Southeastern Health AI Consortium - Collaborative Grants (Track 1 Novel Health Insights)

Southeastern Health AI Consortium - Collaborative Grants (Track 1 Novel Health Insights) is sponsored by Medical University of South Carolina (MUSC) and University of Florida Clinical and Translational Science Institute (UF CTSI). This track supports research collaborations between MUSC and UF that leverage artificial intelligence to address significant health issues in South Carolina and Florida.

The consortium aims to accelerate discovery, improve care, and transform health across the Southeast by supporting interdisciplinary teams using AI and large clinical datasets.

REQUEST FOR APPLICATIONS (RFA) – COLLABORATIVE GRANTS The Southeastern Health AI Consortium is an exciting new collaboration forged between the Medical University of South Carolina (MUSC) and the University of Florida Clinical and Translational Science Institute (UF CTSI) with the purpose of transforming the future of healthcare. Supported by $2.

5 million allocated research dollars, this special funding mechanism aims to promote collaboration between MUSC and UF to conduct research that addresses significant health issues facing South Carolina and Florida by leveraging each institution’s respective insights, computing infrastructure, advances in artificial intelligence, clinical and research strengths of each organization to improve health outcomes in Florida, South Carolina, and the Southeast.

All funded applications must include an artificial intelligence approach or lay the foundation for such and include strong interdisciplinary This mechanism offers two tracks: Track 1 Novel Health Insights: This track supports up to five teams for two years to address an evidence gap that will lead to improved health outcomes at the individual or population level.

With funding from this award, investigators will build a multi -institutional research team, establish the rationale, and generate preliminary data addressing a real -world decision dilemma relevant to clinical care. Use of AI and/or machine learning can be broadly integrated into any aspect of the study.

Teams will be expected to prepare and submit a competitive collaborative proposal application (e.g. ARPA -H, NIH, PCORI) at the end of the study period. Studies that are designed to have actionable insights within the first year will be prioritized. Award Amount : up to $400,000 direct costs for one year.

> •Two PIs, one from each institution, must be full -time FTE at the rank of assistant professor or above > •Data scientist/Informaticist > •Implementation scientist > •Patient engagement from each institution > •Two early career investigators (i.e., mentees, one from each site) Track 2 Methodologic Innovation: This track provides seed funding for up to five teams for one year to develop novel methodological approaches in health services research, biomedical informatics, or data science.

The goal is to stimulate innovative ideas that can lead to future external funding. Use of AI and/or machine learning can be broadly integrated into any aspect of the study. Award Amount : up to $100,000 direct costs for one year.

> •Two PIs, one from each institution, must be full -time FTE at the rank if assistant professor or above > •One early career investigator > •Data scientist/Informaticist COLLABORATION DEVELOPMENT: Need assistance in identifying collaborations?

MUSC and UF will facilitate matching of skills and expertise between institutions to strengthen team composition and coordinate introductory PRIORITIZED RESEARCH TOPICS: For this initial round of funding, we are seeking concept proposals in the following five areas: > •Improving mental and behavioral health > •Preventing maternal morbidity and mortality > •Advancing gene and cellular therapies through immunophenotyping > •Addressing brain health CONCEPT PROPOSAL SUBMISSION REQUIREMENTS: One -page concept proposals must be submitted via InfoReady (PDF format, Arial 11 font, all page margins ≥ 0.

5”). Responsive concept proposals must include the following: > •Rationale outlining areal -world decision dilemma (Track 1) or methodologic gap (Track 2) > •Compare different evidence -based interventions and approaches. If using ‘usual care’ as a comparison group, > clearly define ‘usual care’ and how this will be measured.

(Track 1) > •Anticipated role of each partner institution on the inter -institutional team > •Relevant institutional environment > •Plan to identify and engage patient partners, community organizations and/or other relevant stakeholders > •Plan for early career investigator participation/mentorship Investigators should utilize relevant CTSA consultations (e.g., community engagement, statistical/study design, data source assessment, informatics/data science, dissemination and/or implementation science, as indicated) in developing concepts and to facilitate the identification of collaborators at other institutions.

REVIEW PROCESS and COLLABORATION DEVELOPMENT: Leaders from MUSC’S South Carolina Clinical & Translational Research Institute (SCTR) and UF’s CTSI will review concept proposals and prioritize invitations for full applications and facilitate collaborations between institutional teams.

CONTACT: MUSC investigators with questions and/or needing assistance identifying a collaborator at UF can message SCTR’s Office of Science Development at SCTR -SDO@musc. edu . University of Florida investigators may contact the CTSI Research Connect at info@ctsi.

ufl. edu .