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ITS Health Informatics - i2b2

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The Institute for Translational Sciences provides Informatics for Integrating Biology and the Bedside (i2b2) as an open-source application that allows rapid identification of patient cohorts within data from the Epic- Electronic Medical Record (EMR) and other sources at UTMB. Originally developed by an NIH-funded National Center for Biomedical Computing (NCBC) based at Partners HealthCare System in Boston, MA, this platform enjoys wide international adoption by the CTSA network, academic health centers, and industry.

i2b2


 For additional information about i2b2 please see https://www.i2b2.org/.

 Since all of the data is de-identified, users do not need prior IRB approval to access the system for feasibility studies. However, to release patient level data UTMB requires IRB approval for research studies. If you need help preparing an IRB submission or retrieving patient level information, please feel free to contact us for assistance.

 We also recommend that users receive training on the use of i2b2. One on one training and group training are available to faculty and their staff. Additionally, the ITS provides services for clinical trial feasibility as discussed here.

 You can submit a request for services at https://utmb.us/34y.

***NEW*** We are excited to announce that we are now part of the Accrual in Clinical Trials (ACT) research network that will enable data sharing and increase network capacity between CTSA institutions.  ACT includes over 37 institutions in the CTSA program and contains more than 100 million patients. Use of the network will allow UTMB investigators to perform i2b2 queries over the entire ACT network to guide the design and recruitment for multi-site clinical studies. For more information about ACT, see https://ncatswiki.dbmi.pitt.edu/acts

 Specific uses of i2b2 include:

  • Identifying a population for clinical trials,
  • Identifying geographic areas for clinical trial recruitment,
  • Quick "what if's" - for example where you are trying to link a drug to a procedure or diagnosis,
  • Eliminate manual chart reviews for many data request types - resulting in increased efficiency and data accuracy,
  • Federated queries for discovery research across multiple institutions.
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