User reactions to COVID-19 screening chatbots from reputable providers

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>The objective was to understand how people respond to COVID-19 screening chatbots.</jats:p> </jats:sec> <jats:sec> <jats:title>Materials and Methods</jats:title> <jats:p>We conducted an online experiment with 371 participants who viewed a COVID-19 screening session between a hotline agent (chatbot or human) and a user with mild or severe symptoms.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The primary factor driving user response to screening hotlines (human or chatbot) is perceptions of the agent’s ability. When ability is the same, users view chatbots no differently or more positively than human agents. The primary factor driving perceptions of ability is the user’s trust in the hotline provider, with a slight negative bias against chatbots’ ability. Asians perceived higher ability and benevolence than Whites.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>Ensuring that COVID-19 screening chatbots provide high quality service is critical, but not sufficient for widespread adoption. The key is to emphasize the chatbot’s ability and assure users that it delivers the same quality as human agents.</jats:p> </jats:sec>

Health Chatbots for Fighting COVID-19: a Scoping Review

“…Additionally, our review showed that health chatbots were used in multiple domains. The most prevalent domains were preventive care and risk communication (n=3) ( 3 , 9 , 12 ), public health surveillance by health officials (n=2) ( 2 , 10 ), Telehealth (n=2) ( 1 , 11 ), workplace health and safety (n=1) ( 13 ), and care of the elderly (n=1) ( 14 ).…”

“…For example, an application with a chatbot was developed to record health factors such as nutrition, physical activity, etc. during mandated self-isolation periods ( 9 , 10 , 12 , 14 ). Health chatbots were also used for tracking mood status to mitigate the psychological effects of COVID-19 such as anxiety, distress, and depression that could increase due to self-isolation, fear of becoming infected with the Coronavirus, or the stigma of contracting COVID-19 ( 9 ).…”

“…At the social system level, some health chatbots became virtually inactive due to the lack of interest from the community ( 2 ). In addition, there was a mismatch between how users perceived these technologies in comparison to the actual abilities provided by health chatbots ( 2 , 9 ). Users’ negative perceptions about the chatbot integrity, benevolence, ability to provide accurate information, and ability to preserve their privacy may hinder their acceptance to use health chatbots ( 9 ).…”

“…For example, a decision was made to exclude any article if it solely mentioned chatbots as an example of emerging technologies for combating COVID-19 without any further information or descriptions of health chatbots. This ultimately led to the elimination of 83 articles and left a total of 9 included articles ( 1 - 3 , 9 - 14 ).…”

Machine Learning in Healthcare Communication

“…The study was done with 371 individuals that participated in a COVID-19 screening session and concluded that the primary factor that drove the user's decision was whether they trust the capability of the chatbot or not. Overall, the chatbot providing high-quality service is critical [97]. As the COVID-19 pandemic continues, the mental health of both the infected and noninfected is an important concern.…”