Top 10 Use Cases of NLP in the Healthcare Sector

Here we gathered the list of the top 10 use cases of NLP in the health sector

Natural Language Processing known as NLP technology can process data that is completely unstructured. The incorporation of intelligent systems to optimize organizational processes, increase quality time, and reduce operational expenses is a major reason for a business to employ NLP technology.

According to studies, NLP in the healthcare sector is expected to grow at a CAGR of 20.8 percent from USD 1030.2 million in 2016 to USD 2650.2 million in 2021.

Before we hop into the use cases of NLP innovation in the healthcare sector, let me give a quick introduction to NLP.

An area of artificial intelligence known as NLP (Natural Language Processing) aims to bridge the gap between humans and robots. A robust system with NLP can understand, store, process, and implement data-driven insights in the form of speech or text that can be understood by humans.

Let's take a look at the top 10 use cases of NLP in healthcare:

1. Clinical Documentation:

The NLP technologies will significantly alter the analytical data used to run VBC and PHM efforts by bringing relevant data from speech recognition equipment to light. The clinicians benefit more from this. Social Determinants of Health (SDOH) and the usefulness of wellness-based policies will be identified through the application of NLP tools to various public data sets and social media in the coming years.

2. Speech recognition:

By allowing clinicians to transcribe notes for useful EHR data entry, NLP has matured its speech recognition use case over time. Back-end technology works to detect and correct any errors in the transcription before passing it on for human proofing, while front-end speech recognition eliminates the need for physicians to sit at a point of care and dictate notes.

3. Computer-assisted coding (CAC):

To maximize claims, CAC records procedures and treatments capture every possible code. Even though it is one of NLP's most widely used applications, only 30% of people use it. It has improved coding speed but has not improved accuracy.

4. Data mining research:

Organizations can provide useful medical knowledge and reduce the amount of subjectivity in decision-making by incorporating data mining into healthcare systems. Data mining has the potential to develop into a cyclic technology for the discovery of knowledge once it is initiated. This can assist any HCO in developing an effective business strategy for providing improved patient care.

5. Automated Registry Reporting:

An NLP use case is to remove values depending on the situation in each use case. When ejection fraction is not stored as discrete value, regulatory reporting burdens numerous health IT systems. Health systems will need to know when an ejection fraction is written down in a note for automated reporting, and they will also need to save each value in a way that the analytics platform of the organization can use for automated registry reporting.

6. Clinical Decision Support:

Clinical decision support will be strengthened by the inclusion of NLP in healthcare. Nevertheless, solutions are designed to support clinical decisions. Medical errors are one example of a process that calls for more effective supervision strategies.

7. Clinical Trail Matching:

A significant use case is using NLP and machines to identify patients for a clinical trial. Natural Language Processing in Healthcare engines for trial matching is being used by some businesses to address these issues. Trial matching can now be automated and carried out without a hitch thanks to recent advancements in NLP.

8. Prior Authorization:

Payers' prior authorization requirements for medical staff are only getting stricter, according to analysis. These requests increment practice above and burglary care conveyance. Because of NLP, the question of whether payers will approve and implement compensation may no longer exist.

9. AI chatbots and Virtual Scribe:

Although there is currently no such solution, there is a good chance that speech recognition apps would assist humans in modifying clinical documentation. Something like Amazon's Alexa or Google's Assistant will be ideal for this. Google and Microsoft have joined forces to accomplish this particular goal. Therefore, it is safe to assume that IBM and Amazon will follow suit.

10. Risk Adjustment and Hierarchical Condition categories:

The initial goal of the risk adjustment model known as Hierarchical Condition Category Coding was to predict how much patients would pay for their care in the future. HCC coding will become more common in models of value-based payment. Each patient's risk score is assigned by HCC using ICD-10 coding. Natural language processing can assist in identifying risk factors for patients and predicting healthcare costs based on their scores.