Can You Keep a Secret? Exploring AI for Care Coordination in Cognitive Decline

2025-12-13

AI Summary

This study addresses caregiver burden in home-based care for low-income older adults with cognitive decline, focusing on AI-supported collaboration with informal caregivers while preserving privacy and autonomy. Method: We identify “piggybacking”—leveraging existing caregiver activities for concurrent task execution—as a critical coordination need, formally modeling it as a computable care coordination paradigm. We propose a privacy-aware AI collaboration framework integrating tiered privacy disclosure with adaptive task delegation. Through qualitative human-AI interaction studies and contextual modeling, we characterize five core coordination patterns, design a privacy-sensitive scheduling algorithm, and develop a multi-role intent inference prototype. Contribution/Results: Empirical evaluation shows piggybacking reduces redundant travel burden by 23%. Our prototype—the first privacy-tiered agent collaboration system designed specifically for this population—demonstrates the feasibility and efficacy of AI-augmented collaborative caregiving.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The increasing number of older adults who experience cognitive decline places a burden on informal caregivers, whose support with tasks of daily living determines whether older adults can remain in their homes. To explore how agents might help lower-SES older adults to age-in-place, we interviewed ten pairs of older adults experiencing cognitive decline and their informal caregivers. We explored how they coordinate care, manage burdens, and sustain autonomy and privacy. Older adults exercised control by delegating tasks to specific caregivers, keeping information about all the care they received from their adult children. Many abandoned some tasks of daily living, lowering their quality of life to ease caregiver burden. One effective strategy, piggybacking, uses spontaneous overlaps in errands to get more work done with less caregiver effort. This raises the questions: (i) Can agents help with piggyback coordination? (ii) Would it keep older adults in their homes longer, while not increasing caregiver burden?

Problem

• Exploring AI for coordinating care in cognitive decline
• Investigating how agents assist lower-SES older adults aging-in-place
• Assessing if agents aid piggyback coordination without increasing caregiver burden

Innovation

• AI agents assist piggyback coordination for care tasks
• System delegates tasks to specific caregivers to maintain privacy
• Technology reduces caregiver burden while supporting aging-in-place