Bandit-supported care planning for older people with complex health and care needs

TL;DR

This paper introduces an AI-assisted care planning model using bandit algorithms to optimize personalized care for older adults with complex needs, addressing workforce shortages and improving health outcomes.

Contribution

It presents a novel bandit-based decision model for personalized elder care planning, leveraging sequential feedback to adapt clinical decisions.

Findings

The model effectively adapts to individual needs based on past feedback.

Application to empirical data demonstrates improved care decision optimization.

The approach supports scalable, personalized care in resource-limited settings.

Abstract

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.