SBIR Phase II: System for Patient Risk Stratification through Electronic Health Record Analytics

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is significant; transitions of care impact millions of Americans every year. The healthcare system bears substantial cost and inefficiency
on account of suboptimal care transitions and overspending. This Phase II project will support progress towards a "learning healthcare system" and will extend the capabilities of data mining and machine learning in healthcare The proposed project seeks to improve data mining technologies for healthcare decision support. This project will focus on the analysis of a broad variety
of data types that are common in healthcare settings. The anticipated improvements would allow frontline care staff, operational managers, and healthcare executives to assess and make stronger evidence-driven decisions regarding quality, cost, and access as patients move through the healthcare system. The enhanced data mining system would utilize state-of-the-art pattern recognition and machine learning techniques to dynamically process and interpret clinical, claims, and other types of healthcare data. If successful, this research will impact the state-of-the-art in healthcare analytics.