Technology Use Cases (NLP) in Healthcare

In healthcare, patient medical records, order entries and physician notes are not the only sources of data. In fact, millions of people have already used take-home kits to upload their genetic information to commercial databases.

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

Many use cases for natural language processing (NLP)

Enter Natural Language Processing (NLP) technology, which can process completely unstructured data. One of the primary goals for a business to use NLP technology is to integrate intelligent systems to optimize organizational processes, increase quality time, and reduce operational expenses.

However, the integration of NLP into health app development has other advantages.

  • NLP could help translate human speech into a machine-readable form, allowing machines to derive meaning from the data provided.
  • NLP tools are used in healthcare to mine large amounts of data recorded as speech, text, pictographs, photos, and other forms of data in order to derive useful information.

Natural language processing in healthcare is predicted increase at a CAGR of 20.8%, from USD 1,030.2 million in 2016 to USD 2,650.2 million in 2021, according to studies.

Before we jump into the use cases for this disruptive technology in healthcare, let me give a brief overview of what NLP actually means!

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

The importance and use of natural language processing systems in the development of healthcare applications is increasing day by day. So systems like create chatbots using NLP are causing a stir in the health sector.

When effectively implemented, technology enables clinicians to streamline administrative operations, spend more time on patient care, and improve patient experience by analyzing data in real time.

8 Top Use Cases for NLP Technology in Healthcare

1. Voice recognition

For nearly two decades, NLP has its roots in healthcare with voice recognition, allowing doctors to transcribe notes for quick EHR data entry.

Front-end voice recognition allows doctors to dictate notes rather than sitting at a computer at the point-of-care, while back-end recognition corrects any problems in the transcription before it is passed on for human review.

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

2. Clinical documentation

Due to speech-to-text dictation and structured data entry, the impact of NLP on speech recognition goes hand-in-hand with clinical documentation, freeing physicians from the laborious and constraining structure of EHRs enabling better patient care.

Both Nuance and M*Modal 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 Aided Coding (CAC)

CAC gathers process and treatment data to capture all possible codes and optimize claims. CAC may have increased encoding speed, but it did nothing to improve accuracy.

For example, a Cleveland Clinic study indicated that while CAC reduced coding time, it had worse recall and accuracy when used alone without the help of a credentialed coder.

4. Clinical Trial Matching

Clinical trial matching is perhaps the most talked about use case in the “in development” category. Linguamatics Health and Clinithink, for example, have created NLP engines to solve trial matching issues, while IBM Watson Health and Inspirata have dedicated significant resources to using NLP to aid oncology studies. .

In the not too distant future, NLP appears to have the ability to make clinical trial matching a transparent 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 technique for knowledge discovery, helping 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 applications have a good possibility of helping humans edit clinical documents. Something like Amazon’s Alexa or Google’s Assistant will be ideal for this.

Microsoft and Google have teamed up to achieve this particular goal. Currently, creating a chatbot using NLP can capture symptoms and direct patients to the most appropriate provider.

7. Root cause analysis

Another intriguing aspect of NLP is the ability of predictive analytics to provide solutions to common health issues.

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

8. Reviews management and sentiment analysis

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

Some systems can even listen to the voice of the customer in reviews, which can help the physician understand how consumers are talking about their care and communicate more effectively through the use of shared vocabulary.

The future of the healthcare industry with NLP

The healthcare industry is eager to make efforts to improve natural language processing, although it is 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 creating chatbots using NLP for development.

Financial analysis

Financial analysts are optimistic about the prospects for NLP and related technologies in the coming years. According to Allied Market Researchthe cognitive computing market will be worth $13.7 billion by 2020, growing at a compound annual growth rate (CAGR) of 33.1% from current levels.

Natural language techniques

Natural language processing techniques could one day bridge the gap between the huge amount of data generated daily and the limited cognitive abilities of the human mind.

NLP has virtually endless potential to transform electronic health records from a burden into a boon, from cutting-edge precision medicine applications to the basic process of coding a billing and reimbursement claim.

Ongoing 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 care delivery system to improve the patient experience.

Contact a reputable software development company to provide leading healthcare application development with NLP and AI solutions across a wide range of use cases.

Image credit: Max Mishin of Pexels; Thank you

Smith Johnes

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

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