SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project focuses on using analytics and technology to drive greater efficiency and effectiveness in healthcare. Recent legislative changes are driving all players within the healthcare ecosystem toward greater accountability. This Phase I project specifically includes technologies to automatically assess patient risk and thereby reduce post-discharge readmissions rates. This Phase I project has the potential to support a broad range of customers across both the provider and the payer landscape, by providing cost-effective readmissions control solutions that respond to new legislative pressures. In terms of commercial potential, the Institute of Medicine of the National Academies has estimated that preventable hospital readmissions account for $20 billion/year in wasteful healthcare spending. The addressable market for the proposed Phase I proof-of-concept for patient risk stratification to support readmission control is approximately $100MM. In the future, this research project will serve as a foundation to support broader population health analytics, the addressable market for which exceeds $500MM/year and is growing at a rate of 24% annually. The proposed project aims to develop a data mining system to capture and analyze information from electronic medical records in order to risk-stratify patients after they have been discharged from hospital. Leveraging interoperability standards that are required by federal regulation, the system will seamlessly aggregate data from multiple electronic medical record systems in a vendor-agnostic manner. A custom analytics engine will detect emergent patterns and draw inferences about each patient?s risk of readmission. If successful, this research will validate the end-to-end concept and suggest the broader applicability of this approach to some of the greatest challenges in population health.

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.