Natural Language Processing - How is it beneficial for better clinical decisions

Natural Language Processing - How is it beneficial for better clinical decisions

Until a few years ago, one had to use a bag of words to solve or win a challenge. You remember Siri, right? If it had been 2011, it would face some challenge to understand a non-American accent. But if you switch to 2021, there is no issue like that anymore. 

That’s how natural language processing technology evolved over the years. However, from a semantic process standpoint, consumer data is remarkably easier to understand. So, if we say it is pretty beneficial for the healthcare industry, many leaders would be doubtful. 

And why not when clinical data such as physician notes, emails, or specialist images are more challenging to capture via an automated process. One needs to have the profound clinical knowledge to develop something equivalent to a training set. 

Having said that, the healthcare space is quite interested in NLP applications because of its role in improving accuracy levels. For instance, Minneapolis-based Allinas Health opted for NLP technologies. And it was able to decrease costs for the health system while expediting workflow for providers.

Another example is New Hampshire using an NLP technology that enabled the clinicians to dictate right from their workstations or smartphones. This helped the healthcare system to save money on phone-based transcription services. 

Undoubtedly, NLP has many benefits, but what is it? Let’s find out!

Natural language processing: What is it?

Natural language processing is a term used for computer algorithms to identify or spot different elements in everyday life. Doing so extracts the unstructured spoken input and makes it easier for humans to reach a decision. 

This discipline uses AI, computational linguistics, and other machine learning aspects to create human-like responses to queries or conversations. Ideally, it might help in the following tasks.

  • Summarizing lengthy blocks of narrative text like clinical notes or academic journal articles. 
  • Mapping elements present in the electronic health record to improve clinical integrity.
  • Transforming data into a readable format for educational or reporting purposes.
  • Answering queries by extracting data from multiple sources. 
  • Engaging in optical character recognition to change images into text files that one can quickly analyze. 
  • Speech recognition helps users to dictate clinical notes and later turn them into a text format.

Ideally, these systems “learn” over time after reabsorbing the results from previous interactions or feedback. They work on statistical probabilities and are based on the data. They adjust in the future to meet the evolving needs of the consumers.

How is it Beneficial for Better Clinical Decisions?

Natural language processing or NLP is quite effective for clinical decision support. But how? It enhances the completeness and accuracy of the EHR. It translates free text into standard data. Moreover, it can also fill data warehouses with meaningful information. Further, it will make documentation easier by allowing the providers to dictate their notes and generate tailored educational materials for the patients about to discharge.

If you consider the famous example of machine learning NLP whiz kid, it would undoubtedly be IBM Watson. It dominated headlines for many months because it helped with clinical decision support in cancer care.

In the year 2014, they set up a division where they added an epic NLP system. It helped them to flag patients with heart disease. Using NLP algorithms against the data entered by the physicians, one could highlight pertinent clinical data. 

Further, there are millions of academic articles related to detecting and treating a variety of diseases. No human can read, understand, and most importantly, remember all of this data. So, one can’t expect to distill this information into some concrete recommendation.  

Also, the medical history, physical examination, and other results are usually obtained in text form, so one could use NLP methods to identify the essential elements that can improve the clinical decisions at no additional cost.

Examples of NLP put into action

  • The Department of Veterans Affairs used the NLP technique to review more than 2 billion EHR documents to browse through indications of PTSD, depression, and elements that could accentuate self-harm triggers. The pilot was more than 75% accurate. 
  • MIT researchers achieved around 75% percent accuracy when using NLP to decode the semantic meaning of clinical terms provided in free-text clinical notes and put all the words into context. 
  • NLP helped the healthcare providers to take the speech patterns of the mentally unstable patients. This allowed them to determine the onset of psychosis with 100% accuracy. 
  • This technology helped the researchers at the University of California Los Angeles to flag patients with cirrhosis. 

However, this is just the beginning. There are still some challenges of integrating NLP tools into clinical care. For instance, reliability and accuracy are still in progress. And problems like disambiguation or fragmented “doctor speak” can cause complications even if you have the best NLP systems. 

Key Takeaways: The Future of Healthcare Industry With NLP

Even though natural language processing isn’t a unique technology yet, the healthcare industry uses this technology for better healthcare decisions. For instance, cognitive computing and semantic big data analytics depend on this technology for better results. 

Scientists predict that the technology will grow to $13.7 billion across different industries. Many believe that its role in arranging EHR information will be a lot more in the coming years. It will improve the patient encounter information easier to find by the clinicians.

Plus, it will populate the documents into sections that will allow clinicians to find the data otherwise used to get missed. Thus, resulting in better diagnosis. In other words, it will improve the quality of healthcare. One of the clinicians developed a report card integrated with NLP, which helps to calculate ADR automatically. 

To put it simply, it will improve healthcare data, and its widespread adoption will significantly impact outcomes improvement. A user-friendly NLP tool will improve patient outcomes by helping clinicians save time that usually goes into administrative tasks.

Now you tell us, how will this technology help your organization improve in the coming future? Can you suggest some changes that will help to navigate the challenges that this technology presents?

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