The Revolutionizing Effects of NLP in the Healthcare Industry

Posted By : Priyansha Singh | 23-Feb-2022


The Benefits of NLP Services in the Healthcare Industry


Natural Language Processing or NLP is the ability of computers to comprehend and understand human speech and texts. While it is an active tech enabler of diverse industries, NLP has proven to be extremely beneficial for the healthcare sector in particular.


According to research, NLP in the life sciences and healthcare industry is expected to reach $3.7 billion by 2025, growing at the rate of 20.5% annually. The increased involvement of NLP in the healthcare sector is because of its recognized potential to analyze, search, and interpret mammoth amounts of healthcare professionals and patient datasets. With the help of advanced machine learning and medical algorithms, NLP technology has the potential to harness relevant concepts and insights from data that was considered to be buried in text forms previously.


Natural language processing in the healthcare media can accurately give voice to unstructured datasets in the medical landscape, furnishing incredible insights into improving methods, understanding quality, and obtaining better results for patients.


In this blog, we will discuss some of the prominent use cases and applications of NLP in the healthcare industry and how it is helping in transforming medical processes, healthcare datasets, and business workflows.

NLP in healthcare industry

Understanding Natural Language Processing


NLP is an algorithm-based principle that helps in the study and comprehension of text, images, and other unorganized datasets. It enables computers to understand and process human language in exactly the same way as we do. NLP helps in discovering data patterns and also performs automated tasks for diverse industries such as healthcare, pharma, life sciences, and many more. A myriad of devices such as AI-enabled digital assistants and voice-assisted devices use this technology on a daily basis.


By leveraging the following techniques to evaluate spoken and written texts, natural language processing enables healthcare professionals and service providers to use extracted data in order to develop treatment plans, diagnose illnesses, monitor patients’ progress, and more.


  • Named Entity Recognition (NER): It uses real-world names of places, people, or products to structure and organize data.



  • Text Classification (TC): It is used to classify and assign different labels or tags to specific categories of texts. 


  • Sentiment Analysis (SA): It is used for applying biometrics and text analysis to measure feelings, sentiments, or opinions.


  • Topic Modelling (TM): It is a form of statistical modeling that groups similar phrases and words together.


Harnessing The Potential of NLP in Healthcare


The reason why the adoption of Natural Language Processing or NLP is augmenting is because of its undisputed potential in interpreting complex and unstructured datasets as well as in generating actionable intelligence. This data can be in any form such as speech, visuals, texts, and more. Leveraging this power can open the doors to unprecedented possibilities and amplify the organization’s collective investments in relation to human efforts, capital, and time. Natural Language Processing works on the same lines. It assists in processing massive amounts of data present in linguistic forms coupled with running advanced machine learning algorithms with it in order to achieve valuable business insights.


In relation to the healthcare industry, NLP is even more useful where colossal data volumes are incessantly churned out every day. A few segments of healthcare where the technology is used for digital transformation are clinical documentation enhancement, free text, automated reporting, data mining research, clinical trials, informed decisions, and more.


NLP Applications and Use Cases in Healthcare


Clinical Documentation


For all EMR systems (Electronic Medical Records), improved clinical documentation is the ultimate goal. With natural language processing, healthcare providers find it easy to document physician notes, health history as well as other information.


One research tested if NLP techniques could possibly replace physicians’ standard documentation, while it was concluded that it could be an immensely useful adjunct to clinical notes. An amalgamation of dictation and NLP was the most effective, user-friendly, and accurate way to update patients’ records.


Assisting in Clinical Trial Matching


NLP helps medical professionals to identify suitable candidates for clinical trials from mega quantities of unstructured databases. This accelerates innovation, saves lives, and helps patients who need immediate medical assistance. In fact, pharma giants are already using such technology to anonymize public healthcare information.


It uses NER techniques to manage patients’ Protected Health Information and Personally Identifiable Information using an automated process of entity extraction, detection, anonymization, and relationship management – reducing the efforts from days to hours!


Also Read: How Artificial Intelligence Will Shape The Future Metaverse


Computer-Assisted Coding (CAC)


CAC helps in analyzing large amounts of medical documents to generate the most suitable medical codes for optimum claims. It also helps in increasing productivity, transparency, data security, coding accuracy, and eradicates tedious manual tasks.


Enabling Predictive Analytics


Natural language processing helps boost predictive analysis in the life sciences and pharmaceutical industries, thereby enabling data-driven testing and diagnosis. With the ongoing COVID-19 pandemic, this process has proven to be extremely beneficial for medical professionals to predict and analyze its intensity and enhance medical preparedness. Moreover, for pharma biologists, a literature mining solution based on NLP can establish a link between medicine, target, and disease. 


Phenotyping and Biomarker Discovery


NLP in EHR systems shows remarkable promise for computational phenotyping. The implementation of NLP has upgraded the ability of providers to monitor and document phenome-wide and genome-wide biomarkers. Using natural language processing for biomarker discovery improves the accuracy of records and also prevents dangerous drug interactions for patients.


Summing Up


The complexity and volume of medical data inflow have sharply proliferated with the swift digitization of the healthcare industry due to the pandemic. NLP implementation can drastically help in minimizing the time of big data analysis and garner more accurate results. 


Furthermore, medical natural language processing has constructed exciting opportunities in the patient experience and healthcare delivery. If you are looking to invest in EHR systems, we recommend leveraging NLP-powered algorithms. For more information, you can get in touch with our team of experts. We will get back to you within 24 hours. 


About Author

Author Image
Priyansha Singh

Priyansha is a talented Content Writer with a strong command of her craft. She has honed her skills in SEO content writing, technical writing, and research, making her a versatile writer. She excels in creating high-quality content that is optimized for search engines, ensuring maximum visibility. She is also adept at producing clear and concise technical documentation tailored to various audiences. Her extensive experience across different industries has given her a deep understanding of technical concepts, allowing her to convey complex information in a reader-friendly manner. Her meticulous attention to detail ensures that her content is accurate and free of errors. She has successfully contributed to a wide range of projects, including NitroEX, Precise Lighting, Alneli, Extra Property, Flink, Blue Ribbon Technologies, CJCPA, Script TV, Poly 186, and Do It All Steel. Priyansha's collaborative nature shines through as she works seamlessly with digital marketers and designers, creating engaging and informative content that meets project goals and deadlines.

Request for Proposal

Name is required

Comment is required

Sending message..