An outline of the opportunities for modernizing SNAP to better meet participant needs by streamlining enrollment, improving digital access, and enhancing coordination with other safety net services.
CalAcademy is California’s public sector innovation training program, equipping state employees with modern skills like human-centered design, data analytics, and a product mindset to enhance government services.
A case study documenting how a modular API layer was built to support a state-level paid family and medical leave program, improving interoperability, scalability, and user experience.
This crosswalk compares provisions in H.R. 1 with existing human services policies, focusing on how proposed federal work requirements could affect programs like TANF, SNAP, and Medicaid.
American Public Human Services Association (APHSA)
Created for use in the Digital Doorways research project, this design stimuli shows the steps of submitting an application, sharing personal information, and verifying identity for Arizona's integrated online application that includes SNAP and Medicaid.
This file contains two, state-agnostic service blueprints that visualize how the new work requirements policy passed as part of H.R. 1 impacts the process of applying for, determining, and maintaining eligibility for SNAP and Medicaid benefits.
This guide outlines key strategies, definitions, and procedures for improving SNAP payment accuracy and reducing quality control (QC) error rates across states.
This overview introduces direct cash transfers (DCTs) in the United States, outlining their history, major programs, and findings from contemporary guaranteed income demonstrations that show how cash supports improve family stability, health, and economic mobility
A detailed guide outlining how states can minimize coverage losses and administrative burden while implementing new Medicaid work requirements established under the 2025 federal reconciliation law.
A report that defines what effective “human oversight” of AI looks like in public benefits delivery and offers practical guidance for ensuring accountability, equity, and trust in algorithmic systems.
