How Natural Language Processing Helps Industries Innovate

April 1, 2021 - Emily Newton

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People frequently bring up natural language processing (NLP) when talking about artificial intelligence (AI). That’s because it’s a computer science that often uses algorithms. NLP involves computers making sense of text and speech, often for categorization or contextual purposes. 

For example, NLP helps determine if an email ends up in a recipient’s inbox or gets caught in spam. Devices or apps with autocorrect features also depend on natural language processing when suggesting what words or phrases people should type, saving them keystrokes. 

Plus, the now-ubiquitous voice-enabled smart assistants use NLP to interpret what someone says and provide them with the correct answers. These broad examples emphasize that NLP is already more common than many people realize, and it will become more prominent. Here are some inspiring examples of how sectors could use natural language processing to ensure they remain competitive and well-positioned to serve the people who need them. 

Facilitating Knowledge Sharing Between Farmers 

Many experienced farmers never received formal education to learn what they know. They come from generations of other people with similar career paths who pass down their knowledge and expect the recipients to do the same for younger generations at the appropriate times. 

Similarly, if farmers have trouble with pest infestations or crops that continually don’t deliver the expected yields, they typically ask their peers. Sometimes, that happens online. However, getting such recommendations is more challenging in places without reliable internet access or when people live in remote areas.

When people have fast and effective ways to share their expertise, the agricultural industry becomes stronger for current and future generations. Similarly, it could help farmers cope with emerging issues, such as climate change’s increasingly noticeable effects. 

Making Progress Through Text Messages and NLP

A company called Wefarm seeks to help smallholder farmers, and particularly those in Latin America. Founder Kenny Ewan worked directly with those agricultural professionals and realized they had a wealth of knowledge that could help others. 

The main obstacles were that the information had a siloed structure, and farmers often faced no choice but to follow the guidance of government officials — who typically lacked the firsthand knowledge that was so crucial in the farming community. 

Wefarm provides text message shortcodes that farmers can use to ask their peers for advice. Then, natural language processing comes into play by deciphering the context of each message and ensuring it gets to the right place. There are about 2.6 million farmers currently in the app’s database. NLP plays a vital role in sending a query to the person with the most appropriate knowledge.

The NLP database currently recognizes three regional languages, as well as English. It also can understand a person’s question or answer if the content contains a typo, spelling error or dialect-based variation. People can also provide star ratings after getting questions answered. The system presumably uses those to gauge the overall success in pairing a question-asker with someone ready to respond. 

Reducing Air Travel Challenges

Security checks, delays and cancellations are some of the things that cause headaches for people who travel by plane. The challenges are even more numerous now that airports follow strict procedures to limit the spread of COVID-19. 

Airlines must prove to customers that they’re continually attempting to make things more convenient for all air travel passengers or people who need to pick up those individuals from airports. Some relatively early uses of natural language processing in the airline industry involved voice-activated apps that allowed checking flights. Those examples paved the way for more advanced options that the industry now has. 

COVID-19 hit the airline industry hard, pushing it to make creative changes to welcome customers back and show them that traveling by plane is a safe and efficient way to reach destinations. NLP could help that happen. 

Giving People Updated Information

Airport-related information typically shapes people’s decisions before they depart on trips. For example, hearing that there are particularly long lines at security checkpoints could make someone decide to leave earlier for their business trip to avoid missing the flight.

Scotland’s Glasgow Airport now has a multiplatform chatbot that’s reportedly the first tool offered by a United Kingdom airport to work with Google Assistant and Amazon Alexa that goes beyond providing live flight details. People can get those, as well as content about COVID-19 procedures and assistance with locating lost luggage. The app provides estimated security wait times, too.

The app initially came out for voice-based products. However, it’ll eventually work on text-based platforms, like Facebook Messenger and the airport’s live chat interface. Giving users various ways to engage with the tool is a smart way to encourage them to interact with it. Some people may not have voice-enabled smart speakers, but many are now well-accustomed to text-based chat systems. 

Improving Health Care Outcomes

In health care, failing to stay on top of innovations could cause catastrophic outcomes. The medical sector has plenty of data, such as from patient records and physicians’ details. Natural language processing could emerge as a crucial technology for helping providers unlock new insights. 

There are also NLP apps that assist medical professionals with making more accurate and efficient notes. For example, if a health care provider uses a voice-recognition tool, they could cut down on the average amount of time usually spent transcribing notes. Then, they’d have more time in their schedules to devote to patient care and similarly rewarding tasks that make the best use of their expertise. 

Helping Surgical Review Teams Maintain High Quality Levels

Several hundred hospitals in the United States use the National Surgical Quality Improvement Program (NSQIP) to measure and enhance the quality of care received by people who undergo operations. 

However, specially trained reviewers currently do most of their work by hand, engaging in a time-consuming process of reading information-dense clinical documents. Completing the task can take more than two hours. According to one estimate, only about 20% of surgical cases get examined per year with this system due to its inefficiency. 

A new approach will use natural language processing and AI to evaluate the patient data, then extract meaningful details related to quality metrics. This solution is a work in progress, but it will include features like predictive analytics and expanded data access when finished. 

A Virginia hospital currently uses the tool to monitor how often its endoscopists find non-cancerous tumors called adenomas. The medical center’s team also intends to bring the NLP tool to other aspects of patient care soon. 

Identifying the Social Drivers Associated With Heart Attacks

Heart attacks happen to about 805,000 Americans every year. Smoking, high cholesterol and high blood pressure are some of the most significant risk factors associated with people who have heart attacks. However, other factors, such as an unhealthy diet and too little exercise, can also elevate someone’s likelihood of experiencing this medical issue. 

Medical professionals also know that social factors can also raise a person’s heart attack risk. For example, if a patient experiences ongoing domestic violence, such perilous circumstances could put them under extra stress and make heart attacks more likely. Alternatively, someone with a long history of poverty could lack the income needed to make healthy food choices, even when they want to eat well. 

The tricky part is that the structured data of medical records contain billing codes, medication lists and laboratory findings, but they don’t usually feature the all-important social clues. Physicians often take those as handwritten notes that become unstructured data. 

However, natural language processing could help doctors start seeing the data in new ways that help them more frequently prevent heart attacks than treat them. That’s a tremendous improvement, especially since certain actions often cause related outcomes later. If a person loses their job and decides to stop taking a cholesterol-lowering medication to cut costs, that choice might affect their overall heart attack risk. 

When doctors can refer to information that reveals a fuller picture of someone’s situation, it’s easier to give them optimal care. Plus, health care professionals could use NLP tools that apply this approach to numerous ailments — not just heart attacks. 

Natural Language Processing Spurs Meaningful Improvements

Many people use tools with natural language processing working in the background without realizing it. Others may also feel unsure that NLP could enhance their industries. These examples show that the technology can pay off in ways that people may not expect, benefitting entire industries and those who use those sectors. 

Revolutionized is reader-supported. When you buy through links on our site, we may earn an affiliate commision. Learn more here.


Emily Newton

Emily Newton is a technology and industrial journalist and the Editor in Chief of Revolutionized. She manages the sites publishing schedule, SEO optimization and content strategy. Emily enjoys writing and researching articles about how technology is changing every industry. When she isn't working, Emily enjoys playing video games or curling up with a good book.

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