Evidence map›Paper›PMID 41245198›Full record

ArticleFrontiers in robotics and AI2025

Bringing a socially assistive robot to the paediatric emergency department: design, development, and usability testing.

Mary Ellen Foster, Jennifer N Stinson, Lauren Harris, Kate Kyuri Kim, Sasha Litwin, Patricia Candelaria, Summer Hudson, Julie Leung, Ronald P A Petrick, Alan Lindsay and 4 more

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Mary Ellen FosterSchool of Computing Science, University of Glasgow, Glasgow, United Kingdom.
Jennifer N StinsonResearch Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Lauren HarrisResearch Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Kate Kyuri KimResearch Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Sasha LitwinDepartment of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Patricia CandelariaDepartment of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
Summer HudsonDepartment of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
Julie LeungDepartment of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
Ronald P A PetrickDepartment of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom.
Alan LindsayDepartment of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom.
Andrés Ramírez-DuqueSchool of Computing Science, University of Glasgow, Glasgow, United Kingdom.
David Harris SmithDepartment of Communication Studies & Media Arts, McMaster University, Hamilton, ON, Canada.
Frauke ZellerInstitute for Design Informatics, University of Edinburgh, Edinburgh, United Kingdom.
Samina AliDepartment of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Children undergoing medical procedures in paediatric Emergency Departments (EDs) often experience significant pain and distress. Socially Assistive Robots (SARs) offer a promising avenue for delivering distraction and emotional support in these high-pressure environments. This study presents the design, development, and formative evaluation of an AI-enhanced SAR to support children during intravenous insertion (IVI) procedures. Methods: The robot system was developed through a participatory design process involving healthcare professionals, patients, caregivers, and interdisciplinary research teams. The SAR was designed to autonomously adapt its behaviour to the child's affective state using AI planning and social signal processing. A two-cycle usability study was conducted across two Canadian paediatric EDs, involving 25 children and their caregivers. Feedback was collected through observations, interviews, and system logs. Results: The SAR was successfully integrated into clinical workflows, with positive responses from children, caregivers, and healthcare providers. Usability testing identified key technical and interaction challenges, which were addressed through iterative refinement. The final system demonstrated robust performance and was deemed ready for a formal randomised controlled trial. Discussion: This work highlights the importance of co-design, operator control, and environmental adaptability in deploying SARs in clinical settings. Lessons learned from the development and deployment process informed six concrete design guidelines for future SAR implementations in healthcare.

Indexed as

participatory designreal-world evaluationsocially assistive robotssystem validationtechnology adoption

Identifiers

PMID41245198
PMCPMC12612628

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.