This project brings together six universities to design and construct a patient-focused and personalized health system that addresses the fractured nature of healthcare information, and the lack of engagement of individuals in their own healthcare. By taking advantage of the enormous amount of information being created about our environment, through the confluence of real-time, mobile and wearable devices and the availability of rich social media data on patient behavior, the team will create a detailed and comprehensive picture of a patient's health, and a tool to help manage patients' engagement with their health care providers. The system has four key aims to: (1) provide a human-centered approach for integrating electronic health record data generated by traditional methods with data collected "in the wild" (such as personal fitness devices, mobile phone usage, local weather, pollution or even fast food restaurant maps, etc.); (2) develop a framework for deciding which data sources are trustworthy; (3) create a cloud-based system to allow users to view and track their own data over time and improve healthcare outcomes; and (4) provide educational outreach and community participation, particularly in minority populations, to design a system which benefits users in both the short term (through employment and education) and the long term (through increased engagement and trust).
This project will leverage modern distributed cloud-based computing infrastructure (including mobile phones and Amazon Web Services), and the unique capacities of the South BD Hub to house and analyze the enormous volumes of health-related data that are generated every day by people, and their environment. By linking electronic medical records, external databases and data 'in the wild' harvested from patient's Internet-enabled devices, the project will address several issues related to the integration of high-resolution data for longitudinal tracking of patients. These include acceptability of the technology, particularly by vulnerable groups, usability, veracity of data collected, and scalability/integration across a large heterogeneous landscape. By employing patient-centric agile development, the team will work with communities to implement a cloud-based architecture to improve tracking of study participants, increase the ease with which data can be captured, improve patient engagement, and facilitate care coordination. The resultant platform will integrate big data analytics, real time scalable data collection, and social media analytics on patient behavior to analyze cardiovascular disease outcomes among disadvantaged African American and Hispanic patient populations. Additionally, the team will implement data fusion techniques to ensure the veracity of the varying qualities of data collected, and develop machine learning models to identify at-risk patient populations in order to reduce health disparities. Finally, patient engagement and health outcomes will be measured to assess the validity and success of the system.
