Resources and Toolkits
These 5 Million People Are About to Lose Their Medicaid — But They Don’t Have To
Tools for data-driven understanding Medicaid work requirements, including information about who’s at risk, automatic verification pathways, ex parte capability by state, coverage loss, and maps
SNAP PER Analytics Workgroup
Workshop examining how predictive analytics and risk scoring can improve SNAP implementation and evaluation, using Conneticut as a case study.
Common Missteps on the Road to Lower SNAP Payment Error Rates
Guidance on state policy chocies that tend to increase SNAP payment error rates include requiring change reporting or monthly reporting, imposing asset tests, and limiting discretionary exemptions. In contrast, states can reduce errors by simplifying reporting requirements, using BBCE, applying exemptions, and using one-month lookbacks to minimize reassessments and ensure coverage for eligible populations.
Predictive Analytics for SNAP PER Reduction QC Data Workshop Summary
Workshop to foster collaboration and share modeling practices among state agencies in response to HR1’s new SNAP PER cost share requirements. Gathering encompassed convened a group of nearly 40 research and data analytics staff from across 15 states involved in SNAP QC data modeling.
Metrics That Matter For States Under H.R. 1
Guide on how state agencies can develop legible and flexible metrics to assess implementation of new work requirements, outlining how to build, capture, and effectively utilize metrics to monitor impact for operational awareness and decisionmaking.