Leveraging AI to Advance Age-Friendly Care in the Veterans Health Administration
The aging population presents a pressing challenge for health care systems, necessitating effective strategies to address the complex needs of older adults. The Veterans Health Administration in the Department of Veterans Affairs (VA), the largest integrated health care system in the United States, has embraced the Age-Friendly Health Systems (AFHS) initiative from the Institute for Healthcare Improvement to ensure safe and high-quality care for older veterans. In AFHS, health care providers consistently use the evidence-based 4Ms framework (what matters, medication, mentation, and mobility) to deliver comprehensive care for older adults in all care settings., This viewpoint paper explores the potential of artificial intelligence (AI) to enhance the evidence-based implementation of the AFHS 4Ms framework in the VA to provide optimal care for older adults. By leveraging AI technologies, such as natural language processing, machine learning, large language models, clinical decision support, and data analytics, this viewpoint examines the opportunities and challenges of using AI to support the 4Ms domains in a large, integrated health care system. Furthermore, it discusses the potential benefits of integrating AI-driven decision support systems and predictive analytics to personalize care, reduce polypharmacy and potentially inappropriate medications, enhance cognitive and mood assessments, and better identify mobility issues and interventions. By examining the intersection of AI and age-friendly care in the VA, this viewpoint highlights the transformative potential of AI to expand 4Ms care and improve the experience of providers and older adults across diverse health care settings.
Institutions
- University of Florida Health (US)
- Malcom Randall VA Medical Center (US)
- Geriatric Research Education and Clinical Center (US)
- Office of Patient Care Services (US)
- VA Northeast Ohio Healthcare System (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- JMIR Aging
- Published
- 2025-12-08
- DOI
- https://doi.org/10.2196/75686
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00