| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | June 24, 2016 |
| Latest Amendment Date: | June 24, 2016 |
| Award Number: | 1621996 |
| 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, 2016 |
| End Date: | June 30, 2017 (Estimated) |
| Total Intended Award Amount: | $225,000.00 |
| Total Awarded Amount to Date: | $225,000.00 |
| Funds Obligated to Date: |
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| 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: |
125 Cambridge Park Drive, Suite Cambridge MA US 02140-2392 |
| 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: |
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| 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 benefit patients who require recovery care to get fully well after being discharged from the hospital. Transitions of care from acute (hospital) to post-acute (short-term rehabilitation and skilled nursing) settings impact millions of Americans every year. Seniors overindex on utilization of post-acute care, as a consequence of natural age-related degeneration and the need for longer recovery periods. By improving post-hospital coordination of care across the continuum, clinical outcomes and patient satisfaction stand to improve for America?s aging population. 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 methods for user-centered data capture in healthcare. Our 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 electronic health records and claims data from both acute and post-acute care settings. If successful, this research will impact the state-of-the-art in healthcare analytics and outcomes measurement.
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.
This project was a proof-of-concept application of the use of analytics to benefit patients who require recovery care to get fully well after being discharged from hospital. Transitions from acute (hospital) to post-acute (rehabilitation) care impact millions of Americans every year and account for billions of dollars of medical expenditure. Seniors overindex on utilization of post-acute care, as a consequence of natural age-related degeneration and the need for longer recovery periods. By improving high-level coordination of care across the continuum, clinical outcomes and patient satisfaction stand to improve for America's aging population. The results of this project stand to help reduce costs of care for healthcare providers, payers, and government/society.
This project incorporated and improved upon methods for aggregating, processing, and leveraging user-centered data in healthcare. The research resulted in a platform that harnesses multiple streams of healthcare data, including electronic health records and claims data from acute, post-acute, and sub-acute settings. This project demonstrated the ability of the system to transform high-complexity datasets into actionable insight, with the promise of driving meaningful improvement in clinical practice. Furthermore, the project demonstrated an ability to generate highly customized research and reporting through the repackaging of population-level insights and trends into more accessible data sets.
Last Modified: 07/28/2017
Modified by: Thaddeus R Fulford-Jones
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