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Design, Implementation, and Evolution of the Medicaid Outcomes Distributed Research Network (MODRN).
Zivin, Kara; Allen, Lindsay; Barnes, Andrew J; Junker, Stefanie; Kim, Joo Yeon; Tang, Lu; Kennedy, Susan; Ahrens, Katherine A; Burns, Marguerite; Clark, Sarah; Cole, Evan; Crane, Dushka; Idala, David; Lanier, Paul; Mohamoud, Shamis; Jarlenski, Marian; McDuffie, Mary Joan; Talbert, Jeffery; Gordon, Adam J; Donohue, Julie M.
  • Zivin K; Department of Veterans Affairs, University of Michigan Ann Arbor, MI.
  • Allen L; Northwestern University, Evanston, IL.
  • Barnes AJ; Virginia Commonwealth University, Richmond, VA.
  • Junker S; University of Pittsburgh, Pittsburgh, PA.
  • Kim JY; University of Pittsburgh, Pittsburgh, PA.
  • Tang L; University of Pittsburgh, Pittsburgh, PA.
  • Kennedy S; AcademyHealth, Washington, DC.
  • Ahrens KA; University of Southern Maine, Portland, ME.
  • Burns M; University of Wisconsin, Madison, WI.
  • Clark S; University of Michigan, Ann Arbor, MI.
  • Cole E; University of Pittsburgh, Pittsburgh, PA.
  • Crane D; Ohio State University, Columbus, OH.
  • Idala D; The Hilltop Institute, University of Maryland Baltimore County, Baltimore, MD.
  • Lanier P; University of North Carolina at Chapel Hill, Pittsboro NC.
  • Mohamoud S; The Hilltop Institute, University of Maryland Baltimore County, Baltimore, MD.
  • Jarlenski M; University of Pittsburgh, Pittsburgh, PA.
  • McDuffie MJ; University of Delaware, Newark, DE.
  • Talbert J; University of Kentucky, Lexington, KY.
  • Gordon AJ; Department of Veterans Affairs, University of Utah Salt Lake City, UT.
  • Donohue JM; University of Pittsburgh, Pittsburgh, PA.
Med Care ; 60(9): 680-690, 2022 09 01.
Article in English | MEDLINE | ID: covidwho-2008676
ABSTRACT

BACKGROUND:

In the US, Medicaid covers over 80 million Americans. Comparing access, quality, and costs across Medicaid programs can provide policymakers with much-needed information. As each Medicaid agency collects its member data, multiple barriers prevent sharing Medicaid data between states. To address this gap, the Medicaid Outcomes Distributed Research Network (MODRN) developed a research network of states to conduct rapid multi-state analyses without sharing individual-level data across states.

OBJECTIVE:

To describe goals, design, implementation, and evolution of MODRN to inform other research networks.

METHODS:

MODRN implemented a distributed research network using a common data model, with each state analyzing its own data; developed standardized measure specifications and statistical software code to conduct analyses; and disseminated findings to state and federal Medicaid policymakers. Based on feedback on Medicaid agency priorities, MODRN first sought to inform Medicaid policy to improve opioid use disorder treatment, particularly medication treatment.

RESULTS:

Since its 2017 inception, MODRN created 21 opioid use disorder quality measures in 13 states. MODRN modified its common data model over time to include additional elements. Initial barriers included harmonizing utilization data from Medicaid billing codes across states and adapting statistical methods to combine state-level results. The network demonstrated its utility and addressed barriers to conducting multi-state analyses of Medicaid administrative data.

CONCLUSIONS:

MODRN created a new, scalable, successful model for conducting policy research while complying with federal and state regulations to protect beneficiary health information. Platforms like MODRN may prove useful for emerging health challenges to facilitate evidence-based policymaking in Medicaid programs.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Medicaid / Opioid-Related Disorders Limits: Humans Country/Region as subject: North America Language: English Journal: Med Care Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Medicaid / Opioid-Related Disorders Limits: Humans Country/Region as subject: North America Language: English Journal: Med Care Year: 2022 Document Type: Article