What is Federated Data Networks?
The power of connected health data
Background:
Health data can provide wealth of information about patients, disease diagnosis, monitoring, treatment options and all the different stages of a patient journey. Health data is utilized in observational research and specifically real-world evidence (RWE) studies which play a crucial role in complementing clinical trials data by providing insights into how treatments perform in real-world settings.
The challenge:
To adequately answer a research question, researchers need to collect and analysis an adequate representative sample of health data which usually requires access to data beyond one institution and even beyond one country. However, accessing health data for research purposes presents several challenges, including privacy concerns, data security, regulatory compliance, and the need for interoperability among different systems and data sources. Interoperability sounds like a complicated term however it simply refers to the ability of different systems, applications, or devices to exchange, interpret, and use data seamlessly. In real life, data in each insitution may have a different structure and it is challenging to merge with other datasets generated from other institutions.
Federated data networks (FDNs) as a potential solution:
Data federation represent a potential solution for health data access challenges; it is simply defined as a software process that enables numerous databases to work together as one, data will remain within the premises of the data custodian, security is maximized throughout data analysis process.
Federated analysis (FA) is a technique that may facilitate a centralized combined analysis of multiple decentralized data sources without requiring actual data merging, it allows real-time, interactive, centralized statistical analysis on individual-level data, without actual transfer of sensitive personal data between institutions and countries.; researchers can analysis data across multiple distinct organizations in a secure manner, it brings researcher’s analysis and computation to where the data resides. The federated approach provides identical results compared to analyses of physically merged data has been demonstrated for commonly used statistical models. FA will perform a data harmonization process through building a “Common Data Model” from multiple local datasets with common structure and format for the study variables. This is an essential requirement for combined analysis of individual level data from different data sources. It is essential that the variables have similar definitions by harmonisation using transparent and well documented algorithms.
Federated data networks (FDNs) allow efficient fit-for-purpose assessment and facilitate finding the appropriate patient sample for each research question. They are networks of real-world data sources, with a central hub connecting and interacting with participating local databases while each database is safeguarded by their own local governance.
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In conclusion, federated health data networks offer a scalable, secure, and privacy-preserving infrastructure for unlocking the full potential of healthcare data to facilitate innovation in health data research.