The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project focuses on using analytics and technology to benefit patients who require additional care to fully recover - or simply to maintain their health - after being discharged from the hospital. Whether subacute or community-based care, significant opportunity exists to leverage machine learning, decision science, and advance data mining techniques to better support patients. State and federal budgets cover the costs of care for millions of Americans every year. This expenditure is growing faster than inflation, prompting the development of both optional and mandatory payment reform efforts that stand to simultaneously reduce costs and improve care outcomes. If successful, this project will help reduce costs of care for healthcare providers, payers, and government/society.The proposed project aims to incorporate and improve upon user-centered decision science in healthcare. The proposed platform will combine advanced machine learning techniques with a patient/family-centered business model. The innovation will harness multiple streams of healthcare data, such as claims/billing data from acute, subacute, and community care settings. If successful, this research will impact the state-of-the-art in healthcare analytics and outcomes measurement.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.
