There were over 25 million Medicaid disenrollments in 2023, but national enrollment remained significantly above pre-pandemic levels at over 56 million, with notable state-level variations and near-recovery of child enrollment.
This post explores the lessons learned and opportunities for improvement from USDR's research on families' experiences as they navigate the child care journey.
This assessment aims to help states gain a comprehensive understanding of their successes and shortcomings in their data strategies and enhance their strategic and tactical plans.
There are frameworks available that could inform the standardization of communicating rules as code for U.S. public benefits programs. The Airtable communicates the differences between the frameworks and tools. Each entry is tagged with different categories that identify the type of framework or tool it is.
This GitHub repository includes resources that users of the UI wage data toolkit may find helpful. It covers a variety of topics, including equity, data security, programming, and data QC tips. It also serves as a place for our team to continue to post information that the TANF Data Collaborative (TDC) pilot sites found useful during our partnerships with them.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
As a part of Benefit Data Trust (BDT)’s Medicaid Churn Learning Collaborative, BDT has created a memo describing policy options and state examples for Medicaid administrators to reduce churn for non-MAGI Medicaid enrollees when the federal public health emergency ends.
This summary outlines statewide progress in improving digital services, reducing administrative burdens, and elevating customer satisfaction through human-centered and data-informed government design.
This executive order establishes governance, values, and oversight structures for the ethical and responsible use of generative AI technologies within the Commonwealth of Pennsylvania.
