Projects

CRISPI is modernizing the existing clinical informatics enterprise and implementing advanced data technologies to better support highly innovative and emerging clinical research conducted at the NIH.

REDCap^® (Research Electronic Data Capture) is a secure, web-based application designed to support data collection for all NIH ICs to use for their clinical and translational research. It enables the rapid, user-friendly creation of electronic case report forms (eCRFs), surveys, and study databases, while maintaining compliance with the Privacy Act, HIPAA, and other regulatory standards.

REDCap is ideal for managing clinical research protocols and longitudinal studies, offering user customizable instruments, branching logic, calculated fields, and real-time data validation to ensure high-quality data capture. The platform supports user-friendly CSV imports and seamless data exports to Excel and common statistical packages (SPSS, SAS, Stata, R). It also includes a built-in project calendar, scheduling module, ad hoc reporting tools, and advanced features such as piping, file uploads, and automated survey invitations.

REDCap’s robust and flexible design empowers research teams to collect, manage, and prepare data for analysis efficiently—across a wide range of clinical research settings, from single-site studies to medium sized, multi-center trials.

In addition, CRISPI team is working with Vanderbilt REDCap team on piloting an interoperability tool to promote the use of NIH Common Data Elements (CDE).

For more information, contact Frank Velez at frank.velez@nih.gov.

ChIRP — an NIH-secured Generative AI platform for exploring Large Language Model (LLM) technologies, short for Chatbot for the NIH Intramural Research Program. ChIRP provides a secure, NIH-network-hosted environment ensuring compliance with NIH security policies and is free for all NIH staff. It is specifically tailored for NIH staff and uses the models GPT-4o, 4.1, and o3, Claude-2.1, and Dell-E-3.

For more information and access contact Bryant Jen at bryant.jen@nih.gov. 

Clinical Data Warehouse (CDW) — a collaboration with the Clinical Center and CIT to provide secure access control to explore next-generation AI-enabled research analysis, storage, sharing, and collaboration in a centralized repository containing terminated and de-identified clinical research datasets. These include associated diagnoses, labs, medications, procedures, and clinical notes from Electronic Health Records (EHR) as well as images and genomics data. Additionally, our CDW pilot supports the exploration of general-purpose FHIR support and automated information extraction from clinical notes among other areas of research.

For more information, contact Dr. Nic Dobbins at nic.dobbins@nih.gov.

Clinical Research Dashboard (in development) — provides a consolidated overview of clinical research protocols across the IRP. This tool offers NIH leadership with high-level insights into the status of all projects and protocols while allowing NIH Clinical Directors and PIs to drill down and examine the specific details of the relevant protocols. The dashboard reduces the need to consult multiple data sources.

For more information contact Bryant Jen at bryant.jen@nih.gov.

Terra — a cloud-native platform tailored to genomics research for biomedical researchers to access data, run analysis tools, and collaborate as well as sharing datasets. This is a collaboration with AnVIL, and the NIH National Human Genome Research Institute (NHGRI).

For additional information contact Dr. Nic Dobbins at nic.dobbins@nih.gov.

Clinical AI Pilot — a high-performance computing (HPC) and artificial intelligence (AI) system for clinical data processing. Established in 1999 for a variety of supercomputing needs, Biowulf currently supports the CRISPI pilot in two specific ways: processing of computerized tomography (CT) and magnetic resonance imaging (MRI) brain images of stroke patients, including a simulated real-time component; and implementing machine learning models and platforms for auto-segmentation and disease detection from image scans.

For more information contact Steve Bailey at sb@mail.nih.gov.