How Natural Language Processing Unlocks The Full Healthcare And Patient Journey

By Ana Bargo, M.S., Data Scientist, Jeannine Cain, MSHI, RHIA, CPHI, Healthcare Analyst, Mark Yap, FNP-C, Healthcare Analyst, Shannon Fee, B.A., BioMedical Data Scientist, Ciox Health, LLC.

In 2016, a major transformation occurred in how we evaluate clinical data for real-world effectiveness. The FDA signed into law the 21st Century Cures Act, which impacted the Real-World Evidence Program in the United States. It signaled a paradigm shift in medicine by formally recognizing the importance of real-world data (RWD) in bringing medical innovation to patients. But the immense promise of RWD comes with sizeable hurdles. Simply having massive quantities of data at your disposal does not automatically equate to having meaningful answers—especially when considering the sheer length and complexity of medical records.

We realize that medical records in their raw form are far from being set up in a consistently organized and tabulated format. Due to inconsistencies, redundancies, and format variations in medical records, it is more difficult to easily identify relevant aspects of a patient’s medical journey. To gain deeper clinical insight from information that holds the nuances and critical details about patient care, we must rely on more sophisticated machine learning approaches like natural language processing for healthcare. We need to be able to process and make sense of the information-rich, natural language that exists throughout medical records to gain an understanding at the highest fidelity when it comes to a patient’s health journey. This is needed regardless of where that information is stored.