Natural Language Processing (NLP) in EHRs: Turning Clinical Notes into Insights

Natural Language Processing (NLP) is the branch of Artificial Intelligence (AI) that deals with understanding and communicating natural human language, whereas Electronic Health Records (EHRs) are systems used to digitize patient information.

Together, NLP in EHRs have transformed the way healthcare professionals store and process health records. EHR systems store massive volumes of unstructured data like clinical notes, diagnostic results, prescriptions and more. This data is rich with health insights but the major challenge that healthcare professionals used to face is that often such insights get lost under the sheer volume of data, or go unnoticed. But with NLP, this challenge has been overcome and clinicians are now able to make sense of the unstructured or semi-structured data within EHRs.

The application of natural language processing in healthcare itself is experiencing robust growth in the past few years. According to a report by Grand View Research: “The global NLP in healthcare and life sciences market size was valued at USD 4.9 billion in 2023 and is expected to reach USD 37.0 billion by 2030, growing at a CAGR of 34.7% from 2024 to 2030.”

In this post, we shall look at how healthcare NLP tools are helping healthcare professionals extract meaningful insights from EHRs.

Role of NLP in EHRs

The healthcare industry feeds mammoth volumes of unstructured patient data into their EHR systems on a daily basis, but it’s hard for a computer to help physicians aggregate that critical data. Structured data like claims or CCDAs / FHIR APIs can help clinicians determine the disease burden, but it gives only a limited view of the actual patient record.

A research by IDC has revealed that, “at least 80 percent of healthcare data is unstructured,” in the form of “typed and written text, photos, radiological images, pathology slides, video, audio, streaming device data, PDF files, faxes, PowerPoint slides, and emails.”

Nevertheless, mining and extracting relevant information from these unstructured data is not easy, and is also time- and resource-intensive. However, missing this data can have a negative impact on patient care. Hence, without healthcare NLP tools, that unstructured data is not in a usable format for modern computer-based algorithms to extract and use beneficially.

How NLP Is Transforming Electronic Health Records

Healthcare professionals more than often document patient information like diagnosis, treatment procedures and more in natural language because it is more efficient, faster and intuitive. But this also leads to the formation of information silos, where valuable insights get buried in text that is not easily searchable or analyzable.

Healthcare NLP tools come to the rescue here by automatically extracting relevant information like medications, symptoms and more. It also helps in identifying the relationship between such data and presenting it in simple natural language for better comprehension, thereby improving the overall clinical documentation improvement (CDI).

For example:

A physician’s note says: “Patient complains of irregular bowel movement and chest pain, prescribed Pantoprazole and advised to consult Gastroenterologist.”

NLP can extract:

• Symptoms: Irregular bowel movement, chest pain
• Medication: Pantoprazole
• Condition: Acute Gastritis
• Next Step: Consult Gastroenterologist

This structured information is then input into the EHR system and further used for clinical decision support (CDS), analytics, and patient outcomes.

Benefits of NLP in EHR Systems

Clinical notes thus digitized and structured can then be used for a variety of purposes:

1. Improving clinical decision-making with NLP: AI-driven healthcare insights can be used by clinicians to spot possible risks, missed diagnosis, and medication interactions. In the previous example, insights from the clinical note can help clinicians identify patients at risk of critical gastric-related problems before symptoms worsen.
2. Computer-assisted Coding (CAC): Medical coding is the process of extracting relevant data (diagnoses, drugs, procedures, and equipment used during treatment) from clinical records and the assigning of codes for billing purposes. Manually doing this consumes time and also carries the risk of human error. An AI tool can automatically assign relevant ICD and CPT codes from clinical text, saving time and improving billing accuracy.
3. Research and Drug Development: Pharma companies and biotechnology firms can make use of the capabilities of NLP to find relevant data from documents like clinical-trial reports, research papers and other literature to accelerate drug discovery.
4. Clinical Trial Matching: Recruiting the right patients for clinical trials requires a lot of research and analysis. Healthcare NLP tools can accelerate patient matching by quickly processing various data, such as medical histories, personal details, or test results.
5. Telemedicine: The onset of COVID-19 gave telemedicine the much needed boom, as the requirement by patients to avoid public places while needing medical attention rose. The popularity of telemedicine continues even after the pandemic because of its application in providing quality medical care in remote areas. A report by MarketsAndMarkets says: “The global telehealth and telemedicine market, valued at US$83.62 billion in 2023, stood at US$94.14 billion in 2024 and is projected to advance at a resilient CAGR of 11.5% from 2024 to 2030, culminating in a forecasted valuation of US$180.86 billion by the end of the period.” And, one of the rapidly developing areas in telemedicine is NLP-enabled chatbots and self-service assistants.
6. Unification of Clinical Records: When an individual gets treated at different medical institutions and by different medical practitioners, it’s likely that their medical records would vary in format and structure. NLP, with its entity recognition and semantic analysis capabilities, can considerably facilitate the composition of unified medical records. NLP can extract and classify meaningful data from different documents, and normalize information, making entries in EHR comparable and traceable.
7. EHR Management: Electronic health records (EHR) systems maintained by healthcare institutions contain all health records of patients right from their birth, which are updated whenever patients receive a routine check-up or are diagnosed with a medical condition. Nevertheless, many of these medical records come in non-digital form such as printed or handwritten notes. NLP can digitize such documents, and can assist in structuring any new content added to the EHR: X-rays, CAT scans, lab tests, etc. thereby helping with clinical documentation improvement (CDI). Also, advanced ML models can use this data for deep analysis and treatment predictions.
8. Population Health Management: NLP can easily analyze large datasets contained within EHRs to help identify trends in chronic conditions, track disease outbreaks, and assess health disparities across different demographics.

These are but some of the practical applications of natural language processing in healthcare.

Time is of the essence when it comes to medical care, and so is accuracy. Late or one wrong diagnosis could be life-altering. AI-driven NLP tools and advanced LLMs go a long way in addressing this challenge. Equipping the EHRs with accurate data, and smooth data processing can provide clinicians with smart AI-driven healthcare insights, which could positively affect the life of patients, and also make things easier and beneficial for healthcare professionals and institutions.

Nevertheless, the shift to a smart NLP-enabled EHR system requires a partner with established experience in the field of AI implementation, and this is where players like DeepKnit AI can help you. DeepKnit AI has a proven track record of serving clients across the healthcare industry with smart AI solutions, which has benefitted them by providing intelligent insights from clinical notes.