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Natural Language Processing - Filling the Gap

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Jose Horta
July 19, 2018

One of the most talked-about buzzwords in healthcare is natural language processing (NLP). While electronic health records (EHR) offer rich and generalized data to improve clinical outcomes and staff efficiency, vital information tends to get trapped within the free text fields often used for physicians’ notes.

NLP uses computer algorithms to pull out key elements and mine meaning from large amounts of unstructured, hand-typed or dictated notes to convert it into actionable items.  In essence, NLP is a collaboration of computer science and computational linguistics, that work together to fill the gap between human communication and computer understanding.

NLP is the driving force behind many forms of AI and in the medical community it offers a way to sift
through mountains of data in an EHR and produce a true record for patients. If done correctly, NLP can help with locating, extracting, and summarizing key concepts or phrases, which can enhance the accuracy of capturing risk factors and impact care delivery.

Specific tasks for NLP systems may include:

  • Summarizing lengthy blocks of text by identifying key concepts or phrases
  • Conducting speech recognition that allows users to dictate notes and information that can be turned into text
  • Converting data into natural language for reporting and educational purposes
  • Answering free-text queries without having to sort through multiple data sources

NLP has the ability to pull data that might not be in a designated field and produce quality reports. Considering today’s machines can analyze more language-based data than humans and considering the vast amount of unstructured data generated every day, including medical records, website content and social media, it’s clear why automating the function of not only pulling data but analyzing and categorizing it appropriately is critical for business success. In fact, one of the biggest challenges and cost factors, exchanging patient information between physicians or healthcare facilities, including treatment directives and claims for medical services, can be effectively reduced with NLP applications.

The key to the success of NLP will be to develop accurate and intelligent algorithms. Natural Language Processing is constantly adapting, and algorithms will become more accurate as advancements continue and more data becomes available. This process will deliver a savings in both time and money for everyone in the medical community and beyond.

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