2336417
Cooperative Agreement
Overview
Grant Description
Sttr Phase II: Advancing health equity using interactive condition assessment and monitoring.
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is to potentially improve patient outcomes and reduce healthcare costs by enhancing communication between patients and their medical providers.
In the U.S., 78.9% of misdiagnoses are caused by miscommunication, resulting in 80,000 to 200,000 avoidable hospital deaths each year, and 56.3% of those communication gaps are related to the history-taking during the patient-provider encounter.
Enhancing communication in healthcare is crucial for improving both the efficiency and quality of healthcare services.
Literaseed’s project proposes electronic health record (EHR) integration and natural language processing (NLP) data extraction to enable automated chart review, facilitating possible access to critical patient data and allowing health systems to reclaim previously lost revenue due to the misclassification of patient risk.
This project aims to improve the long-term efficiency of our healthcare system by addressing incomplete and conflicting EHR information, providing alerts of vital medical history, and mitigating the effects of poor health literacy, all in an effort to help empower the patient.
The proposed project performs electronic health records (EHR) integration of the platform and integrates it with natural language processing (NLP) to extract valuable information from complex and unstructured medical records.
These learnings led to the prioritization of three major technical objectives: (1) EHR integration to simplify workflow and enhance access to patient data, (2) enhancing the ML/AI risk assessment model by incorporating NLP techniques for extracting valuable information from complex, fragmented, incomplete, and contradictory medical records, and (3) conducting validation testing by clinicians to ensure the reliability and efficacy of ML/AI outputs.
The integration of NLP for data extraction, combined with the patient’s self-reporting, ensures a comprehensive and accurate representation of the patient's present condition and medical history.
This innovation could enable real-time risk adjustment, expedite patient care, address missed care opportunities, and boost revenue in global capitation and value-based care delivery models.
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.
Subawards are planned for this award.
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is to potentially improve patient outcomes and reduce healthcare costs by enhancing communication between patients and their medical providers.
In the U.S., 78.9% of misdiagnoses are caused by miscommunication, resulting in 80,000 to 200,000 avoidable hospital deaths each year, and 56.3% of those communication gaps are related to the history-taking during the patient-provider encounter.
Enhancing communication in healthcare is crucial for improving both the efficiency and quality of healthcare services.
Literaseed’s project proposes electronic health record (EHR) integration and natural language processing (NLP) data extraction to enable automated chart review, facilitating possible access to critical patient data and allowing health systems to reclaim previously lost revenue due to the misclassification of patient risk.
This project aims to improve the long-term efficiency of our healthcare system by addressing incomplete and conflicting EHR information, providing alerts of vital medical history, and mitigating the effects of poor health literacy, all in an effort to help empower the patient.
The proposed project performs electronic health records (EHR) integration of the platform and integrates it with natural language processing (NLP) to extract valuable information from complex and unstructured medical records.
These learnings led to the prioritization of three major technical objectives: (1) EHR integration to simplify workflow and enhance access to patient data, (2) enhancing the ML/AI risk assessment model by incorporating NLP techniques for extracting valuable information from complex, fragmented, incomplete, and contradictory medical records, and (3) conducting validation testing by clinicians to ensure the reliability and efficacy of ML/AI outputs.
The integration of NLP for data extraction, combined with the patient’s self-reporting, ensures a comprehensive and accurate representation of the patient's present condition and medical history.
This innovation could enable real-time risk adjustment, expedite patient care, address missed care opportunities, and boost revenue in global capitation and value-based care delivery models.
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.
Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23516
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Phoenix,
Arizona
85083-5836
United States
Geographic Scope
Single Zip Code
Literaseed was awarded
Cooperative Agreement 2336417
worth $997,693
from National Science Foundation in August 2024 with work to be completed primarily in Phoenix Arizona United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Cooperative Agreement was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase II Programs (SBIR/STTR Phase II).
SBIR Details
Research Type
STTR Phase II
Title
STTR Phase II: Advancing Health Equity using Interactive Condition Assessment and Monitoring
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II
project is to potentially improve patient outcomes and reduce healthcare costs by enhancing communication
between patients and their medical providers. In the U.S., 78.9% of misdiagnoses are caused by
miscommunication, resulting in 80,000 to 200,000 avoidable hospital deaths each year, and 56.3% of
those communication gaps are related to the history-taking during the patient-provider encounter.
Enhancing communication in healthcare is crucial for improving both the efficiency and quality of
healthcare services. LiteraSeed’s project proposes Electronic Health
Record (EHR) integration and Natural Language Processing (NLP) data extraction to enable automated
chart review, facilitating possible access to critical patient data and allowing health systems to reclaim previously
lost revenue due to the misclassification of patient risk.
This project aims to improve the long-term efficiency of our healthcare system by addressing incomplete
and conflicting EHR information, providing alerts of vital medical history, and mitigating the effects of
poor health literacy, all in an effort to help empower the patient.
The proposed project performs Electronic Health Records (EHR) integration of the platform and integrates it with Natural Language Processing (NLP) to extract valuable information from complex and unstructured medical records. These learnings led to the prioritization of three major technical
objectives: (1) EHR integration to simplify workflow and enhance access to patient data, (2) enhancing
the ML/AI risk assessment model by incorporating NLP techniques for extracting valuable information
from complex, fragmented, incomplete, and contradictory medical records, and (3) conducting validation
testing by clinicians to ensure the reliability and efficacy of ML/AI outputs. The integration of NLP for
data extraction, combined with the patient’s self-reporting, ensures a comprehensive and accurate
representation of the patient's present condition and medical history. This innovation could enable
real-time risk adjustment, expedite patient care, address missed care opportunities, and boost revenue
in global capitation and value-based care delivery models.
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.
Topic Code
DH
Solicitation Number
NSF 23-516
Status
(Ongoing)
Last Modified 8/27/24
Period of Performance
8/15/24
Start Date
7/31/26
End Date
Funding Split
$997.7K
Federal Obligation
$0.0
Non-Federal Obligation
$997.7K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2336417
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
CMMARFT4L8D9
Awardee CAGE
986R0
Performance District
AZ-08
Senators
Kyrsten Sinema
Mark Kelly
Mark Kelly
Modified: 8/27/24