CTSI Resource Spotlight: Integrated Data Repository
Supported by the UF CTSI and Shands HealthCare, the Integrated Data Repository (IDR) is a large-scale database that collects and organizes information from across UF Health’s clinical and research enterprises. The UF Health IDR enables new research discoveries as well as improvements in the quality and safety of patient care.
The IDR consists of a secure, clinical data warehouse (CDW) that aggregates data from the university’s various clinical and administrative information systems, including the Epic electronic medical record system. As of July 2013, the IDR contains data for encounters that occurred between June 2011 and June 2013, with a total of more than 46 million observational facts pertaining to approximately 371,000 patients. The IDR also contains data about biospecimen availability and patient research consent through UF Consent2Share. IDR data is refreshed monthly.
Accessing IDR Data for Research
- Access to IDR data is provided through the NIH-funded i2b2 tool, which provides researchers access to a HIPAA-compliant and IRB-approved “Limited Data Set.” Faculty researchers can query the i2b2 Limited Data Set to identify cohort counts as they prepare grant proposals, plan clinical trials, and write IRB protocols.
Two recent success stories illustrate how researchers are using the IDR:
- Institute on Aging: A team of researchers from the UF Institute on Aging, including Drs. Marco Pahor, Stephen Anton and Todd Manini, recently used the IDR and i2b2 cohort discovery tool to support their participation as a study site for the new Cardiovascular Inflammation Reduction Trial, a major new randomized trial led by Brigham and Women’s Hospital and sponsored by the National Heart Lung and Blood Institute: Read More
- College of Medicine: With the help of a CTSI Pilot Project grant awarded in March 2011, Drs. Azra Bihorac and Mark Segal used the newly launched IDR to identify a unique cohort of patients undergoing in-hospital surgical procedures for their pilot project titled “Database Communication Enables Machine Learning Classifiers To Predict Postoperative Acute Kidney Injury In Intensive Care Unit (Declare).” Dr. Bihorac recently received the Society of Critical Care Medicine’s 2013 Vision Grant to continue the collaborative research: Read More