Many healthcare providers enter important patient information using free-form text in the “notes” section of the EHR. This can result in clinical information that is unstandardized or uncategorized – meaning that important patient details may get lost. Natural language processing (NLP) solutions scan unstructured text to extract important words, concepts and relationships. However, the complexity of clinical language, as well as acronyms and abbreviations that may be specialty-specific, complicate this task for healthcare applications. An NLP solution grounded in robust clinical terminology is important for success in minimizing ambiguity in the patient record. IMO solutions help capture precise clinical data at the point of care and standardize it across settings and sources to power more informed decisions.

Find out more about the challenges facing NLP engines in healthcare:

  • Acronyms and abbreviations – as well as common misspellings
  • Specialty-specific language and context clues
  • Time-based relationships and use of negation

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