This paper outlines common challenges agencies face while administering benefits and gives examples of how technology can streamline the process. It also discusses the importance of user-centered design, and the necessity of utilizing technology as part of a holistic strategy to implement public benefits.
This article describes the necessity of building an inclusive research environment that empowers participants, as well as techniques for creating such environments in both in-person and remote capacities.
Building modular, open-source, human-centered software is necessary to create equitable government services fit for the digital age. Nava emphasizes addressing large scale digital service challenges by building and releasing small, modular software components that are loosely-coupled by well-defined APIs. This enables agencies to quickly and conistently deliver services that help people immediately, whilst also building a flexible foundation for long-term technical evolution.
Digital interfaces are the bridges to our communities. Technology can’t fix the systemic inequality revealed by COVID19, but it can help make connections with our communities better. This tool kit and website brings together emergent best practices, workflows, and tools that communities, educators, mutual aid groups, designers, artists and activists are using for community building, and how design needs to change to best suit people, right now.
Professor Don Moynihan discusses how administrative burden is an effective tool to make it difficult for people to access certain types of benefits, noting that this is particularly harmful to communities of color.
Less than 10% of applications for VA health care are submitted digitally. This article describes how the U.S. Digital Service partnered with the Health Eligibility Center to change that, making it possible for anyone with an internet connection to apply anytime, anywhere, from any device.
This guide explains the U.S. Digital Service’s “discovery sprint,” a process by which teams can quickly build a common understanding of the status of complex organization, system, or service.
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.
In a time of crisis, behavioral science offers insights into how to reduce the paperwork and other administrative burdens that prevent people from taking advantage of crucial support services.
Innovators inside and outside of government are working to improve access to the social safety net using data, technology, and design. This report highlights innovations carried out by The Rockefeller Foundation’s Data and Technology grantees from 2018 to 2021, including extraordinary efforts to meet the challenges of the pandemic. Those grantees are: Benefits Data Trust, Code for America, Georgetown University’s Beeck Center for Social Impact and Innovation, U.S. Digital Response, and the Digital Innovation and Governance Initiative at New America. In 2020, these projects secured more than $200 million in benefits for close to 100,000 people across at least 36 states, and helped millions more through policy change, training, and guidance.
Policymakers, lawmakers, and government leaders are increasingly exploring new ways to ensure that laws and policies are centered around people’s needs while improving how services are delivered to the public. To help policymakers interested in following these successful models, the Beeck Center for Social Impact + Innovation launched the User-Centered Policy Organization Assessment. Teams crafting policy inside and outside government can use the assessment to center their policy-making activities around those most impacted by their proposed programs and policy ideas.
County workers typically spend most of their time trying to get income information right during eligibility interviews. This article provides several recommendations for asking about income, accounting for cognitive biases, under-reporting, and complexities in reporting income.
