5 Cutting Edge Applications of NLP in Healthcare

Key Takeaways

• According to research, doctors who use NLP-enabled speech recognition, get at least two hours free per day, which they can spend with patients.
• A research is aimed at using a natural language processing enabled CDS (Clinical Decision Support) system to capture both free-text and discrete data of patients to improve the system’s ability for cervical cancer screening.
• The Washington University School of Medicine has adopted AI-based NLP tools to sort out unstructured EHR data for better treatment and diagnosis.

The healthcare sector is experiencing technological advancements in the field of AI (Artificial Intelligence). According to a report, the US AI (Artificial Intelligence) in healthcare market is estimated to surge at a CAGR of 36.1 percent during the 2023-2030 timeframe.

Market growth is due to the adoption of deep learning technologies, content analytics, NLP (Natural Language Processing), and predictive analytics by healthcare providers to diagnose patients. Amongst these technologies, NLP in healthcare is significantly gaining traction.

The global NLP in healthcare and life sciences market size is estimated to garner a revenue of $7.2 billion by the end of 2027, at a CAGR of 27.1 percent. NLP in healthcare can mimic human behavior, execute multiple tasks, and extract critical data from EHR (Electronic Health Records).

Natural language processing in healthcare offers the opportunity to leverage unstructured data and utilize it in healthcare. Let’s look at 5 cutting-edge applications of NLP in healthcare.

1. Speech Recognition For Enhanced Provider Productivity

Cerner Corporation

One of the key benefits of NLP in healthcare is speech recognition, as it not only ensures enhanced productivity for healthcare providers, but also reduces physician burnouts. Rather than manually typing out diagnosis and treatment processes, providers can simply dictate their information in the NLP speech recognition server, and the work is done. Thus, curbing providers’ burnout rate.

According to research by Nuance, a computer software company, based in Massachusetts, doctors who use speech recognition have two free hours per day, which they can spend with patients or take time off. Additionally, the report states that dictation is three times faster than typing on a computer. Thus, saving time and enhancing productivity.

Further, voice-assisted technology helps physicians to document patient data on a mobile device, thus easing the burden on healthcare providers.

In December 2019, Cerner Corporation, a computer and information technology company based in North Kansas City, achieved its 500th patent. The innovation was a voice-assisted technology designed to make it easier for providers to document all information about patient visits on a mobile device.

NLP in healthcare has tremendous scope for automation. Speech recognition can be automated for tasks such as reminders, recording complicated clinical names, and organizing data from virtual visits.

An article published in mobihealthnews.com in 2020, stated that one of the giants of Silicon Valley, NVIDIA has been researching the same concept. The tool is trained to understand the biomedical and clinical language and is focused on transcribing for telemedicine applications.

2.  NLP for Cancer Screening

Agency for Healthcare Research and Quality

Blood tests, medical imaging, and genetic testing are some of the cancer screening methods. Apart from this, NLP, a branch of artificial intelligence is used for cancer screening as well.

Though NLP is a new and emerging technology in the healthcare space, researchers have devised advanced techniques to screen cancer. For instance, the Agency for Healthcare Research and Quality posted project details about NLP-enabled decision support for cervical cancer screening and surveillance.

The project aimed at using natural language processing enabled CDS (Clinical Decision Support) system to capture both free-text and discrete data of patients to improve the system’s ability for cervical cancer screening.

Additionally, several companies are investing heavily in NLP research and development for effective cancer screening and treatment methods. For example, Project Hanover by Microsoft aspires to advance machine reading and improve CaaS (Curation-as-a-Service) in precision medicine. The project focuses on three representation areas, namely: molecular tumor board, clinical trial matching, and real-world evidence.

3. NLP to Extract Unstructured EHR Data

Washington University School of Medicine

Precision medicine is an emerging approach for disease prevention and treatment. It takes into account the genetic variability, lifestyle, and environment of a patient, and based on this providers decide on treatment strategies.

NLP in healthcare is widely being used for precision medicine. The Washington University School of Medicine has adopted AI-based natural language processing tools to sort out unstructured EHR (Electronic Health Records) data. With this AI-based tool, the university can extract key information from EHRs about treatment, diagnosis, and outcomes.

However, one of the major challenges faced while using AI-based tools is to create algorithms that include diverse populations to predict all outcomes. Furthermore, the need to transform care for chronically ill patients and to improve diagnosis procedures is pushing notable companies to launch new products.

In November 2022, Zephyr AI, Inc., a healthcare technology company partnered with MedStar Health, a popular healthcare provider based in Maryland. The goal of this partnership is to develop an AI-enabled ‘Insights’ product for chronically ill patients.

4. NLP for Better Clinical Data Management

Consensus Cloud Solutions, Inc.

Clinical data comprises patients’ medical history, which can be used by healthcare professionals to offer better treatment. Vast clinical data is collected from patients each day, which is difficult to manage.

Structured data must be extracted from EHR, telemedicine, and RMP for further diagnosis. In August 2022, Melax Tech, a natural language processing technology company, partnered with the University of California, Irvine (UCI). This partnership is focused on analyzing EHR data using NLP.

Healthcare staff often encounter unstructured documents and they have to manually review them before taking any action. Hence, it’s essential to counter this issue and streamline workflows.

In March 2022, Consensus Cloud Solutions, Inc., announced the launch of Consensus Clarity which integrates NLP and AI to help healthcare organizations convert unstructured patient documents to structured consumable data. With this new launch, healthcare providers can easily use data to improve clinical decision-making and patient outcomes.

5. Targeted Sentiment to Detect Patients’ Emotions

Amazon Comprehend

The purpose of sentiment analysis is to optimize the quality of care provided and improve patient satisfaction. Sentiment analysis is also known as opinion mining and is used to measure patients’ perspective about care, diagnosis, and treatment. It helps to discover attitudes, opinions, and emotions that a patient feels about their care.

Accurate sentiment analysis enhances patient retention rate as providers can address negative issues and improve them. As per an article posted on March 2022, Amazon Comprehend launched Targeted Sentiment, a novel API that offers sentiment insights by detecting all types of emotions towards entities within the text. Healthcare organizations can make use of it to pinpoint patients’ sentiments and identify problems within their care.

Apart from this, happy patients are likely to recommend a healthcare practice to their friends and family, thus increasing patient referrals. Referrals help practitioners to increase their ratings through review platforms such as Google and Yelp.

Final Thoughts

The core function of the NLP in healthcare is to analyze texts and convert them into speech format. This approach is beneficial for providers who wish to save time by dictating their diagnosis in the NLP system. Moreover, patients who are unable to note down essential parts of the medication can easily record the speech and listen to it when needed.

Applications of NLP in healthcare are based on this core function. From managing clinical data to precision medicine, NLP is augmenting the healthcare sector. If you’re looking to integrate NLP or AI in your healthcare organization, then get in touch with Arkenea, a top-class healthcare software development company.