This impact report showcases the office's initiatives and achievements in enhancing state government services through innovative, user-centered approaches.
This report outlines state Medicaid program priorities, including expanding access to services, addressing health disparities, and implementing cost-containment measures amid post-pandemic uncertainties.
This article advises government agencies to prioritize cybersecurity methods over AI-driven approaches when combating identity fraud in benefits programs, highlighting potential risks that automated systems pose to legitimate applicants.
The Digital Identity Community of Practice kick-off event featured key resources, a new research publication on account creation and identity proofing, and insights from multiple speakers.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
An event recap from one of FormFest 2024's breakout sessions featuring speakers from the state of Maryland's Department of Social Services and the U.S. Department of Health and Human Services (HHS).
The article examines the participation of adults aged 50 and older in the Supplemental Nutrition Assistance Program (SNAP) in 2022, highlighting the program's role in reducing food insecurity and poverty among older adults, especially those with disabilities.
The Digital Benefit Network's Digital Identity Community of Practice held a session to hear considerations from civil rights technologists and human-centered design practitioners on ways to ensure program security while simultaneously promoting equity, enabling accessibility, and minimizing bias.
