| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | June 3, 2014 |
| Latest Amendment Date: | December 17, 2014 |
| Award Number: | 1416215 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jesus Soriano Molla
jsoriano@nsf.gov (703)292-7795 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | July 1, 2014 |
| End Date: | June 30, 2015 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $179,999.00 |
| Funds Obligated to Date: |
FY 2015 = $29,999.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
50 BEHARRELL ST CONCORD MA US 01742-1750 (617)855-8214 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
38 Ossipee Rd #2 Somerville MA US 02144-1610 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase I |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The impact of this work centers on improving the state-of-the-art in analytics to drive greater efficiency and effectiveness in healthcare delivery. The technology developed under this award improves the ability to automatically assess patient risk and thereby reduce post-discharge readmissions rates. Importantly, the risk assessment takes into account a multitude of patient characteristics, thereby offering a comprehensive picture of risk drivers.
Specific outcomes of this work include the development of technologies to capture and analyze information from electronic medical records. An additional outcome is a set of analytics tools to detect emergent patterns and draw inferences about each patient’s risk of readmission. The research is broadly applicable to a wide variety of patient demographics, conditions, and care trajectories.
Societal benefits of these technological advances include the opportunity to control total medical expenditure while maintaining or improving quality of care and outcomes. With US healthcare spending exceeding 17% of GDP, there exists significant opportunity to improve the efficiency of care delivery. The technological foundations of this work can help respond to this opportunity. The risk-stratification system can help providers, payers, and other caregivers to identify where medical, administrative, and/or socio-behavioral interventions may be most appropriate in order to manage identified risk factors.
Last Modified: 07/15/2015
Modified by: Thaddeus R Fulford-Jones
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