The Medicaid program now provides health care coverage for over 82 million people in the United States, including many low-income and vulnerable populations. Yet, the program is understudied relative to Medicare and lacks a cohesive, national administrative claims data infrastructure. The Centers for Medicare and Medicaid Services (CMS) has undertaken significant efforts to enhance the Medicaid Statistical Information System (MSIS), which collects claims data from state Medicaid agencies to guide improvements in the program. In 2019, CMS released the first T-MSIS (Transformed-MSIS) Analytic Files (TAF), the latest generation of federal Medicaid claims data that replaced the now retired Medicaid Analytic eXtract (MAX).
While the TAF data represent a significant improvement in quality and usability over MAX, they remain highly complex, with varying data quality issues across states, eligibility categories, and data elements. As research teams begin to leverage these data, there is an important opportunity to share learnings and approaches developed to avoid duplicating work. Furthermore, there is a value in collaborative efforts to develop key methodological standards to ensure Medicaid research using the TAF data is high quality, timely, and impactful. With support from the Commonwealth Fund and the Robert Wood Johnson Foundation, AcademyHealth’s Evidence-Informed State Health Policy Institute is leveraging its extensive network of Medicaid policymakers, researchers, and stakeholders to establish the Medicaid Data Learning Network (MDLN). The goals of the MDLN are as follows:
- To provide a forum for TAF researchers to share learnings about the dataset;
- To develop consensus on best practices for key TAF methods and share those methods with the broader health services research community;
- To expand opportunities for health services researchers to use Medicaid claims data and increase the number of researchers engaged in Medicaid-focused work; and
- To share learnings with CMS, as well as state Medicaid agencies, on steps to improve the quality of TAF data over time.
To learn more, please feel free to contact AcademyHealth through our contact page.