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.
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.
Authors: Andrew J McMurry; Clint A Gilbert; Ben Y Reis; Henry C Chueh; Isaac S Kohane; Kenneth D Mandl Journal: J Am Med Inform Assoc Date: 2007-04-25 Impact factor: 4.497
Authors: Sengwee Toh; Laura J Rasmussen-Torvik; Emily E Harmata; Roy Pardee; Rosalinde Saizan; Elisha Malanga; Jessica L Sturtevant; Casie E Horgan; Jane Anau; Cheri D Janning; Robert D Wellman; R Yates Coley; Andrea J Cook; Anita P Courcoulas; Karen J Coleman; Neely A Williams; Kathleen M McTigue; David Arterburn; James McClay Journal: JMIR Res Protoc Date: 2017-12-05
Authors: Julie M Donohue; Marian P Jarlenski; Joo Yeon Kim; Lu Tang; Katherine Ahrens; Lindsay Allen; Anna Austin; Andrew J Barnes; Marguerite Burns; Chung-Chou H Chang; Sarah Clark; Evan Cole; Dushka Crane; Peter Cunningham; David Idala; Stefanie Junker; Paul Lanier; Rachel Mauk; Mary Joan McDuffie; Shamis Mohamoud; Nathan Pauly; Logan Sheets; Jeffery Talbert; Kara Zivin; Adam J Gordon; Susan Kennedy Journal: JAMA Date: 2021-07-13 Impact factor: 56.272