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The Collaborative Chronic Care Model for Mental Health Conditions: From Evidence Synthesis to Policy Impact to Scale-up and Spread. | LitMetric

Background: Extensive evidence indicates that Collaborative Chronic Care Models (CCMs) improve outcome in chronic medical conditions and depression treated in primary care. Beginning with an evidence synthesis which indicated that CCMs are also effective for multiple mental health conditions, we describe a multistage process that translated this knowledge into evidence-based health system change in the US Department of Veterans Affairs (VA).

Evidence Synthesis: In 2010, recognizing that there had been numerous CCM trials for a wide variety of mental health conditions, we conducted an evidence synthesis compiling randomized controlled trials of CCMs for any mental health condition. The systematic review demonstrated CCM effectiveness across mental health conditions and treatment venues. Cumulative meta-analysis and meta-regression further informed our approach to subsequent CCM implementation.

Policy Impact: In 2015, based on the evidence synthesis, VA Office of Mental Health and Suicide Prevention (OMHSP) adopted the CCM as the model for their outpatient mental health teams.

Randomized Implementation Trial: In 2015-2018 we partnered with OMHSP to conduct a 9-site stepped wedge implementation trial, guided by insights from the evidence synthesis.

Scale-up And Spread: In 2017 OMHSP launched an effort to scale-up and spread the CCM to additional VA medical centers. Seventeen facilitators were trained and 28 facilities engaged in facilitation.

Discussion: Evidence synthesis provided leverage for evidence-based policy change. This formed the foundation for a health care leadership/researcher partnership, which conducted an implementation trial and subsequent scale-up and spread effort to enhance adoption of the CCM, as informed by the evidence synthesis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749976PMC
http://dx.doi.org/10.1097/MLR.0000000000001145DOI Listing

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