Applying Natural Language Processing to Healthcare Text at Scale
July 1, 2021 in Platform Blog
This is a co-authored post written in collaboration with Moritz Steller, AI Evangelist, at John Snow Labs. Watch our on-demand workshop, Extract Real-World Data with NLP, to learn more about our NLP solutions for Healthcare.
In 2015, HIMSS estimated that the healthcare industry in the U.S. produced 1.2 billion clinical documents. That’s a tremendous amount of unstructured text data. Since that time, the digitization of healthcare has only increased the amount of clinical text data generated annually. Digital forms, online portals, pdf reports, emails, text messages and chatbots all provide the backbone for modern healthcare communications. The amount of text generated across these channels is too vast to measure and too comprehensive for a human to consume. And because these datasets are unstructured, they are not readily analyzable and often remain siloed.
This poses a risk for all healthcare organizations. Locked within these lab reports, provider notes and chat logs is valuable information. When combined with a patient’s electronic health record (EHR), these data points provide a more complete view of a patient’s health. At a population level, these datasets can inform drug discovery, treatment pathways, and real-world safety assessments.
Uncovering novel health insights with natural language processing
There’s good news. Advancements in natural language processing (NLP) - a branch of artificial intelligence that enables computers to understand written, spoken or image text - make it possible to extract insights from text. Using NLP methods, unstructured clinical text can be extracted, codified and stored in a structured format for downstream analysis and fed directly into machine learning (ML) models. These techniques are driving significant innovations in research and care.
In one use case, Kaiser Permanente, one of the largest nonprofit health plans and healthcare providers in the US, used NLP to process millions of emergency room triage notes to predict the demand for hospital beds, nurses and clinicians, and ultimately improve patient flow. Another study