Data & Statistical Policy

In recent years several researchers and child welfare agencies have begun developing predictive risk models to support child welfare decision-making. Predictive analytics is a sophisticated form of risk modeling that uses historical data to understand relationships between myriad factors to estimate a probability score for the outcome of interest.

The Department of Health and Human Services (HHS) has several different policy groups such as the HHS Data Council, Data Governance Board, Evidence and Evaluation Policy Council, and the HHS AI Council that frequently use many terms related to data, but likely with inconsistent understanding of their definitions and how these terms should be used.

This posting includes a report prepared by the RAND Corporation, “State All Payer Claims Databases Understanding the Current Landscape and Challenges to Use,” which builds on a 2021 report “The History, Promise and Challenges of State All Payer Claims Databases.” The new report provides additional detail on the objectives of and use cases for APCDs, the current APCD landscape, and implementati

This report analyzes newly-released data from CMS that provides information on changes in ownership among hospitals and skilled nursing facilities (SNFs), based on information reported to CMS through the Provider Enrollment, Chain, and Ownership System (PECOS).

Program Integrity and Effectiveness through Data and Analysis (PIEDA) aims to enhance the capacity to share and link data between state child welfare and Medicaid agencies on issues at the nexus of the two systems. PIEDA intends to sustainably improve the data infrastructure of states to increase their ability to analyze challenges experienced by families involved in child welfare systems.

The Child and Caregiver Outcomes Using Linked Data project provides technical assistance to states to develop state-specific datasets linking the Medicaid administrative claims of parents with the records of their children from the child welfare system. The data will be combined into a multi-state, de-identified data sets for secondary data analysis.

Summary of panel discussion concerning the challenges of measuring the impact of disease management programs, especially for individuals with multiple chronic conditions.