Natural Language Processing in Healthcare Explained
Natural language processing (NLP) allows computer programs to understand written and spoken human language. Natural language processing in healthcare enables organizations to extract vital data from documents quickly. It organizes uncategorized clinical information, gives valuable insights, and streamlines manual workflows.
In this guide, you’ll learn how NLP works, its benefits in healthcare companies, and common real-world applications.
How Does Natural Language Processing Work?
When healthcare professionals put medical documents into an NLP system, its algorithms try to understand the input. Natural language processing is designed to not only recognize characters and words in a document, but also comprehend what they mean.
For instance, NLP can tell the difference between a patient’s name and a disease symptom. NLP algorithms can also recognize whether a medical document contains details of multiple patients.
Once NLP analyzes and understands the input, it uses a combination of artificial intelligence (AI) techniques to extract data from medical documents. NLP and AI can:
- Segment the details accurately, if they are from a multi-patient medical document
- Format the data so it can be fed into electronic health record (EHR) systems.
This data extraction and formatting involves identifying different types of clinical details in a document. For example, NLP tools can spot patient IDs, demographic information, prescriptions, symptoms, and risk factors. Then, they map each detail to the right fields in a digital file. This reduces manual data entry.
Some advanced NLP solutions combine machine learning, NLP, and AI. These tools can become more intelligent over time. They work by learning from patterns in health systems data. As they get more information, they can more accurately process complex medical data.
The Relevance of Natural Language Processing in Healthcare
Medical documents in random formats, such as scans, PDFs, and text, are simply unstructured data. That means the information is random and unorganized. Unstructured data is less useful because it lacks meaningful context that EHR systems can use.
For unstructured data to be usable and fit into healthcare workflows, it must be converted to structured data. Structured data is formulaic and conforms to a preset template. In other words, you need patterns of data organized by category. That way, it’s easy to analyze and record in healthcare systems.
Within healthcare organizations, making unstructured data structured often means hospital staff manually reviewing physical medical records, PDFs, or images. Then, they sort the information before feeding it into the organization’s system. This makes operations slower, inefficient, and time-consuming.
NLP for healthcare is a faster and more efficient alternative. Instead of burdening already-busy hospital staff with manual documentation processes, hospitals can use NLP to:
- Identify and interpret unstructured data in documents
- Extract intelligence from unstructured documents and get usable insights
- Enter the structured data into EHR systems
In short, NLP for healthcare automates data extraction. As a result, clinicians and administrators get actionable insights from documents. And the process happens in less time so they can make informed decisions quickly. Fast decisions translate to faster patient care.
Main Benefits of NLP in Healthcare
There are multiple NLP benefits for organizations in the healthcare setting. Here are its primary advantages.
Improved Clinical Documentation
Around 80% of all medical data in healthcare organizations is unstructured. Making this data usable manually is time-consuming. Plus, it increases the chances of data entry errors.
NLP automates this process to make clinical documentation better. It extracts information from unstructured documents like handwritten clinical notes. It’s also speeds up the process.
Additionally, NLP healthcare systems are designed to be accurate. Therefore, they minimize the risk of mistakes, duplication, and misfiled medical records. This helps ensure patient details are reliable and error-free.
Enhanced Patient Care and Diagnosis
On average, primary care physicians spend about six hours daily on manual data entry. That’s more than half of their typical 11.4-hour workday. This means healthcare providers have less time for patient care.
NLP takes care of data extraction and data entry so hospital staff have more time to focus on other tasks.
NLP is also fast in identifying, organizing, and formatting medical data from random documents. So, doctors get easy access to insightful information they can use to make important decisions. Healthcare organizations can then create treatment plans faster and administer care earlier.
Quick and Thorough Analysis of Medical Literature
Healthcare organizations need to stay on top of the latest medical literature. Continuously analyzing research papers, clinical trial reports, and case studies supports quality care and innovation. However, making sense of all this data can be challenging, especially without the help of technology.
Natural language processing models use AI-enabled intelligent data extraction to make this information accessible. Specifically, they analyze unorganized and uncategorized medical data. From there, they can summarize important points within the literature. NLP algorithms can also identify data patterns in medical literature to reveal key connections.
Automation of Administrative Tasks
Administrative burdens such as clinical documentation and data entry can be repetitive and time-consuming. By automating these tasks with NLP, healthcare organizations can:
- Minimize the time staff spend on manual processes
- Reduce physician burnout
- Increase accuracy in clinical documentation
Given these NLP benefits, it’s easy to see why natural language processing in healthcare is good for an organization’s workflows. Read on to find out how hospitals can apply the benefits of AI and NLP in the healthcare sector.
NLP Applications in Healthcare
Below are some common NLP in healthcare use cases.
Electronic Health Records (EHRs) and NLP
Healthcare organizations are always looking for faster ways to take advantage of medical data and record it in EHRs.
Organizations use NLP healthcare tools like Clarity to extract patient information from random documents. Then, the NLP automatically records the data into electronic health record systems via Direct Secure Messaging. This prevents sensitive information from getting into unauthorized hands.
NLP also converts extracted data into a Continuity of Care Document (CCD). Then, it structures the information with the Consolidated Clinical Document Architecture (C-CDA) format. The healthcare industry commonly uses this format to share clinical data in EHR systems.
The bottom line is that NLP automates the whole EHR entry process. Hospital staff no longer have to extract data and convert data.
Clinical Decision Support Systems (CDSS)
NLP also also powers clinical decision support systems. CDSS can process, interpret, and provide context to patient-specific data. This helps clinicians with a treatment plan for each patient.
For instance, healthcare enterprises can use NLP-driven CDSS to categorize symptoms from clinical notes. CDSS identifies specific terms related to a patient’s conditions, helping healthcare providers make timely and accurate diagnoses.
In other words, NLP in CDSS can make a patient’s health information easily accessible when physicians need it most. This not only streamlines the decision-making process, but also reduces costs and improves patient care, according to research.
Natural Language Processing for Medical Imaging
One of the more common NLP applications in healthcare is medical imaging. Health organizations can use NLP in almost every aspect of medical imaging delivery, including evaluating radiology reports. For instance, medical professionals can use powerful NLP algorithms to analyze radiology reports. This helps them identify skeletal fractures associated with a specific disease.
Additionally, healthcare providers can use NLP to obtain image data in bulk from picture archiving and communication systems medical technology. PACS can store and share clinical images like X-rays.
Once NLP collects the image data, healthcare professionals can use the technology to analyze, interpret, and label the images with text. This support for medical imaging helps providers better understand their patients’ conditions. It also supports organizations as they scale and innovate.
Improve Your Healthcare Outcomes Today With Natural Language Processing
Unlock intelligent NLP data extraction in healthcare with Consensus Cloud Solutions. You might be seeking NLP-powered tools to optimize interoperability. Or, perhaps you want to achieve more efficiency with less manual data entry. However you want to improve, Consensus has powerful cloud-based solutions to streamline your healthcare administrative tasks.
Many advanced NLP tools are often complex and difficult to set up. But implementing an NLP solution from Consensus is simple and quick, and you don’t have to set up any additional on-premise infrastructure. The solution runs on the cloud and integrates with your existing healthcare systems. You can have it up and running it almost immediately, all while keeping your workflows intact.
Additionally, NLP technologies from Consensus are AI-enabled. That means they can learn and become smarter over time to introduce even more efficiencies in your healthcare workflows. Request a demo to see how our healthcare tools can help you optimize your processes, increase accuracy, and improve patient care — all while meeting regulatory requirements.