Other Titles
Harnessing AI-Driven Technology to Empower Dementia Caregivers Across Home, Clinic, and Community
Other Titles
Symposium Presentation
Abstract
Informal dementia caregiving imposes a substantial financial burden on families, compounded by systemic factors such as insurance coverage gaps, prohibitive long-term care costs, and the administrative complexity of navigating public and private benefit programs [1-2].
To address this challenge, we developed an artificial intelligence (AI)-driven application designed to streamline access to financial resources. The system is built on a curated dataset that links multidimensional caregiver profiles with relevant financial support programs. Profiles, encoded as input feature vectors, are derived from structured questionnaire responses capturing variables such as employment status, weekly caregiving hours, out-of-pocket medical expenses, and insurance coverage. Each profile is algorithmically paired with output labels representing verified financial resources, including Social Security Disability Insurance (SSDI) Compassionate Allowances, Medicaid waiver programs, state-specific respite care funding, and nonprofit grants.
The system’s core engine employs a supervised machine learning framework to predict optimal mappings between caregiver circumstances and resource eligibility criteria. To enhance contextual understanding, natural language processing (NLP) techniques extract salient themes from open-ended caregiver narratives, supplementing structured data. Following model training and validation, the application functions as an intelligent recommendation engine: caregiver inputs are transformed into feature vectors and processed to generate a ranked list of potential financial supports.
A critical innovation is the embedded feedback loop, wherein user interactions and outcomes iteratively refine predictive accuracy and the personal relevance of recommendations. This methodology demonstrates the potential of AI to reduce the administrative burden of navigating fragmented financial support systems by providing caregivers with personalized, data-driven guidance. By synthesizing structured demographic data, NLP-enhanced qualitative inputs, and supervised learning, the application offers a scalable, adaptive solution for connecting vulnerable families with essential economic resources.
Future directions include expanding the training dataset to improve incorporating regional policy variations into algorithmic logic and conducting rigorous larger studies. This innovation contributes to nursing science by addressing a critical need for caregiver financial support.
Notes
Presenter notes available in attached slide deck.
References:
[1] Akintomide, M., Zhang, X., Iwuagwu, L., & Ramos, M. (2025). Experiences and Support Needs of Informal Caregivers in Managing Behavioral Symptom of Dementia. OBM Geriatrics,9(2), 306. https://doi.org/10.10.21926/obm.geriatr.2502306
[2] Qiu, L., Blair, J., & Abdullah, S. (2024). Managing finances for persons living with dementia: Current practices and challenges for care partners. https://saeedabdullah.com/files/pubs/2024-chi-lbw-dementia-fintech.pdf
Sigma Membership
Non-member
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Health Equity or Social Determinants of Health, Public and Community Health, Competence
Recommended Citation
Akintomide, Modupe; Zhang, Xinyue; and Iyer, Divya, "Leveraging AI-Driven Financial Resource Identification in Dementia Caregiving-Development Phase" (2026). International Nursing Research Congress (INRC). 8.
https://www.sigmarepository.org/inrc/2026/presentations_2026/8
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-07-19
Leveraging AI-Driven Financial Resource Identification in Dementia Caregiving-Development Phase
Toronto, Ontario, Canada
Informal dementia caregiving imposes a substantial financial burden on families, compounded by systemic factors such as insurance coverage gaps, prohibitive long-term care costs, and the administrative complexity of navigating public and private benefit programs [1-2].
To address this challenge, we developed an artificial intelligence (AI)-driven application designed to streamline access to financial resources. The system is built on a curated dataset that links multidimensional caregiver profiles with relevant financial support programs. Profiles, encoded as input feature vectors, are derived from structured questionnaire responses capturing variables such as employment status, weekly caregiving hours, out-of-pocket medical expenses, and insurance coverage. Each profile is algorithmically paired with output labels representing verified financial resources, including Social Security Disability Insurance (SSDI) Compassionate Allowances, Medicaid waiver programs, state-specific respite care funding, and nonprofit grants.
The system’s core engine employs a supervised machine learning framework to predict optimal mappings between caregiver circumstances and resource eligibility criteria. To enhance contextual understanding, natural language processing (NLP) techniques extract salient themes from open-ended caregiver narratives, supplementing structured data. Following model training and validation, the application functions as an intelligent recommendation engine: caregiver inputs are transformed into feature vectors and processed to generate a ranked list of potential financial supports.
A critical innovation is the embedded feedback loop, wherein user interactions and outcomes iteratively refine predictive accuracy and the personal relevance of recommendations. This methodology demonstrates the potential of AI to reduce the administrative burden of navigating fragmented financial support systems by providing caregivers with personalized, data-driven guidance. By synthesizing structured demographic data, NLP-enhanced qualitative inputs, and supervised learning, the application offers a scalable, adaptive solution for connecting vulnerable families with essential economic resources.
Future directions include expanding the training dataset to improve incorporating regional policy variations into algorithmic logic and conducting rigorous larger studies. This innovation contributes to nursing science by addressing a critical need for caregiver financial support.
Description
Overall Symposium Summary: This symposium presents preliminary findings on AI-driven interventions to enhance Alzheimer’s and dementia caregiving across home, clinical, and community contexts. Projects include: a voice assistant app guiding informal caregivers in managing behavioral symptoms; a gamified VR training tool cultivating dementia care competencies in healthcare students; and a data-driven app matching caregivers with tailored financial resources. These innovations aim to empower caregivers and improve outcomes.