Perspectives of Healthcare Providers to Inform the Design of an AI-Enhanced Social Robot in the Pediatric Emergency Department

Healthcare professionals' attitudes to animal assisted activity with dogs in paediatric care

BACKGROUND

Hospitalization for children often involves stress induced by fear and pain. Complementary therapies, such as Animal Assisted Activities (AAA) with dogs, can alleviate the hospital experience.

PURPOSE

The first aim of this study was to initiate the development of an instrument that measures healthcare professionals' attitudes toward complementary therapy, specifically dogs in AAA. The second aim was to elucidate the emerging effects of introducing dogs to children in healthcare settings, as reported by healthcare professionals.

MATERIALS AND METHODS

A questionnaire covering demographics, rating attitudes, and allergy and hygiene risks, followed by open-ended questions, was completed by 61 healthcare professionals (HCPs). Quantitative data were analysed statistically, while qualitative data underwent content analysis.

RESULTS

The Attitude Instrument of Complementary Therapy (AICT) included 7 items and was evaluated using exploratory factor analysis. Healthcare professionals had a median score of 25 (range 18-28), high scores indicating a more positive attitude. However, 36 % of the HCPs perceived a risk of allergies, and this group had a significantly lower median score, 22 versus 26. Open-ended answers were analysed into four categories: "Dogs could positively affect children in hospital", "Dogs may pose a risk of allergies in children", "Dogs might be frightening for children" and "Dogs can affect healthcare professionals' working situation".

CONCLUSION

The AICT can serve as a valuable tool for investigating HCPs' attitudes to dogs in AAA as complementary therapies. Professionals view dogs as beneficial for hospitalized children, but attitudes toward dogs working in paediatric care can be influenced by concerns about risks such as allergies.

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.

Exploring the needs of children and caregivers to inform design of an artificial intelligence-enhanced social robot in the pediatric emergency department

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.