Federated data health networks hold potential for accelerating emergency research

Abstract

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.

Keywords

• data model
• emergency care
• global research
• pediatric emergency medicine