Knowledge discovery in electronic health records (EHRs) is a central aspect for improved clinical decision making, prognosis, and patient management. While EHRs show great promise towards better data integration, automated access, and clinical workflow improvement, the vast information they capture over time pose challenges not only for medical practitioners, but also for the information analysis by machines. The objective of this paper is to promote and emphasize the importance of exploratory analytics that are commensurate with human capabilities and constraints. Within this realm we present a novel temporal event matrix representation and learning framework that discovers complex latent event patterns, which are easily interpretable by humans. We demonstrate our framework on synthetic data and on EHRs together with an extensive validation involving over 70,000 computed latent factor models. The present study is the first to link temporal patterns of healthcare resource utilization (HRU) against a diabetic disease complications severity index to better understand the relationships between disease severity and care delivery.