2432755
Project Grant
Overview
Grant Description
SBIR Phase I: Health communication software integration with AI and LLMs to target localized, high-quality health information messaging.
The broader/commercial impact of this SBIR Phase I project is to enhance public health communication by leveraging artificial intelligence (AI) and natural language processing (NLP) technologies.
This project aims to support health communicators in creating, customizing, sharing, and measuring the effectiveness of health messages tailored to diverse cultural and linguistic contexts.
By addressing the challenges of delivering accurate and engaging health information, this innovation seeks to improve health outcomes and reduce health disparities in communities across the United States.
The project has significant commercial potential, with an initial market focus on health agencies, hospitals, and community-based organizations.
Driven by the unique value proposition of providing a user-friendly platform that integrates multiple health communication functions tailored to diverse audiences, the project offers a promise to advance scientific and technological understanding while offering a comprehensive solution to meet the specific needs of health communicators and the patients they serve.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical need for culturally and contextually relevant health communication.
The research objectives include developing capabilities to generate and customize health messages, creating a dataset to train novel artificial intelligence (AI) models, and evaluating the effectiveness of these messages in real-world settings.
The proposed research involves collecting health communication materials, processing and tagging this data using natural language processing (NLP), and employing large language models (LLMs) to generate initial drafts of health messages.
Customization tools will refine these messages to reflect local cultural and linguistic nuances.
The project will implement A/B testing to determine message effectiveness and collect feedback for continuous model improvement.
Anticipated technical results include a scalable platform that enhances the ability of health communicators to deliver effective health messages, supported by robust data on message usage and impact.
This research aims to bridge the gap between advanced AI technologies and practical health communication needs, ultimately contributing to improved health outcomes and reduced disparities in underserved communities.
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 not planned for this award.
The broader/commercial impact of this SBIR Phase I project is to enhance public health communication by leveraging artificial intelligence (AI) and natural language processing (NLP) technologies.
This project aims to support health communicators in creating, customizing, sharing, and measuring the effectiveness of health messages tailored to diverse cultural and linguistic contexts.
By addressing the challenges of delivering accurate and engaging health information, this innovation seeks to improve health outcomes and reduce health disparities in communities across the United States.
The project has significant commercial potential, with an initial market focus on health agencies, hospitals, and community-based organizations.
Driven by the unique value proposition of providing a user-friendly platform that integrates multiple health communication functions tailored to diverse audiences, the project offers a promise to advance scientific and technological understanding while offering a comprehensive solution to meet the specific needs of health communicators and the patients they serve.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical need for culturally and contextually relevant health communication.
The research objectives include developing capabilities to generate and customize health messages, creating a dataset to train novel artificial intelligence (AI) models, and evaluating the effectiveness of these messages in real-world settings.
The proposed research involves collecting health communication materials, processing and tagging this data using natural language processing (NLP), and employing large language models (LLMs) to generate initial drafts of health messages.
Customization tools will refine these messages to reflect local cultural and linguistic nuances.
The project will implement A/B testing to determine message effectiveness and collect feedback for continuous model improvement.
Anticipated technical results include a scalable platform that enhances the ability of health communicators to deliver effective health messages, supported by robust data on message usage and impact.
This research aims to bridge the gap between advanced AI technologies and practical health communication needs, ultimately contributing to improved health outcomes and reduced disparities in underserved communities.
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 not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Asheville,
North Carolina
28803-1428
United States
Geographic Scope
Single Zip Code
Arclet was awarded
Project Grant 2432755
worth $274,920
from National Science Foundation in September 2024 with work to be completed primarily in Asheville North Carolina United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Health Communication Software Integration with AI and LMLs To Target Localized, High-Quality Health Information Messaging
Abstract
The broader/commercial impact of this SBIR Phase I project is to enhance public health communication by leveraging artificial intelligence (AI) and natural language processing (NLP) technologies. This project aims to support health communicators in creating, customizing, sharing, and measuring the effectiveness of health messages tailored to diverse cultural and linguistic contexts. By addressing the challenges of delivering accurate and engaging health information, this innovation seeks to improve health outcomes and reduce health disparities in communities across the United States. The project has significant commercial potential, with an initial market focus on health agencies, hospitals, and community-based organizations. Driven by the unique value proposition of providing a user-friendly platform that integrates multiple health communication functions tailored to diverse audiences, the project offers a promise to advance scientific and technological understanding while offering a comprehensive solution to meets the specific needs of health communicators and the patients they serve.
This Small Business Innovation Research (SBIR) Phase I project addresses the critical need for culturally and contextually relevant health communication. The research objectives include developing capabilities to generate and customize health messages, creating a dataset to train novel Artificial Intelligence (AI) models, and evaluating the effectiveness of these messages in real-world settings. The proposed research involves collecting health communication materials, processing and tagging this data using Natural Language Processing (NLP), and employing large language models (LLMs) to generate initial drafts of health messages. Customization tools will refine these messages to reflect local cultural and linguistic nuances. The project will implement A/B testing to determine message effectiveness and collect feedback for continuous model improvement. Anticipated technical results include a scalable platform that enhances the ability of health communicators to deliver effective health messages, supported by robust data on message usage and impact. This research aims to bridge the gap between advanced AI technologies and practical health communication needs, ultimately contributing to improved health outcomes and reduced disparities in underserved communities.
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 23-515
Status
(Complete)
Last Modified 9/25/24
Period of Performance
9/15/24
Start Date
8/31/25
End Date
Funding Split
$274.9K
Federal Obligation
$0.0
Non-Federal Obligation
$274.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2432755
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
QM8DSKSTCUZ4
Awardee CAGE
None
Performance District
NC-11
Senators
Thom Tillis
Ted Budd
Ted Budd
Modified: 9/25/24