HealthNovember 25, 2020

Applying AI in healthcare: Practical applications of clinical NLP

There’s been a lot of buzz about the promise of artificial intelligence (AI) in healthcare. We’re moving rapidly from hype to real-world use cases, and health leaders are recognizing that AI holds the potential to diagnose and treat disease, improve processes, and better manage underlying operational, financial, and patient health data through which they can innovate and maximize value.

What is Natural Language Processing? Natural Language Processing (NLP) is a branch of AI in healthcare that’s quickly proving its value to healthcare organizations by enabling the use of unstructured text, which accounts for 80% of all healthcare data. Without NLP, accessing clinical insights locked in unstructured text requires manual review, which is time-consuming, expensive, and non-scalable.

NLP has the power to transform these workflows by quickly reviewing patient records, including clinical codes and other data in healthcare, and then identifying relevant clinical insights that can improve high-value use cases such as clinical decision support, risk adjustment, and quality measure reporting.

4 Practical Applications of Clinical NLP in Healthcare:

  • Provides clinical insights for healthcare organizations
  • Empowers earlier and accurate sepsis detection via real-time clinical surveillance solutions
  • Accelerates chart review and finds supporting evidence for accurate risk adjustment coding
  • Automates the interpretation of pathology reports to increase accuracy and speed of detection, resulting in improved quality measure reporting

Watch now to learn how hospitals and health plans can take advantage of clinical NLP to improve patient outcomes and enhance performance


  • Stephen Claypool, MD - Medical Director of Surveillance, Wolters Kluwer
  • Chris Funk, Ph.D - Sr. Medical Informaticist, Health Language, Wolters Kluwer
  • Brian Diaz - Sr. Director of Strategy , Health Language, Wolters Kluwer
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Health Language Clinical Natural Language Processing
Automate the review of unstructured data, extract clinically relevant data, and codify extracted data to industry standards.
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