About the Medicaid Data Learning Network
What We Do
The Medicaid Data Learning Network (MDLN) supports researchers who use federal Medicaid claims data (the T-MSIS Analytic Files or TAF). We help members learn from one another, share practical tools, and improve the quality, transparency, and reproducibility of Medicaid data research.
MDLN works to:
Provide a forum for TAF researchers to share learnings about the dataset;
Develop consensus on best practices for key TAF methods and share those methods with the broader health services research community;
Expand opportunities for health services researchers to use Medicaid claims data and increase the number of researchers engaged in Medicaid-focused work; and
Share learnings with CMS, as well as state Medicaid agencies, on steps to improve the quality of TAF data over time.
Our History
The MDLN was co-founded in 2022 by Sarah Gordon, John McConnell, and William Schpero as a collaborative home for researchers working with federal Medicaid administrative data. The network was built around a simple premise: given the complexity of Medicaid data, researchers make faster progress — and produce better, more useful work — when they share knowledge, tools, and lessons learned, rather than working in isolation or competition.
MDLN initially partnered with AcademyHealth, with support from the Commonwealth Fund and the Robert Wood Johnson Foundation, to host the network and expand opportunities for peer learning, technical exchange, and collaboration across institutions.
In 2026, MDLN moved to the Medicaid Policy Impact Initiative at Cornell University, part of the Cornell Health Policy Center.