We’re happy to announce that next January 10th our founder Cristina Santamarina will join the experts of the swiss healthcare network healthetia to talk about our journey using conversational interfaces in healthcare.

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The talk will discuss the current challenges with health data collection, the advantages of the conversational interface, our experiences with Eva, our women health chatbot prototype, and the potential of this family of technologies in the future.

Learn more about the event on the healthetia website or stay tuned to hear the recordings.

Our friend Megan, part of the team behind the HelpHer chatbot, pinged us with a link to a very interesting medical paper.

We read through it and were happy to have a study to back our believe: Conversations are a great interface for healthcare.

The medical paper is called “Improving Access to Online Health Information With Conversational Agents: A Randomised Controlled Experiment“. It was written by a group of scientists who studied a group of people who were asked to look for medical trials about specific diseases using a traditional keyword-based search engine and a conversational interface. They were then asked to do the same, making it a bit harder by adding criteria the trial had to match.

The sample for their study was of 89 people of an average of 60 years old (very interesting!) with a very good 50/50 gender balance and 23 people with low health literacy. A fifth of them had previous experience looking for clinical trials.

The main finding was that participants were definitely more satisfied using the conversational interface.

“Results indicated that all participants were more satisfied with the conversational interface […] compared to the conventional Web form-based interface.”

Must be noted that looking at the description of the conversational interface it was an advanced one and probably a pleasure to use, with features like read-out-loud, bookmarking and different levels of detail.

The paper also mentions a previous study in which the success rate was also higher in the conversational interface for those users with low health literacy, and gives a figure that may surprise some: 36% of USA adults are included in that definition.

“[Another study] demonstrated that individuals with low health literacy had lower success rates when using these interfaces to search for general health information on the Web. Usability by people with low health literacy is important because this population comprises 36% of US adults.”

Interestingly enough none of these low health literacy users managed to find the clinical trial with the constrains using the keywords search, but a third did using the conversational interface.

“In our standardised task (task 2), it is notable that none of the low health literacy participants were able to find a correct clinical trial using the conventional search engine interface, whereas 36% (5/14) were able to do so with the conversational agent.”

What I read here is: maybe because you understand what your problem is you are able to find the best solution on google. For a person with not so good of a health literacy, that may be a way more challenging task. As in non of the low health literacy passed.

On the down side the time it takes is a bit longer in the conversational interface (around 30%) but participants were not bother and the time difference was actually subjectively perceived as shorter.

The final bits are encouraging:

Apparently several studies have shown that traditional keyword search does simply not work for kids, the elder, or people who speak a different native language.

Another study worked with conversation interfaces and proved their success making health easier to understand for those not familiar with it in health areas such as physical activity promotion, hospital discharge instruction, explanation of medical documents, and family health history-taking.

“Our findings suggest that conversational agent-based search engine interfaces could be a good alternative to conventional Web form-based interfaces for many kinds of applications, but especially for those intended for low health literacy users or those with limited computer experience or skills.”

We are thrilled to hear this and looking forward to a lot more serious research papers. If you stumble upon any please send them our way at cristina@bots4health.com.

Eva 1.0

In September Eva was born as a tool to help young women access information about their sexual and reproductive health.

We thought that girls who know about their bodies and the means at their reach to enjoy their sexualities in a safe way are at a lower risk of getting pregnant or contracting a sexually transmitted disease.

Research

Uruguay, as a small country with a lot of interest from the authorities in setting up a digital strategy for health, was home to our first user research. Coming from Africa we knew that an initiative like Eva lied too heavily on the domain of public health authorities, and we knew they had to be involved for the project to succeed.

We spent a month talking to people, getting more insights about their 16% undesired teenage pregnancy rate, and about the other initiatives the Ministry of Health had conducted in the field of sexual education.

Pivoting

In parallel to this research about the Uruguayan use case we had conversations with women of various ages, as well as friends with experience in product design and other fellow chatbot developers.

At the end of October we decided to pivot and redefine Eva.

What if we used Eva to gather information about the potential users and users of a chatbot for women health? To validate our assumptions and add functionalities as we progressed, in the leanest possible way?

Eva 2.0

Eva is now an english speaking chatbot (to reach more people) and she does not specifically focus on the teen pregnancy problem – instead she will get to know you better and we will develop features and information sections as we go.

Our two main goals with this new strategy are:

  1. Reach a wider public
  2. Gather user insight (demographics, goals, conversation style)
  3. Define most wanted use cases

Eva is available as of NOW on out Facebook page, bots4health, for you and your friends to test. Don´t forget to let me know how many 💙 Blue Hearts and 🎈 Red Balloons you get!

Chat with Eva 2.0

Empathy is the capacity to understand and share the feelings of another.

In the novel Do androids dream of electric sheep? Philip K. Dick imagined a world in which humans faced the challenge of spotting bots and used for this a simple empathy test. If you have seen the movie Blade Runner, based in the book, you know what a Voight-Kampf test is. If you have not, here’s what it is:

Replicants (just another word for androids and robots) don’t react naturally. Think about it: most of us would have had an emotional reaction to the situation narrated. Leon, instead, gets lost in details such as which desert this happened at or what a tortoise is.

Most chatbots we’ve used in the last couple of months feel like that: out of context, not responding to calls for help… even Poncho, one of the few bots that seem to have users and a strategy, and that is otherwise really awesome, does not get this right. See below what happened when I tried to change the city of my weather report, restart the process or get the help page of the bot:

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Chatbots, let’s face it, are still quite stupid. No NLP or AI API is there yet, and good luck with trying to DIY. Some other people have written about how annoying chatbots are and you can get a feeling by yourself just by looking at some of the chatbots already out there. If you speak Spanish you could even see for yourself how Eva, our chatbot for teen pregnancy prevention, also fails at many things.

One thing we can do, though, is using empathy, user research and the right team members and consultants to build empathic bots.

Introduce yourself

Explain what a chatbot is, what your chatbot’s purpose is, and the basic mechanics of your chatbot. Depending on your public this will need more or less detail. Run user interviews as soon as possible to learn how much your users know about chatbots and their functioning. You’d be surprised about how many people know about Siri but have never heard about chatbots.

Have a way for users to access help, reach a human, exit and restart the conversation

We like doing this with commands like help, human, restart… other chatbots have permanently visible buttons. This is, however you decide to implement it, key in faking some kind of understanding – or at least in not blocking the users in stupid never ending loops just because they want to do something a little different that what you designed your bot for.

Do one thing and do it well

Choose one domain and redirect the users to that field any time they try to ask you about the weather, the existence of God or the number of flies in Taiwan. Eva will laugh at your comments, but ask you to select a high level topic from the main menu immediately after admitting she actually did not understand what you asked.

Use language (and emoji) wisely

Short, concise, simple… make it easy to read and understand, and use the words your users use and read on their real lives. This can get tricky with languages like English or Spanish, where regional differences are important to get right.

Analyse the hell out of your bot

Measure conversation length, read them out loud to spot things that sound weird, check the keywords people use the most… There are many tools for this, we like to use a combination of old school analytics (provided by our chat building frameworks) and good old reading whole conversations. This last one is a must-do, as they give you the most human look at your audience’s behaviour. It does not scale as good, though.

Fake it ’til you make it

Can’t make your chatbot learn automatically from conversations? Change your messages and flows using what you learn from your analytics. For an extra wow effect fix broken flows as they are used for the first time and notify users in chat of the fix.

Don’t be scared to be needy

Push push push. Don’t wait for users to go there and find you for a chat. Specially if you have something to say. One of the features we want to add to Eva is a reminder to have responsible sex  and buy condoms sent every second Friday. You can also use broadcast messages for research, to announce fixes…

We are at the beginning of the chatbot revolution, so a lot of the lessons are still to be learnt. What seems clear is that chatbots will be, for now, a task for the social scientists. It sounds like 2017 will be the year the inquisitive psychologist, the occurrent script writer and the nerdy domain expert join the code ninjas, devops rockstars and growth hacking gurus of tech teams. People that will make chatbots feel like they can understand and, why not, dream.