NPL in healthcare
Healthcare, Technology / December, 02 2022

Possible Applications of Natural language Processing (NLP) in Healthcare

The Natural Language Processing (NLP) market is expected to snowball in the next few years. Statista reports that it will be 14 times larger in 2025 than in 2017, which is $3 billion in 2017 to over $43 billion in 2025 of which $3.7 billion in healthcare alone. This shows the rapid adoption of Natural language processing (NLP) as it empowers computers to understand, interpret and manipulate human languages, enhancing collaboration. Incorporating computer science and computational linguistics, among other disciplines, it bridges human communication and computer understanding to give a human touch to computers. Let’s find out what NLP can do in healthcare.

NLP in healthcare supports medical professionals in diagnosing ailments better, developing treatment plans, and optimizing patients’ experience. In our previous blog, we talked about Natural Language Processing (NLP) in ai in detail. Check it out to get a comprehensive understanding of NLP. In this blog, we will precisely see how NLP can revolutionize healthcare. Stay tuned to know more about NLP in healthcare.

NLP is not a new term. It’s been around for a while now. Whenever you interact with a customer service chatbot or your personal virtual assistants like google assistant, Siri, Alexa etc. the language understanding of these is powered by NLP an advanced capability of Artificial Intelligence (AI). Isn’t that interesting? This is the power of NLP. When it is incorporated effectively it can do wonders in any sector.

NLP techniques

A quick glance of the important NLP techniques as I will refer to them in this blog. As we need digital documents, the first step is t o digitize the medical documents. Thanks to the technological advancements. Most of the medical documents today are digital easing the process.

Optical Character Recognition (OCR) – You can use OCR to digitize any document. It can be handwritten, image or document of any format. OCR can extract data from the document and present it in a digestible format. It is then fed into the NLP pipeline for further analysis.

Named Entity Recognition (NER) – This is a unique information extraction technique. NER segments named entities in real-world subjects like person, places, organization, or anything into predefined categories.

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

Text Classification – Text categorization happens here. It analyzes 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 – It is a statistical modeling techniques. 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 of different words and phrases to group them.

Applications of NLP in Healthcare:

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. This accommodates patients to receive experimental care that improves their condition and promotes medical innovation.

Clinical Decision making: 

NLP aids medical professionals make 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.

Clinical Assertion:

Clinical assertion helps medical professionals to diagnose and treat patients better by enabling them to analyze clinical notes and identify what and when the patient is suffering. For example, a patient has been suffering from a headache for the past two weeks and feels anxious when she walks quickly. After examining the patient, the doctor notices that she has no alopecia symptoms and does not appear to be in pain.

The doctor could later use NER and text classification to analyze the patient’s clinical data from that appointment and identify headache, anxiety, alopecia, and pain as PROBLEM entities. The doctor could further categorize those issues by asserting whether they were present, conditional, or absent. In this case, the headache would be present, anxiety would be conditional, and alopecia and pain would be absent. Thus, NLP in healthcare allows physicians to optimize patient care by identifying the most pressing problems and providing immediate treatment.

Clinical deidentification:

Health Insurance Portability and Accountability Act (HIPAA) states that healthcare providers, health plans, and other covered entities should not disclose sensitive patient health information without the patient’s consent or knowledge. So, healthcare providers can use NLP to identify content containing Protected Health Information (PHI) and deidentify them by replacing PHI with semantic tags. Thus, healthcare organizations can avoid HIPAA non-compliance issues.

Clinical entity resolver:

NLP in healthcare helps medical providers to extract information from patient records about various conditions and diagnoses and assign them an ICD-10 Clinical Modification (ICD-10-CM) code. The ICD-10-CM is a valuable resource that helps physicians make better decisions. Cross-referencing symptoms and diagnoses against ICD-10-CM codes allow healthcare professionals to understand medical complications, prescribe better treatments, and determine care outcomes.

Clinical relation extraction:

The Clinical Extraction Model draws connections between different entities detected by NLP algorithms. NLP in healthcare helps in clinical documentation by identifying appropriate data based on the relationships between keywords and phrases. Healthcare providers use NLP to identify a particular drug’s strength, frequency, form, and duration.

Financial contract-based entity recognition:

NLP in healthcare empowers health insurance providers by automating the financial contract review process and flagging any potential errors or fraudulent data. Thus, decreasing the time to process the applications. Also, here entity recognition and text classification come into play.

The above applications are fewer comparing the actual intensity of applications of NLP in healthcare. Wherever you come across a bot or an automated workflow that can understand human language, it is NLP that is behind the scenes. Not limiting the applications of NLP to just numbers, it has infinite possibilities in any sector.

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