This report highlights lessons learned from improving economic stability and well-being outcomes for young parent families, focusing on interagency collaboration, community engagement, data-driven improvement, and aligned services to guide future efforts.
Building modular, open-source, human-centered software is necessary to create equitable government services fit for the digital age. Nava emphasizes addressing large scale digital service challenges by building and releasing small, modular software components that are loosely-coupled by well-defined APIs. This enables agencies to quickly and conistently deliver services that help people immediately, whilst also building a flexible foundation for long-term technical evolution.
This study evaluates the use of RPA technology by three states to automate SNAP administration, focusing on repetitive tasks previously performed manually.
The team explored the performance of various AI chatbots and LLMs in supporting the adoption of Rules as Code for SNAP and Medicaid policies using policy data from Georgia and Oklahoma.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This report examines Georgia’s Medicaid demonstration testing work requirements—the only such active program in the nation—and provides detailed findings on administrative costs, implementation challenges, and federal oversight weaknesses.
