When you think to build quick chatbot with an open source framework system the first framework might pops will be Rasa. No doubt its a great framework, provide you to develop chatbot application with very easily with nice organization of documentation. After all the setup and training, the application seems to perform well. But when you go for the load testing of the application things starts to look messy : Response time increases, concurrency drops etc.
So based upon this, here in this blog I will be writing few things which I observed should be taken care off while setting up the configuration of Rasa.
1.Is there a way to solve concurrency issue? Ans : One solution I came up with is by increasing the Sanic workers. Rasa 2.x use Sanic web server. As per my exposure I haven’t found any command line argument predefined by Rasa to change the workers. But you can the change the worker of Rasa and Rasa-sdk by making hard-coded changes of the Sanic Workers values in the Constant.py of both or writing a wrapper function to set the environment variable ACTION_WORKER_SANIC_SERVER = #Desired Worker. Same to be done for Rasa.
2. How to handle increasing response time? Ans : There are various reasons which can be looked upon but I guess most ignored reason while doing any testing is consideration of sender id. The tracker of event is build based upon the sender id. As the list increases so do increase of response time. One could think of using setting up a Session timeout variable but problem is, it only works in cases of inactivity for that sender id after a period of time which is set in config. So question arises is how to solve it.
I will answer this question in my next blog. Thank you for your time. Please let me know if you have any suggestion and thought, or if I have misinterpreted.
Now that the digital and real worlds are merging, having an online presence is critical. Consistency is one of the most significant means for businesses to set themselves apart from their competition. Brands may establish themselves as thought leaders in their sector by producing high-quality, consistent content. The consistency of your material has a direct impact on people’s perceptions of your credibility. The more consistent you are, the more credible you are.
We have an incredible chance to harness automation to boost our productivity many times over in today’s digital and data-driven society. The audience will return for more if the content is compelling. They may offer their own user-generated content if it is really intriguing. However, if a company does not produce it on a regular basis or does not keep the same personality, it is more likely to lose its audience. It’s worthwhile, though, because keeping existing customers engaged can result in more revenue and is less expensive than acquiring new consumers. This emphasizes the significance of maintaining consistency in content, not just in frequency but also in tone. The usage statistics are eye-opening, and posting your material at the right moment can improve the accuracy and efficacy of your Twitter marketing efforts significantly.
High levels of user interaction can be extremely beneficial to your company, particularly in the following ways:
• Increased audience attention • Increased audience attention • A better emotional connection with customers • More long-term recurring customers • Brand advocacy from your strongest followers • Shorter sales cycles
In this article, we’ll show you how to make a bot using Bot Libre, a free open source chatbot platform that lets you automate your Twitter engagement with your audience through your own Twitterbot.
Click 1 — Create a bot
First you need to create your own bot, this only takes a few clicks
Click 2 — Create a Twitter account
You need to create a Twitter account for your bot. If you are automating your own account, then you can use your existing account. You can create your own Twitter account from the Twitter website here.
Click 3 — Authorize your Twitter account
From the Bot Libre website browse to your bot and click the
Admin Console button. This will take you to the Admin section that provides several tabs of administrative functions. Click on the Twitter tab. From the Twitter tab click the Authorize button.
This will give you a link to Twitter. Click on the link, this will bring you to Twitter where you must accept the application authorization request, you will need to login to Twitter if you are not already logged in. Twitter will give you a code, you must go back to the Bot Libre page and enter the code, and click Done. This will return you to the Twitter properties page, next click on the Connect button.
Click 4 — Configure your Twitterbot
You can configure your bot’s Twitter usage in several ways. Use caution when configuring your Twitter bot, do not use your bot for spam, or to violate the Twitter terms of service.
Tweet when someone chats with the bot
This property configures if the bot should tweet when someone chats with it on Bot Libre. It will tweet some like “Talking with anonymous on #botlibre”.
Reply to mentions
A mention is when another Twitter user tweets to you, or about you using your Twitter ID, i.e. @brainchatbot. This configures your bot to reply to all tweets that mention it. The bot will reply the same way it replies to chat messages.
Reply to direct messages
A direct message is private message from another Twitter user. This configures your bot to reply to all direct messages. The bot will reply the same way it replies to chat messages.
Configures if your bot will process its friends tweets. The bot will only process its friend’s tweets that include one of its ‘Reply Keywords’ sets. If the tweet does not include any of the keywords, it will be ignored. The bot will process the tweet like a chatroom message, it will only respond to the status update if it knows a good response, (requires a 90% match to respond). Scripted responses will also be used for tweets that are questions. The ‘Read-only’ option can be used to have the bot read all of its friends tweets, but not reply.
Max Status Updates
Configures the maximum number of friend tweets to process in one cycle.
Reply Keywords/Hashtags
The bot will only process its friend’s tweets that include one of its ‘Reply Keywords’ sets. If left blank, the bot will not process any of its friend’s tweets, unless the ‘Read-only’ option is used.
Tweet Search
Configures if the bot should search and process tweets that match a certain criteria. The bot will process, and possibly reply to the tweet, or retweet the tweet. Use this feature with extreme caution, and do not spam. Tweet search uses Twitter’s search API which can include a list of keywords, and some other advanced syntax.
Retweet Keywords/Hashtags
Configures if the bot should retweet certain tweets from its friends, or its tweet search results. If the tweet contains any of the keyword sets, the bot will retweet it. Note, this use only keyword set, not Twitter’s search syntax. Since the retweet keywords are apply on top of the search results, you only need to include the keywords, as the results have already been filtered.
Follow Messages
Follow Messages configures if your bot will listen to ‘follow me’, or ‘unfollow me’ requests. If the bot gets a direct message, or tweet mention that request a follow, or unfollow it will perform the request. The purpose of this is to allow the bot to provide some service to users that subscribe with it. Be careful in enabling this, it should not be used to allow Twitter users to increase their follow counts. The bot will only follow at most 100 users.
Welcome Message
You can set a welcome message to be sent as a direct message to anyone who follows your bot.
Max Friends
You can configure the maximum number of friends your bot will follow on its own. The maximum is 100 friends. You can still add more friends manually if you wish.
RSS Feed
Configures the bot to process an RSS feed, and post each new feed item to Twitter. An RSS feed is a standard XML format for news and posts. You can get an RSS feed link from your blog, or from many news and forum services. Make sure you give the full HTTP link to your RSS feed, this should be an XML file, not a webpage or HTML file.
RSS Keywords
You can filter the RSS feed results using keywords. To post the entire RSS feed, leave this blank.
Auto Tweet
You can configure your bot to tweet every set number of hours. The bot will tweet a random tweet from its ‘Auto Tweets’ list.
Auto Tweet Hours
The number of hours to wait between auto tweets. Note, if you bot only checks it status once per day, it will only auto tweet once per day, even if you put 1 hour here. To force your bot to auto tweet every time its checks its status, you can put -1 here.
Auto Tweets
You can enter a list of tweets here. Each tweet is separated by a new line. You can use formulas and AIML template to construct dynamic tweets.
Click 5 — Train your bot
You can train your bot how to answer tweets, mentions, and direct messages through several mechanisms.
You can chat with your bot, and correct any incorrect responses it gives.
You can add question answer pairs, or default responses from the Training & Chat Logs page in your bot’s Admin Console.
You can review your bot’s chat logs, or upload or import chat logs, response list, or AIML.
If you are a programmer, you can script your bot’s responses in Self, or AIML from your bot’s Scripts page.
That’s it, now your bot is ready to start tweeting.
A Twitter account can be linked to any Bot Libre bot. The bot will take care of your Twitter account, including checking status updates, maintaining followers, responding to mentions, retweeting key articles, tweeting your blog entries or RSS feed, and auto-tweeting. No programming is required, and you can train the bot using natural language.
Bots are automated software program that runs predesigned tasks over the Internet. They are automated which means they run according to the instructions they receive without a human user needing to manually start them every time. Bots imitate humans and replace human behavior.
More than half of all internet traffic can be credited to bots. For some e-commerce stores, bot traffic can be even higher than 90%. Bots scan content, interact with sites and social media accounts, or chat with users.
Bots are typically networked; bots account for more than half of Internet traffic, reading material, interacting with webpages, conversing with people, and searching for attack targets.
Some bots, such as search engine bots that index content for search or customer service bots that assist users are valuable.
Other bots are “evil,” and they’re programmed to hack into user accounts, search the web for contact information to send spam, or do other destructive things.
Types Of Bots –
Chatbots: These bots are typically used to strengthen a company’s customer care department. A chatbot is an artificial intelligence (AI) software that can imitate a natural language discussion (or chat) with a user via messaging apps, websites, mobile apps, or the telephone. A chatbot is often described as one of the most advanced and promising forms of human-machine interaction.
Social Bots: These bots are used on social media platforms for a range of tasks, including account creation and increasing the number of followers for specific accounts. For example, they’re designed to respond to certain words and phrases in social media posts automatically, either by reposting the messages or replying to them all at once. As a result, they can be used to influence public opinion and promote a particular point of view. On Twitter, such bots are widely used.
Web Crawlers/ Spider Bots: The friendly spider bots crawl the web for indexing and retrieval of web material by following hyperlinks. They’re designed to run in the background and continuously fetch data from websites and APIs. The Google Bot is the best example of a spider bot, as it crawls the web to make content search simple and convenient.
Spam Bots: A spambot is a computer application that sends spam emails in enormous quantities automatically. It gathers email addresses from a variety of online sources automatically. A spambot compiles mailing lists and sends junk mail, often known as spam, using the vast number of email addresses obtained.
Transactional Bots: Transactional bots allow customers to complete a transaction while conversing.
Monitoring Bots: This type of bot is used to monitor the health of a website or system. Downdetector.com isan example of an independent site that provides real-time status information, including outages, of websites and other kinds of services.
Bots help you in getting to know your customers: Conversational bots can help you learn even more about your customers. They assist you in determining their queries and requirements, as well as the items or services that they are interested in, and in making strategic decisions to improve the experience.
Bots are a selling machine: Your bot could be the ideal partner for promoting new products and sending proactive alerts. They can also provide rapid support to your prospective customers, speeding up the decision-making process.
Customer service at any time: One of the most significant advantages is that bots are available to assist customers 24/7. Additionally, they react to every question promptly. This ensures that no matter what time of day it is, your audience will always be able to solve their concerns.
Improves client satisfaction: People are all diverse, and their communication styles are as well. As a result, it’s important to respond to each query with the proper and empathic response.
How businesses can use Bots?
People nowadays prefer messaging as compared to other forms of communication. The vast majority of individuals want to engage with a company via messaging. That’s when understanding how to create a business bot comes into use. Bots enable businesses to engage with customers automatically using chat applications, SMS, and text. Messenger bots have a high open-rate and are effective at converting clients without being annoying.
Using a marketing bot for your company will certainly result in a higher return on investment. Facebook Messenger is the most popular chat app used in the United States.
Determine a purpose for using Messenger bots in your business, such as increasing engagement, discovering leads, or generating sales. Then write personal bot copy that educates and entertains users throughout the user experience.
How do bots work?
Bots, in most cases, operate across a network. Bots that can speak with one another will do so through internet-based services such as instant messaging, Twitterbots interfaces, or Internet Relay Chat (IRC).
Bots are made up of algorithms that assist them in doing their tasks.
Examples and uses of bots:
Facebook Messenger, WhatsApp, Slack, and Telegram are a few examples of Instant Messaging apps that use chatbots.
Google Assistant and Siri are examples of chatbots.
The World Health Organization created a WhatsApp bot to provide public information about the coronavirus outbreak.
To promote their show Genius, National Geographic created a talking software that purportedly spoke like Albert Einstein.
Users can use Facebook Messenger to search and share tunes on Spotify.
Consumers now expect firms to have some kind of automated channel so that they can talk to 24/7.
The modern consumer does not want to be put on hold or wait for a callback. Customers want to interact with your brand when they need it, not when you’re available or ready to reply. Adopting a bot-building strategy as a business today makes sense.
So what are you waiting for? You can Contact Us if you need any help! We’re always there to help you 🙂
The lessons we learned from our first foray into the world of chatbots
It is mid-2018. The air here at XIMNET is abuzz with a nervous excitement — we had just landed our first chatbot project.
‘Artificial intelligence — the future of tech!’
‘NLP — the next big thing in business automation!’
Up until now, these were just things we had watched grow from the sidelines, with equal amounts of skepticism and trepidation. Just words we’d throw around the office: “AI? Yeah, we’ll see.” But now we were going to have a stake in it.
Even way back then, there were plenty of Natural Language Understanding (NLU) platforms to choose from, and no shortage of advocates for each provider.
Google’s Dialogflow seemed like an obvious choice: Creepily accurate answers to our sometimes nonsensical questions is basically their whole business model.
On the other hand, IBM’s Watson had already made big waves with their Jeopardy win, and was now poised to revolutionise healthcare (a promise stillpending).
In retrospect, it really didn’t matter which one we chose — we ended up trying them all anyway as we ventured deeper and deeper into our chatbot journey, eventually going beyond the platforms to explore the underlying models for ourselves (Huggingface is a great resource for this).
Our takeaway: You can’t go wrong with either platform. Do a quick comparison and just get started.
Despite what our optimistic selves would have us believe, chatbots are not magical, omniscient beings. By this point, everyone in the team had had their own horror stories about having to deal with an ineffective chatbot — creating a meaningful one was going to be a deliberate effort.
Enter, the 80–20 rule. For the uninitiated, here’s a quick breakdown from Investopedia:
The 80–20 rule, also known as the Pareto Principle, is an aphorism which asserts that 80% of outcomes (or outputs) result from 20% of all causes (or inputs) for any given event. In business, a goal of the 80–20 rule is to identify inputs that are potentially the most productive and make them the priority.
In chatbot terms, if we identify the right 20% of problems to address, we will be able to satisfy the needs of 80% of users. But how do we find those problems? Here are a few things we did to get the conversation going:
Profile the users: Who will be using the chatbot? Is it going to be on a public-facing channel, or will it be behind a log-in screen? Taking a user’s point of view serves as a good starting point into identifying what would be most helpful without the bias of being on the inside.
Speak to the stakeholders: When speaking to stakeholders, there are bound to be specific problems that they need addressed. Is there a painful but key business process, long overdue for automation? What are the repetitive tasks bogging them down from more productive work?
Get down to the sources: How are the users currently communicating with the brand? If there are any logs available (e-mail trails, Messenger chat histories, etc.) it will be good to get a look through as this will not only help in identifying problems, but also serve as a guide to building a feasible solution.
Once we had pinpointed the problems we wanted to address, it was time to get to work.
Establishing a Foundation
While we were designing our chatbot, it was very tempting to become mired in the world of utterances, intents, entities, dialogs and responses. And for a while, it felt like duty for us to educate and encourage more people to do the same.
But reality doesn’t quite work that way, and we found that it made discussions tedious and frustrating for everyone involved. If we were going to come up with something great, we had to start communicating effectively, and fast.
Building process flows for our very first chatbot.
In the end, we boiled down the problems into a series of process flows: The idea being that each dialog could be essentially reduced to a flow chart. Sure, it does leave out quite a bit of the nitty-gritty (e.g. How does a user tell the chatbot to get recent transactions?), but it gives all stakeholders a chance to be involved even at the early stages of chatbot building.
Developing Human-First
Over the course of building our chatbot, it started to become painfully apparent that the old adage is really true: ‘No two humans are alike’. From slight variations in word choice to complete changes in sentence structure, when it comes to developing intents, you really need to catch ’em all. It pays to be thorough, and it is always a good idea to involve more than a few brains.
On the other hand, when it comes to responses we found it best to keep it short and sweet. While it is always tempting to provide all the information at one go in a chatbot message, often times all this does is overload the user, resulting in skimming and subsequent confusion. As a rule, it is much better to provide only information that is necessary, directing users to additional information when prompted.
And as always, when it comes to anything human — test, test, and test.
We launched our first chatbot — Affin Hwang AM’s NADIA in late November, 2018 to great results. Over the years we have built new chatbots and improved upon our overall solution: Teaching our chatbots new tricks every now and then, and in turn, learning some new tricks ourselves.
Is it just us or are virtual assistants actually becoming quirkier and sassier by the day? If you remember your first interaction with a virtual assistant like Siri, Cortana, or Alexa, you would recollect bland responses and plain execution of tasks.
However, their responses are not the same they used to be. Over the years, they have grown to become sarcastic, witty, and in simple words — more human-like. It’s like they are just a step away from cracking the Turing Test. But this has been a journey, hasn’t it?
To get here, close to a decade of AI training has happened at the backend. Thousands of data scientists and AI experts have meticulously worked for hours together to source the right datasets to train their speech projects, annotate key aspects and make machines learn them intact. From tagging parts of speech to teaching machines quirkiness and funny responses, tons of complex tasks have happened in the development phases.
But what is the process actually? What does it take for experts to train and develop speech projects? If you’re working on a speech project, what are the factors you need to keep in mind?
Know-How Your Audience WIll Interact With Your Solution
One of the first steps in training speech modules is to understand how your audience will interact with them. Work on getting insights on what they would say to activate your speech module, use it through dictation, and listen to results. So, in this case, know the triggers, responses, and output mechanisms.
For this, you need to collect massive volumes of representational data that are accurately close to your source. From call transcriptions to chats and everything in between, use as many volumes of data as possible to zero in on these crucial aspects.
Domain-specific Interactions
Once you have a general understanding of how your audience will interact with your speech module, realize the specific language they would use that is in line with your domain of operation. For instance, if your speech project is for a mhealth application, your system needs to be familiar with healthcare jargon, processes, and diagnostic phrases to accurately do its job. If it’s a project for an eCommerce solution, the language and the terms used would be completely different. So, know the domain-specific language.
Develop A Script And Record It
By now, you have a compilation of phrases, sentences, and text of value with you. Now, you need to turn these into a solid script and record it from humans for your machine learning modules to understand and learn. In every piece of recording, you could ask recorders to specify their demographics, accent, and other useful information you could use as metadata during data annotation.
How accurately your speech module responds to triggers depends on your recording data. Meaning, it should have data from your actual target audience. Using the same example of mhealth application, if it’s a specialized module for the elderly, you need to have data recorded from older people for your module to understand precisely.
Their accents, the way they talk, diction, pronunciation, modulation, and command are all different from people who are younger than them. That’s why we mentioned that your data should be as close to your source.
Collect As Many Datasets As Possible
Depending on your domain and market segment, collect as much data as possible. Compile call recordings, schedule real-time recordings from people, crowdsource, approach training data service providers and do more to get datasets.
Transcribe Your Recordings To Eliminate Errors
Your contributors are not trained professionals (mostly). When they talk, there are bound to be some mistakes such as the use of errs and umms. There could also be instances of repeating words or phrases because they couldn’t get them right the first time.
So, manually work on eliminating such errors and transcribe your recordings. If manual labor sounds too much like a task, use speech-to-text modules. Save them as documents with proper naming conventions that accurately define the type of recording.
Start The Training Process
You have a good source of speech data with you now. With the data you compiled in step 2 and with the actual recordings and transcriptions, you can trigger the training process for the development of your speech module. As you train, test your module for accuracy and efficiency and keep making iterations for optimization. Do not let go of errors because it takes another round of training. Fix all loopholes, gaps, and errors and make way for an airtight module in the end.
We understand that this could be quite overwhelming at first. Speech modules require complex efforts over a period of time to train conversational AI / virtual assistants. That’s why such projects are tedious as well. If you find this too technical and time-consuming, we recommend getting your datasets from quality training data vendors. They would source the most relevant and contextual data for your project on time that are machine-ready.
Social Media Description: Sourcing quality data for speech projects is tough. You need to know your audience, how they speak, how they access solutions, and more to develop an airtight solution. For those of you getting started with a speech project, here are effective steps on how you could approach data sourcing.
Description: Acquiring data for speech projects is simplified when you take a systematic approach. Read our exclusive post on data acquisition for speech projects and get clarity.
Author BioVatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
Does adding customers to your business matter to you?
Pop Quiz, which company on the planet has the most number of customers? It’s everywhere. Yeah, that’s the Ad pitch/slogan for the company. Its Visa. With 302 million customers, it’s towering above the next largest one which is Apple iTunes with 225 million customers.
Does business growth, revenue, and brand depend on the number of customers an organization has? Of course, it does. The more customers you reach the more robust is the moat you build for your business. So if your organization has to grow large and accumulate a lot of customers and build a moat that is impenetrable around your business, you need to start acquiring more customers.
The top dollar every company spends on is customer acquisition. That’s because business survival, growth, stakeholder satisfaction, and market influence are all dependent on the number of customers who use your products or services.
What do Visa and iTunes have to do with automation and your company? I’m sure you are always focused on growing your business and acquiring new customers. So how do you grow your customer base? Where do you find customers in large numbers?
As even the most traditional of businesses go digital due to the pandemic and the lockdowns, creating an interactive digital presence and getting on board with the digital economy is a do-or-die task for businesses.
How would you create amazing experiences while you move your business online, grow it to have a primary digital presence, or just expand it limitless on the digital superhighway?
Imagine having a storefront in a mall that billions of people visit and spend 38 mins in on a daily basis. Wouldn’t you like to capture that traffic by having a brand presence there?
There is an accessible set of almost a third of humanity on one platform that is used by 2 billion+ people and has users in 180 countries.
Here is why WhatsApp represents an opportunity for businesses to grow exponentially and leverage a platform that is used globally
100 billion messages are sent daily
An average user uses WhatsApp for 38 mins a day
600 million users downloaded it last year itself
WhatsApp supports 60 languages
The pandemic has seen usage increase by 40%
How do you leverage your business on this ubiquitous platform to acquire new customers, scale your operations and serve more customers for less? Going digital is imperative and opening your storefront on WhatsApp, leveraging automation with the WhatsApp Business API will be a very powerful way to get started on adopting digital in your business mix and you will be surprised to see Customer Acquisition Costs and Service Costs drop by 98% over traditional costs when you do so.
What can I do with my business on WhatsApp?
The short answer? Almost everything. When you use Engati and the WhatsApp Business API to deploy a WhatsApp bot, you can get leads, run a store, let customers order and pay for their purchases, answer customer queries, provide customer support, and much more. And guess what’s the best part?
If you sell digital products then it’s truly based on automation and absolutely no human intervention is required, artificial intelligence and machine learning would pretty much take care of everything. If you are selling non-digital products, only the fulfillment aspect of the order would need to be handled by humans.
WhatsApp for Business enables you to run your business on WhatsApp, automation platforms like Engati let you build, grow and support your store using chatbot automation with WhatsApp business tools.
If you really need to scale your store, make your presence global, and be on 24×7 not just for orders but also for queries, service requests, and customer engagement, a WhatsApp chatbot from Engati is the silver bullet you’re looking for.
What is WhatsApp automation?
WhatsApp automation is all about using intelligent WhatsApp chatbots to send automated messages to your customers and even have entire conversations with them. Companies use WhatsApp bots to interact with customers at scale, making sure that all their customers get instant replies, even outside of office hours.
It’s all about reaching your customers on the messaging apps that they prefer using, and since they open WhatsApp 23 times a day, it’s the perfect messaging app to engage your customers over.
You can even use live chat along with WhatsApp automation so that your agents can handle the complex queries that come in and your customers don’t have to shift from the bot to call your customer support team or even write an email. They can get in touch with your customer support agents right over WhatsApp without needing to change platforms, which means that you are creating a seamless experience for your customers.Instead of having to write an entire email, get on a call, or even have to load up your website and navigate through it, your customers can get all their queries answered in just a few clicks and a couple of messages when you use WhatsApp chatbot automation and live chat.
Can I scale my business without limits?
Yes, you can. It is automation technology so set it up once, (it comes pre-connected with the WhatsApp Business API and integrated with standard software for CRM, billing, payments, etc for you to get going quickly) and use it again and again. The most exciting part of the solution, you don’t have to ramp up hiring to manage a growing business, the platform scales as your business evolves, allowing you to automate your business processes with WhatsApp bots.
To be continued…
Now that we have gotten you interested in how organizations around the world are expanding rapidly into the WhatsApp automation space, stay tuned for our Part 2 in the series of how do you exploit a WhatsApp chatbot to your benefit at a cost factor that is peanuts compared to what you spend today.
Making Patient Experience Better and Helping Healthcare have PSAT Patient Satisfaction Using Conversational AI
Evolving Omnipresence Of AI Chatbots In Healthcare
Industry insiders have predicted exponential growth in the Healthcare AI Market, which is expected to reach USD 45.2 Billion by 2026 from USD 4.9 Billion recorded in 2020. The evolving omnipresence of intelligent medical AI Chatbots is only a reflection of this rather speedy progress.
So, what exactly are AI Chatbots? Going by the simplest definition, they are little chat bubbles that pop up on the screen of your computers / mobile devices when you visit a website for information. Mimicking human conversations, these bots are designed to offer essential information on commonly asked questions and connect users to relevant personnel in case of emergencies.
AI chatbots in healthcare, however, do much more than answer basic queries. With improving sophistication in AI technology, they have become critically essential components in saving time and resources on part of healthcare providers and more importantly, maximizing patient experience in their interaction with the former.
“Fact Check: Did you know, Health care is among the top 5 industries that derive maximum benefits from Chatbots. Statistically stable at 75% usage, it is preceded by E-Commerce and Insurance; and followed by retail and hospitality.”
Role of Healthcare Conversational AI…What Do They Do?
The key role of chatbot technology in healthcare is to connect healthcare providers, patients, and insurance companies seamlessly with minimum human interventions. An idea is to automate repetitive processes while saving time and revenues without compromising on the overall provider-patient experience.
Making essential information available anytime, anywhere though, is the primary function of conversational AI in healthcare. These intelligent chatbots are programmed to maximize patient engagement (like, remember/scheduling appointments, booking consultations, follow-up with insurance providers, etc.), a service that is expected to save USD 2–5 million annually in favor of medium to large emergency departments.
“Fact Check: Did you know; Healthcare industry can save about USD 150 billion with medical AI chatbots?”
How Medical AI Chatbots Improve Patient Experience
Statistically speaking, healthcare conversational AI has a satisfaction rate of 73%… a figure that is expected to soar with more sophisticated personalization features waiting to be implemented in future applications.
Existing chatbot solutions for healthcare, however, have been very successful in enhancing patient experience in many different ways…
Anonymity
Patients are observed to be more open with chatbots while revealing their symptoms and other critical information than with their human counterparts. The advantage of anonymity helps tremendously when it comes to getting help for sensitive cases.
24/7 Availability
Healthcare chatbots are available round clock for just about any informational and scheduling services that patients may require. Minimized dependence on human intervention/communication for routine services has been a big plus-point for later.
Personalized Experience and Symptom Reading
Chatbots in healthcare are programmed to remember and store patient details and provide critical health information instantly. They can read and understand symptoms, tally the same with medical records as entered by respective patients, and offer accurate next-step guidance.
“Fact Check: Did you know, ELIZA was the first healthcare chatbot that was used in 1966!”
Booking Appointments
Booking appointments or making alterations to existing schedules is often a one-click affair with chatbots. It saves ample time, energy, and funds on the part of patients, especially for those who are semi or non-ambulatory.
Connecting with Healthcare Providers on Emergencies
Days of dialing numbers and waiting for someone to answer from the other end during emergencies are practically over with AI chatbot technology in healthcare. These intelligent bots can connect users with the right healthcare providers quickly and efficiently. Peripheral requirements for admission etc. can also be addressed by chatbots.
Managing and Tracking Insurance
Accessing health covers, premium payment management, filing claims, and tracking status of the same are included among core features of advanced healthcare and conversational AI solutions. Timely reminders and updates on existing health insurance policies have contributed to maximizing the self-responsibility of patients while enhancing their app usage experience phenomenally.
Offering Mental Health Assistance
Conversational AI in healthcare has been instrumental in offering dependable mental health assistance to thousands of patients globally. Programmed to mimic human conversations, these bots can understand and read anxiety levels in patients and offer humanized experiences through empathetic conversations.
Reduced Wait Time and Saving Money
This is one of the best-liked features of AI medical chatbots, in the opinion of users worldwide. No queues, no uncertainties, no traveling all the way to healthcare centers to schedule appointments, no manual follow-ups for treatments, insurance, etc.; the amount of time and money saved by patients through healthcare chatbots is rather exemplary.
Feedbacks
Feedbacks are important when it comes to helping healthcare facilities improve their services. More often than not, manual feedback solutions through paper or e-forms become a hassle for patients. Chatbots, however, can help patients offer valuable feedback by asking highly relevant questions that can be answered with a click.
Final Thoughts
There is no end to patient-friendly features of healthcare conversational AI. In fact, many have used these bots for effective weight management assistance, dental care assistance, and critical prescription refilling as well without having to communicate with live agents.
Global COVID-19 Pandemic has fueled the surge of AI in healthcare in order to reduce human contact as much as possible. Everything from reading symptoms to answering frequently asked questions in seconds has been handled very efficiently by these technological wonders.
