Leveraging AI to Advance Age-Friendly Care in the Veterans Health Administration
Bibliographic record
Abstract
Unlabelled: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".