This report explores innovative solutions and insights from CMS Innovation Center's Hackathon series to address the unique healthcare challenges faced by rural, Tribal, and geographically isolated communities.
This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.
This study describes the potential of human-centered design principles to identify burdens, reducing the effects of what we label as administrative checkpoints.
Recapping the work and achievements of the Digital Benefits Network (DBN), Digital Service Network (DSN), and the State Chief Data Officers Network (CDO) in 2025.
This field guide provides research-based design principles for creating clear, usable forms that help voters accurately complete election-related paperwork and successfully take action.
This report focuses on the impact of adopting a national PFML program modeled on existing state programs, known as the Family and Medical Insurance Leave (FAMILY) Act.
Building on our February 2022 report Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net, the Beeck Center’s Digital Benefits Network (DBN) recently held a convening to share progress and potential in digitizing benefits eligibility and to begin addressing how a national approach could be started.
In Austin, there are over 2,000 individuals without a safe place to sleep. There are many reasons a person can become homeless, and these reasons range from the lack of affordable housing to the loss of family and community. In 2017, the Innovation Office secured a three-year $1.25m grant from Bloomberg Philanthropies to focus on the city's goal of ending homelessness. The grant funds an i-team to help the city identify the best ways for City Council, departments, and the community to collaborate towards a shared understanding of homelessness in Austin.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
These recommendations outline privacy-focused guidelines for states adopting digital IDs, emphasizing protections against surveillance, ensuring equitable access, and maintaining control over personal data.
