The digitalization of the healthcare systems has resulted in a deluge of big data and has prompted the rapid growth of data science in medicine. Many informatics tools, such as data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, can also introduce benefits into implementation science, quality improvement (QI), and primary care research. The increased amount of primary care QI initiatives, availability of practice facilitation-related data, the need for better evidence-based care, and the complexity of challenges make the use of data science techniques and data-driven research particularly appealing to primary care. Recent advances in the usability, applicability, and interpretability of data science models offer promising applications to implementation science. Despite the increasing number of studies and publications in the field, thus far there have been few examples of combining informatics and implementation framework to facilitate primary care studies. We designed and developed an informatics-driven implementation research framework to provide a coherent rationale and justification of the complex interrelationships among features, strategies, and outcomes. The proposed framework is a principle-guided tool designed to improve the specification, reproducibility, and testable causal pathways involved in implementation research projects in primary care settings.