NLP in Healthcare: Techniques, Applications, Challenges & Future

Natural Language Processing (NLP) is a technique that enables a machine to recognize and decipher the nuances of human language.

NLP algorithms organize textual data by analyzing it for relevancy, differences in spelling, correlation, and semantic meaning. They try to understand and memorize different lexicons, grammatical syntaxes, and the relation between words and phrases, just as a human.

In this way, the NLP models help the computer system understand the literal meaning and recognize the sentiments, tone, opinions, thoughts, and other components that construct a proper conversation.

Various industries, such as finance, retail, education, and healthcare, leverage NLP to improve their processes. In this post, we will discuss how NLP is used in healthcare to improve patient care and healing.

NLP in Healthcare Industry

In the healthcare industry, natural language processing has many potential applications. NLP can enhance the completeness and accuracy of electronic health records by translating the free text into standardized data.

It can fill data warehouses and semantic data lakes with meaningful information accessed by free-text query interfaces. It can make documentation requirements easier by allowing providers to dictate their notes or generate tailored educational materials for patients ready for discharge.

NLP Techniques in Healthcare

NLP systems pre-process data by first cleaning the dataset. It essentially involves organizing the data into a more logical format — for example, breaking down text into smaller semantic units, or tokens in a process known as tokenization. Pre-processing makes the data set easier for the NLP system to interpret.

From there, the system applies algorithms to the text to interpret it. The two primary algorithms used in NLP are:
1. Rule-based Systems, which interpret text based on predefined grammatical rules
2. Machine learning Models, which use statistical methods and “learn” over time by being fed training data

These are some of the NLP techniques commonly employed in the healthcare sector:

Optical Character Recognition (OCR)

OCR is used to digitize any document. It can be handwritten, an image, or a computerised text of any format. OCR can extract data from them and present it in a format suitable to feed into the NLP pipeline for further analysis.

Named Entity Recognition (NER)

NER is a unique information extraction technique. It segments named entities in real-world subjects like persons, places, organizations, or anything into predefined categories.

Sentiment Analysis

Sentiment Analysis uses a combination of NLP, computational linguistics, text analysis, facial recognition, and biometrics to a text or speech to ascertain the underlying sentiment.

Text Classification

Text categorization helps to analyze text data and assigns tags or labels to semantic units or clauses based on predefined categories. With this technique, a healthcare provider can identify at-risk patients based on specific keywords in the medical records.

Topic Modeling

Topic modeling classifies the documents collected and groups them based on common keywords or phrases to recognize semantic structures. Latent Dirichlet Allocation is a standard topic modeling technique that uses algorithms to identify semantic relationships between different words and phrases to group them.

Applications of NLP In Healthcare

While there is ongoing research and development on the use of NLP for various tasks in the healthcare sector, below we have discussed some of the popular ways in which NLP is employed in the healthcare sector.

Clinical Documentation

NLP extracts critical data from Electronic Health Records (EHR) at the point of care using speech-to-text dictation and formulated data entry. Furthermore, It allows physicians to concentrate on providing patients with the necessary care and ensures that clinical documentation is accurate and up to date.

Clinical Trial Matching

Healthcare providers can use NLP to automatically review massive amounts of unstructured clinical and patient data to identify patients for clinical trials. It accommodates patients to receive experimental care that improves their condition and promotes medical innovation.

Clinical Decision Making

NLP aids medical professionals in making informed and intelligent decisions by providing access to health-related information exactly when required. Thus, It enables medical professionals to make more accurate decisions at the point of care.

Medical Coding

NLP can be used to automatically assign codes to medical diagnoses and procedures. It can help streamline the billing process and reduce errors. There would be no confusion between the staff, doctors, and patients related to the advised treatment.

Patient Engagement

NLP can be used to communicate with patients through chatbots and virtual assistants. It can help improve patient engagement and provide personalized care.

Chatbots and virtual assistants can encourage patients to undergo treatment and boost their mental health during treatment. It can also send reminders of medications, pending appointments and tests, etc.

Clinical Research

NLP can analyze large amounts of clinical data for research, speeding up the R&D process. It can help identify new treatments and improve healthcare outcomes.

Challenges for NLP in Healthcare

As beneficial as NLP is to the healthcare sector, it faces various challenges. First, adapting to newer software technologies like NLP becomes a major challenge in the healthcare sector. Secondly, developing solutions for the healthcare industry requires a good understanding of medical content.

Besides these, NLP models require sufficient data for training to be able to perform the desired task. They need to understand medical terminology to read and understand medical data. So, there are barriers to NLP in healthcare, but efforts are being made to overcome them.

The Future of NLP in Healthcare

In the future, medical NLP tools are expected to become an integral part of healthcare organizations. They would serve as an intelligent assistant for healthcare providers, quickly processing large volumes of text-based unstructured data. With them, medical professionals will make informed decisions and improve predictive analytics.

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