Unlocking the full value of health data for machine learning Part II

Towards a Federated Future

With the continued explosion of digital health data, health AI requires data scientists to explore new approaches to breakthrough data silos beyond centralizing the data. In the future, teams will be able to activate health data silos around the world through a federated approach that preserves patient privacy without moving data. To enable this approach, practitioners are turning to powerful techniques like federated learning.

Unlike traditional machine learning, federated learning and analytics enable data scientists and researchers to train models and do analytics without bringing the data together. A central federated learning server hosted by a trusted party transmits training instructions to each hospital’s data server, where a local model is trained. Local model parameters are sent back to the federated learning server, where they are aggregated into one global model. The nature of federated learning makes it the ideal solution for health AI, because data never moves, and it is privacy-preserving.

Federated learning directly solves three major issues that make data centralization impractical:

Sensitivity

Institutions continue to require patient consent or a legal basis to share data for a specific purpose. However, since data does not move and federated learning is privacy-preserving, data sharing becomes much easier, while still complying with regulations like GDPR and HIPAA.

Volume

Again, as data does not move in a federated architecture, the costs and compute time for moving and storing large volumes of data are significantly reduced. Aggregate data like model parameters still move between servers, but this volume is minuscule compared to the raw data sets.

Interoperability

While federated learning does not solve challenges with data standardization and interoperability, in a federated architecture, all data must be standardized for model training to execute properly. Existing efforts to drive adoption of FHIR standards will continue to benefit everybody, even as teams transition to a federated architecture.

The Benefits of Improved Health Data Access

Although the health AI ecosystem is still in early experimentation with federated learning and analytics, there is growing interest in the opportunities that it may unlock. Already, the technology has been applied across a number of use cases, such as predictive diagnostics, precision medicine, and drug discovery, to name a few.

In this future of improved access, researchers and data scientists will be able to activate data from connected medical devices without moving data to centralized servers. Health application developers will recognize new revenue opportunities by enabling machine learning and analytics across their data networks, while their partners retain full control over their own data. And all stakeholders across the health ecosystem will reap the benefits of new and better insights from AI, to efficiently deliver better patient outcomes.

While barriers to the adoption of privacy-preserving tools for increased data access abound, crucial elements of healthcare delivery suffer, including diagnostic accuracy, patient outcomes, pipeline development speed, drug approval time, and more, all at the costs of patients and an overburdened and understaffed healthcare ecosystem.

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