How HealthcareConsumersCommunicate WithChatbots

A comprehensive analysisof how healthcareconsumers interact andconverse with chatbots

Contents

As chatbotscontinue to play alarger role in howhealthcareorganizationscommunicateand engage withconsumers, weanalyzedthousands ofconversations tounderstand howhealthcareconsumersactually interactwith the platform.

IN THIS REPORT

Introduction

Research Questions

Analysis

How Healthcare ConsumersCommunicate

Methodology

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Overview

OverviewHealthcare organizations and providers areincorporating solutions to make healthcareand the digital patient experience moreconsumer-friendly. One solution that isquickly gaining traction and adoption acrossthe sector is AI-powered chatbot andconversational platforms. As conversationsbetween website visitors and chatbotscontinue to increase, the question of howhealthcare consumers actually interact withthe platform becomes increasingly moreimportant to understand.  When Loyal’s chatbot platform, Guide,surpassed 100,000 conversations, our AILabs team set out to analyze a sample set ofdata to better understand how actual healthcare consumers interact and conversewith the platform.  In an age where consumers expect accessand convenience, our results indicate thatthe overall majority of users utilize thechatbot platform as an avenue to quicklyreceive answers and assistance in findinginformation. However, users understandthat chatbots are not going to behave likesearch engines, and instead communicatewith the bot in a conversational manner thatis similar to a human-human conversation,but with less richness, depth, and length ofa typical conversation.

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As conversationsbetween websitevisitors and chatbotscontinue to increase,the question of howhealthcare consumersactually interact withthe platform becomesincreasingly moreimportant tounderstand.

Introduction

The digital patient experience and providing accessand convenience has become a top priority for thehealthcare industry, and will transform how healthsystems communicate, interact, and empower thepatients in the near future. One of the fastestgrowing solutions to address this shift towards amore consumer-friendly digital experience areconversational chatbot platforms powered by AI andmachine learning. Chatbot platforms are already transforming howbrands interact and communicate with websitevisitors. In fact, Gartner predicts that by 2020 we’llhave more conversations with chatbots than ourspouses, and 85% of customer service interactionswill be powered by chatbots.  As chatbots become more commonplace, healthcareprofessionals want to understand how site visitorsinteract and communicate with these chatbots, aswell as how much PHI is included in theseconversations.  In essence, do healthcare consumers converse withchatbots like a human or a robot, and does amachine on the other end of the conversationinfluence the inclusion of PHI in the conversation?

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Do healthcare consumers converse with chatbots like a human or a robot,and does a machine on the other end of the conversation influence theinclusion of Protected Health Information (PHI) in the conversation?

85Percent of customer service

interactions powered bychatbots by 2020

%

Research QuestionsTo answer these questions, we gathered data from a sample size of 1,000conversations across five health system clients, and analyzed conversationsbased on the following three questions:

Definitions

Utterance - Complete entryfrom the user into thechatbot environment PHI - Protected HealthInformation

Statement - Using language suchas, "I'm sick" without a specificrequest Request - Using language such as, “I want” or “I need”

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Is the utterance a request, question, or statement?

How often is PHI included in the utterance?

What’s the average character/word count per utterance?

Access and Convenience Consumers expect quick answers and immediate access to information.This aligns with the type of results uncovered from the breakdown of theutterances the chatbot experienced in the conversations we analyzed.

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Breakdown of Utterance Type

50% Requests

33% Questions

17% Statements

Template Usage Templates are pre-populated calls to action located within the chatwindow. These templates are designed as a quick and easyreference for commonly asked questions on each page. Byanalyzing the usage of templates in the chatbot environment weuncovered:

50of the time the utterance type was aquestion, the template did notprovide the call to action the userneeded

33of the time when a template wasn'tused, the user typed out an utterancefor an existing template instead ofselecting it

of users selected a template, withthe most used templates beingrequests for information (i.e. PayMy Bill, Find a Doctor)

33

7

%

%

%

Example template

The Significance of Requests

As the majority of utterances wererequests, it indicates that whenconsumers use a chatbot it isprimarily to receive informationwithout having to search for it. In a2017 study by researchers from theEuropean research institutionSINTEF titled, Why People UseChatbots, similar findings wereuncovered as 68% of surveyrespondents indicated that theyused a chatbot for the conveniencefactor of finding informationimmediately.

The requests indicate users were unable to find the answers theyneeded, which provides insight into the type of information that'smissing or difficult to locate on the website. It also demonstrates that theuser wanted information immediately and bypassed searching for italtogether.

68Survey respondents who

indicated they used a chatbotfor the convenience factor offinding information quickly

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Consumers use chatbots primarily to receiveinformation immediately

%

Utterance Length Most internet users are familiar with how to use a search engine andthe ability to type in keywords and receive a ranked list of closelyrelated results for them to find the most accurate result. As chatbots arerelatively new to the digital space and still growing in their adoption,there’s the concern that consumers will use chatbots in the similar veinof search engines. Search engines and chatbots both find answers, but operate andfunction differently, with chatbots relying on natural languageprocessing (NLP) to mimic humans and are designed to beconversational. Our research indicates that users do not treat chatbots like a searchengine.

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Most common wordsused in all the utterances

Utterance Length Cont. The average word length forutterances fell between 5-13words, indicating that users aren’ttyping in simple keyword searcheslike, “mens shoes,’ or “heartdoctor,” but rather formingcomplete sentences. These findingsalign with a study titled, Computersin Human Behavior, which foundthe average utterance length forhumans communicating withchatbots to be 2-13 words. 

Interestingly, Computers in HumanBehavior uncovered the average lengthof a human-human conversation to bebetween 2-25 words. Both our findingsand the aforementioned study indicate afew things about human behavior andhow it relates to the length ofutterances.

5-13 WordsAverage length of an utterance

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Vertical axis shows number of conversations that contained the word count amount (horizontal axis)

Utterance Analysis

Differing Behavior Online consumers don’tcommunicate with chatbots as ifthey are a search engine, butsimilarly, they don’t communicatethe same way as if a human-agentis on the other end of theconversation, either. The chatbotconversations lack the richness of ahuman-human conversation, butstill contains complete sentencesand thoughts.

Utterance Length The average length of anutterance also demonstrates thatpeople aren’t having complexconversations with the chatbot.The longest utterance across all ofthe conversations totaled 127words, which far exceeded theaverage length and no otherutterance nears that word count.Users understand there is not ahuman-agent on the other end ofthe conversation, and therefore,it’s reasonable to infer thatconsumers simplify their wordchoice in order to receive the mostaccurate response.

Comparative Length Comparing the length of a chatbotconversation with that of a human-human conversation on a live chatplatform, email, or a contact centerphone call demonstrates that ittakes significantly less time to fulfilla request with a chatbot than anyother form of communication. Forhealthcare contact centers, thismeans less time on the phone andcost savings when a chatbot canaddress frequently asked questionsor simple requests.

The chatbotconversations lack therichness of a human-human conversation,but still containcomplete sentencesand thoughts

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Inclusion of PHI Protecting sensitive health information or any type of personal identifyinginformation is a top priority with any healthcare organization, andespecially for our HIPAA-compliant chatbot. Without requesting anypersonal information, we wanted to uncover how much identifyinginformation users provided and how frequently it was includedthroughout the conversations.

Percent of conversations thatcontained PHI information

Typical PHI InformationIncluded:

Name

Account Number

Bill Amount

Although the chatbot neverrequested any identifyinginformation from users, theinclusion of the informationprovides interesting insight intohow consumers interact andcommunicate with the platform.For instance, we can speculatethat patients are accustomed toproviding identifying information(name, account number, etc)when interacting with a contactcenter agent, and thereforeprovide the information upfrontunder the assumption it will berequested later, or that it’snecessary in order to continue theconversation.

25

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%

How Healthcare ConsumersCommunicate With Chatbots The healthcare chatbot market will continue to grow in the upcoming years andhelp healthcare consumers with everything from locating a provider, paying abill, or scheduling an appointment. Forecasts estimate that by 2025, the marketvalue for healthcare chatbots will reach $470 million, a 21.2% increase since2018. With chatbots becoming more mainstream, understanding how sitevisitors actually interact with the platform becomes even more important.

Healthcare consumers are familiar with chatbotsand how to communicate with them.

InteractBy analyzing actual conversations we learn that consumers understand theplatform, how it’s intended to be used, and they are engaging with chatbotsas conversational platforms. In other words, they converse and interact in asimilar way that they would with a human-agent, and less like a searchengine.

PHI Similarly, the inclusion of PHI in 25% of the utterances, demonstrates thatconsumers already provide basic information without the need to be promptedfor it. Whether it’s because there’s the belief a human-agent is on the otherend, or that it’s generally a common request from call centers or otherindustries influences these results is uncertain at this time. However, the datastill provides valuable insight into how people engage with the platform andrepresents an opportunity for chatbots to reduce contact center call times bygathering patient information prior to connecting with a human-agent.

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How Healthcare ConsumersCommunicate With Chatbots

Frictional Experience The use of templates provides valuable insight into what type ofrequests or information is important to website visitors, and can beused to provide a better patient experience based on the frequency orinfrequency consumers utilize the templates. By understandingconsumer intent, health systems can provide a more seamless digitalexperience for patients. As AI-powered chatbot platforms continue to become morecommonplace on healthcare websites and interactions with themmore regular, it’s apparent that digital consumers are familiar with theplatform, how to use it, and how to communicate with it. As thetechnology advances, chatbots are slated to become a valuablesolution to improve the digital patient experience.

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About Loyal

Loyal's AI-driven conversation and consumer experience platform provideshealth systems with the tools needed to connect with patients and guide

them throughout their digital experience. With its multi-disciplined team ofengineers, marketers, and data scientists, Loyal partners with the nation's

leading health systems to bring together the voice of the consumer and thevoice of the health system with smart, consumer-first technologies

designed to inspire loyalty.

Methodology From the 1,000 conversations, we used200 first utterances, or the initial statementby the consumer, per each of the fivehealth systems This ensured that the typeof utterances were balanced and notreceived from one single source that wasinfluenced by the website layout,information provided, or other factors.  Each utterance was then coded to identifyone of three speech acts: request, question,or statement. Additionally, we put anindicator in place to search for the presenceof PHI, and an indicator for whether theutterance was generated by the template.The data set was then programmaticallyevaluated to determine the variables ofinterest addressed above. We removed any form of PHI that mayhave been included (name, date of birth,account numbers), and also capturedinformation surrounding the usage oftemplates. This would help get a betterunderstanding of the effectiveness of thetemplates and whether people used them,or typed out the questions themselves.

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Sources

Hill, Jennifer, W. Randolph Ford, and Ingrid G.Farreras. "Real Conversations with ArtificialIntelligence: A Comparison between Human-human Online Conversations and Human-chatbot Conversations." Computers in HumanBehavior 49 (March 20, 2015): 245-50.Accessed June 25, 2019. Elsevier.

Brandtzaeg, Petter & Følstad, Asbjørn. (2017).Why people use chatbots.

Levy, Heather Pemberton. "Gartner Predicts aVirtual World of Exponential Change." SmarterWith Gartner. October 18, 2016. AccessedJune 20, 2019.https://www.gartner.com/smarterwithgartner/gartner-predicts-a-virtual-world-of-exponential-change/.

"Global Healthcare Chatbots Market."Healthcare Chatbot Market. February 05,2019. Accessed June 25, 2019.https://www.zionmarketresearch.com/report/healthcare-chatbots-market.