How NLP can help with at-Risk Patients, SDOH and Pop Health

A physician expert in natural language processing technology explains how it can help clinicians sift through unstructured data to better manage chronic conditions and boost health equity. The new Centers for Medicare & Medicaid Services Interoperability and Patient Access final rule requires the interoperability of full-text medical records. "The Interoperability and Patient Access final rule puts patients first by giving them access to their health information when they need it most, and in a way they can best use it," according to CMS. "This final rule focused on driving interoperability and patient access to health information by liberating patient data using CMS authority to regulate Medicare Advantage, Medicaid, Children's Health Insurance Program and Qualified Health Plan issuers on the Federally-facilitated Exchanges."

There is, of course, a massive amount of unstructured text in electronic health records. As a result, healthcare provider organizations are in need of a way to plow through all that text to comply with the CMS rule. One technology more providers are turning to is natural language processing, a form of artificial intelligence. NLP can do that by plowing and turning free text into structured text, making it much more accessible and usable for end users – and patients. Dr. Calum Yacoubian is associate director for healthcare strategy at Linguamatics, an IQVIA company, which develops a natural language processing-based artificial intelligence platform. He offered a brief primer on NLP, and explained how the technology can help clinicians identify at-risk patients, and how it can help clinicians and health IT leaders uncover insights into social determinants of health and advance population health efforts.