How NLP is Transforming Healthcare? Use cases and More

Are you spending too much time going through patient records or struggling to keep up with ever-growing documentation? Do you worry about missing key insights hidden in your data? All of these challenges can be addressed with NLP in healthcare. Natural language processing in healthcare enables machines to process and analyze vast amounts of unstructured medical data, allowing for quicker diagnosis, streamlined administrative tasks, and improved patient outcomes. These are just a few of the many NLP use cases in healthcare poised to transform the field.

Before we understand how NLP development is bringing positive changes to the healthcare sector, let’s first understand what NLP is and how it works. 

What is NLP and How does it Work?

Natural language processing is a subfield of artificial intelligence specifically designed to let computers grasp and decipher human speech.

NLP systems initially “clean the dataset,” thereby pre-processing data. This basically entails, in a process known as tokenization, breaking down text into smaller semantic pieces, or “tokens,” therefore putting the data into a more logical framework. Pre-processing basically facilitates NLP system interpretation of the dataset.

From there, the system uses algorithms to understand the text. Rule-based systems—which analyze text according to established grammatical rules—and machine learning models—which use statistical techniques and “learn” over time by being given training data—are the two main algorithms applied in natural language processing.

Though it is a significant technical development that crosses computer science and linguistics, NLP is more widespread than you would know. Whether you express a customer care concern to a chatbot or engage with an at-home virtual assistant like Siri or Alexa, you are really using NLP every time. Having said that, NLP has more complex applications, particularly in the healthcare sector, which we will discuss in this post.

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Top 10 NLP Use Cases in Healthcare

The market for NLP in Healthcare and Life Sciences was $2.9 billion in 2023. It’s going to grow very quickly, by 30% per year, and hit over $18.2 billion by 2030. These statistics demonstrate the huge impact of NLP in healthcare. Now, let’s look at some key NLP use cases in healthcare that are driving this change.

1. Speech Recognition

Natural language processing in healthcare systems frees up doctors to spend more time with patients by taking them out of the time-consuming physical processes of EHRs. Speech-to-text notes and made data input tools are very helpful to me. The teamwork and speech recognition features of Nuance and M*Modals make it possible to collect structured data while patients are being cared for and to create standardized languages that can be used in the future.

Voice recognition software can be used with natural language processing (NLP) tools to get useful information. This will greatly change the analysis data that powers VBC and PHM projects. This helps the doctors do their jobs better. It will eventually use natural language processing (NLP) tools on different public data sets and social media to find out what social determinants of health (SDOH) are and how well wellness-based policies work.

2. Patient Sentiment Analysis

NLP methods examine patient feedback from a variety of sources, including social media, questionnaires, and electronic health data. Understanding patient attitudes and feelings helps healthcare professionals to spot areas for development in patient care experiences and services. Sentiment analysis enables one to see patterns, track patient satisfaction rates, and react quickly to patient problems or concerns. By means of this proactive strategy, patient involvement, contentment, and loyalty toward healthcare services improves.

3. Computer-Assisted Coding

Medical coding is the method used to give clinical papers regular procedural and diagnostic codes. Processing insurance claims, billing, and trend analysis of healthcare depend on this coded data.

Analyzing clinical data using NLP and machine learning, Computer-Assisted Coding recommends appropriate medical codes for patient records. This calls both pattern recognition methods and semantic analysis.

As for instance, NLP might detect terms like “development of type 2 diabetes” and extract relevant data to assign the proper code, therefore guaranteeing billing accuracy and compliance.

4. Root Cause Analysis

Root Cause Analysis is a more advanced use of natural language processing in healthcare that tries to find out where health problems come from. When you combine natural language processing with electronic health data, you can find health trends based on race and location that affect the health of the whole community. Natural language processing (NLP) can look at how culture and society affect health on a much bigger scale than many other sources can.

5. Drug Discovery

NLP is changing the way drugs are made by sorting through huge amounts of data from clinical studies, academic papers, and biology books. Algorithms that have been trained on NLP in healthcare can now pull out and combine data on treatment results, molecular relationships, and drug targets. One of the most important uses of NLP in healthcare is its ability to find hidden trends in large datasets, which speeds up the process of finding new medicines and helps in idea generation. This approach based on data leads to better personalized medicine, shorter time to market for new treatments, and more work being done in research. what is federated learning?

6. Unification of Clinical Records

Since patients get care at different sites or from different doctors, their notes are not always organized and contain the same information. This makes it hard for researchers and therapists to come to a decision. It is easier to make uniform medical records with NLP in Healthcare’s object recognition and semantic analysis tools. NLP in healthcare standardizes and organizes data by pulling helpful information from different papers using natural language processing in healthcare. This makes it much easier to make decisions by making sure that comments are similar and can be tracked.

7. Computational Phenotyping

Natural language processing is changing clinical trial matching in many ways; it may even be able to assist doctors with the difficult task of phenotyping patients before an examination. Using natural language processing (NLP), phenotypes may be determined by patients’ actual circumstances rather than experts’ opinions.

It may employ natural language processing (NLP) to evaluate speech patterns, which may prove to be diagnostically useful for neurocognitive impairments including Alzheimer’s disease, dementia, cardiovascular disease, and mental illness. 

8. Dictation and EMR Implications

EMRs, which usually hold 50 to 150 MB per million data, are not nearly as long as clinical notes, which can be over 150 times longer. For this reason, a lot of doctors are switching from making notes to voice notes. NLP in Healthcare can quickly review voice notes and add them to EMR systems. Because of this, doctors may be able to put more focus on taking care of patients. Despite the fact that the majority of clinical notes are unorganized, natural language processing in the healthcare industry can instantly evaluate and collect crucial information on medical reports and doctors’ letters. 

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9. Automating Registry Reporting

One important NLP use case in healthcare is extracting values based on each person’s reporting needs. Many health IT systems have trouble with regulatory reporting because they don’t store factors like ejection fraction as precise numbers. Health systems need to find out when ejection fractions are written down in clinical notes and keep each number in a format that works with their analytics tool in order to set up automated reporting.

It’s not always easy to set up automated register reports. It is very important to use NLP in healthcare to get the best results.

10.  Precision Medicine and Genomics

Precision medicine, which customizes treatment for each person based on their unique traits, is a well-known NLP use case in healthcare. Genomic science is a key part of this plan to learn more about how genetic variations affect health. The amount of genomic data doubles every seven months, but we aren’t keeping up with our ability to understand it. The data is spread out across many databases and file types, so powerful tools are needed to get information from it. The use of NLP in healthcare makes it possible to automatically pull out clinically relevant information from a huge body of biological literature. This helps doctors make correct diagnoses, predictions, and plans for treatment.

To help combine different types of data for more personalized care, natural language processing could look at clinical notes to find out about the patient’s traits and find connections to DNA markers. The ability of NLP to make sense of genetic data and target specific illnesses has helped doctors treat heart and brain problems better, among other things.

Enhance Care with NLP in Healthcare

NLP is a powerful tool in healthcare for tracking health and helping doctors make decisions because it can greatly improve care for patients and make things easier. For the application to work, it’s important to understand the unique words that patients and healthcare workers use.

Working with an experienced AI and ML development company like Innvonix is a good idea if you want your business to get the most out of generative AI services. Our natural language processing systems, which are powered by AI and offer personalized apps, virtual helpers, and search results, can be used by hospitals and other healthcare centers.

If you want better results for patients and more efficient healthcare operations, you need to start using NLP right away.