Cardiovascular disease (CVD) is the leading cause of death in the United States and globally, accounting for approximately one in three deaths. Early detection and frequent monitoring of traditional risk factors and biomarkers is necessary to save lives and reduce unnecessary costs due to CVD morbidity and mortality. This dissertation aims to develop new tools and processes that can contribute more effective means to assist in prevention and early intervention at the point-of-care through integrated instrumentation and predictive models for CVD. The Programmable Bio-Nano-Chip (p-BNC) is a platform to digitize biology in which small quantities of patient sample generate immunofluorescent signal that is optically extracted and converted to antigen concentrations. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the p-BNC cartridge’s blister packs. A portable analyzer instrument was designed and fabricated so as to facilitate fluid delivery, optical detection, automated data analysis, and intuitive mobile health interfaces, representing a universal system for acquiring, processing, and managing clinical data streams and overcoming many of the challenges facing the widespread clinical adoption of lab-on-a-chip technologies. The p-BNC system is also a biosensor system that learns—a diagnostic device that implements machine-learning algorithms for diagnosis and prognosis for a variety of disease applications. In this capacity, a Cardiac ScoreCard system was developed to predict a spectrum of CVD using multiplexed biomarker measurements, symptoms, medical history, and demographics. The combination of a platform to digitize biology and predictive analytics has the potential to alter the trajectory of medicine, where the current linear thinking—mainly based on late-stage disease diagnosis using expensive and cumbersome tools—is replaced by a pathway to exponential medicine made possible through the introduction of scalable tools with the capacity to learn.