Clinical and Translational Science Research Program
Clinical and Translational Science Research Program
The 2025 award will fund a highly meritorious interdisciplinary clinical and translational science (CTS) projects. We are seeking competitive proposals that develop equitable AI tools that address health disparities and advance health equity.
One new grant is awarded per year, for a total of seven funded projects.
Funding per Award
$125,000 annually for two years direct costs
Pre-Application Due Date
October 22, 2024
Invited Full Application Due Date
December 12, 2024
Clinical and Translational Science Research Program Awards are based on a new framework, Clinical and Translational Data Science Equity, which include representation equity, feature equity, access equity and outcome equity.
This Clinical and Translational Data Science Equity framework comprehensively conveys the challenges of ensuring diversity, equity, inclusion and accessibility (DEIA) in Clinical Translational Science (CTS), as well as the major translational barriers to effective dissemination, implementation and achievement of population health benefits. Most importantly, this framework, for the first time, allows us to systematically solicit, review, prioritize, select and disseminate CTS research projects addressing Data and AI Equity challenges.
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Representation inequity can arise from the systematic exclusion of certain individuals or groups, such as those from marginalized or minoritized communities. For example, the lack of drug efficacy data in maternal or pediatric populations is due to clinical trials not being adequately inclusive of these two populations; COVID testing cases can have racial disparities in both the availability of the testing and in the comfort level of individuals to be tested. Consequently, representation inequity in data can lead to systemic biases in the decisions based on the data and can have a major impact on the appropriateness of healthcare recommendations for certain populations.
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Feature equity is defined as the availability of variables for needed representation and data analysis in each data subgroup. One salient example is that social determinants are not necessarily well captured in clinical data sets, especially among underrepresented racial groups.
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Access equity refers to equitable access to data and models, regardless of domain expertise. A classic example is electronic medical records (EMRs). While researchers are generally well educated in EMR data access policy, researchers with diverse research backgrounds have different challenges in accessing the data. Data scientists with inadequate experience in developing clinical research protocols sometimes struggle with obtaining IRB approval before being granted access to the data. Clinical scientists, on the other hand, have been more successful in requesting access to the data, but they need help in implementing computational tools for rigorous and innovative data analysis.
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Outcome equity focuses on the research impact from data. Because dissemination and implementation of clinically significant research results and tools can be highly different in various patient populations or contexts, outcome inequity is a persistent challenge.
Call for Applications
The Clinical and Translational Science Institute (CTSI) is accepting applications for the 2025 Clinical and Translational Science Research Program Award, funded by the National Institutes of Health's (NIH) National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Awards (CTSA) Program.
The goal of the 2025 award is to fund a highly meritorious interdisciplinary clinical and translational science (CTS) project. We are seeking competitive proposals that develop equitable AI tools that address health disparities and advance health equity.
Upcoming Sessions
Information Session (Virtual): October 1, 4:30 – 5 p.m.
A brief overview of the Clinical and Translational Science Award funding mechanism, followed by question and answer.
Ideation Session (Virtual): October 14, 1 - 3 p.m.
Prospective applicants are encouraged to participate in a virtual ideation workshop to present their initial ideas in a professional and friendly forum, because research shows that ideas that have gone through several iteration processes are stronger and have better potential of securing funding. The audience will include a mock review panel to provide expert comments, and general members of The Ohio State University and Nationwide Children’s Hospital Community who have the opportunity to ask probing questions and offer comments on presented ideas.
Participation in this ideation workshop is not prerequisite for application to the RFA, and feedback in this workshop is not indicative of application success. Up to 15 presentations will be accommodated in the workshop, on a first come, first served basis.
To participate as a presenter, choose the "presenter" option in the registration form and prepare a three-minute presentation that includes:
- Translational science research question(s);
- Research strategy, including data management plan and rigor of the study;
- Significance to clinical and translational science
Funded Projects
Project 1
AI-guided information retrieval and recommendation for clinical decision support. (MPI: Drs. Xia Ning and Courtney Hebert, Department of Biomedical Informatics, College of Medicine)
Project 2
Unleashing the Potential of Large Language Models for Clinical and Translational Science: Application to Addressing Unmet Social Needs of Patients (MPI: Drs. Macarius Donneyong, Huan Sun, and Yu Su, College of Pharmacy and College of Engineering)
Primary Contacts
Lang Li
Program Co-Director of the Clinical and Translational Science Research Program, Professor and Chair of the Department of Biomedical Informatics
Tanya Berger Wolf
Program Co-Director of the Clinical and Translational Science Research Program, Director of the Translational Data Analytics Institute and Professor of Computer Science and Engineering, Electrical and Computer Engineering and Evolution, Ecology and Organismal Biology
Jenny Grabmeier
Program Manager of the Clinical and Translational Science Research Program, Director of Research Strategy at the Translational Data Analytics Institute