Research Grants in Clinical Informatics (R01 Clinical Trial Optional)

The National Institutes of Health (NIH), through the National Library of Medicine (NLM), has launched the "Research Grants in Clinical Informatics (R01 Clinical Trial Optional)" funding opportunity (PAR-26-042). As part of the U.S. Department of Health and Human Services, the NLM plays a central role in advancing biomedical knowledge and informatics innovation. This grant initiative aligns with NLM’s mission to promote data-driven research and transform complex clinical data into actionable knowledge that enhances healthcare delivery and public health outcomes. This program supports innovative clinical informatics research projects aimed at creating scalable, generalizable tools and methodologies that can be applied across various clinical domains. The funding is open to projects focusing on the design, implementation, and evaluation of informatics tools that utilize diverse data types such as electronic health records (EHRs), clinical notes, imaging data, and patient-generated information. The overarching goal is to empower healthcare professionals and patients alike through improved clinical decision-making and enhanced health data management. Research priorities include AI integration, clinical decision support systems (CDSS), predictive analytics for preventative care, EHR interoperability, and workflow optimization to reduce clinician burden. Funding under this opportunity is offered through the NIH R01 Research Project Grant mechanism, with up to $250,000 in direct costs per year for a maximum project period of four years. Applications proposing NIH-defined clinical trials are permitted but not required. Projects must demonstrate scientific rigor, methodological innovation, and strong potential for broad impact. Evaluation criteria will emphasize significance, innovation, scalability, and the feasibility of proposed approaches. Outputs must be reproducible, well-documented, and made available for open access, with a clear plan for long-term maintenance and dissemination. Proposals narrowly focused on specific diseases or duplicating existing tools are not responsive to this NOFO. Applications will be accepted according to the NIH standard due dates: February 5, June 5, and October 5 each year through 2029, with renewals and resubmissions due one month later respectively. The next earliest due date is June 5, 2026, followed by October 5, 2026, and then February 5, 2027. Applicants must submit their proposals by 5:00 PM local time via one of three platforms: the NIH ASSIST system, Grants.gov Workspace, or a system-to-system institutional solution. While there is no required pre-application (e.g., LOI or concept paper), applicants are strongly encouraged to contact the NLM program staff at [email protected] to ensure alignment with the NOFO’s objectives. The program is open to a wide range of U.S. and foreign organizations, including higher education institutions, nonprofits, for-profits, and government entities at the state, local, and tribal levels. However, NIH does not fund foreign subawards/subcontracts unless specifically allowed under a designated NOFO, which does not apply here. All applicants must have active registrations in SAM.gov, Grants.gov, eRA Commons, and related systems. Program Directors/Principal Investigators must also have an eRA Commons ID. All submitted applications will undergo peer review to assess scientific and technical merit. The process includes both an initial review by a Scientific Review Group and a secondary review by the NLM Board of Regents. Funding decisions will consider peer review outcomes, program relevance, and availability of funds. Successful applicants will be notified via a formal Notice of Award. Performance monitoring includes annual Research Performance Progress Reports (RPPR) and adherence to NIH’s Data Management and Sharing Policy. Projects involving human subjects or clinical trials must comply with applicable oversight, registration, and reporting requirements.

Highlight scalability, generalizability, and reproducibility. Demonstrate significant innovation over existing methods. Share open-access outputs.