Version 1
: Received: 15 August 2024 / Approved: 16 August 2024 / Online: 16 August 2024 (04:33:39 CEST)
How to cite:
Acosta, C. G.; Ye, Y.; Wong, K. L. Y.; Zhao, Y.; Lawrence, J.; Towell, M.; D'Oyley, H.; Mackay-Dunn, M.; Chow, B.; Hung, L. Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care. Preprints2024, 2024081188. https://doi.org/10.20944/preprints202408.1188.v1
Acosta, C. G.; Ye, Y.; Wong, K. L. Y.; Zhao, Y.; Lawrence, J.; Towell, M.; D'Oyley, H.; Mackay-Dunn, M.; Chow, B.; Hung, L. Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care. Preprints 2024, 2024081188. https://doi.org/10.20944/preprints202408.1188.v1
Acosta, C. G.; Ye, Y.; Wong, K. L. Y.; Zhao, Y.; Lawrence, J.; Towell, M.; D'Oyley, H.; Mackay-Dunn, M.; Chow, B.; Hung, L. Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care. Preprints2024, 2024081188. https://doi.org/10.20944/preprints202408.1188.v1
APA Style
Acosta, C. G., Ye, Y., Wong, K. L. Y., Zhao, Y., Lawrence, J., Towell, M., D'Oyley, H., Mackay-Dunn, M., Chow, B., & Hung, L. (2024). Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care. Preprints. https://doi.org/10.20944/preprints202408.1188.v1
Chicago/Turabian Style
Acosta, C. G., Bryan Chow and Lillian Hung. 2024 "Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care" Preprints. https://doi.org/10.20944/preprints202408.1188.v1
Abstract
Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the Interpretive Descriptive approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30-50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings.
Keywords
bed sensors; sleep monitoring; implementation science; focus groups
Subject
Public Health and Healthcare, Nursing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
