In healthcare, there are many ways providers enter clinical terminology into electronic health records (EHRs). A popular choice? Using the notes section of the EHR – a place where clinicians can document patient encounters using free-form text to describe an individual’s symptoms, diagnoses, and treatment plan.
But while this method of documentation makes things easier on providers, using it comes at a cost. These notes aren’t always easy to translate into standardized coding systems, which is critical for billing and reimbursement purposes. And the unique, nuanced language of healthcare increases the difficulty of this task – even when tools like natural language processing (NLP) are used.
So, why doesn’t an NLP solution quickly solve this problem? It’s because learning the language of healthcare – and deciphering notes that use free-form text – requires a specific type of teacher. Take a look at an example of a complex clinical note below, and download IMO’s Insight Brief, Reading like a human: The key to successful natural language processing in healthcare to learn more.
