Medical Tech

What Is NLP and How Can It Be Used for Clinical Text


What Is NLP and How Can It Be Used for Clinical Text

You may have heard of natural language processing, or NLP, before, but do you know what it is? It’s been studied for decades now, and you actually use it all the time, though you may not realize it.

Natural language is simply the way humans talk. We use speech and text to express ourselves, and this makes it very easy to understand each other. Text is a regular part of everyday life, and you use it constantly. For example, you use text when you read:

  • Menus
  • Books
  • Signs
  • Emails
  • Computer sites
  • Text messages
  • Social media posts
  • Notes

It’s very much an integral part of life. So is speech, and speaking is one of the major ways we communicate. What you may not realize however, is that we actually have developed methods and rules to guide natural language. Children learn natural language data automatically. They pick it up and learn it all without actually being taught most of the rules.

Of course, language also doesn’t stay the same. It’s constantly changing, and that makes it more difficult to understand and express its rules. However, computers have to learn how humans speak in order to work with us. Consider a search engine, for example. When you type a query, if you’re like most people, you use natural language.

Instead of typing “dog breed dangerous” which might make more sense to a computer, you write “What’s the most dangerous dog breed.” The point of NLP in healthcare is to help AI understand what you are talking about when you ask it something.

In short, natural language processing is part of AI that focuses on understanding, interpreting, and manipulating the language as humans use it. This process is translated to computers so they can understand you as well.


Examples of NLP

You use NLP every single day, guaranteed, apart from just speaking. Using a computer or a mobile device has you employing natural language processing. Here are a few examples of it:

  • Spam filters
  • Autocomplete sentences
  • Spell check
  • Voice to text messages
  • Related keywords when searching online
  • Auto responders like Siri or Alexa

Each of these examples shows that computers are able to use context in order to figure out what a person is attempting to say. It looks at the other words included and then manages to work out what you’re talking about.

NLP, Naturallanguageprocessing, clinicaltext, healthcare

How NLP Works in Healthcare

Healthcare records are notorious for being unusual in the way they were processed. For many years, people learned to use computer language in order to process everything. However, NLP is now in use, and healthcare systems are designed to use it.

NLP in healthcare is very important and becoming even more so. It allows for simple searching and analyzing of patient information.

There is a lot of unstructured data in the world of medicine. Large swaths of text that were never properly processed are often buried in files, and these can be all but useless until someone sits down and reads them. Unfortunately, there are a few very important pieces of information that get hidden away in these files and people don’t realize just how hard it is to find it.

However, with natural language processing, a simple query can pull information that may be relevant from the files that were previously ignored because they didn’t have the specific dataset that was requested.


Medical Records Are Written in Human Text

When it comes to writing chart notes, nurses and physicians tend to write down their notes just how they would say them to someone. This is not easy for a computer to analyze, and the information generally gets lost once it has been uploaded into electronic health record systems, or EHRs. The notes are stored as free text, but it’s not easily accessible to the search algorithms.

Massive amounts of data are input every day like this, and much of this data is completely ignored because the machine learned algorithms have never caught a specific disease mention. In addition, this can be very frustrating for physicians who are attempting to find their previous notes and can’t get the search function to work correctly.

More than a few doctors have suggested retiring is preferable to trying to learn how to use an EHR database. That was before NLP was available. Physicians spend, on average, 16 minutes per patient inputting information to EHR, which means roughly half their clinical time is spent on data entry and charting.


NLP in Healthcare to the Rescue

Technology can be hard, and when you haven’t learned how to speak like a computer, you may find it nearly impossible to manage the EHR. However, when NLP is utilized in healthcare records, you immediately have broader access to information and the struggle with technology is over, or at least greatly reduced.

Search engines have used natural language processing for a long time now and that has made it much simpler to do searches online and find what you are looking for. As NLP software for healthcare is set up, it becomes more and more likely that those missed notes will be picked up and found again. It’s estimated that around 80% of information in EHR is unstructured data, which could be important for the patient and research. Doctors may not all talk identically, but they do speak like humans, and that means you will be able to find what you’re looking for much more easily.

If search engines can do it, so can healthcare software.


The Next Step

At this point, NLP in healthcare is still relatively new, and it is certainly not widespread. It’s something that needs to become more common and should be a natural part of the healthcare record system. The sooner it’s implemented, the sooner doctors can go back to spending time with their patients instead of struggling with a database they don’t understand.

The future may not be right now, but it’s coming fast, and when NLP is standard throughout healthcare software, everyone will be much happier. It will also reduce the chances of missing a diagnosis or a symptom simply because it got lost in the database.


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