Until recently, the use of natural language processing (NLP) in healthcare has been primarily limited to research efforts and population health within academic medical centers. However, with the proliferation of unstructured data from electronic medical records, providers are now seeking to harness the potential of their data and considering a variety of use cases for NLP technology. That’s the conclusion of a recent KLAS report entitled “Natural Language Processing: Glimpses into the Future of Unstructured Data Mining.”
The report includes insights from 58 provider organizations and examines the various ways providers are currently leveraging NLP technology, as well as some of use cases poised for wider adoption. Coding and documentation applications represent the broadest use of NLP engines. But it is clear providers have a growing interest in NLP solutions that advance their population health initiatives. An increasingly popular use case, involves applications that use NLP to mine unstructured data within patient populations and include predictive analytics to identify at-risk patient populations.
A few of the major findings from KLAS’s report are summarized below.
How is NLP being used today?