Federated data health networks hold potential for accelerating emergency research

Abstract

Multi-center research networks often supported by centralized data centers are integral in generating high-quality evidence needed to address the gaps in emergency care. However, there are substantial costs to maintain high-functioning data centers. A novel distributed or federated data health networks (FDHN) approach has been used recently to overcome the shortcomings of centralized data approaches. A FDHN in emergency care is comprised of a series of decentralized, interconnected emergency departments (EDs) where each site’s data is structured according to a common data model that allows data to be queried and/or analyzed without the data leaving the site’s institutional firewall. To best leverage FDHNs for emergency care research networks, we propose a stepwise, 2-level development and deployment process—creating a lower resource requiring Level I FDHN capable of basic analyses, or a more resource-intense Level II FDHN capable of sophisticated analyses such as distributed machine learning. Importantly, existing electronic health records-based analytical tools can be leveraged without substantial cost implications for research networks to implement a Level 1 FDHN. Fewer regulatory barriers associated with FDHN have a potential for diverse, non-network EDs to contribute to research, foster faculty development, and improve patient outcomes in emergency care.