The Atlanta Fed’s CLIFF tools provide greater transparency to workers about potential public assistance losses when their earnings increase. We find three broad themes in organization-level implementation of the CLIFF tools: identifying the tar- get population of users; integrating the tool into existing operations; and integrating the tool into coaching sessions.
This report puts forth an anti-racist reimagining of Medicaid and CHIP that actively reckons with the racist history of the Medicaid program and offers principles and recommendations that capitalize on the transformative potential of the programs. The principles center the voices and agency of program participants and prioritize direct community involvement at all stages of the policy process.
This report explores how state and local agencies can enhance customer service in health and human services by implementing technologies such as web-based tools, mobile applications, and call center innovations, aiming to streamline processes and improve client interactions.
This page includes data and observations about authentication and identity proofing steps specifically for online applications that include MAGI Medicaid.
The Performance.gov team hosted a webinar featuring federal customer experience (CX) changemakers from across government and focused on the nine CX projects that aim to address pain points learned about through human-centered design research.
This resource is a research paper examining the role of the public safety net in insuring job losers against income loss, analyzing which government programs provide financial support and how benefits vary based on pre-job loss income levels.
The team explored the performance of various AI chatbots and LLMs in supporting the adoption of Rules as Code for SNAP and Medicaid policies using policy data from Georgia and Oklahoma.
The Technology Transformation Service at GSA recently created a new Public Benefits Studio to focus on fostering a more cohesive, coordinated experience for the public, across programs.
The team developed an application to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
