Analytics, AI/ML

14 Use Cases of NLG In Healthcare

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14 Use Cases of NLG In Healthcare

Natural Language Generation (NLG) is becoming increasingly popular in the healthcare industry. It involves automating the process of generating text from structured data. The healthcare sector uses NLG to generate patient summaries, tailor health information, and develop medical reports.

As per the latest reports, the adoption of NLP solutions in the healthcare and life sciences market is expected to increase from US$2.2 billion in 2022 to US$ 7.2 billion by 2027 at a CAGR of 27.1%. The contributors to this market growth include:

  • Increasing demand for predictive analytics to address significant health issues
  • The need to make electronic health record (EHR) data more usable
  • The necessity to analyze and extract insights from narrative text.

In this blog post, we will discuss 14 use cases of NLG in the healthcare sector..

What is NLG?

NLG is a subfield of artificial intelligence (AI) that generates natural languages, such as written or spoken text, from structured data sources like databases or semantic graphs. NLG systems can create reports, summaries, dialogue systems, and answers to questions.

In a healthcare setting, NLG takes data from patient records and EHRs and turns them into clear and easy-to-understand reports and summaries. It helps to speed up processes as doctors and other healthcare professionals don't have to spend time on paperwork and can instead focus on looking after patients.

How is NLG Used in the Healthcare Industry?

The healthcare industry is becoming increasingly data-driven. Hence, healthcare organizations are turning to NLG to help them make sense of the mountain of information they have to work with. 

NLG systems can quickly examine large amounts of data and derive useful information. It is used in the health-tech industry to help with decision-making, improve the patient experience, and make administrative tasks more manageable with greater efficiency.

Top 14 Use Cases of NLG in Healthcare

Automatic text summarization

Automatic text summarization is a way to quickly identify essential parts of a large piece of text. It can save time by creating a shorter text version that still contains all the vital information.

It makes it easier for healthcare professionals to review information quickly and find essential points. It can be helpful for medical diagnosis, like when a doctor needs to look at a patient's medical history, different lab test results, and medical reports.

Conversational AI (chatbots)

Conversational AI, such as chatbots, can help make healthcare more accessible. They can provide 24-hour access to medical advice, schedule appointments automatically, and create personalized health care plans.

Virtual assistants like chatbots can save money. They make processes more efficient and reduce the need for people to do the work manually. They can help customers 24 hours a day, seven days a week, and remind people when they need to get checkups or fill a prescription.

Advanced clinical diagnosis

Using NLG systems, healthcare providers can look through large amounts of unstructured data, like patient notes, medical records, and other clinical documents. NLG systems help them derive helpful information from the data and determine the patient's medical conditions.

NLP can help healthcare professionals recognize possible illnesses faster and more accurately. It saves time on diagnosis and improves the care the patient receives. It can also automate and simplify paperwork such as bills and appointments, freeing doctors to focus on giving care.

Improved clinical documentation

Clinical documentation helps healthcare workers keep track of patient's medical information, which is necessary for making correct decisions and diagnoses.

Healthcare sector can use NLG to make clinical documentation easier. It gives healthcare professionals access to more accurate information to make decisions with and reduces the risk of errors in documentation. NLG systems can take unstructured information from patient notes and turn it into structured reports.

Computer-assisted coding (CAC)

CAC technology uses natural language processing (NLP) and NLG algorithms to read medical records and assign the correct codes for diagnosis and treatment. It looks for keywords and ideas in the document to give valid regulations.

CAC helps healthcare organizations ensure their coding is accurate and quick. It reduces the chances of making mistakes, which can cause improper payments and fines. It also makes coding faster by automatically linking codes to concepts to ensure the correct codes are used.

Automatic speech recognition (ASR)

ASR has been used in healthcare to streamline the process of recording and accessing patient information. It can reduce the time spent manually entering data into EHRs, allowing healthcare providers to focus on providing quality care. 

ASR also increases accuracy and consistency in data entry. It ensures that patient records are as comprehensive and accurate as possible. It can also be used to quickly and accurately transcribe conversations between providers and patients. As a result, it reduces the need for transcription services and makes sharing information more efficient. 

Clinical trial matching

Clinical trial matching can help healthcare providers identify therapeutic options to improve patient outcomes. By using NLP-based clinical trial matching, healthcare providers can quickly find clinical trials that meet the criteria of their patients. 

It can reduce the time, energy, and resources needed to manually search for a suitable clinical trial. It can also reduce the risk of patients being enrolled in a clinical trial unsuitable for their condition.

Computational phenotyping

Computational phenotyping is a technique for transforming EHR data into medical concepts. It can then be used to estimate an individual's risk of developing a disease or their response to the medicine. This process was initially created to identify groups of patients to be included in scientific studies. 

Computational phenotyping with NLP can automatically identify patient characteristics, such as age, gender, race, etc., from medical records and other unstructured data. This information is then used to provide a more comprehensive picture of a patient's medical history, which helps to make better decisions about their care.

Sentiment analysis

Sentiment analysis provides an effective way to measure and understand the feelings, attitudes, and opinions of patients' experiences. It can be used to identify potential areas for improvement, understand patient behavior and preferences, and develop more effective treatments and services.

By analyzing large volumes of patient feedback, NLP systems can provide a clearer picture of the overall patient experience with insights into the following:

  • An understanding of patient satisfaction with their doctors
  • The quality of care they received
  • Their experience with their health insurance provider
  • Patient concerns, such as wait times and the availability of services.

Automated reporting

NLG can be used to automate creating reports for compliance purposes. Healthcare organizations can quickly generate reports that meet regulatory requirements.

NLG systems can quickly extract relevant information from patient records to identify trends or correlations in patient data. This information can then be used to better understand patients' health and inform healthcare decisions.

Clinical decision support (CDS)

CDS is a system of software, tools, and services that allow healthcare professionals to make more informed evidence-based decisions. CDS systems are designed to provide real-time feedback on diagnoses, treatments, and other clinical findings. Using NLG in CDS systems can provide several advantages: improved accuracy, faster decision-making, and better patient outcomes.

Hierarchical condition category coding (HCC coding)

HCC coding is a method of assigning diagnosis codes to patients based on their clinical data. It helps to identify patients who are at risk for certain health conditions. The codes are organized in a hierarchical structure.

NLP extracts relevant information from a patient's medical records to create a valid HCC code. The algorithm scans the text to identify keywords and phrases related to the patient's condition. For example, the algorithm may identify words such as "diabetes," "hypertension," and "heart disease." The algorithm then uses these words to assign a code based on the condition's severity level.  

Predictive analysis

By utilizing NLP, healthcare professionals can analyze medical records, process medical terminology faster, and gain insights into patient data. It is also being used for predictive analytics in healthcare. 

NLP-powered predictive analytics is helping healthcare professionals more accurately predict patient outcomes and detect patterns in patient data. For instance, treatments that are most effective for specific conditions. It also helps to predict which patients are at higher risk for certain diseases.

Personalized educational materials and health reminders

NLG can improve patient engagement by automatically generating personalized educational materials and health reminders. It can automatically generate patient-specific educational materials tailored to a patient's needs. It can also provide automated reminders for patients to take medications or to schedule follow-up appointments.

Benefits of NLG in healthcare

Some of the benefits of using NLG in the healthcare industry are:

Improved data accuracy

NLG can help healthcare providers accurately extract, format, and analyze data. By automating the process, NLG can help healthcare providers generate more reliable and accurate insights from data and make more informed decisions and improve patient outcomes.

Improved decision-making

 NLG can help healthcare providers quickly and accurately generate insights from clinical data to make more informed decisions and improve patient outcomes.

Automated Clinical Decision Support

NLG can be used to provide automated clinical decision support, such as generating evidence-based recommendations to help with treatment options based on patient data and medical guidelines.


NLG is a powerful technology that can help improve many areas, including patient care, cost reduction, and decision-making in the healthcare industry. Contact us to know more.

Cogent Infotech helps organizations by implementing solutions that meet their specific business needs, leveraging data to gain competitive insights, and developing effective business strategies.

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