Minnesota is a good example of an organization that started small in its drive to integrate benefits programs. For instance, its recent statewide rollout of its online integrated benefit application website, MNbenefits.mn.gov, started as a pilot in 2020 with Code for America. The pilot encompassed two counties including Hennepin County, where Minneapolis is located. The pilot later expanded to four counties, then 16 and a tribal nation. The final roll out, which took 12 months to implement, included the state’s 87 counties and three tribal nations.
Digitizing public benefits policy will make the biggest impact for administrators and Americans, but only if it happens at the highest level of government.
This report outlines the guiding principles, policy priorities, and tools for the National Collaborative for Integration of Health and Human Services, aimed at improving health and well-being outcomes through the integration of health care and human services programs.
American Public Human Services Association (APHSA)
The examples in this guide describe how peer-to-peer training and updated interview scripts can help connect residents to the benefits they are eligible for.
A case study on how North Carolina leveraged human-centered design, interagency collaboration, and data-sharing strategies to improve cross-enrollment in SNAP, WIC, and Medicaid, aiming to reduce administrative burden and better serve families.
American Public Human Services Association (APHSA)
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 report by EPIC investigates how automated decision-making (ADM) systems are used across Washington, D.C.’s public services and the resulting impacts on equity, privacy, and access to benefits.
This case study details the development of a document extraction prototype to streamline benefits application processing through automated data capture and classification.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
