Use cases for (NLP) technology in healthcare

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In healthcare, patient records, incoming orders, and doctor’s notes aren’t the only sources of data. In fact, millions of people have used take-home kits to upload their genetic information to commercial databases.

Wearable technologies have also opened up new channels for consumer health data. However, adding it to the sea of ​​health data won’t help you much if you don’t use it. And many experts agree that the application of the data is missing.

Many Use Cases for Natural Language Processing (NLP)

Enter Natural Language Processing (NLP) technology that can handle data that is completely unstructured. A key reason for using NLP technology for a business is to embed intelligent systems to streamline organizational processes, increase quality time and reduce operational costs.

The Inclusion of NLP in development of health applications has other advantages.

  • NLP could help translate human language into machine-readable form, allowing machines to derive meaning from the data provided.
  • NLP tools are used in healthcare to leverage vast amounts of data stored in the form of speech, text, pictograms, photos and other forms of data to derive useful insights.

natural language processing in healthcare predicted According to studies, to grow at a CAGR of 20.8% from USD 1030.2 million in 2016 to USD 2650.2 million in 2021.

Before we dive into the use cases of this disruptive technology in the healthcare industry, let me give a quick overview of what NLP actually means!

NLP (Natural Language Processing) is an area of ​​artificial intelligence that aims to bridge the gap between humans and robots. NLP is a skill that enables a robust system to understand, store, process, and execute data-driven insights in the form of speech or text understandable to humans.

The importance and use of natural language processing systems in the development of healthcare applications is increasing day by day. Therefore, systems like Building chatbots with NLP cause a stir in the healthcare industry.

When implemented effectively, the technology enables physicians to streamline administrative processes, spend more time on patient care, and improve the patient experience by analyzing real-time data.

8 Top Use Cases for NLP Technology in Healthcare

1. Speech Recognition

For nearly two decades, NLP has its origins in healthcare with speech recognition allowing physicians to transcribe notes for rapid EHR data entry.

Front-end speech recognition allows physicians to dictate notes instead of sitting at a computer at the point-of-care, while back-end recognition corrects any issues in the transcript before sending it on for human review.

Speech recognition is one of the most cost effective solutions as it eliminates the need and high cost of medical transcriptionists.

2. Clinical documentation

Through speech-to-text dictation and structured data entry, NLP’s impact on speech recognition works hand-in-hand with clinical documentation, freeing physicians from the tedious and restrictive structure of electronic medical records, enabling better patient care.

Nuance and M*Modal both have technologies that work in unison with their speech recognition technology to collect structured data and standardized terminology at the point of care for future use.

3. Computer Assisted Coding (CAC)

CAC collects data on procedures and treatments to capture every possible code and optimize claims. CAC may have increased encoding speed, but it did nothing to improve accuracy.

For example, a Cleveland Clinic study showed that while CAC reduced coding time, it had poorer recall and accuracy rates when used alone without the assistance of a certified coder.

4. Comparison of clinical studies

Clinical trial matching is perhaps the most discussed use case in the development category. For example, Linguamatics Health and Clinithink have developed NLP engines to solve study matching problems, while IBM Watson Health and Inspirata have dedicated significant resources to using NLP to support oncology studies.

In the not too distant future, NLP appears to be able to make clinical trial matching a seamless and automated process.

5. Data mining research

Data mining in healthcare systems enables companies to reduce subjectivity in decision-making while providing relevant medical knowledge. Once started, data mining can become a cycle of knowledge discovery techniques that will help any healthcare company develop a sound financial strategy to deliver better patient care.

6. AI Chatbots and Virtual Scribe

Although no such solution currently exists, speech recognition apps have a good opportunity to help people modify clinical records. Something like Amazon’s Alexa or Google’s Assistant are ideal for this.

Microsoft and Google have partnered to achieve this specific goal. Currently, building a chatbot with NLP makes it possible to capture symptoms and route patients to the most appropriate provider.

7. Root Cause Analysis

Another fascinating aspect of NLP is its ability to use predictive analytics to provide solutions to common health problems.

Large caches of digital medical records can help identify subsets of geographic regions, racial groups, or other distinct demographic segments that face different types of health disparities when applied to NLP. NLP systems evaluate unstructured responses to determine what is causing the patient’s difficulties or poor outcomes.

8. Rating Management and Sentiment Analysis

NLP can also help manage internet ratings for healthcare facilities. Every day, it can collect and analyze hundreds of third-party healthcare reviews. It can also quickly assess human emotions and the context in which they are expressed.

Some systems can even listen to the customer’s voice in reviews, which can help the doctor understand how consumers are talking about their treatment and communicate more effectively by using common vocabulary.

The future of healthcare with NLP

Healthcare is keen to make efforts to improve natural language processing, although it’s not quite there yet. Some well-known companies are investing heavily in cognitive computing and semantic big data analytics initiatives, both of which rely heavily on building chatbots using NLP for development.

financial analysis

Financial analysts are optimistic about the prospects for NLP and related technologies in the coming years. Accordingly Associated Market ResearchThe cognitive computing market will be worth $13.7 billion by 2020 and will grow at a compound annual growth rate (CAGR) of 33.1 percent from current levels.

Natural Language Techniques

Natural language processing techniques may one day be able to bridge the gap between the massive amount of data generated every day and the limited cognitive abilities of the human mind.

NLP has virtually infinite potential for turning electronic health records from a liability into a blessing, from cutting-edge precision medicine applications to the fundamental process of coding a billing and reimbursement claim.

Continued need for natural language processing

The many applications of natural language processing discussed here offer the healthcare industry an opportunity to break down old barriers and fill gaps in the delivery system to improve the patient experience.

Turn to a reputable software development company to offer leading healthcare application development with NLP and AI solutions for a variety of use cases.

Photo credit: Max Mishin from Pexels; Thanks

SmithJohn

Smith is a content writer who loves to write his understanding and knowledge in a simplified and engaging way. He is an early adopter and likes to keep up to date with the latest trends and technologies.

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