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.

User-tested application templates and resources for Medicaid work requirements

Evidence-based recommendations for states to optimize implementation of work requirements include: integrating compliance and exemption information and reporting into a single streamlined process, designing clear and actionable notices, utilizing text communications, and building mobile-friendly platforms.

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