5 Things to Know About NLP in Healthcare

Natural Language Processing

Natural Language Processing, or NLP, has been a revolutionary technological innovation since its introduction in the 20th century. As we continue to talk about the benefits of technology in healthcare, we must also acknowledge that NLP programs such as Chat GPT and its competitors from all across the tech space will become more prominent in everyday use. Let’s take a closer look at NLP in healthcare, and the benefits and challenges that come with integrating this technology.

What Is NLP?

NLP, short for Natural Language Processing, is a subfield of artificial intelligence focusing on the interaction between humans and computers using natural language. In other words, NLP enables computers to understand, interpret, and generate human language in a way that is both meaningful and useful to humans. You might not even know it, but NLP is already commonly used on a daily basis. Smart assistants such as Alexa and Siri, auto-fill-in search results, and autocorrect are all versions of NLP that consistently make our lives easier and more efficient.

As NLP continues to evolve and its applications across various industries grow, it will become impractical to ignore the use of this technology, especially in the healthcare sector. The following are three ways NLP in healthcare will help improve and benefit the industry altogether.

NLP in Healthcare: What Can It Help Improve?

1. Sorting Unstructured Data

A significant problem that the healthcare industry currently faces is the amount of unstructured data present. Unstructured data is information that does not have a predefined format, schema, or organizational structure. Because of that, unstructured data is typically harder to analyze without the help of machine learning algorithms and NLP.  In the healthcare industry, 80% of data is unstructured. Much of this data—including clinical notes, patient feedback, and medical literature—is found within a medical provider’s Electronic Health Record (EHR). Typically an EHR sorts data by patient encounter rather than keywords, making it harder sometimes to find critical patient information. With the digital transformation of NLP in healthcare, however, clinicians will be better able to find information buried in big data that contains valuable insights. This can lead to more accurate diagnoses, better treatment plans, and more effective patient monitoring. 

2. Healthcare Costs

As is the case in many other industries, the number of administrative tasks (such as data entry and analysis) burden healthcare professionals and prevent them from delivering high-quality patient care. With NLP in healthcare, however, these tasks can be drastically reduced in number, allowing healthcare professionals to focus more on patients that require human attention rather than menial tasks that do not. Luckily, we have already seen NLP in healthcare begin to impact the reduction of healthcare costs overall. An estimated $20 billion in U.S. healthcare costs will be reduced by 2026 due to virtual NLP nursing models that can attend to and communicate with patients without the need for human assistance. 

3. Strengthening Predictive Analytics

In the future, NLP in healthcare will play a crucial role in improving patient care with predictive analytics. Essentially, NLP in healthcare will be able to scour large amounts of data and identify patients facing health disparities or risk factors that could make them susceptible to conditions at a higher percentage than human observation. For example, in 2016, a study was done where an NLP-based algorithm spotted high-risk patients of Kawasaki disease at a sensitivity of 93.6% compared to clinician notes and oversight. As NLP in healthcare continues to improve, the number of risk factors found will grow as well, and more and more patients will benefit from the early detection capabilities of NLP.

NLP in Healthcare: Challenges

While there are many benefits to the future of NLP in healthcare, there are still some challenges that need to be addressed as well. Here are some ways NLP must still improve for it to be adopted across multiple healthcare systems.

1. Understanding Context and Meaning

One of the primary challenges of implementing NLP in healthcare is the complexity of deriving meaning and inferring context from clinical text. Medical language is often ambiguous, and the meaning of a word or phrase can depend on its context and who writes it. Additionally, healthcare professionals often use abbreviations, acronyms, templates, and domain-specific jargon, making accurate interpretations and text analysis difficult for NLP algorithms. Overcoming this challenge requires developers to program NLP models with scale and meaning in mind, creating a sophisticated system that can learn to understand the nuances of medical language. 

2. Learning Sublanguages

Another challenge to implementing NLP in healthcare is the existence of specific sublanguages within the medical domain. Different medical specialties and platforms have their own terminologies, which can vary significantly from one another. To continue being effective, NLP solutions must be tailored to already know or continue to learn the terminology of the system for which it was built. However, this requires extensive domain knowledge and training data.

NLP in Healthcare: Final Thoughts

Although NLP in healthcare is far from perfect, it is a step in the right direction toward a more efficient and effective healthcare system. To detect risk factors of preventative diseases earlier and with less difficulty, it will be crucial for healthcare providers to integrate NLP into their systems and keep in mind the future of technology in healthcare. One disease that can already be detected early is diabetic retinopathy.

Diabetic retinopathy is the number one cause of blindness in adults. However, with the IRIS solution, licensed physicians at the IRIS Reading Center can detect this condition early with 95% accuracy. Want to learn more? Contact us to see how our teleretinal screening solutions are helping to fight against preventative blindness and ensure better patient treatment for all.


How Is NLP Used in Healthcare?

NLP is used in healthcare to process and analyze unstructured data, such as clinical notes, patient feedback, and medical literature. Applications include extracting information from clinical documents, supporting decision-making processes, and identifying trends that help healthcare providers deliver better patient care.

What Does NLP Mean in Medical Terms?

In medical terms, NLP stands for Natural Language Processing, a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language meaningfully. In the context of healthcare, NLP can help process and analyze unstructured data to support decision-making and improve patient care.

How Can NLP Benefit the Patient Experience?

NLP can benefit the patient experience by helping healthcare providers make better-informed decisions, leading to more accurate diagnoses, improved treatment plans, and more effective patient monitoring. Additionally, NLP can help reduce healthcare costs by automating tasks, optimizing resource allocation, and identifying areas for cost savings.

What Is NLP in Nursing?

In nursing, NLP refers to the application of Natural Language Processing to support nursing practice and decision-making. NLP can be used to analyze nursing documentation, identify trends and patterns in patient care, and provide insights that can help nurses deliver more effective and personalized care.



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