In healthcare, patient medical records, order records, and physician notes are not the only sources of data. In fact, millions of people have already used kits to upload their genetic information to commercial databases.
Wearable technology has also opened up new channels for obtaining consumer health data. However, adding to the sea medical data will not help you if you do not use it. And many experts agree that the application of data is lacking.
Many uses for natural language processing (NLP)
Introduce natural language processing (NLP) technology that can process data that is completely unstructured. The main goal of the company, which uses NLP technology, is to introduce intelligent systems to optimize organizational processes, improve time and reduce operating costs.
However, the inclusion of NLP in developing healthcare applications has other benefits.
- NLP can help translate human speech into machine-readable form by allowing machines to derive meaning from the data presented.
- NLP tools are used in healthcare to use vast amounts of data stored in the form of speech, text, pictographs, photographs and other forms of data to obtain useful information.
Natural language processing in healthcare predicted increase with a CAGR of 20.8% from $ 1,030.2 million in 2016 to $ 2,650.2 million in 2021, according to research.
Before moving on to the uses of this destructive technology in the healthcare industry, let me give a brief overview of what NLP really means!
NLP (natural language processing) is an area of artificial intelligence that aims to bridge the gap between humans and robots. NLP is the ability to enable a robust system to understand, store, process, and perform data-driven understanding in the form of speech or text that people can understand.
The importance and use of natural language processing systems in the development of medical applications is increasing day by day. So systems like creating chatbots using NLP cause a stir in health care.
With effective implementation, the technology allows clinicians to streamline administrative operations, devote more time to patient care, and improve patient experiences by analyzing data in real time.
8 main examples of the use of NLP technology in healthcare
1. Speech recognition
For nearly two decades, NLP has originated in healthcare with speech recognition, allowing physicians to transcribe notes for rapid EHR data entry.
Front voice recognition allows doctors to dictate notes instead of sitting at a computer at a help point, while internal recognition fixes any problems in the transcript before it is passed on for human verification.
Speech recognition is one of the most cost-effective solutions as it eliminates the need and high cost of medical transcriptionists.
2. Clinical documentation
Through dictation of speech into text and structured data entry, the impact of NLP on speech recognition works hand in hand with clinical documentation, freeing physicians from the time-consuming and restrictive structure of EHR, allowing for better patient care.
Nuance and M * Modal have technologies that work in unison with their speech recognition technology to collect structured data and standardized terminology at the point of care for future use.
3. Computer Coding (CAC)
The CAC collects data on procedures and treatments to cover all possible codes and optimize claims. The CAC may have increased the coding speed, but it did nothing to improve accuracy.
For example, a Cleveland Clinic study found that while the CAC reduced coding time, it had poorer levels of memory and accuracy when used alone without the help of an authorized coder.
4. Coincidence of clinical trials
The coincidence of clinical trials is perhaps the most debated use in the “evolving” category. For example, Linguamatics Health and Clinithink have established NLP mechanisms to address trial matching issues, while IBM Watson Health and Inspirata have allocated significant resources to the use of NLP to aid in oncology research.
In the near future, NLP seems to be able to make clinical research an appropriate continuous and automated process.
5. Data mining research
Data mining in health systems allows businesses to reduce subjectivity in decision making as well as providing relevant medical knowledge. Once started, intelligence can become a cycle method for discovering knowledge, helping any healthcare firm in developing a sound financial strategy to provide better patient care.
6. AI chatbots and virtual transcriber
Although such a solution does not currently exist, speech recognition applications have good opportunities to assist people in changing clinical documentation. For this, something like Alexa from Amazon or Google Assistant is ideal.
Microsoft and Google have teamed up to achieve this goal. Currently, creating a chatbot using NLP allows you to capture symptoms and refer patients to the most appropriate provider.
7. Analysis of the root causes
An intriguing aspect of NLP is the ability of predictive analysis to provide solutions to common health problems.
Large digital medical record caches can help identify subsets of geographic regions, racial groups, or other diverse demographic segments that face different kinds of health differences when applied to NLP. NLP systems evaluate unstructured responses to determine what is causing difficulties or poor outcomes in patients.
8. Review management and mood analysis
NLP can also help in managing online reviews of medical facilities. Every day he can collect and analyze hundreds of health reviews from third-party lists. He can also quickly assess human emotions as well as the context in which they are expressed.
Some systems may even listen to the customer’s voice in reviews, which can help the doctor understand how consumers are talking about their help, and communicate more effectively using a common vocabulary.
The future of the healthcare industry with NLP
The healthcare business is committed to making efforts to improve natural language processing, even if it is not now. Some well-known companies are investing heavily in initiatives for cognitive computing and big data semantic analysis, which depend heavily on creating chatbots using NLP for development.
Financial analysts are optimistic about the prospects of NLP and related technologies in the coming years. According to Allied Market Researchby 2020, the cognitive computing market will be worth $ 13.7 billion, up 33.1 percent from its annual growth rate (CAGR) compared to current levels.
Natural language techniques
Natural language processing techniques may one day bridge the gap between the vast amount of data generated daily and the limited cognitive abilities of the human mind.
NLP has virtually endless potential to change electronic medical records from burden to benefit, from advanced precision medicine applications to the basic process of coding requirements for billing and compensation.
The constant need for natural language processing
The many natural language processing applications discussed here enable the healthcare industry to break down old barriers and fill in the gaps in the care system to improve the patient experience.
Contact a reputable software development company to provide leading-edge medical application development with NLP and AI solutions for a wide variety of uses.
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