Electronic medical records and research documents capture large quantities of clinically significant information. However, much of this information is often in a form that is not amenable to analysis because of its free-text, unstructured nature. Our NLP solution - IBIL-IntelliCP built on UIMA framework transform this information into structured data for use in quality improvement, research, population health surveillance, and decision support.
IBIL-IntelliCP evolved out of the necessity to scale-up the existing framework to customize and tune techniques that fit a variety of tasks, including document classification, tuned concept extraction for specific conditions, patient classification, and information retrieval related to the patient, history, disease manifestation, details of laboratory tests, prescribed interventions, and outcomes and much more.
As part of the solution, we have implemented several informatics tools to leverage clinical data for research: