RESUMO
Children commonly experience pain and distress in healthcare settings related to medical procedures such as blood tests and intravenous insertions (IVIs). Inadequately addressed pain and distress can result in both short- and long-term negative consequences. The use of socially assistive robotics (SARs) to reduce procedure-related distress and pain in children's healthcare settings has shown promise; however, the current options lack autonomous adaptability. This study presents a descriptive qualitative needs assessment of healthcare providers (HCPs) in two Canadian pediatric emergency departments (ED) to inform the design an artificial intelligence (AI)-enhanced social robot to be used as a distraction tool in the ED to facilitate IVIs. Semi-structured virtual individual and focus group interviews were conducted with eleven HCPs. Four main themes were identified: (1) common challenges during IVIs (i.e., child distress and resource limitations), (2) available tools for pain and distress management during IVIs (i.e., pharmacological and non-pharmacological), (3) response to SAR appearance and functionality (i.e., personalized emotional support, adaptive distraction based on child's preferences, and positive reinforcement), and (4) anticipated benefits and challenges of SAR in the ED (i.e., ensuring developmentally appropriate interactions and space limitations). HCPs perceive AI-enhanced social robots as a promising tool for distraction during IVIs in the ED.
RESUMO
Background & Objective: Socially assistive robots (SARs) are a promising tool to manage children's pain and distress related to medical procedures, but current options lack autonomous adaptability. The aim of this study was to understand children's and caregivers' perceptions surrounding the use of an artificial intelligence (AI)-enhanced SAR to provide personalized procedural support to children during intravenous insertion (IVI) to inform the design of such a system following a user-centric approach. Methods: This study presents a descriptive qualitative needs assessment of children and caregivers. Data were collected via semi-structured individual interviews and focus groups. Participants were recruited from two Canadian pediatric emergency departments (EDs) between April 2021 and January 2022. Results: Eleven caregivers and 19 children completed 27 individual interviews and one focus group. Three main themes were identified: A. Experience in the clinical setting, B. Acceptance of and concerns surrounding SARs, and C. Features that support child engagement with SARs. Most participants expressed comfort with robot technology, however, concerns were raised about sharing personal information, photographing/videotaping, and the possibility of technical failure. Suggestions for feature enhancements included increasing movement to engage a child's attention and tailoring language to developmental age. To enhance the overall ED experience, participants also identified a role for the SAR in the waiting room. Conclusion: Artificial intelligence-enhanced SARs were perceived by children and caregivers as a promising tool for distraction during IVIs and to enhance the overall ED experience. Insights collected will be used to inform the design of an AI-enhanced SAR.
RESUMO
In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social conversation is fluent, flexible linguistic interaction; face-to-face dialogue is both the basic form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the field of computational linguistics devoted to the automated production of high-quality linguistic content; while this research area is also an active one, in general most effort in NLG is focused on producing high-quality written text. This article summarizes the state of the art in the two individual research areas of social robotics and natural language generation. It then discusses the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate to the needs of socially interactive robots. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.
