Abstract
Undergoing an MRI scan can be a stressful experience for young children. For this reason, pediatric patients are often scanned under general anesthesia or sedation, which is costly and may carry health risks. By preparing children for their scan, anesthesia may be avoided; however, existing preparation solutions are generally hospital-specific and not easily scalable. We describe the iterative design process and feasibility test of the Scan Buddy app: a scalable app to prepare children 4–10 for an MRI scan.
Interviews and Questionnaire: Interviews with 19 healthcare practitioners (US and EU) revealed a desire to reduce anesthesia for imaging through better patient preparation. A questionnaire administered to 73 caregivers of children who recently had an MRI scan exposed the need for well-designed tools to explain the MRI procedure as well as caregivers’ willingness to invest time in at-home preparation if it could increase the likelihood of an awake scan.
Design Rationale: We developed a learning framework based on Informing, Familiarizing and Training children for MRI (FIT-for-MRI), and designed a gamified app with 7 learning goals.
Method: Scan Buddy v1 was used in the weeks prior to an MRI examination by 28 children in a feasibility test at Cincinnati Children’s Hospital Medical Center. Results: Scan Buddy prepared the children for their MRI: learning goals were met, children enjoyed playing with the app, awake scanning was successful, and parents felt it supported their child in their preparation. Scan Buddy v2 was subsequently developed, and the rationale for design is detailed.
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Notes
- 1.
The study was not designed to be powered. We inspected trends and interesting patterns.
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Acknowledgements
We would like to thank Janneke Verhaegh and Edwin Heijman for kick-starting this project and their contributions in the initial phase.
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Saini, P. et al. (2022). Scan Buddy: A Gamified App to Prepare Children for an MRI Scan. In: Kurosu, M. (eds) Human-Computer Interaction. Theoretical Approaches and Design Methods. HCII 2022. Lecture Notes in Computer Science, vol 13302. Springer, Cham. https://doi.org/10.1007/978-3-031-05311-5_42
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