Turning Compliance Into Action: An Augmented Intelligence–Enabled Framework for Advocacy Organizations to Drive Population Health Improvement

Abstract

Community Health Needs Assessments (CHNAs), mandated by the Affordable Care Act for tax-exempt hospitals, represent an underutilized yet rich data source for disease-specific advocacy. This commentary proposes a novel framework in which disease advocacy organizations—such as Alzheimer’s Los Angeles, the American Heart Association, and the National Alliance on Mental Illness—deploy artificial intelligence (AI) agents to systematically analyze CHNAs, identify gaps in condition-specific care, generate personalized outreach to hospital leadership, and publicly score health systems on their responsiveness to identified needs. Using Alzheimer’s disease and dementia care in Los Angeles County as a primary case example, this article describes how AI-driven automation of data collection, natural language processing of CHNA documents, and coordinated advocacy campaigns can transform the current passive CHNA cycle into an active mechanism for population health improvement. The framework combines reputational accountability through public scorecards with constructive, evidence-based recommendations, creating a “carrot-and-stick” dynamic that existing literature on public performance reporting suggests can achieve engagement rates of 40%–70% and meaningful institutional change in 30%–60% of targeted systems. This approach is adaptable across chronic conditions and disease advocacy organizations, wherever publicly reported community needs data intersect with organized patient advocacy. Implications for population health management, health system quality improvement, and the responsible integration of AI in public health advocacy are discussed.