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Catalyzing Medicaid policy research with T-MSIS Analytic Files (TAF): learnings from year 1 of the Medicaid Data Learning Network (MDLN)
Catalyzing Medicaid policy research with T-MSIS Analytic Files (TAF): learnings from year 1 of the Medicaid Data Learning Network (MDLN)
- Collection:
- Health Policy and Services Research
- Author(s):
- Gordon, Sarah, (Of Boston University), author
Johnson, Annaliese, author
Kennedy, Susan, (Of AcademyHealth), author
McConnel, John, (Of Oregon Health & Science University), author
Schpero, William, author - Contributor(s):
- AcademyHealth, issuing body.
- Publication:
- Washington, DC : AcademyHealth, May 15, 2023
- Language(s):
- English
- Format:
- Text
- Subject(s):
- Datasets as Topic
Health Policy
Health Services Research
Medicaid -- statistics & numerical data
Ethnicity
Fee-for-Service Plans
Health Expenditures
Information Storage and Retrieval
Managed Care Programs
Maternal Health
Opioid-Related Disorders -- drug therapy
Racial Groups
Reproductive Health
Utilization Review
United States - Genre(s):
- Technical Report
- Abstract:
- The Medicaid program remains relatively understudied despite pro viding health care coverage to over 92 million people in the United States as of April 2023. This is due, in large part, to its federalist structure and the lack of a cohesive, national administrative claims data infrastructure. The Centers for Medicare and Medicaid Ser vices (CMS) has made significant efforts to enhance the Medicaid Statistical Information System (MSIS), which compiles data from state Medicaid agencies to inform overall program improvements. In 2019, CMS released the latest generation of federal Medicaid claims data, the T-MSIS (Transformed-MSIS) Analytic Files (TAF), to replace the Medicaid Analytic eXtract (MAX). Though the TAF data represent a significant improvement in quality and usability over MAX, they remain highly complex, with varying data quality, eligibility categories, and data elements across states. As researchers begin to work with these data, there is an important opportunity to share learnings and approaches to avoid duplicative efforts and to distill key methodological standards. This effort will help ensure Medicaid research using the TAF is high quality, relevant, and impactful. With support from the Commonwealth Fund and the Robert Wood Johnson Foundation, AcademyHealth’s Evidence-In formed State Health Policy Institute (ESHPI) established the Medicaid Data Learning Network (MDLN) to provide the opportunity to foster peer-shared learning among TAF users. Through a learning series curriculum, the MDLN provides a forum for TAF researchers to share what they have learned using the dataset and to develop consensus on best practices. These insights can then be disseminated to CMS, state Medicaid agencies, and the broader health services research community. The MDLN’s ultimate goal is to improve the quality of the TAF data over time, expand opportunities for health services researchers to use Medicaid claims data, and increase the number of researchers engaged in Medicaid-focused research.
- Copyright:
- Reproduced with permission of the copyright holder. Further use of the material is subject to CC BY license. (More information)
- Extent:
- 1 online resource (1 PDF file (8 pages))
- NLM Unique ID:
- 9918663774606676 (See catalog record)
- Permanent Link:
- http://resource.nlm.nih.gov/9918663774606676