Natural language processing (NLP), at its most basic, is leveraging computer algorithms to process and draw meaning from natural language such as written text or speech. When it comes to medical natural language processing, it can help in finding, extracting, and summarizing crucial bits of information from clinical notes or patient’s accounts. This can be further analyzed.
For example, director of Medical Informatics at Intermountain Healthcare Scott Evans, has claimed that an NLP program based on Java, has helped them effectively identify at-risk patients with heart failure with greater degree of accuracy.
NLPs Gather Crucial Information in No Time
Besides, such a sophisticated algorithm can also reverse data from machine-readable formats into speech or written text. It is seen that medicos waste almost 49% of their work hours on paperwork and organizing or inputting data in the electronic health record (EHR) system. By automating such tasks using NLP, staff productivity can be upped drastically. Further, NLP apps can considerably bring down the time needed to find out valuable insights from all the available data, thereby making physician’s diagnosis and treatment more effective.
An NLP-powered context engine, particularly, can mine unstructured documents in a speedy manner to come up with actionable insights pertaining to stroke management, sepsis management, clinical decision support, etc.
EHR natural language processing is particularly helpful for patients having chronic diseases. This is because it is difficult for most physicians to sort the Hierarchical Condition Category (HCC) codes in order to uncover the type of diabetes the patient has or if they are afflicted with chronic obstructive lung disease. An AI-powered system, on the other hand, can glean relevant information from dozens of unstructured documents in a jiffy to find out accurately the severity of the particular condition.