Since its inception, chatbots have been used by industries worldwide to serve many use cases. From enabling simple conversations to help desk support to making shopping easier, chatbots have come a long way.
If you do the numbers, research shows that 1.4 billion people are using chatbots today.
There is no doubt that the chatbot presence in healthcare is booming. In fact, if things continue at this rate, the healthcare chatbot industry will reach $967.7 million by 2027.
There are several interesting applications for healthcare chatbots. If you’re interested in learning more, just give our article on the best use cases for healthcare chatbots a spin.
It’s also important to pause and think about how chatbots and conversational AI systems are able to effortlessly converse with humans. That too in a language that is simple and easy for us to understand.
This is where natural language processing (NLP) comes in.
In order to fully understand how you can build and deploy healthcare chatbots for various use cases, it is important to understand how to build such chatbots. And this is what we intend to cover in this article.
Let’s start with the most important question.
Natural language – the language that people use to communicate with each other.
Programming language – the language that a person uses to make a computer system understand his intentions. Python, Java, C++, C, etc. are all examples of programming languages.
Imagine a situation where you can interact with machines and computers without using such programming languages. Easy and seamless. Right?
Fortunately, you don’t have to go to great lengths trying to imagine such a situation, because NLP makes it possible.
Natural language processing is a computer program that converts both spoken and written forms of natural language into input or code that a computer can make sense of.
NLP-powered chatbots are able to understand the intent behind conversations and then generate contextual and relevant responses for users.
With NLP, you can train your chatbots through multiple conversations and content examples. This, in turn, gives your healthcare chatbots access to a wider pool of data to learn from, equipping them to predict what questions users might ask and how to formulate appropriate responses.
It is interesting. Right?
We hope you now have a better understanding of natural language processing and its role in building artificial intelligence systems. Now let’s go into more detail.
A chatbot built using NLP has five basic steps in how it works to convert natural language text or speech into code. Let’s explore each of these steps and what it entails.
This is the process by which you can break down entire sentences into any number of words. This process is called word marking or sentences that are called sentence marking. This is a data processing method.
Extract the symbols from the sentences and use them to make a vocabulary, which is just a collection of unique symbols. These tokens help the AI system understand the context of the conversation.
Imagine you are sending a message to your partner. Naturally, different people tend to misspell certain words, use short forms, capitalize some words and lowercase others. Basically, there is a lot of randomness in different people’s texts.
Now, extrapolate this coincidence to how people interact with chatbots. If the system can’t get rid of such randomness, it won’t be able to provide intelligent inputs to the machine for a clear and distinct interpretation of the user’s conversation. Normalization refers to the process in NLP by which such randomness, errors and inappropriate words are removed or converted to their “normal” version.
InputCan I book an appointment with my doctor for 2 days?
Result after normalization. Can I make an appointment with my doctor today?
Now that the sentence has been tokenized and normalized, the system begins to understand the difference entities in a sentence.
Entities are nothing but categories to which different words belong. Some examples of entities include Name, Location, Organization, and more. Subject recognition allows the chatbot to understand the topic of the conversation.
For example, take the sentence: Mary works at North Dakota’s Mt. Sinai Medical Hospital.
In this example, the chatbot will recognize Mary as a name, Mt. Sinai Medical Hospital as an organization and North Dakota as a location.
Check out our guide to Intents vs. About Entities to learn more.
In natural language processing, dependency analysis refers to the process by which a chatbot identifies dependencies between different expressions in a sentence. It is based on the assumption that each phrase or linguistic unit in a sentence is dependent on each other, thereby determining the correct grammatical structure of the sentence.
This is the last step of NLP where the chatbot collects all the information obtained in the previous four steps and then determines the most accurate answer to give to the user.
One of the most important things to understand about NLP is that not every chatbot can be built using NLP. However, for the healthcare industry, NLP-based chatbots are a surefire way to increase patient engagement. This is because only NLP-based healthcare chatbots can truly understand the intent of patients’ communications and formulate appropriate responses. This is in stark contrast to systems that simply process inputs and use default responses.
You can continuously train your NLP-based healthcare chatbots to provide simplified, tailored responses. This is especially important if you plan to use healthcare chatbots in your patient engagement and communication strategy.
As demand for healthcare systems grows, the only way to reduce overhead and increase patient engagement efficiency is to deploy conversational AI-powered chatbots built using NLP as the first point of contact between your patient and healthcare practice.
Building your own healthcare chatbot using NLP is a relatively complicated process, depending on which route you choose. Healthcare chatbots can be developed either with support from third-party vendors, or you can opt for custom development.
Here’s what this means
In this method of developing healthcare chatbots, you rely heavily on either your own coding skills or the skills of your tech team.
To make it work, you need to have the expertise to create and develop NLP-powered healthcare chatbots. These chatbots should perfectly suit the needs of your healthcare business.
Of course, the biggest advantage of this method is that you can customize it to your liking. However, when you consider factors such as time and cost, it may make more sense to consider a third-party vendor.
If you don’t want to go the DIY development route for your healthcare chatbot using NLP, you can always choose to build chatbot solutions with third-party vendors.
For example, Kommunicate, a customer support automation software, enables users to create NLP-powered healthcare chatbots that are not only customized to their business requirements, but can also be easily built. Their NLP-based no-code bot builder uses a simple drag-and-drop method to create your own conversational AI-powered healthcare chatbot in minutes.
You can check it out here.
Their powerful NLP chatbot builder provides a simple and intuitive interface on top of a powerful conversational AI system so you can easily build your healthcare chatbot. In fact, you can create a bot using Communication in just five easy steps. Here’s how.
First, you need to sign in to Kommunicate using your email address. After logging in, open the dashboard and go to “Bots”. Click Create Bot and it will take you to Kompose, Kommunicate’s bot builder.
Click “Create Bot” to build your bot.
Choose from available templates to get started or build your bot from scratch, tailored to your needs.
Once you’ve chosen your template, you can then go ahead and choose your bot’s name and avatar, and set the default language you want your bot to communicate in. You can also choose the “Automatic bot to human” feature, which allows the bot. seamlessly transfer the conversation to a human agent if it doesn’t recognize the user’s request.
Once you’ve set up your bot, it’s time to write the welcome message. The welcome message is basically how your bot greets the user. You can add both images and buttons to your welcome message to make the message more interactive.
The next step is to add phrases that your user is likely to ask and how the bot responds to them. The bot builder offers suggestions, but you can also create your own. Best of all, since the bots are powered by NLP, they are able to recognize intent for similar phrases as well. The more expressions you add, the more data your bot can learn and the higher the accuracy.
Your chatbot is almost ready. Now all you have to do is test it.
In the Chatbot preview section, you will find the “Testbot Chatbot” option. This will take you to a new page to display.
The chatbot will then display a welcome message, buttons, text, etc. once you’ve set it up, and then proceed to respond according to the phrases you’ve added to the bot.
Healthcare chatbots are here to stay. What we see today with chatbots in healthcare is just a small part of what the future holds.
These conversational AI-powered systems will continue to play a critical role in communicating with patients. Some of their other applications include answering medical queries, collecting patient records, etc. And with NLP advancing rapidly, it’s inevitable that healthcare chatbots will address much more complex use cases going forward.
If you want to learn more about medical chatbots, their use cases and how to build them, check out our recent article here.
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