The question for community health and healthcare systems isn’t whether to use AI—it’s where to begin. Per a 2025 assessment from the National Association of Community Health Centers, 64% of health centers are not yet using generative AI tools (but plan to), and 77% do not use machine learning or predictive models, (though many are preparing to implement them). Interest is high, but the path forward is uncertain.
A disciplined use case approach is the answer. It starts with identifying where time, effort, or data are being lost; refining those ideas to ensure they’re realistic, safe, and aligned with mission goals; and prioritizing the ones with the highest value and lowest risk. By following these steps, healthcare organizations can turn AI from an abstract idea into a practical roadmap for action.
Abt’s AI Use Case Scoring Matrix offers a way to make those choices measurable—scoring each potential AI investment by value, feasibility, risk, and scalability. It’s a simple way to ensure grantees start with the right projects and build evidence for sustainable implementation.
Abt’s 60-year history delivering results for CMS, HRSA, and other federal partners gives us a unique vantage point: we don’t just build tools -- we help agencies and their recipients apply AI responsibly, with measurable outcomes that advance mission and accountability.
See how structured use case development can strengthen AI readiness and program performance.
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Health in the United States
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