OBJECTIVES: Detailed data on type 2 diabetes mellitus (T2DM) patients and treatment in clinical practice are scarce. The Nordic region offers unique opportunities for research on patient-level data from various complementary data sources, by availability of  homogenous public healthcare systems with clinical information registered in electronic medical records (EMR) and mandatory national registers, and data linkage using the unique personal identification numbers for all inhabitants. This paper describes the implementation of a novel research methodology utilized in the Nordic countries to provide new T2DM insights based on a hybrid utilization of EMR data combined with national health registers, and supports the ongoing global DISCOVER program (NCT02322762). METHODS: This observational study collects both prospective health care data and retrospective longitudinal data. Patient-level information (e.g. demographics, diagnoses, clinical notes, laboratory results, health care contacts and referrals) of the enrolled T2DM patients will be extracted from EMR at each study site. To facilitate clinical data completeness, specific variables will be collected by electronic case report forms. In addition, the national full population registers (patient-, cause of death- and prescribed drug registers) provide longitudinal data for the enrolled T2DM patients, the complete study site T2DM cohorts, and the nationwide full T2DM population. RESULTS: This approach, bridging data from the enrolled T2DM patients, the complete cohort of T2DM patients at the study sites, and the nationwide full population, generates extended observational data with high internal and external validity from approximately 1 million T2DM patients. Iterative data collection spanning several years (2015 – 2018), thus potentiating interim analyses, will provide contemporary insights of T2DM disease progression and treatment development. CONCLUSIONS: This novel methodology presents a new era for observational research, providing efficient ways of generating comprehensive data with high completeness and minimal interference with ordinary clinical practice. This is highly relevant for diabetes and other chronic diseases.