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Family In The Loop: Enabling Family Involvement and Person-Centered Dementia Care at Long-Term Care Facilities with Collaborative AI Tools
Dylan Edward Moore, Songyun Tao, Emma Ricci-De Lucca, Christina Sapp Tadin, Dio Tadin, Brian L. G. Morgan, Elizabeth L. Murnane. Proceedings of the ACM on Human-Computer Interaction, 2025-10-16
Topic: Risk, prevention and care in population studies
Person-centered care provides a critical framework for dementia support in long-term care facilities, emphasizing attention to each resident's evolving needs, personal history, preferences, and values. Achieving this level of personalized attention depends on coordination among staff and active family involvement to record and share this essential context. However, fragmented communication, caregiver burnout, limited resources, and regulatory complexity frequently disrupt the flow of information, creating conditions in which person-centered care becomes difficult to sustain in practice. To address these challenges, we introduce Family In The Loop (FITL), an AI-enabled web platform that enhances continuity and collaboration for person-centered dementia care. FITL automatically synthesizes diverse care data-including video, notes, and records-into contextually relevant, emotionally resonant updates tailored for staff and family members. It offers four interlinked views: ''Reels'' provides families with concise, meaningful video updates; ''Essentials'' supplies caregivers with resident-specific summaries for immediate use; ''Explanations'' presents narrative insights into care decisions; and ''Archives'' maintains a searchable, longitudinal record of care activities. Together, these views enable flexible yet coordinated information sharing, respecting the fluid nature of family involvement and supporting continuity across caregiver roles and shifts. Through an iterative co-creation process spanning two years and seven resource-constrained facilities, we designed, developed, and deployed FITL in real-world dementia care contexts. Findings indicate that FITL improved staff access to critical resident information and facilitated sustained family engagement without increasing caregiver burdens. By surfacing timely, personalized, and meaningful information, FITL strengthened trust between families and staff, reduced misunderstandings, and bridged communication gaps. Our work highlights the potential of AI tools to stitch fragmented care interactions into cohesive, person-centered caregiving ecosystems. We conclude the paper by discussing inherent tensions around automation, oversight, and privacy, and we propose design principles for adaptive, collaborative, and person-centered care technologies.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Moore, D. E., Tao, S., Lucca, E. R.-D., Tadin, C. S., Tadin, D., Morgan, B. L. G., et al. (2025). Family In The Loop: Enabling Family Involvement and Person-Centered Dementia Care at Long-Term Care Facilities with Collaborative AI Tools. Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3757599
Vancouver
Moore DE, Tao S, Lucca ER.-D, Tadin CS, Tadin D, Morgan BLG, et al. Family In The Loop: Enabling Family Involvement and Person-Centered Dementia Care at Long-Term Care Facilities with Collaborative AI Tools. Proceedings of the ACM on Human-Computer Interaction. 2025. doi:10.1145/3757599.
BibTeX
@article{moore2025-0d72c8cf,
author = {Moore, Dylan Edward and Tao, Songyun and Lucca, Emma Ricci-De and Tadin, Christina Sapp and Tadin, Dio and Morgan, Brian L. G. and Murnane, Elizabeth L.},
title = {{Family In The Loop: Enabling Family Involvement and Person-Centered Dementia Care at Long-Term Care Facilities with Collaborative AI Tools}},
journal = {Proceedings of the ACM on Human-Computer Interaction},
year = {2025},
doi = {10.1145/3757599},
url = {https://doi.org/10.1145/3757599}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Findings indicate that FITL improved staff access to critical resident information and facilitated sustained family engagement without increasing caregiver burdens.
- We conclude the paper by discussing inherent tensions around automation, oversight, and privacy, and we propose design principles for adaptive, collaborative, and person-centered care technologies.
Why it matters
This paper introduces an AI-enabled web platform designed to share care information among staff and families in long-term care and reports that it improved staff access to resident information and sustained family engagement without increasing caregiver burden. It is a care-coordination tool and does not test treating, curing, or preventing Alzheimer's disease.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 46/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Proceedings of the ACM on Human-Computer Interaction) |
| Journal's citation record | 1 / 4 | Proceedings of the ACM on Human-Computer Interaction: journal h-index 108 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 6.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Elizabeth L. Murnane: h-index 30 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Dartmouth College: institution h-index 771 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 46 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1145/3757599
- Original source →
- Free full text: Proceedings of the ACM on Human-Computer Interaction →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
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