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What you need to know about data fluency and federated AI | 7wData

What you need to know about data fluency and federated AI | 7wData

Sharecare is a digital health company that offers an artificial intelligence-powered mobile app for consumers. But it has a strong viewpoint on AI and how it is used.

Sharecare believes that while other companies use augmented analytics and AI to understand data with business intelligence tools, they are missing out on the benefits of data fluency and federated AI. By using federated AI and data fluency, Sharecare says it digs deeper to find hidden similarities in the data that business intelligence tools would not be able to detect in health settings.

To gain a deeper understanding of data fluency and federated AI,Healthcare IT News sat down with Akshay Sharma, executive vice president of artificial intelligence at Sharecare, for an in-depth interview.

Q: What exactly is federated AI, and how is it different from any other form of AI?

A: Federated AI, or federated learning, guarantees that the user's data stays on the device. For example, the applications that run specific programs on the edge of the network can still learn how to process the data and build better, more efficient models by sharing a mathematical representation of key clinical features, not the data.

Traditional machine learning requires centralizing data to train and build a model. However, with edge AI and federated learning combined with other privacy-preserving techniques and zero trust infrastructure, it's possible to build models in a distributed data setup while lowering the risk of any single point of attack.

The application of federated learning also applies in cloud settings where the data doesn't have to leave the systems on which it exists but can allow for learning. We call this federated cloud learning, which organizations can use to collaborate, keeping the data private.

Q: What is data fluency, and why is it important to AI?

A: Data fluency is a framework and set of tools to rapidly unlock the value of clinical data by having every key stakeholder participate simultaneously in a collaborative environment. A machine learning environment with a data fluency framework engages clinicians, actuaries, data engineers, data scientists, managers, infrastructure engineers and all other business stakeholders to explore the data, ask questions, quickly build analytics and even model the data.

This novel approach to enterprise data analytics is purpose-built for healthcare to improve workflows, collaboration and rapid prototyping of ideas before spending time and money on building models.

Q: How do data fluency platforms enable analysts, engineers, data scientists and clinicians to collaborate more easily and efficiently?

A: Traditional healthcare systems are very siloed, and many organizations struggle to discover the value within their data and unlock actionable trends and clinical insights. Not only are data creation systems and teams isolated from data transformation systems and teams, but engineers and data scientists use coding languages while clinicians and finance teams use Word or Excel.

The disconnect creates a situation where the data knowledge is translated outside of the programming environment. The transformations between system boundaries are lossy and without feedback loops to improve an algorithm or the code. Yet, all stakeholders need early and iterative access to the data to build health algorithms effectively and with greater transparency.