This report outlines how the New Jersey Department of Human Services’ Division of Family Development (DFD) and the Department of Health (NJDOH) are increasing SNAP & WIC co-enrollment through data sharing, outreach, and systems integration.
American Public Human Services Association (APHSA)
The article discusses key takeaways from BenCon 2023, highlighting the importance of creating equitable and ethical public benefits technology. It emphasizes the need for tech solutions that address systemic inequalities, ensure accessibility, and promote inclusivity for underserved communities in accessing public services.
This toolkit is designed to assist state and local TANF agencies in accessing, linking, and analyzing employment data from unemployment insurance (UI) systems.
This resource provides examples and practical guides that explain how to use existing regulations and data sharing agreements to transfer client information or eligibility status between benefit programs.
“Interoperability” refers to systems’ ability to interact with each other to share data so that a customer is connected with as many benefits as possible in an efficient way. The Affordable Care Act (ACA) was originally intended to be interoperable, but this has not occurred yet. Promoting interoperability in the ACA is imperative, as it would help alleviate food insecurity through automatic benefits enrollment.
This data playbook created by the California Health and Human services agencies discusses five plays designed to help Departments utilize data to inform program and policy development.
California Health and Human Services Agency (Cal HHS)
Accessing safety net benefits can involve complicated and duplicative processes that create barriers to access. Using cross-enrollment strategies can minimize the difficulties community members face in getting access to life-saving resources.
The document outlines revisions to OMB's SPD No. 15, which updates the standards for collecting and presenting federal race and ethnicity data, including the integration of race and ethnicity questions, the addition of a MENA category, and requirements for more detailed data collection.
This report reviews global AI governance tools, highlighting their importance in ensuring trustworthy AI, while identifying gaps and risks in their effectiveness, and offering recommendations to improve their development, oversight, and integration into policy frameworks.
