In healthcare, patient health records, order entries, and physician notes aren’t the only data sources. In reality, millions of people have already used take-home kits to upload their genetic information to commercial databases.
Wearable technologies have also opened up new channels for consumer health data. However, adding to the sea of healthcare data won’t help you much if you don’t use it. And many experts concur that the data’s application is lacking.
Many Use Cases for Natural Language Processing (NLP)
Enter Natural Language Processing (NLP) technology, which can process data that is completely unstructured. A major purpose for a firm to employ NLP technology is to embed intelligent systems to optimize organization processes, increase quality time, and reduce operational expenses.
However, incorporating NLP into healthcare application development has other benefits.
- NLP could aid in the translation of human speech into machine-readable form, allowing machines to derive meaning from the data provided.
- NLP tools are being used in healthcare to harness vast amounts of data saved in the form of speech, text, pictographs, photos, and other forms of data in order to draw useful insights.
Natural Language Processing in Healthcare is predicted to expand at a CAGR of 20.8% from USD 1030.2 million in 2016 to USD 2650.2 million in 2021, according to studies.
Before we jump into the use cases of this disruptive technology in the healthcare industry, let me give a quick overview of what NLP actually means!
NLP (Natural Language Processing) is an area of artificial intelligence that aims to bridge the gap between people and robots. NLP is a capability that enables a robust system to understand, store, process, and execute data-driven insights in the form of speech or text that is comprehensible by humans.
The importance and use of natural language processing systems in healthcare application development is expanding by the day. Hence, systems like building chatbots using NLP are causing a stir in the healthcare industry.
When effectively implemented, technology allows clinicians to streamline administrative operations, devote more time to patient care, and improve the patient experience by analyzing real-time data.
8 Top Use Cases for NLP Technology in Healthcare
1. Speech Recognition
For nearly two decades, NLP has had its origins in healthcare with speech recognition, allowing physicians to transcribe notes for quick EHR data entry.
Front-end voice recognition allows physicians to dictate notes rather than sitting at a computer at the point of care, while back-end recognition corrects any problems in the transcript before it is passed along for human verification.
Speech recognition is one of the most cost-effective solutions, as it eliminates the need for and high expense of medical transcriptionists.
2. Clinical Documentation
Because of speech-to-text dictation and structured data entry, NLP’s impact on speech recognition works hand in hand with clinical documentation, liberating physicians from the laborious and restricting structure of EHRs enabling better patient care.
Nuance and M*Modal both have technologies that work in unison with their speech recognition technology to gather structured data and standardized terminology at the point of care for future usage.
3. Computer-Assisted Coding (CAC)
CAC gathers data on procedures and treatments in order to capture every possible code and optimize claims. CAC may have increased coding speed, but it hasn’t done anything to improve accuracy.
For example, a Cleveland Clinic study indicated that while CAC reduced coding time, it had a worse recall and precision rate when used alone without the assistance of a credentialed coder.
4. Clinical Trial Matching
Clinical trial matching is perhaps the most talked-about use case in the “developing” category. Linguamatics Health and Clinithink, for example, have created NLP engines to solve the issues of trial matching, while IBM Watson Health and Inspirata have devoted significant resources to employing NLP to help oncology studies.
In the not-too-distant future, NLP appears to have the ability to make clinical trial matching a seamless and automated process.
5. Data Mining Research
Data mining in healthcare systems allows businesses to reduce subjectivity in decision-making while also providing relevant medical knowledge. Once started, data mining can become a cycle technique for knowledge discovery, assisting any healthcare firm in developing a sound financial strategy for providing better patient care.
6. AI Chatbots and Virtual Scribe
Although no such solution exists at the moment, speech recognition apps have a good possibility of assisting humans in the modification of clinical paperwork. Something like Amazon’s Alexa or Google’s Assistant will be ideal for this.
Microsoft and Google have teamed up to achieve this particular goal. Currently, building a chatbot using NLP empowers to capture symptoms and direct patients to the most appropriate provider.
7. Root Cause Analysis
Another intriguing aspect of NLP is the ability of predictive analysis to provide solutions to common health issues.
Large caches of digital medical records can aid in recognizing subsets of geographic regions, racial groups, or other varied demographic segments that face different sorts of health disparities when applied to NLP. NLP systems evaluate unstructured responses to determine what’s causing patients’ difficulties or poor outcomes.
8. Review Management & Sentiment Analysis
NLP can also aid in the management of internet reviews for healthcare institutions. Every day, it can collect and analyse hundreds of reviews on healthcare from third-party listings. It may also quickly assess human emotions as well as the context in which they are expressed.
Some systems can even listen to the customer’s voice in reviews, which can assist the physician to understand how consumers talk about their care and communicate more effectively with the usage of shared vocabulary.
The Future of Healthcare Industry with NLP
The healthcare business is eager to put in the effort to improve natural language processing, even if it isn’t quite there now. Some well-known companies are investing heavily in cognitive computing and semantic big data analytics initiatives, both of which rely heavily on building chatbots using NLP for development.
Financial analysts are optimistic about the prospects for NLP and related technologies in the coming years. According to Allied Market Research, the cognitive computing market will be worth $13.7 billion by 2020, growing at a 33.1 percent compound annual growth rate (CAGR) over current levels.
Natural Language Techniques
Natural language processing techniques may one day be able to bridge the gap between the enormous amount of data generated on a daily basis and the human mind’s limited cognitive ability.
NLP has practically endless potential to change electronic health records from a burden to a boon, from cutting-edge precision medicine applications to the basic process of coding a claim for billing and reimbursement.
Continued Need for Natural Language Processing
The numerous applications of natural language processing discussed here provide an opportunity for the healthcare industry to break down old barriers and fill holes in the care delivery system in order to improve the patient experience.
Contact a reputable software development company to provide leading healthcare application development with NLP and AI solutions across a wide range of use cases.
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