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The ADVANCE Clinical Research Network Past, Present, and Future: Accelerating Partnerships for Patient-Centered
Wyatt P Bensken1, Erika K Cottrell1,2, Anna R Templeton1
1OCHIN, Inc, Portland, OR.
Background:
The Accelerating Data Value Across a National Community Health Center Network (ADVANCE) collaborative is 1 of 8 clinical research networks participating in PCORnet®, the National Patient-Centered Clinical Research Network. Providing data, engagement, and regulatory infrastructure, ADVANCE facilitates the inclusion of people who receive primary care in community-based health centers (CHC) across over 90 patient-centered research studies. Centrally coordinated by OCHIN, in partnership with Fenway Health, Health Choice Network, Oregon Health & Science University, and the University of Washington, ADVANCE comprises the nation's most comprehensive network for health care and outcomes research in community-based primary care and hospital-based settings.
Objective:
This manuscript discusses ADVANCE's unique past, present, and future approaches to strengthen infrastructure and capacity to enhance research in community-based primary care settings.
Main Arguments:
As community-based primary care organizations, CHCs have relationships, trust, and expertise with the communities they serve. ADVANCE provides critical data and engagement resources for including CHCs and their patients in research to improve patient-centered care and outcomes. Despite past investment, there is a recognized need for additional engagement, investment, capacity building, and research infrastructure to realize the full potential of CHC partnerships in clinical research and PCORnet® studies. By partnering with CHCs and tertiary hospitals, ADVANCE and the PCORnet infrastructure can provide enhanced access to clinical research opportunities that further support patient-centered, high-quality care delivery, and positive health outcomes while meeting CHCs' priorities and goals, progressing their own research capacity and infrastructure, and contributing to scalable research readiness models.
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