Natural Language Processing (NLP) in Healthcare: Overview and FAQ
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that involves translating spoken or written language into information that computers can understand, interpret, and analyze to deliver insights. Though sophisticated and reliable use of this process is relatively new, it’s already built into many of the AI-powered products used in the healthcare industry. The accelerated adoption of NLP in healthcare can be connected to its ability to solve very real, practical problems, such as time-consuming documentation requirements and inefficient billing cycles.
With some of the biggest electronic health record (EHR) systems in the industry embedding AI-powered tools that rely on NLP to function, this process is conveniently built into many clinical workflows. In fact, healthcare has become a leader in AI implementation, adapting at twice the rate of other industries.
Whether you’re already using NLP-enabled tools to complete your daily tasks or you’re looking into it for the first time, you likely have questions — and we have answers. Here’s a big-picture view of the NLP process and how providers have integrated it into practice, followed by answers to frequently asked questions, giving you crucial pros and cons of NLP and examples of its use across the healthcare ecosystem.
Natural Language Processing (NLP): Overview
Everyday technology like customer-service chatbots and car GPS systems use NLP to enable communication between consumers and technology. The process relies on principles of linguistics, computer sciences, and a blend of machine learning and deep learning to translate human language into computer-friendly data. This is usually done for the purpose of generating responses and insights. For example, a car GPS system is using NLP when it responds to a request for directions.
Older, traditional iterations of NLP were limited to rudimentary functions, like generating text messages based on statistical models. Advances have made the process more refined and reliable, and therefore more useful.
The Rise of NLP in the Medical Field
In healthcare, the newer models of the technique have proven particularly relevant and helpful. This is because of the vast amount of unstructured, raw text that is generated in a typical patient engagement with a healthcare provider. Thanks to advances in NLP, this information can be processed and used to the advantage of both the provider and the patient.
How NLP Is Applied in Healthcare Settings
In clinical settings, NLP is often used within the framework of AI-powered tools. Most generative AI tools that process text rely on NLP to function. In practical, day-to-day operations, clinicians who engage with EHR systems are likely applying NLP to their workflows. This is because the prevalent EHR companies are aggressively integrating AI functions into their platforms.
For example, Epic (a market-leading EHR vendor) reported that their client hospitals Mercy, UNC Health, and Houston Methodist have seen 90% of staff nurses using Epic AI to draft end-of-shift notes. Employees don’t have to go out of their way to find NLP applications in healthcare — the process is already built into the technology they’re using to complete daily tasks.
Using Natural Language Processing in Healthcare: FAQ
It’s important for clinical leaders, managers, educators, and other healthcare personnel to understand the basics of using NLP in healthcare. Examples of everyday applications, such as those discussed below, can help facility leaders understand how the process affects patient care, clinician workloads, and facility operations.
What is NLP in healthcare?
Natural language processing is a technique that AI-powered software applications use to understand human language. The exact process may vary from one product to another. NLP is used in healthcare to streamline workflows, leverage existing information, ease administrative burdens, and more.
What are the steps of natural language processing?
The steps of NLP have gone through multiple iterations over the decades, influenced by advances in computer engineering and AI. The modern versions of medical NLP models involve three steps.
| 1. Pre-processing text | Unstructured text, which holds value information but isn’t yet translated into data, is prepared.An example of unstructured text in healthcare is a nurse’s progress note about a patient. The note is considered unstructured, or raw because it can’t easily be categorized, queried, or analyzed — until NLP is applied.This step entails:
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| 2. Feature extraction | The cleaned, standardized text is then turned into numerical data that machines can understand. This step often involves identification of specialized medical terms. It also typically includes processing to account for the common phenomenon in healthcare of symptom negation (such as “Patient denies chest pain.”) |
| 3. Analyzing the text | This step involves using various methods of analysis (like syntactic and semantic analysis) to interpret the text and assign meaning. Text may be analyzed with machine learning or deep learning models.In this step, both short chunks of data and larger bodies of text are analyzed. |
What are some applications of NLP in healthcare?
There’s a rapidly growing number of applications for NLP in healthcare, spanning all areas of the industry. Here are some prominent areas of use.
| Health literacy | Tools like Automated Patient Instruction Generation (APIG) and Generative Pre-trained Transformer (GPT) generated instructions and teaching materials can be used to translate complex medical information into simplified, tailored materials, complete with visual aids. |
| Clinical documentation | Ambient AI tools such as Nuance’s Dragon Medical One, translate unstructured text into information that can be queried, categorized, and analyzed to contribute to patient care. |
| Clinical decision-making | AI-powered clinical support technology leverages unstructured data (like progress notes), medical literature, and clinical guidelines to deliver insights, reducing cognitive burden on clinicians. |
| Billing | Available information, such as clinical documentation, can be automatically assigned ICD-10 and CPT codes for billing purposes, speeding the revenue cycle and reducing errors. |
| Interpreting patient feedback | Text in the form of reviews, survey comments, and social media posts can be mined and distilled to inform marketing decisions and quality improvement initiatives. |
| Public health | NLP-enabled tools can analyze large amounts of public health information to discern patterns and trends, make predictions, and inform public health decisions. |
| Research | Formerly “unusable” unstructured, raw text can now be turned into valuable information that can be used for research purposes. |
What are the advantages and disadvantages of applying NLP to healthcare tasks?
When using tools that rely on NLP to function, be aware of the limitations of the process. Here are some important benefits and drawbacks to consider.
| Streamlines documentation efforts
Can efficiently analyze vast amounts of data Reduces cognitive load on providers Reduces administrative burden |
Inherent risk of technical failures and inaccuracies, which can be particularly dangerous in healthcare due to what is at stake
Requires human oversight Providers may develop an over-reliance on AI, which can weaken critical thinking skills If the data used to train the model (input) was not diverse, the responses and insights (output) may be biased |
Need More Info About Emerging Technologies and Strategies?
There’s an increasing number of uses for NLP in healthcare, and more on the horizon. We know the landscape is changing quickly — making it a challenge to stay current. Our valuable healthcare resources and guides deliver the streamlined information you need to keep your facility on the leading edge.