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.
|Title of host publication||2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016|
|Number of pages||4|
|State||Published - Oct 13 2016|
|Event||38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States|
Duration: Aug 16 2016 → Aug 20 2016
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016|
|Period||8/16/16 → 8/20/16|
Bibliographical noteFunding Information:
The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant number UL1TR000117 and by the National Institute of General Medical Sciences, NIH, through IDeA CTR award number U54GM104942. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
© 2016 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics