How a Clinician - Friendly Approach to NLP Produces Results That Make a Difference

Clinicians rely on data to provide a comprehensive view of a patient’s health. While much of this data lives in the electronic health record (EHR), some come from other clinical systems, insurance claims, new medical research, and even from patients themselves. Unfortunately, simply having access to data isn’t the same as being able to effectively use the data. Roughly 80% of healthcare data is considered unstructured, regardless of its source. The presence of disparate and unstructured data has a negative impact on the care experience as well as the revenue cycle.

More recently, advanced technology such as natural language processing (NLP) has emerged as a potential solution to addressing these challenges by augmenting the review of large, unstructured data sets. Most NLP technology focuses on extracting information from large data sets, and while this is valuable in a clinical setting, it’s not enough.

This is certainly an important attribute of NLP solutions, but it’s also important to consider the business issues that an NLP solution could be positioned to solve. For clinical use cases, it’s not enough to just find the relevant results. Rather than sifting through a long list of results, a clinician with limited time is best served by reviewing answers to the question they are trying to solve for.