Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
TD‐P‐011: The Efficacy of Interactive Ibooks in Educating Older Patients on TBI, Concussion, and NPH
0
Zitationen
6
Autoren
2016
Jahr
Abstract
Individuals with Alzheimer’s disease (AD) or other cognitive alterations may have difficulty understanding their medical condition and any associated surgical procedures. We developed an interactive and user-friendly iBook to educate patients with cognitive deficits on their medical condition, specifically, Concussion, Traumatic Brain Injury (TBI), or Normal Pressure Hydrocephalus (NPH). Upon consent and with IRB approval, patients were presented with a 5 question pre-survey to assess baseline knowledge and understanding of their medical condition. Then, an informative and interactive iBook pre-loaded on standard 32gb iPad tablet was presented to the patient. Either an iBook on Concussion, TBI, or NPH was provided based on the patient’s pathology. A post-survey was then conducted in order to assess the interim improvement in understanding and knowledge acquisition. A total of 52 (N=29 < 60, N=24 >60) patients participated in this study. The average pre-survey score across all questions was 2.42, while the average post-survey score across all questions was 3.97 (P < .05). Patients 60 and over scored 2.26 on the pre-survey and 3.74 on the post-survey (P < .05), while patients under 60 years of age scored 2.57 on the pre-survey and 4.15 on the post-survey (P < .05). Although older patients showed a statistically significant increase in the post-survey score, it was significantly lower then younger controls (P < .05, .001). Furthermore, 40 family members were surveyed, and a similar trend was noted with older family members scoring lower on both pre- and post-surveys, but having a significant improvement in understanding (P < .05, P = .027, .010). Patients with NPH showed a trend towards lower pre- and post-survey scores and attenuated post-survey improvement.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.380 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.243 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.671 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.496 Zit.