Chatbots have been used by businesses all over the world to service a wide range of use cases since their inception. Chatbots have gone a long way, from permitting simple interactions to performing helpdesk assistance and facilitating purchases.
In terms of statistics, research indicates that 1.4 billion individuals utilise chatbots now.
Without a doubt, chatbots are becoming increasingly prevalent in the healthcare business. In fact, assuming current trends continue, the healthcare chatbot sector will be worth $967.7 million by 2027.
There are several uses for healthcare chatbots. If you want to learn more, check out our post on the best healthcare chatbot use cases.
It's also worth pausing to consider how chatbots and conversational AI-powered systems can speak naturally with people. That, too, in a primary and easy-to-understand phrase.
This is where Natural Language Processing (NLP) enters the picture.
It is vital to learn how to design such chatbots in order to fully comprehend how to build and execute healthcare chatbots for various use cases. This is what we plan to discuss in this post.
Let's begin with the most pressing question.
What exactly is NLP?
Natural language is used by humans to communicate with one another.
A programming language is human employes to communicate with a computer system. Programming languages include Python, Java, C++, C, and more.
Consider the possibility of communicating with machines and computers without the usage of programming languages. Simple and smooth. Right?
Fortunately, you don't have to exert much effort to picture such a scenario because NLP makes it conceivable.
Natural language processing is a computational programme that translates spoken and written natural language into inputs or codes that computers can understand.
NLP-powered chatbots can grasp the intent of discussions and then provide contextual and appropriate replies to users.
You may train your chatbots using NLP by using various chats and content examples. As a result, your healthcare chatbots will have access to a larger pool of data from which to learn, allowing them to forecast what kinds of queries users will ask and how to structure appropriate replies.
Interesting. Right?
We hope this article has given you a better knowledge of natural language processing and its significance in the development of artificial intelligence systems. Let's get into the specifics now.
How do NLP-powered healthcare chatbots work?
A chatbot constructed with NLP follows five fundamental processes to translate natural language text or speech into code. Let's take a look at each of these phases and what they entail.
1. Tokenization is the first step.
This is the method for breaking down full sentences into words. This technique is known as word tokenization, whereas phrases are known as sentence tokenization. This is a data processing approach.
Tokens from sentences are extracted and used to create a vocabulary, which is basically a collection of unique tokens. These tokens assist the AI system in comprehending the context of a discussion.
2. Normalization
Assume you are messaging your coworker. Naturally, various people misspell words, utilise abbreviations, and type certain words in capital letters while others are in lowercase. Essentially, the way various individuals text is highly erratic.
Extend this unpredictability to how individuals interact with chatbots. Unless the system is able to eliminate such unpredictability, it will be unable to give the machine with meaningful inputs for a clear and crisp interpretation of a user's speech. Normalization is the procedure in NLP that eliminates such unpredictability, mistakes, and irrelevant words or converts them to their 'normal' version.
As an example:
Input: cn i book an apptmnt with my dr 2day?
Output after normalization: Can I book an appointment with my doctor today?
3. Identifying entities
After a sentence has been tokenized and normalised, the system proceeds to comprehend the various things in the sentence.
Entities are just categories to which certain words belong. Entities include things like Name, Location, Organization, and so on. Recognizing entities enables the chatbot to comprehend the topic of the discussion.
Consider the following sentence: Mary works at Mt. Sinai Medical Hospital in North Dakota.
The chatbot would recognise Mary as a name, Mt. Sinai Medical Hospital as an organisation, and North Dakota as a place in this case.
4. Parsing of dependencies
Dependency parsing in natural language processing refers to the process through which a chatbot detects the interdependence between various sentences in a sentence. It is founded on the premise that every phrase or linguistic unit in a sentence is dependent on each other, allowing the right grammatical structure of a sentence to be determined.
5. Production
This is the last phase of NLP, in which the chatbot compiles all of the information gathered in the previous four steps and determines the most correct response to deliver to the user.
Why should you think about developing an NLP-based healthcare chatbot?
One of the most crucial things to grasp about NLP is that it cannot be used to build every chatbot. NLP-based chatbots, on the other hand, are a certain approach to enhance patient involvement in the healthcare business. This is due to the fact that only NLP-based healthcare chatbots can really grasp patient communication intent and create suitable answers. This contrasts sharply with systems that just process inputs and apply preset replies.
You may educate your NLP-based healthcare chatbots to offer simplified, personalised replies indefinitely. This is especially true if you want to use healthcare chatbots as part of your patient engagement and communication strategy.
Create a healthcare chatbot using NLP
Building your own healthcare chatbot using NLP is a rather hard procedure depending on whatever method you take. Healthcare chatbots may be produced either with support from third-party providers, or you can choose for a bespoke creation.
DIY Development on Your Own
This way of creating healthcare chatbots mainly relies on either your own coding talents or the expertise of your tech staff.
To make it work, you must have specialist expertise in building and developing NLP-powered healthcare chatbots. These chatbots must be completely aligned with the requirements of your healthcare business.
Of course, the major advantage of this approach is that you may personalise it to your liking. However, when time and money are issues, it may be better to consult a third-party provider.
Using third-party bot builders to create your healthcare chatbot
If you don't want to design your healthcare chatbot using NLP yourself, you can always work with third-party providers to produce chatbot solutions.
For example, Kommunicate, a customer service automation programme, allows customers to create NLP-powered healthcare chatbots that are not only personalised to their company needs but also simple to create. Their NLP-based codeless bot builder allows you to create your own conversational AI-powered healthcare chatbot in minutes using a simple drag-and-drop technique.
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