Building confidence in Medicaid and CHIP data quality

Make sense of inconsistent Medicaid and CHIP data

To manage Medicaid and the Children’s Health Insurance Program (CHIP), the Centers for Medicare & Medicaid Services (CMS) relies on state administrative data to track national rates of enrollment, service use, costs, and outcomes. But because these data are reported differently across states, they can be difficult to uniformly interpret and compare. Without a clear, efficient way to assess data quality, CMS staff, policymakers, and researchers risk drawing incomplete or misleading conclusions and spending significant time validating information before they can use it for analysis and decision making.

Increase the transparency of data quality

To help CMS staff, policymakers, and researchers navigate differences in how states report data, Mathematica developed the Data Quality (DQ) Atlas. Grounded in Mathematica’s deep expertise working with Medicaid and CHIP data, the DQ Atlas is an interactive, web-based platform for assessing and comparing data across states and over time. The DQ Atlas provides standardized assessments that help users quickly determine whether data are usable, reliable, and appropriate for specific policy and research needs.

Faster, more confident analysis through standardized data quality insights

The DQ Atlas is now a publicly available tool and key resource for evaluating the usability of data associated with nearly 80 million beneficiaries. It helps users assess whether data are fit for analysis and policy use while strengthening confidence in state-submitted data; improving transparency; and supporting rigorous, methodologically sound analysis. The DQ Atlas offers:

• Accelerated analytic readiness. Users can quickly evaluate data quality across states, topics, and years before beginning complex analyses, enabling them to plan analyses more efficiently and with a stronger focus on complete, reliable data.
• Improved transparency and usability. Standardized information on data quality enables researchers and policymakers to identify potential quality issues—such as inconsistencies in claims and enrollment—early in the analytic process, reducing uncertainty and avoiding time-consuming and costly rework.
• Broader access to insights. Data visualizations and downloadable analytic briefs and data quality assessments provide additional context on patterns and variability in data quality, helping users better understand strengths and limitations across states and over time.