Virtual voice-based assistants, such as Siri or Amazon’s Alexa, are becoming more common, transforming our houses into smart homes.
In the US, about 30 percent of our interactions with technology are happening through conversation now. Data-driven, connected experiences can transform your customer interactions.
Voice-enabled Digital Health
Voice-enabled technology can provide novel ways of interacting with patients, especially those who are at-risk in some way or live in remote areas. People are generally more engaged with technology when they can have a two-way conversation—as such, voice appears to be a powerful user interface. We are more likely to get involved and adopt new habits if we are given information in the form of a meaningful conversation.
Supporting Evidence-Based Information With Voice
Can You Speak the Way I Speak? Researchers around the globe are continuously working on developing and improving intelligent virtual health advisory systems by adding human characteristics. They are looking for optimal design strategies, particularly in relation to their programs’ communication styles.
One study, conducted by a research team from the Northeastern University in Liaoning, China, and Ren Min University in Beijing, showed that similarity in communication style (to the end user) can increase a sense of credibility, thus making the user more likely to trust the virtual advisor. When a user is interacting with an avatar, the avatar’s communication style can influence the user’s engagement level and enjoyment. Authors of the research concluded that, for best results, the virtual advisor’s language should align with the vernacular of the user. The study found that when the digital health advisor is programmed to emulate the communication style of the user, it supports emotional rapport.
It is evident that when people enjoy their interactions with technology, they are more likely to use that technology again. In fact, according to recent studies, this aspect might be more important to users than the technology’s credibility and informativeness. Experts suggest that designers of virtual advisory systems in healthcare should investigate the communication patterns of local users before developing their systems communication style. By understanding the end user first, developers can create language that supports end-user intimacy and acceptance. Especially if it makes it easy to put all the moving parts of their health in one place. As opposed to human-based coaching, LIFEdata PersonalHEALTH is a data-driven, contextual based behavior change intervention platform which continuously learns and adapts to the user, thus enabling scalable chronic conditions prevention cost effectively.
Chatbot technology is not as new as you might think. It has been around since the 1960s. Recently, it has been increasingly used in mental health, but also in other healthcare domains. A study led by Professor Gerhard Andersson of Linköping University, Sweden, showed good adherence rates in people using a smartphone-based automated chatbot for positive psychology and cognitive behavioral therapy interventions. However, the study also revealed some limitations of an automated conversational agent; for example, the responses of the app’s agent often were repetitive. In fact to succeed in today’s age of assistance—when empowered consumers expect experiences designed exactly for their needs—you also have to get the timing right. New research from Bain & Company in partnership with Google reveals how brands who show up with the right message in just the right moment are the ones finding pathways to growth and revenue.
Chatbot for health
Hospitals and healthcare systems are beginning to appreciate the input of virtual advisory services and the amount of data they can collect and dispense at scale with these systems. It is almost impossible for health providers to build a deep relationship with their clients and share all their health information efficiently using strictly human interaction. Chatbots can, to some degree, fill this gap while still providing an individualized approach. Moreover, chatbots are economical to run (once built) and are generally available to answer questions whenever needed (as opposed to a staffing model that is expensive to run around the clock).
Although interactive technology and conversational artificial intelligence have their limitations—including safety concerns and the possibility of misunderstandings—the global chatbot market is expected to continue to develop, reaching $1.25 billion by 2025. Many consumers are accepting chatbots as their preferred mode of communication.
Behavioral and data science
Changing behaviors represents the single biggest opportunity to improve the quality of our life.
Lifestyle recommendations, such as “walk 10,000 steps” or “eat clean” are not effective because they lack of contextual understanding of each user. LIFEdata PersonalHEALTH takes data from consumer devices and puts them into a biometric machine-learning platform and – linking them to medically-validated lifestyle interventions – designs a behavioral coaching program around the person’s health goals. Enhance your brand experience with a technology which automatically turns various smartphone-originated data streams – geolocation, schedule, activity patterns, driving and walking routes, weather, surroundings and more – into personalized and contextual recommendations that guide the user, in real-time, toward achieving the desired goals.
Read more here.
Fitzpatrick KK, Darcy A, Vierhile M. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial.JMIR Mental Health 2017;4(2):e19
Li M, Mao J. Hedonic or utilitarian? Exploring the impact of communication style alignment on user’s perception of virtual health advisory services. International Journal of Information Management, 2015;35:229-243.
Ly K, Ly A, Andersson G. A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods. Internet Interventions, 2017;10:39-46.
Miner A, Milstein A, Hancock J. Talking to machines about personal mental health problems. JAMA, 2017;318(13):1217-1218.
Also published on Medium.