CareInsights: AI-enabled Infrastructure for Person-centered Dementia Care in Resource-constrained Facilities

Long-term dementia care is often shaped by fragmented documentation practices that fail to capture the relational and evolving nature of caregiving. We present CareInsights, an AI-enabled infrastructure that supports caregivers by passively capturing interaction data, enriching it with contextual and clinical signals, and generating narrative-based feedback to support day-to-day person-centered care. Our system was developed through a two-year collaboration with seven long-term care facilities and integrated into existing electronic health record systems and memory therapy platforms to minimize disruption and streamline uptake. We evaluated CareInsights through a combination of system performance analysis, content assessments, caregiver surveys, and interviews. Our model demonstrated expert-level performance, and caregivers reported outcomes consistent with more effective person-centered dementia care. Adoption was supported by integration into existing staff workflows and design choices that mirrored caregiver reasoning. We conclude with design implications for “narrative AI infrastructure,” offering guidance for scaling person-centered, AI-enabled support in real-world care environments.

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Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2025-12-02
DOI
https://doi.org/10.1145/3770687
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00
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article

CareInsights: AI-enabled Infrastructure for Person-centered Dementia Care in Resource-constrained Facilities

S. Moore, Elizabeth L. Murnane, Songyun Tao, Dylan Edward Moore et al.
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Dementia and Cognitive Impairment Research
article

CareInsights: AI-enabled Infrastructure for Person-centered Dementia Care in Resource-constrained Facilities

S. Moore, Elizabeth L. Murnane, Songyun Tao, Dylan Edward Moore, B. L. Morgan, Christina Sapp Tadin, Dio Tadin
article en

Abstract

Long-term dementia care is often shaped by fragmented documentation practices that fail to capture the relational and evolving nature of caregiving. We present CareInsights, an AI-enabled infrastructure that supports caregivers by passively capturing interaction data, enriching it with contextual and clinical signals, and generating narrative-based feedback to support day-to-day person-centered care. Our system was developed through a two-year collaboration with seven long-term care facilities and integrated into existing electronic health record systems and memory therapy platforms to minimize disruption and streamline uptake. We evaluated CareInsights through a combination of system performance analysis, content assessments, caregiver surveys, and interviews. Our model demonstrated expert-level performance, and caregivers reported outcomes consistent with more effective person-centered dementia care. Adoption was supported by integration into existing staff workflows and design choices that mirrored caregiver reasoning. We conclude with design implications for “narrative AI infrastructure,” offering guidance for scaling person-centered, AI-enabled support in real-world care environments.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 9(4)
Dartmouth College (US), St. John's Pleasant Valley Hospital (US), University of California, Davis (US)
Openalex Percentile: Top 56%
Dementia and Cognitive Impairment Research
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