Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation
- PMID: 40484798
- DOI: 10.1016/j.jagp.2025.05.007
Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation
Abstract
As the aging population in the United States grows, the need for an integrated approach to support older adults has become increasingly urgent. The SUNSHINE framework, Seniors Uniting Nationwide to Support Health, INtegrated Care, and Evolution, offers a model for advancing resilience, defined as the capacity of individuals, families, systems, and communities to adapt and thrive in the face of adversity. SUNSHINE promotes this goal through the alignment of older and aging adults, families, healthcare systems, public health agencies, social services, and community resources. Using the Theory of Change modeling, SUNSHINE emphasizes whole-person health, interdisciplinary collaboration, and the strategic use of technology to address the evolving needs of aging populations. The framework promotes systems integration supported by research infrastructure and multi-sector collaboration to enhance the well-being of older adults and family caregivers. SUNSHINE places a strong emphasis on mental health, particularly depression, and highlights the importance of social connection and prevention in addressing health disparities and care gaps associated with aging. It conceptualizes resilience as both a desired outcome and a driver of transformation, guiding the redesign and evaluation of health and social systems. The framework also identifies opportunities to leverage artificial intelligence and machine learning (AI/ML) technologies, grounded in scientific evidence, to support personalized prevention, treatment, and care strategies. These technologies are critical for optimizing decision-making, improving care delivery, and enhancing system flexibility. Finally, SUNSHINE aspires to advance a future of aging that is healthy, resilient, and fair, guided by principles of equity, defined as fairness and impartiality in health opportunities and outcomes.
Keywords: AI/ML; Aging health; Collaboration; Depression; Health disparities; Integration; Resilience.
Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
DISCLOSURES No conflicts of interest. Jie Chen and Teagan Maguire are supported by theNational Institute on Aging(R01AG062315andRF1AG083175). Jie Chen and Rozalina G. McCoy are investigators at the University of Maryland-Institute for Health Computing, which is supported by funding from Montgomery County, Maryland and The University of Maryland Strategic Partnership: MPowering the State, a formal collaboration between the University of Maryland, College Park and the University of Maryland, Baltimore.
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