This report explores how public benefit systems can better support young adults by addressing the barriers they face in accessing and maintaining vital services like SNAP, Medicaid, and WIC.
The New Mexico Human Services Department and Department of Health, as part of the Coordinating SNAP & Nutrition Supports program, leveraged data sharing to align SNAP, Medicaid, TANF, and WIC.
American Public Human Services Association (APHSA)
As a part of Benefit Data Trust (BDT)’s Medicaid Churn Learning Collaborative, BDT has created a memo describing strategies for states to collect current mailing addresses of Medicaid beneficiaries in advance of the Medicaid continuous coverage requirement — in effect under the federal public health emergency — unwinding.
Technology that automates different processes can save time for caseworkers and constituents, but it can also significantly reduce the transparency of government operations. This paper describes how Pennsylvania advocates addressed the low rate of automated Medicaid renewals.
This toolkit provides links to a multitude of resources to help health and human services leaders evaluate and intentionally design programs to meet the unique needs of young families.
American Public Human Services Association (APHSA)
On December 5, 2022, an expert panel, including representatives from the White House, unpacked what’s included in the AI Bill of Rights, and explored how to operationalize such guidance among consumers, developers, and other users designing and implementing automated decisions.
This award documentation from the National Association of State Chief Information Officers (NASCIO) explains how agencies in Ohio used automation to support administration of public benefits programs.
National Association of State Chief Information Officers (NASCIO)
A study shows that Benefits Data Trust’s outreach and application assistance significantly increased SNAP enrollment among North Carolina seniors, improving health outcomes and reducing Medicaid costs.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
In response to the COVID-19 crisis, the federal government authorized a new emergency program, Pandemic EBT (P-EBT), to replace school meals with money for groceries while schools are closed. Code for America describes its efforts to launch an accessible, online P-EBT application under an accelerated timeline due to to immense demand caused by the pandemic.
Approximately 12 million low-income individuals risk missing out on federal stimulus payments due to non-filing status, prompting the Center on Budget and Policy Priorities (CBPP) to recommend targeted state outreach to connect eligible non-filers with their Economic Impact Payments (EIPs).
