ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2016
Using i2b2 to Bootstrap Rural Health Analytics and Learning Networks.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Using Closure Tables to Enable Cross-Querying of Ontologies in Database-Driven Applications.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We demonstrate that the open-source i2b2 (Informatics for Integrating Biology and the Bedside) data model can be used to bootstrap rural health analytics and learning networks. These networks promote communication and research initiatives by providing the infrastructure necessary for sharing data and insights across a group of healthcare and research partners. Data integration remains a crucial challenge in connecting rural healthcare sites with a common data sharing and learning network due to the lack of interoperability and standards within electronic health records. The i2b2 data model acts as a point of convergence for disparate data from multiple healthcare sites. A consistent and natural data model for healthcare data is essential for overcoming integration issues, but challenges such as those caused by weak data standardization must still be addressed. We describe our experience in the context of building the West Virginia/Kentucky Health Analytics and Learning Network, a collaborative, multi-state effort connecting rural healthcare sites.
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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.