Bandit-supported care planning for older people with complex health and care needs
This addresses the challenge of improving care quality and health outcomes for institutionalized older people with complex needs, though it appears incremental as it applies existing bandit algorithms to a new domain.
The paper tackled the problem of providing personalized care for older people in nursing homes despite a shortage of care workers, by proposing an AI-assisted care planning model using bandit algorithms that adapt to sequential feedback, and evaluated it on empirical data from the SPEC study.
Long-term care service for old people is in great demand in most of the aging societies. The number of nursing homes residents is increasing while the number of care providers is limited. Due to the care worker shortage, care to vulnerable older residents cannot be fully tailored to the unique needs and preference of each individual. This may bring negative impacts on health outcomes and quality of life among institutionalized older people. To improve care quality through personalized care planning and delivery with limited care workforce, we propose a new care planning model assisted by artificial intelligence. We apply bandit algorithms which optimize the clinical decision for care planning by adapting to the sequential feedback from the past decisions. We evaluate the proposed model on empirical data acquired from the Systems for Person-centered Elder Care (SPEC) study, a ICT-enhanced care management program.