in generating patient-specific models of the heart. In this paper, we present the state-of-the-art in multimodal data fusion for EP procedure guidance and how such integrated data can be used to personalize models and guide interventions.