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The Role of NLP In Healthcare

March 23, 2020 - by Parul Saini, Webmedy team

Why is there a huge interest in Natural Language Processing (NLP) in the healthcare industry?

To know this, we have to understand the kind of atmosphere healthcare works in. Powerful regulatory and agreement laws like Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), the various refinements of the EHR incentive framework and data interoperability, and other issues like the Medicare Access and CHIP Reauthorization Act (MACRA) framework, all add to the complexity of healthcare data collection, processing, and analysis.

Being aware of all this, the healthcare industry is attempting to transform the huge amount of data that it has into smart data, and in doing so, is utilizing technologies such as Natural Language Processing.

What is Natural Language Processing (NLP)?

NLP is an algorithm-based principle that aids in the study of text data, images, and other unorganized data. It sums the entire journal data from which one can then make actionable insights. Today, the large majority of the data in healthcare is unorganized. This involves data in the form of records, instructions, blogs, and social media communications. In a situation like healthcare, it creates more sense to draw into the potential of unorganized data.

Use of Natural Language Processing (NLP) in healthcare

The section of unorganized data in healthcare is as high as 80%. Without NLP, it is near to impossible to get the data understandable and useful. With NLP, it becomes simpler to handle this chunk. It is no wonder that the entire investment on NLP in healthcare was a big USD 1 billion in 2016. It is only continuing to rise, and it is foretold that it will become USD 2.6 billion in 2021.

One of the immediate results of NLP has been a decrease in the time needed to reach a clinical guideline. According to specialists, it has got down by 60%. In one such research, IBM Watson conducted a pilot program wherein IBM ran its algorithm over a humongous 21 million recordings and, with an efficiency of 85%, recognized 8500 patients who run a danger of congestive heart failure in a week.

Some areas where NLP can aid healthcare:

  • Summing long blocks of fictional text, such as clinical notes or scholarly newspaper article, by recognizing key ideas or expressions existing in the reference body.
  • Mapping data components present in unorganized text to organized lists in an electronic health record to enhance clinical data honesty.
  • Transforming data in the other way from machine-readable forms into natural language for writing and informative purposes.
  • Solving unusual free-text questions that need the combination of multiple data references.
  • Prepossessing in visual character identification to convert images, like PDF reports or scans of care surveys and imaging news, in-text lists that can then be parsed and interpreted.
  • Handling speech identification to enable users to manage clinical notes or other data that can then be converted into text.

Success of NLP in Healthcare

The healthcare ecosystem is growing. With the generation of intelligent technologies, the atmosphere is shifting more favorable to the use of NLP. First of all, the capacity to manage upsurge in health data has grown. The entire big data ecosystem has arrived collectively to manage the upsurge in data. Thanks to cloud computing, collecting data has become economically viable. By creating a sense of the data, the providers have understood that it is simpler to give a more accurate treatment, alias personalized healthcare.

Patient cooperation has also matured more fruitful as NLP has begun making sense of the EHR information. A set of algorithms can operate through humongous volumes of medical records and get sense out of those and gain useful knowledge, which can be utilized for developing patient literacy. With this, the requirement for advancement in healthcare status is approached right at its roots. Analytics also improves the classification of the patients who require care or drive the risk of an attack.

Real-World Examples of NLP in Healthcare

  • A real-life use case is the description of the distress signs in US war vets. With 80% efficiency, the code munched all the data of more than 2 million long EHR records. It originally recognized notices of suicide or suicide tries.
  • Speech perception, equipped with NLP, is performing a crucial role in transforming healthcare. It is saving time for caregivers with notes dictation and has streamlined the method of verbal instruction translation to EHR data and clinical documentation.
  • NLP is witnessing a rising use is clinical trial matching. This is assisting in reaching the goals for registration. Using NLP, healthcare organizations can recognize which problems require more extra heightened care coordination. NLP is unquestionably offering significant benefits to increasing the bar as far as the quality of care is involved.

How NLP can be used to deliver value-based care?

NLP tools may also allow a more effective way to estimate and enhance care quality. It can allow patients to shift from the current treatment plan to a more suitable one.

Healthcare is a complicated process. A notable degree of human engagement in the care cycle can introduce errors, sometimes probably dangerous ones. In a delicate situation, these mistakes can have a jeopardizing impact on care offerings. NLP algorithms can be applied to evaluate patient care and recognize these errors.

NLP tools can also establish a benchmark for doctors. Besides reducing the range of errors included in data extraction, they can discover a quality measure of care delivery.

In brief, one can reliably state that NLP has opened the information once resting unexploited below heaps of a document. With the right partner on board, healthcare organizations can begin harvesting the profits.

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