Community health organizations have long served as the primary point of contact between underrepresented populations and the healthcare system. Yet the data generated through that contact — the conversations, the barriers encountered, the outcomes observed — rarely makes it into the research literature that shapes clinical guidelines, algorithmic training sets, or health policy. This brief describes DaisyMe Foundation's e-DICT™ model (Engagement → Data → Insight → Change → Trust), a structured methodology for converting direct community health engagement into research-grade evidence. Drawing on 12,400+ direct program interactions since 2022, we describe the model's five phases, the data infrastructure required to implement it, and the implications for health systems, clinical AI developers, and policymakers seeking community-sourced evidence on health equity gaps.

Clinical AI systems are increasingly used to support diagnosis, triage, and treatment recommendations across U.S. health systems. These systems are trained primarily on electronic health record data — data that systematically underrepresents the populations most affected by health inequity. This brief presents evidence from DaisyMe Foundation's community health programs on the gap between clinical AI performance as measured in controlled validation studies and real-world outcomes as experienced by underrepresented community members. We document four categories of algorithmic failure observed through community navigation data, describe the structural conditions that produce them, and recommend specific actions for health systems, clinical AI developers, and regulators.

Health inequity is routinely framed as a moral and social justice issue. It is also an economic one. The United States spends an estimated $320 billion annually on excess medical costs attributable to health disparities — costs borne by health systems, employers, insurers, and public budgets. This brief presents the economic case for health equity investment, drawing on national research and DaisyMe Foundation's program data to quantify the financial burden of the status quo and the return on investment from community-based health equity programs. We argue that health equity investment is not charity — it is cost reduction, risk mitigation, and workforce optimization. And we present DaisyMe Foundation's community health programs as a documented, measurable model for achieving that return.