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The Community Health Applied Research Network (CHARN) Data Warehouse: a Resource for Patient-Centered Outcomes Research and Quality Improvement in Underserved, Safety Net Populations
BACKGROUND: The Community Health Applied Research Network, funded by the Health Resources and Services Administration, is a research network comprising 18 Community Health Centers organized into four Research Nodes (each including an academic partner) and a data coordinating center. The network repr...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AcademyHealth
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371501/ https://www.ncbi.nlm.nih.gov/pubmed/25848623 http://dx.doi.org/10.13063/2327-9214.1097 |
Sumario: | BACKGROUND: The Community Health Applied Research Network, funded by the Health Resources and Services Administration, is a research network comprising 18 Community Health Centers organized into four Research Nodes (each including an academic partner) and a data coordinating center. The network represents more than 500,000 diverse safety net patients across 11 states. OBJECTIVE: The primary objective of this paper is to describe the development and implementation process of the CHARN data warehouse. METHODS: The methods involved regulatory and governance development and approval, development of content and structure of the warehouse and processes for extracting the data locally, performing validation, and finally submitting data to the data coordinating center. PROGRESS TO DATE: Version 1 of the warehouse has been developed. Tables have been added, the population and the years of electronic health records (EHR) have been expanded for Version 2. CONCLUSIONS: It is feasible to create a national, centralized data warehouse with multiple Community Health Center partners using different EHR systems. It is essential to allow sufficient time: (1) to develop collaborative, trusting relationships among new partners with varied technology, backgrounds, expertise, and interests; (2) to complete institutional, business, and regulatory review processes; (3) to identify and address technical challenges associated with diverse data environments, practices, and resources; and (4) to provide continuing data quality assessments to ensure data accuracy. |
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