You probably know that most medical professionals spend most of their time dealing with paperwork and documentation. But they still find it difficult to understand the remaining 80% of the unstructured data, which is really risky in the highly sensitive healthcare field. 

How will healthcare professionals be able to treat patients if they are unable to understand medical data? This is actually a big concern!

But here comes the role of NLP. Natural Language Processing (NLP) is a field of artificial intelligence that empowers machines with the ability to interpret, modify, and comprehend human language. In other words, NLP focuses on the connection between computers and human language.

Thanks to natural language processing and healthcare related machine learning services, which have paved the way for personalized diagnosis and patient care. 

In this article, we have discussed different facets of NLP in healthcare – applications, techniques, and the revolutionary benefits it offers to the medical world.

How does NLP work in healthcare?

To analyze and comprehend human language, NLP leverages a variety of machine-learning algorithms and linguistic rules. It is only NLP that has the ability to dissect unstructured data like research papers, clinical notes, patient feedback, etc., and convert it to structured data.

This structured data is a treasure trove of medical information that doctors can use to provide their patients with personalized treatment.

Applications of NLP in healthcare

80% of documentation in the healthcare industry is unstructured, according to the report of Connected Content Better Healthcare. Unstructured documentation means it hinders accurate information processing and recognition. NLP addresses this issue.

In addition to this example, there are many other applications of NLP in healthcare, but the following are the top 5 key patient care areas.

Clinical documentation: Manual clinical documentation is not only time-consuming, but also prone to errors. NLP streamlines the extraction of critical information from patient records, reduces administrative burden, while improves accuracy.

Diagnostic assistance: NLP technologies have introduced multiple tools that swiftly analyze the symptoms and medical history of a patient. Doctors can now gain data insights and provide immediate potential diagnoses to their patients.

Drug discovery: When it comes to the pharmaceutical industry, NLP helps organizations prepare their drugs more quickly and efficiently. NLP technology has the ability to scan an immense volume of research papers and suggest potential ways to discover drugs.

Medical coding and billing: NLP analyzes clinical paperwork to automate the coding process. Furthermore, it reduces errors while increasing billing accuracy and efficiency. 

Radiology and imaging: NLP assists radiologists in interpreting medical images. It provides context through patient histories and makes the process of diagnosis more accurate and efficient.

4 Important NLP techniques you must know

Following are some important NLP techniques that healthcare professionals must know:

Named Entity Recognition (NER): NER has the ability to identify specific entities within data. Examples of entities are medical conditions, medical terms, and medications. All of these are necessary for creating structured medical records.

Sentiment Analysis: This technique determines the sentiment or emotional tone within text. It also helps doctors understand the opinions and feedback of patients.

Text Classification: NLP can classify medical texts according to predetermined categories. For example, it categorizes patient reviews into positive, neutral, or negative, which helps clinicians deal with patients.

Topic Modeling: NLP identifies the major themes in a collection of documents. It assists healthcare professionals, doctors, and physicians in understanding current challenges and developments.

How healthcare organizations can benefit from NLP

Here are some ways NLP can benefit healthcare organizations:

Time efficiency: NLP automates basic administrative tasks and allows healthcare practitioners to devote more time to patient care.

Enhanced accuracy: NLP technology has the power to extract important information from high volumes of data. And it extracts that data with enhanced accuracy, which reduces errors in patient diagnosis.

Data-driven insights: NLP extracts useful information from massive volumes of medical data. It sets the path for decision-making that is evidence-based and completely accurate.

Patient-centric care: Patients receive personalized care that suits their specific needs thanks to customized treatment programs and better diagnostics.

Research advancement: Researchers can harness NLP to examine enormous amounts of medical literature. It will undoubtedly expedite medical advancements.

Future of NLP in healthcare

There is no doubt that the future of healthcare looks bright. We can expect more from AI technology as it advances:

Real-time diagnostics: Quick analysis of patient data could lead to on-the-fly diagnostics and therapies. It will save the precious time of doctors in critical situations.

Preventive care: Based on previous data, NLP could enable prediction algorithms that identify health hazards. This is how NLP enables proactive preventative care.

Improved patient engagement: AI-powered chatbots that use NLP could provide patients with 24/7 assistance, answering patient inquiries and providing them with required information.

Global healthcare access: Language barriers could also be ended as NLP-supported translation services facilitate communication among patients and doctors from all around the world.

Final remarks

Now you can see how the marriage of NLP and healthcare-related machine learning services is revolutionizing the healthcare landscape. Whether it is drug discovery, patient diagnosis, or personalized patient care, NLP has applications in all roots of the healthcare industry.

Those healthcare organizations and hospitals that have welcomed NLP and artificial intelligence technologies are at the forefront of the industry.

Now, it’s your time to lead the industry, but the only way is to accept the AI revolution and use its highly beneficial ML technologies (like NLP, Data Mining, Social Intelligence, Computer Vision, etc.) during this golden era. Otherwise, it will be too late.