As a part of Benefit Data Trust (BDT)’s Medicaid Churn Learning Collaborative, BDT has created a memo describing policy options and state examples for Medicaid administrators to reduce churn for non-MAGI Medicaid enrollees when the federal public health emergency ends.
The toolkit provides strategies for state and local WIC agencies to enhance enrollment by utilizing data from Medicaid and SNAP for cross-program data matching and targeted outreach.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
This playbook provides government-wide guidance for planning, procuring, and managing digital, data, and technology (DDaT) projects with a focus on innovation, agile delivery, cybersecurity, sustainability, and commercial best practices.
This toolkit outlines actionable changes for government practitioners looking to improve the accuracy and accessibility of the questions on their forms that collect information about a user’s gender.
This hub introduces the UK government's Algorithmic Transparency Recording Standard (ATRS), a structured framework for public sector bodies to disclose how they use algorithmic tools in decision-making.
An analysis showing that a proposed plan to shift some cost of SNAP benefits to states could push nearly 900,000 additional people into poverty during a recession.
This report examines how governments use AI systems to allocate public resources and provides recommendations to ensure these tools promote equity, transparency, and fairness.
This 11x17 service blueprint visualizes every step, system, and policy decision involved in implementing Medicaid work requirements under H.R. 1—from application to renewal—identifying pain points, questions, and opportunities for states to streamline and humanize the process
This framework provides a structured approach for ensuring responsible and transparent use of AI systems across government, emphasizing governance, data integrity, performance evaluation, and continuous monitoring.
