This technical guide provides a framework for state agencies to minimize procedural terminations and health coverage losses resulting from new Medicaid work reporting requirements.
This article provides an overview of the Medicaid Payment Error Rate Measurement (PERM) program and examines how the 2025 budget reconciliation law introduces new federal funding reductions for states that exceed specific eligibility error thresholds.
This article examines the historic structural changes to the Supplemental Nutrition Assistance Program (SNAP) enacted through H.R. 1 and their potential consequences for public health and food security.
This issue brief provides a comprehensive framework for state officials to monitor and evaluate the far-reaching impacts of the H.R.1 budget reconciliation bill on the Medicaid program.
This technical brief uses predictive analytics to identify the primary drivers of SNAP payment error rates (PER) following the implementation of the One Big Beautiful Bill (OBBB).
This report analyzes the critical role of SNAP’s "broad-based categorical eligibility" (BBCE) policy and the widespread consequences of its potential elimination by the Trump Administration.
This blog post details the development of a human-centered screening tool designed to help SNAP clients identify and report exemptions from work requirements.
This discussion paper advocates for states to use the implementation of OBBBA (One Big Beautiful Bill Act) as a catalyst to build integrated, cross-agency data systems.
This paper evaluates the technical vendor landscape as states prepare to implement Medicaid work requirements mandated by the H.R. 1 reconciliation law.
This blog post serves as a guide for state agencies to develop flexible and actionable metrics systems for tracking the implementation and impact of new work requirements under H.R. 1.
This presentation focuses on data-driven and analytic strategies for identifying and verifying medical frailty exemptions within Medicaid work requirements.
This workshop summary synthesizes key takeaways from a convening of nearly 40 research and data analytics staff from 15 states focused on SNAP Quality Control (QC) data modeling.
