The Doctor will See You Now: Could a Robot Be a medical Receptionist?

Sutherland, Craig J.; Ahn, Byeong Kyu; Brown, Bianca; Lim, JongYoon; Johanson, Deborah L.; Broadbent, Elizabeth; MacDonald, Bruce A.; Ahn, Ho Seok

doi:10.1109/icra.2019.8794439

Cited by 10 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Robots have been used to quantify significant harm levels in autistic children, by professional caregivers of the elderly, to accompany the elderly while walking, to help persons with motor impairment (i.e., quadriplegia), to monitor and correct during rehabilitation for head, neck, and back pain [16,36], and as a mediator in the interaction with the physician or nurse who performed the treatment [18]. Additionally, social robots can be rehabilitation therapists at home [20] or do administrative tasks, such as reception [29,51] in hospitals [22,60]. They have also been used for edutainment purposes [30,70,80].…”

Section: Discussionmentioning

confidence: 99%

Social Robots in Hospitals: A Systematic Review

2021

View full text Add to dashboard Cite

Hospital environments are facing new challenges this century. One of the most important is the quality of services to patients. Social robots are gaining prominence due to the advantages they offer; in particular, several of their main uses have proven beneficial during the pandemic. This study aims to shed light on the current status of the design of social robots and their interaction with patients. To this end, a systematic review was conducted using WoS and MEDLINE, and the results were exhaustive analyzed. The authors found that most of the initiatives and projects serve the elderly and children, and specifically, that they helped these groups fight diseases such as dementia, autism spectrum disorder (ASD), cancer, and diabetes.

show abstract

Section: Discussionmentioning

confidence: 99%

Social Robots in Hospitals: A Systematic Review

2021

View full text Add to dashboard Cite

show abstract

“…Similarly, Goetz et al [35] indicated that a cheerful service robot was liked more than a serious robot. Sutherland et al [36] also found that the robot friendliness (reflected via speech and smiles) had a positive effect on people's perception.…”

Section: B Design Parametersmentioning

confidence: 97%

An Iterative Interaction-Design Method for Multi-Modal Robot Communication

Saad

Broekens

Neerincx

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

View full text Add to dashboard Cite

The design space of human-robot interaction is large and multi-dimensional. A sound design requires a systematic theory-driven exploration, specification and refinement of design variables. There is a need for a practical method and tool to iteratively specify the content of the dialogue (e.g., speech acts) with the accompanying expressive behavior (e.g., gesture openness) as prescribed by social science theory, e.g., task-and person-oriented communication. This paper presents an iterative interaction-design (ID) method for multi-modal robot communication. Following the ID-method, a designer first creates his/her "own" individual design and, subsequently, provides an iteration to the evolving iterative design. To support the design method, we developed an ID-tool (available for download). The tool support entails (a) selecting the theory-based communication style; (b) creating and linking the dialogue act components for the concerning use case; and (c) setting the associated expression parameters. We conducted a study with Industrial Design students (N = 13) who followed the ID-method and used our tool to design person-and task-oriented communications for a reception robot. Our method produced distinctive task-and person-oriented dialogue styles, i.e., provided the predicted theory-based multimodal communicative behaviors. The task-oriented style showed a more formal, shorter and less chatty communication. Overall, there was a rather smooth design convergence process, in which the individual designs were harmonized into the iterative design. For the selected design problem, the ID-tool had a satisfactory usability. Next steps include validation of the communication styles in an empirical study and, subsequently, identification of reusable design patterns.

show abstract

“…Hamilton et al (2016) found that interaction satisfaction and interaction immersion produced perceived value on a Facebook fan page. Sutherland et al (2019) showed that participants who interacted with a "friendly and professional" robot had higher levels of interaction satisfaction with communication. In the present study, therefore, we define interactant satisfaction with communication as a user's subjective evaluation of the outcome of communication with AI chatbots.…”

Section: Interactant Satisfaction With Communicationmentioning

confidence: 99%

Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach

Lee

Pan

Hsieh

2021

INTR

View full text Add to dashboard Cite

PurposeThis study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.Design/methodology/approachMicrosoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.FindingsRegarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.Practical implicationsBrands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.Originality/valueThis research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

show abstract

The Doctor will See You Now: Could a Robot Be a medical Receptionist?

Cited by 10 publications

References 32 publications

Social Robots in Hospitals: A Systematic Review

Social Robots in Hospitals: A Systematic Review

An Iterative Interaction-Design Method for Multi-Modal Robot Communication

Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach

Contact Info

Product

Resources

About