Resources and Toolkits

Video outlining best practices for states to improve their risk assessment and error detection processes, including model design, review sequencing, feature engineering, review process optimization, and quick wins

Workshop examining how predictive analytics and risk scoring can improve SNAP implementation and evaluation, using Conneticut as a case study.

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.

A CMS-developed suite of open-source tools that streamlines income and community engagement reporting for Medicaid applicants and enrollees

By Sarah Esty (Aspen Institute Financial Security Program) and Eric Giannella (Georgetown University Better Government Lab) To assist states working to rapidly reduce SNAP payment error rates to avoid new cost-share requirements, the Safety Net Response Network has been convening monthly meetings of state data practitioners for peer learning around successful error reduction strategies. Through…

Guide explaining automatic benefit notice generation process while walking through common underlying system errors (e.g. missing or incorrect information) providing insight on how back-end design decisions cause these issues. Provides strategies to improve notice process and output such as targeted questions on system logic, workarounds for caseworkers, and structural reform through data flow, boilerplate language,…

Guide to provide states with policy and technical strategies to reduce procedural terminations under Medicaid work requirements, including simplifying verification processes, mitigating automation risks, managing vendor

States can act to reduce their SNAP PERs but must move quickly for their efforts to impact their share of program costs in 2027.