ArticleEGEMS (Washington, DC)2019
Innovative Solutions for State Medicaid Programs to Leverage Their Data, Build Their Analytic Capacity, and Create Evidence-Based Policy.
Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Improving information sharing in Medicaid home and community-based services with learning health systems: Implications for older adults and individuals living with disabilities and dementia.Learning health systems · 2025Article
- STI Testing among Medicaid Enrollees Initiating PrEP for HIV Prevention in Six Southern States.Southern medical journal · 2023Article
- Policies and Practices to Address Cancer's Long-Term Adverse Consequences.Journal of the National Cancer Institute · 2022Article
- Postpartum Follow-up Care for Pregnant Persons With Opioid Use Disorder and Hepatitis C Virus Infection.Obstetrics and gynecology · 2022Article
- Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Operationalizing and selecting outcome measures for the HEALing Communities Study.Drug and alcohol dependence · 2020Article
- Understanding the Impact of Insurance Coverage Across the Cancer Care Continuum: Moving Beyond Fragmented Systems and Cross-Sectional Data to Inform Policy.Journal of the National Cancer Institute · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As states have embraced additional flexibility to change coverage of and payment for Medicaid services, they have also faced heightened expectations for delivering high-value care. Efforts to meet these new expectations have increased the need for rigorous, evidence-based policy, but states may face challenges finding the resources, capacity, and expertise to meet this need. By describing state-university partnerships in more than 20 states, this commentary describes innovative solutions for states that want to leverage their own data, build their analytic capacity, and create evidence-based policy. From an integrated web-based system to improve long-term care to evaluating the impact of permanent supportive housing placements on Medicaid utilization and spending, these state partnerships provide significant support to their state Medicaid programs. In 2017, these partnerships came together to create a distributed research network that supports multi-state analyses. The Medicaid Outcomes Distributed Research Network (MODRN) uses a common data model to examine Medicaid data across states, thereby increasing the analytic rigor of policy evaluations in Medicaid, and contributing to the development of a fully functioning Medicaid innovation laboratory.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.