Evidence map›Paper›PMID 41204646›Full record

ArticleSubstance use & misuse2026

Blended Payment Models and Care Transitions: Analysis of a Statewide Payment Reform's Impact on Transitions in Care and Care Termination From Outpatient Substance Use Treatment.

Daniel Baslock, Yuanyuan Hu, Patrick Del Giudice-Walsh

Abstract read
In one paragraph

Article in Substance use & misuse, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daniel BaslockSchool of Social Work, Virginia Commonwealth University, Richmond, Virginia, USA.ORCID 0000-0003-0092-7300
Yuanyuan HuSchool of Social Work, University of Minnesota, Minneapolis, Minnesota, USA.
Patrick Del Giudice-WalshInstitute for Family Health, New York, New York, USA.

Funding

BEHAVIORAL SCI TRAINING IN DRUG ABUSE RESEARCHT32DA007233 · NIDA · PUBLIC HEALTH SOLUTIONS · PI GOLDSAMT, LLOYD A · 1985 to 2023
$20.2M
NIDA NIH HHS T32 DA007233
6 · The paper itself

Abstract

backgroundBlended payment models have been hypothesized as one approach to incentivizing service delivery that reduces premature termination of healthcare. In 2019, Vermont implemented a blended fee-for-service and capitated Medicaid payment structure for mental health and substance use treatment provided by community mental health centers. This study assesses the blended payment model's impact on the odds of premature termination and transitions in care for Medicaid recipients in Vermont's substance use disorder treatment system.

methodsThis study employs a difference-in-differences design with a generalized two-way fixed effects model using Vermont-specific Treatment Episode Data Set-Discharges data for 2017 through 2019. Treatment episodes of adult Medicaid utilizers were compared to episodes of adult non-Medicaid utilizers pre-and-post implementation of the blended payment model.

resultsThe odds ratio for termination from intensive outpatient and outpatient programs for Medicaid utilizers post-intervention was OR = 0.730 ( CONCLUSIONS AND IMPLICATIONS: These results provide evidence that incentivizing co-occurring care through blended payment models may increase the availability of co-occurring mental health and substance use disorder treatment within community mental health centers. This increase in availability may also enable transfers of care from the rest of the substance use disorder treatment system while reducing premature termination from care.

Indexed as

Fee-for-Service PlansMedicaidSubstance-Related DisordersAdultAmbulatory CareFemaleHumansMaleMiddle AgedUnited StatesVermontBehavioral healthblended paymentscommunity mental healthhealthcare financingsubstance use treatment

Identifiers

PMID41204646
PMCPMC12706725

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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.