Turning data into intelligence
MagnolAI ingests raw and processed data from all connected devices leveraged in clinical studies — whether those are off-the-shelf wearable devices to measure heart rate, or a Lilly innovation such as its sensor used to measure defecation for inflammatory bowel disease (IBD). The platform then makes this connected data accessible to Lilly’s data and analytics experts, who in turn create algorithms to better understand the disease journey, help measure the effect of Lilly medicines, and build new products that support successful patient outcomes.
“The team took a device-agnostic approach when designing and implementing MagnolAI’s data capabilities, making it a powerful tool regardless of the device being used. MagnolAI has enough scalability to visualize data from different devices, profile them and generate reports of data quality, including the ability to aggregate and synthesize data from across clinical trials,” Carter says.
But what sets the sensor cloud apart is that, while most solutions focus on data collection, MagnolAI is engineered to turn data into intelligence, he says.
“It empowers users with a unique analysis-driven abilities: to view data at scales and resolutions that fit for analysis purposes, capture exact data points with unprecedented accuracy, and to deliver digital data at full spectrum from cloud to analysis environment anywhere and anytime,” he says. “This level of detail and flexibility, previously unseen in the sector, positions MagnolAI as a game-changer for professionals who demand more from their sensor cloud platforms and their data assets.”
The solution, which is private to Lilly, was built with an important human-in-the-loop design principle, Carter adds, “to allow our researchers to view the data, develop initial hypotheses of data, create algorithms to quantify and verify the hypotheses through iterations and learning cycles.”
Overcoming data challenges
MagnolAI has been used to support around 20 connected trials to date. As its use grows, Carter’s team will need to develop new systems, tools, and pipelines to enable the collection and analysis of new forms and sources of data — no easy task given the volumes of data involved.
“The ability to capture a tremendous amount of data is exciting, but early on, it was challenging to make sense of this amount of data, especially as we look across different trials,” says Carter. “In some cases, we’re collecting more than 4 million data points in a single day from one patient.”
