10 ways NLP is transforming the healthcare industry

The healthcare industry is benefiting from technological advancements that are helping to provide and maintain value-based care for patients.

One of the most useful technologies is natural language processing (NLP), especially when it comes to dealing with clinical documentation, such as electronic health records (EHR).

It can also be useful in many other ways, such as transforming raw data into insights that can improve healthcare outcomes.

What is natural language processing (NLP)

Natural language processing involves the process of extracting information from unstructured data, such as written text or speech, and summarizing this information into structured data. This is only one out of many NLP examples.

It is difficult for computers to make sense of human language with all its ambiguities and the importance of understanding context. NLP is a branch of artificial intelligence (AI) that helps to reduce the distance between humans and machines and makes it possible for computers to understand human speech.

The digital transformation of medicine has resulted in a spike in the volume of available data and healthcare organizations need to be able to make sense of this data and draw meaningful insights from it. Here are just some of the ways in which NLP is making a difference in the healthcare environment.

1. Entering patient information into Electronic Health Records (EHR)

A patient’s medical history includes age, demography, past medical history, laboratory data, radiology reports, medications and much more. This is an enormous amount of data.

Many physicians report that entering data into electronic health records (EHR) reduces their efficiency and the time they spend with patients. Systems that use NLP allow physicians to enter notes into the EHR by speaking instead of typing (speech-to-text).

2. Gaining actionable intelligence from EHR

An NLP application can process patient data into structured data. It analyzes it and summarizes it to offer actionable insights. This can save time for clinicians as they can visualize a patient’s data in the form of a chart and understand all the relevant information more quickly.

Using NLP not only speeds up the collection of critical data but offers insights that help with making a diagnosis and determining the correct treatment plan.

3. Automating repetitive tasks

study found that physicians spent two hours on documentation for every one hour tending to patients. Most of this time spent on documentation is repetitive and can be automated by using advanced NLP technology in the form of chatbots and virtual assistants. This could alleviate the burden of repetitive work and save time that could be better spent on patient care.

4. Using virtual nursing assistants

Virtual nursing assistants that can communicate with patients using NLP will extend beyond the walls of hospitals and clinics. They will be able to make sure that patients stick to treatments and more.

Triggers can notify providers of any issues that need human attention. These virtual nursing assistants will help ensure that patients have around the clock access to support and answers.

NLP-based chatbots already have the capability to mimic human behavior and execute a number of minor tasks, such as sending reminders about appointments and taking medications.

5. Streamlining medical coding and billing

Computer-assisted coding (CAC) can be improved with NLP as it streamlines the way medical coders extract clinical information. NLP will read documents, assign codes and the human coder then verifies the code instead of having to read the documents and convert them to code.

Human coders, therefore, only have to work on documents that NLP can’t process, reducing the costs of coding medical information. More accurate and thorough coding results in more timely and accurate billing and improves patient experience.

6. Improving the quality of care

NLP can be used to measure physician performance and identify any gaps in the delivery of care. It can help to assess and improve the quality of care by identifying and mitigating potential errors.

7. Helping patients improve health literacy

Health illiteracy significantly increases the costs of healthcare. When patients don’t understand medical terms and more, it can prevent them from making the right medical decisions. Natural language processing can play an important part in educating patients.

For example, it can explain the meaning of abbreviations and medical terminology to educate a patient. Patients could have more understanding of their symptoms and access the health information they need to make more informed medical decisions.

8. Identifying patients who need improved care

Machine Learning and NLP tools can mine patient data to help detect patients with complex health conditions who may need more care.

It is often hard to locate data, such as the social status or demography of patients, as it is in an unstructured format. Using NLP can solve this problem and improve care coordination with patients who need improved care, such as those with behavioral health conditions.

9. Quickly accessing information in narrative texts

Use of NLP offers summarized updates of important ideas, concepts, and conclusions in large amounts of narrative texts such as journal articles and clinical notes. This gives practitioners quick access to information that would take many hours for them to read through.

10. Enhancing robot-assisted surgery

Surgical robots are already using AI to use information from past surgeries to offer better outcomes in current cases. Beyond all the advantages of robotic surgery, adding an NLP component means that surgeons can query the system and give verbal instructions to direct actions.

A final word

From the simple task of coding a claim for billing to the most cutting-edge precision medicine applications, NLP has great potential. The key to success will be to develop accurate, intelligent and healthcare-specific algorithms. There’s also a need to create user-friendly interfaces to present clinical decision support data. If it can meet these extraction and presentation goals, it will be interesting to see how much influence NLP will have in the future of healthcare.