Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care

Area of Science:

• Geriatric Medicine
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Sleep is vital for hospitalized older adults' rehabilitation.
• AI-driven sleep monitoring offers enhanced patient assessment and tailored interventions.
• Sleepsense technology provides real-time sleep data for clinical decision-making.

Purpose of the Study:

• To explore interdisciplinary healthcare team perspectives on implementing AI-driven sleep monitoring (Sleepsense) in geriatric hospital care.
• To identify barriers and facilitators to the adoption of Sleepsense technology.
• To assess the perceived impact of Sleepsense on patient monitoring and rehabilitation.

Main Methods:

• Qualitative study using focus groups with physicians, nurses, care aides, and activity workers.
• Interpretive description approach for data collection.
• Thematic analysis guided by the Consolidated Framework for Implementation Research (CFIR).

Main Results:

• Sleepsense was viewed as a time-saving, data-driven tool enhancing patient monitoring and assessment.
• Key barriers included resistance to change and concerns regarding patient comfort and safety.
• Facilitators for implementation comprised adequate training and active staff engagement.

Conclusions:

• AI-driven sleep monitoring tools like Sleepsense have potential in geriatric care but require careful implementation strategies.
• Addressing staff concerns and fostering interdisciplinary support are crucial for successful technology adoption.
• Further research should focus on optimizing team support to improve innovation uptake and patient outcomes in rehabilitation.