RAPID: Bridging the Health Care Skill Gap
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$199,114Funder
National Science Foundation (NSF)Principal Investigator
Robert RobsonResearch Location
United States of AmericaLead Research Institution
Eduworks CorporationResearch Priority Alignment
N/A
Research Category
Policies for public health, disease control & community resilience
Research Subcategory
Policy research and interventions
Special Interest Tags
Innovation
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
The project aims to serve the national need of increasing the capacity to have more qualified healthcare providers to address the challenge of the Coronavirus Disease 2019 (COVID-19). By developing and deploying web-based tools that individuals and employers can use to explore healthcare-related competency frameworks, self-identify skill gaps, and find credentials and training, this project seeks to fill gaps in the healthcare workforce. These tools will help individuals identify healthcare areas where they have adjacent skill sets and will also help companies identify employees who are candidates for upskilling into healthcare. Once individual skills are inventoried, the tool will help users identify where they can obtain the requisite healthcare skills and credentials. The tool will be usable on desktop and mobile devices which broadens the reach of this project.
The project seeks to address the COVID-19 crisis by providing end users with the ability to compare their own skills with those required for available healthcare roles and credentials and, in some cases, find ways to fill gaps. The frameworks and related credentials will come from the Credential Engine and additional data will come from the Open Syllabus Project. This project will apply artificial intelligence (AI), specifically natural language understanding (NLU), to (a) crosswalk competency frameworks associated with these credentials, (b) compare skills entered by users to those required to earn those credentials, (c) generate an "alignment score" that measures and quantifies the gap between the two skill sets, and (d) suggest courses and credential programs with the potential to close those gaps. This will contribute to research into applications of NLU to competency-based credentialing and training and will provide data in a concrete domain (healthcare). Including validation and sharing of the underlying competency framework translation and alignment score services via an open data infrastructure will improve our nation?s healthcare talent pipeline.
This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities and is associated with the Convergence Accelerator Track B: Future of Work and the Human-Technology Frontier.
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.
The project seeks to address the COVID-19 crisis by providing end users with the ability to compare their own skills with those required for available healthcare roles and credentials and, in some cases, find ways to fill gaps. The frameworks and related credentials will come from the Credential Engine and additional data will come from the Open Syllabus Project. This project will apply artificial intelligence (AI), specifically natural language understanding (NLU), to (a) crosswalk competency frameworks associated with these credentials, (b) compare skills entered by users to those required to earn those credentials, (c) generate an "alignment score" that measures and quantifies the gap between the two skill sets, and (d) suggest courses and credential programs with the potential to close those gaps. This will contribute to research into applications of NLU to competency-based credentialing and training and will provide data in a concrete domain (healthcare). Including validation and sharing of the underlying competency framework translation and alignment score services via an open data infrastructure will improve our nation?s healthcare talent pipeline.
This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities and is associated with the Convergence Accelerator Track B: Future of Work and the Human-Technology Frontier.
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.