SBIR Phase II: Linking eLearning to patient outcomes

Award ID: 1926846

The broader/commercial impact of this SBIR Phase II project focuses on improving the management of chronic disease by enabling team-based primary care is key to achieving clinical results and taking advantage of new value based" payment reforms. According to the United States Centers for Disease Control, six out of ten adults have a chronic disease and 90% of the national annual healthcare expenditures are spend on people with these conditions. Our technology proposes to apply precision-education instructional theory to enhance primary care team competencies and promote situational awareness, enhanced communication, defined role clarity, improved coordination and leadership support to improve patient outcomes which is directly aligned to the National Science Foundations mission of promoting science to advance the nations health. This project is being designed for commercial use in Federally Qualified Health Centers (FQHCs) which serve one in twelve people in the United States. There are 1,373 FQHCs in the US serving 27 million patients annually in medically underserved areas. Public and private payment models are rapidly moving toward incentives/bonuses for team-based care and demonstrated outcome improvements. Improvements in the cost and outcomes of care for this patients with chronic disease will have enormous social and economic benefit for the Nation. This SBIR Phase II project uses machine learning to integrate individual-level clinical and social characteristics into suggested treatment paths and to apply precision training techniques that improve the skills of individual members of the care team. Our objectives focus on validating the feasibility of machine learning to provide health professionals with recommended workflows and continued education based on trends and gaps in care identified from patient data. The method includes a computational engine to guide reinforcement learning. Machine learning has made possible the development of statistical models to establish effect sizes of clinical interventions, enabling personalized instruction and support to health team members based on patient outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Henry Ahn