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About TBIC

The Translational Biomedical Informatics Center (TBIC) focuses on enabling and easing access and use of unstructured clinical data for primary and secondary uses.

Most clinical data (i.e., patient electronic health record data) is unstructured text. Reuse typically relies on manual chart abstraction, a costly and lengthy process. Innovative and more efficient approaches based on Natural Language Processing (NLP) enable or ease reuse by extracting structured and coded information from clinical text, or transforming clinical text for easier sharing and patient privacy protection.

We offer a range of software and services for free or as paid consultation. See the appropriate pages for a complete listing of our offerings and contact us with any questions.

See the Projects page for examples of current and past projects.

Main objectives:

  • Development, implementation, and management of Natural Language Processing (NLP) infrastructure to:
    • enable and enhance clinical text secondary use/re-use
    • extract structured and coded data from unstructured data sources
  • Creation of a recharge center to enable and enhance unstructured clinical data use; establish local, statewide, and regional collaborations for either NLP expertise addition to other projects or NLP expertise collaborative development.
  • Development of a NLP and text mining experts team, for collaborative efforts and consulting with MUSC, state, and regional collaborations.

Clinical Data Reuse Vision:

A more efficient, scalable, and precise approach to unstructured clinical data reuse or secondary use could involve the following:

  • Clinical Information Extraction Improvements:
    • Real-time, accurate, and generalizable extraction of structured data from narrative text
    • Customization to specific data reuse needs (i.e., specific terminologies and formats)
    • Interactive training, testing, and configuration workflows
    • Preservation of original text and formatting keeps the highest level of details over for the long term
  • Patient Privacy Protection Improvements:
    • Reliable de-identification and anonymization of structured and unstructured data in general, with optimal clinical data preservation