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The Contribution of Natural Language Processing in the Healthcare Industry

Feb 11, 2022
Natural Language Processing in healthcare

The healthcare industry generates tremendous amounts of unstructured data – digital forms, pdf reports, online portals, emails, chatbots, and text messages from NLP in Healthcare. The amount of data generated across these channels is too huge to measure and too comprehensive for a human to consume. And due to the unstructured format of these datasets, they are not readily analyzable and often remain siloed. This is why a growing number of healthcare providers embrace Natural Language Processing (NLP) to give structure to the unstructured data contained in electronic health records (EHRs) and provide patients with more comprehensive care. According to a recent report, the global Natural Language Processing in the healthcare and life sciences market is expected to grow from USD 1.8 billion in 2021 to USD 4.3 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.0% (source).

Need for NLP in Healthcare

NLP is a specialized part of artificial intelligence that helps computers comprehend and interpret unstructured data. The healthcare industry is increasingly adopting natural language processing solutions due to its recognized capabilities to search, analyze and interpret colossal amounts of patient information. The usage of advanced medical algorithms and NLP technologies can harness relevant concepts and insights from unstructured data. NLP facilitates the healthcare providers to automate the managerial job, enhance patient care, and enrich the patient’s experience. According to KMG, with NLP, text data is tokenized, lemmatized, and other feature extraction activities are done. To know more, please visit

Natural Language Processing in the Healthcare Industry
  • Named Entity Recognition (NER): NER is a data extraction technique that segments named entities such as a person, product, location, or organization into predefined categories. NER is also called entity extracting, entity identification, or entity chunking.
  • Optical Character Recognition (OCR): OCR is a text recognition method by which a computer understands a handwritten or printed text and transforms it into a digital format. It is also used to scan unstructured data sets, such as text files or images, extract text and tables from that data, and present it in an understandable format. In the healthcare industry, OCR is commonly used for digitizing medical history records, clinical notes, discharge summaries, patient intake forms, medical tests, etc.
  • Text Classification: This NLP technique is also known as text categorization. It analyzes text data and assigns labels to different semantic units based on predefined categories.
  • Topic Modeling: Topic modeling is a sort of statistical modeling where NLP is used to classify collections of documents to identify semantic structures or “topics.”
  • Sentiment Analysis: This technique is also known as sentiment detection or opinion mining. Sentiment analysis includes a combination of NLP in Healthcare, computational linguistics, text analysis, and biometric to a text to ascertain its underlying sentiment. A healthcare provider can apply sentiment analysis for analyzing patients’ comments about their facility on social media to get an exact picture of the patient experience.

End Note

Natural language processing solutions have an enormous capability to streamline work and enhance care delivery. Here at Key Management Group Inc., we are committed to helping data analytics in healthcare organizations manage data using NLP solutions.  

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