As software, chatbots recognize and respond properly to a user’s text or voice messages. Due to the complexity of a natural-language processing, the user is often constrained to a set of available commands. In messenger-like apps, the user selects them through a graphical user interface. Chatbots can also be regarded as an evolutionary step in the design and development of human-computer interaction. Whereas the common desktop metaphor replaced the command-line decades ago, the ongoing convergence of smart, mobile technologies and the power of artificial intelligence and machine learning enables a user-friendly control of computers through the natural language. The language can be thought of as a natural user interface (NUI). The word “natural” implies the possibility to take advantage of a user’s innate skills, reducing the difference between a novice and long-time user. Therefore, chatbots as NUI are “leveraging the potential of modern technologies to better mirror human capabilities” (Wigdor & Wixon, p. 10), significantly lowering the entry barriers, costs and time devoted to learning a new product or service. When compared to virtual reality or Xbox Kinect, the Wigdor and Wixon’s call for using the modern input technology does not play a central part in chatbot development; however, their potential to accelerate the full integration of technology into our lives and fulfil the promise of ubiquitous computing is certainly present. With chatbots we are on the brink of a next paradigm shift, from the design of graphical layouts to AI-powered software where a conversation is simultaneously the medium and content of user experiences. We can call it a “conversational interface” (Knight 2016).
The rise of chatbots
Chatbots represent a point where two prominent features of human culture intersect: the emphasis on the verbal expression which is unique to humans, and the post-industrial logic of design and production, structured around the just-in-time delivery, personalized content, one-to-one communication, and adjusting based on the immediate context. We find all these features in any typical conversation. The conversational element of chatbots is also stressed by the industry. Satya Nadella and Microsoft announced their commitment to chatbots, stating that the rise of chatbots “is inevitable” (Pall) and describe “a conversation as a platform”. (Microsoft News Center). The attributes critical to a successful conversation are, among other things, the sensitivity to contextual clues and delivering content that is meaningful and useful.
Digital technology and designers have been answering these demands since the beginning of personal computing. The human-computer interaction field, or HCI, arose from the need to study and propose solutions to ease the use of computer technology. During its first-wave, the HCI field brought about developments in understanding of cognitive aspects of computer use that eventually materialised in usability engineering guidelines and suggestions. After the advent of mobile computing — in a mainstream culture largely recognized as the introduction of Apple iPhone in 2007 — the HCI field as well as interaction designers had to take into account the temporal and spatial dimensions of technology use, thus accepting the broader changes in human-computer relationship, where technology moved from a periphery of specialized problem-solving domains to the central positions as a cultural artefact. Apps, which developers crafted with mobile context and experiences in mind, started to dominate. Web designers reacted accordingly, discovering the mix of front-end technologies and browser-supported functionalities that allowed the Web platform to become “responsive” to the variety of browsers, display sizes, and different contextual capabilities of devices. The proof-of-concept itself was introduced in 2010. Following three years, the concept became a trend, when a well-known site in the industry, Mashable, called 2013 the year of responsive web design. The mass adoption of responsive web design coincided with what the data and statistics had been showing: it was the year 2012 when a smartphone use surpassed desktops and notebooks (counted together).
Two years later, another shift occurred when statistics showed that, for the first time, the users in the US spent more time using apps on their mobile devices than using anything else on desktops. (Murtagh) Finally, in the 1Q of 2015 the fraction of mobile functionality, the messaging, overshadowed not only the the other mobile apps, but also the social networking apps. (BI Intelligence)
Chatbots as a platform
While Microsoft’s phrase “conversation as a platform” pinpoints to the disruptive character of verbal interaction with AI in a more theoretical realm, i.e. describing the possible movement away from the dominance of graphical user interfaces, chatbots as a platform have already proven to be a viable ecosystem for business. Therefore chatbots are new digital music or apps, where the equivalences to iTunes and Apple store platforms are Facebook Messenger, Amazon’s Alexa, Skype, Telegram, Kik, Slack and other upcoming solutions to the chatbot-enabled platforms. To asses the “burgeoning bot economy”, Business Insider summarizes its report on chatbots by stating that:
“Chatbots could be lucrative for messaging apps and the developers who build bots for these platforms, similar to how app stores have developed into moneymaking ecosystems.” (Beaver)
The mentioned tech companies provide a shop-like list of bots, except for Facebook and its Messenger, which has not released its own centralised shop to date, although the profiles of prominent users such as CNN or Barack Obama already provide a chatbot-enhanced messaging, whose capabilities range from news to music suggestions.
Chatbots in Digital Health
The digital health turn that reshapes the ingrained processes of medical practice has delivered so far many smart devices bundled with biosensors, mobiles apps analysing data and other digital services. While these solutions offer personalised data and suggestions, the real disruptive step seems to come from the interaction of the new digital ecosystems and the old, albeit well-established, and necessarily conservative environment of the public healthcare systems. It is for this transition that chatbots may play a lead role by embodying the function of a virtual assistant, bridging the gap between patients and clinicians.
Powered by AI and machine learning algorithms, chatbots are forecasted to save “health care costs when used in place of a human, such as a preliminary step of helping to assess a condition and providing self-care recommendations” („The chatbot will see you now: AI may play doctor in the future of healthcare“ 2016) Such prediction is reinforced by the recent successful attempts to automate diagnosis of mental health issues by the analysis of speech patterns with 100% predictive power. (Bedi, et al. 2015)
The digital health field is already saturated with chatbots. The company Your.MD provides a chatbot solution based on the machine-learning algorithms and natural-language processing that predicts probabilities between the symptoms and conditions, which are analysed on the background of a user’s personal profile. („The chatbot will see you now: AI may play doctor in the future of healthcare“ 2016)
TigerConnect offers secure, encrypted, HIPAA compliant messaging platform and chatbots, helping to foster communication between clinicians and patients by providing EHR/EMR records management, physician appointments, patient adherence etc.
Another high-profile chatbot deployment came from the announcement of National Health Service in the UK, which intends to betatest chatbots with Babylon, a UK-based telemedicine startup, supporting video conferencing with doctors, in order to “ replace NHS 111, a non-emergency phone hotline staffed by call center workers who aren’t necessarily medical professionals”. („UK’s NHS will test Babylon’s triage chatbot to replace non-emergency hotline“ 2017)
Among many others stands out the Amazon’s Alexa-based app created with the cooperation of Boston Children’s Hospital called KidsMD. Compared to the previously mentioned chatbot solutions relying on the text-based messages, KidsMD is a voice-recognition app that has been tested at patients’ homes and hospitals as well. Parents can used the app to obtain information about common illnesses and quick treatments, all of which is curated by doctors and accessible on the cloud. The app also helps doctors to take notes and photographs with crucial metadata while operating. (Bailey 2016)
The quest of Digital Health for optimizing the Patient Engagement Experience (PEx) and the general process of interacting with patients will benefit most from adopting chatbots and voice-recognition apps in the cases where the preliminary assessment is routine enough to be automated. The possibilities to streamline a patient-doctor communication exist as we could see in the examples such as KidsMD or Babylon’s trial with NHS in the UK. The more chatbots will become employed in the whole digital and mobile universe, the more familiar they will be to patients willing to try them as alternatives. However, the direct link to doctors will not go away and is currently being deployed in several projects. If the Babylon-NHS cooperation succeeds, other major transformations in public sector health may follow, stimulating the whole Digital Health industry.
Apart from the technical aspects, chatbots are a service and as such directly intertwined with human beings, their skills, fears, prejudices and cognitive spans, which means that chatbots as any service must be designed with a human in mind. It has been said that from the perspective of User interface design, the development of chatbots is less concerned with the visual, and focuses more on the narrative, conversational dimensions. Chatbots pose new challenges to the whole HCI/UX community, which requires their reorientation to the new platform. Therefore, we can expect a time delay between the available technological opportunities and appropriate design solutions that will emerge some time later, after designers test and find functional design patterns that work for all the stakeholders.
Finally, we have to assume that the extreme use cases and users-outliers will constitute major dilemmas as far as ethical and legal issues are concerned. What should chatbot do in case of a dying patient? What would be the legal ramifications when the bug occurs and causes health damages? Those questions are pertinent and hopefully will be sorted out soon, so that Digital Health will be rejuvenated by yet another digital transformation, this time spawned by the progress in AI research.
Bailey, Melissa. „‘Alexa, pull those lab results’: A hospital tries out virtual assistants.“ STAT. STAT, 20 July 2016. Web. 2 Feb. 2017.
Bedi, Gillinder, Facundo Carrillo, Guillermo A. Cecchi, Diego Fernández Slezak, Mariano Sigman, Natália B. Mota, Sidarta Ribeiro, Daniel C. Javitt, Mauro Copelli, and Cheryl M. Corcoran. „Automated analysis of free speech predicts psychosis onset in high-risk youths.“ Npj Schizophrenia 1 (2015): 15030. Web.
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„The chatbot will see you now: AI may play doctor in the future of healthcare.“ Digital Trends. N.p., 07 Oct. 2016. Web. 02 Feb. 2017.
„UK’s NHS will test Babylon’s triage chatbot to replace non-emergency hotline.“ MobiHealthNews. N.p., 04 Jan. 2017. Web. 2 Feb. 2017.
Wigdor, Daniel, and Dennis Wixon. Brave NUI world: designing natural user interfaces for touch and gesture. Burlington, MA: Morgan Kaufmann, 2011. Print.