Stronger together: A convergence of minds and data

Data convergence is anything but dull when you pair a veteran data scientist with a leader who connected human genome reading to clinical care.

Where data and DNA meet

It’s the early 1990s. Phil Hajduk is hunkered down in a lab in North Chicago, Illinois, where he’s focused on developing small molecule medicines that target cancer cells. It’s trial and error, with a dearth of data for this trained chemist to pore over and leverage for new technologies.

Across the state line in Wisconsin, Howard Jacob is serving as the founding director of the Human and Molecular Genetics Center at the Medical College of Wisconsin. During his nearly 20-year tenure at the university, he will be part of the first team ever to use genome sequencing, or a full DNA blueprint, to diagnose and treat a patient.

Little did they know at the time, but these two PhDs working in different states on different types of science would end up crossing paths decades later. And not just cross paths, but bring their disciplines together to lead one of the largest data initiatives in the biotech industry.

A collaboration for convergence

Flash forward to 2021: Hajduk has just celebrated 28 years advancing science at AbbVie, but he’s graduated from the bench and now leads the company’s IT information research team. In an office across campus, Jacob is marking four years at AbbVie overseeing genomics research and data integration.

They liken their relationship to the same way they view science data: seemingly disparate and unrelated but in reality, different yet complementary pieces of a bigger puzzle. This has become apparent over the past year, as Hajduk and Jacob came together to tackle the next piece of the digital health revolution: data convergence.

Simply put, convergence is bringing data together. But why is this important for a biopharma company?

“The challenge many industries face, including ours, is that it’s difficult to pull out knowledge as human beings,” Jacob says. “We’re limited by how we can process mass amounts of data, so instead we’re changing everything about how we leverage and generate knowledge around data.”

Haven’t we heard this all before?

Data mining, machine learning, artificial intelligence. The promise of a digital health care revolution has been in the headlines for years, across industries. What’s so different now?

Well, now there’s enough data. And more importantly, a vision and commitment from AbbVie leadership to double down and create a better data infrastructure and enable knowledge sharing. The ultimate goal: to better treat disease and manage health care more broadly.

This commitment sparked a collaboration between every part of the company’s science organization that maintains, manages and analyzes data (read: all of them). Early discovery science. Chemistry. Genomics. Health economics & outcomes research team. Patient safety. Clinical trials. And on and on.

Each of these groups have built IT strategies, databases and processes that enable how they work. Now, the walls have come down. The past year, leaders focused on building the foundation, leaning on behind-the-scenes data engineering to create and populate a single internal platform with strong governance.

That’s important, but the real value comes a few steps after you bring the parts together, Hajduk says, because mass amounts of data alone won’t help scientists make better decisions.

“What we're building is distinct. It's not a data swamp. It's a knowledge platform,” he says. “Not only do we bring the data in and harmonize it, but we actually sit down with the subject matter experts and ask, what do those data mean? What’s the level of interpretation we can put on this data, and how do we scale that to our entire scientific community?”

Putting it all into practice

So how do you introduce a new data sharing mindset to thousands of scientists? A little bit at a time, learning and improving as you go.

The convergence team identified a few specific use cases, tailored around critical problems to solve and ultimately improve treatment for patients. Take one use case, “clinical trials are not enough,” centered on the need to better leverage real-world evidence (RWE).

This use case leans into how we take RWE (gathered outside of clinical trials) and create a more complete picture of a particular disease. It’s not only the combination, but the assessment of demographic information, patient behavior, treatment patterns and current standard of care.

Jacob likens this to building a blueprint, or many blueprints at scale that create a more complete picture of what it’s like to have a certain disease, and over a long period of time.

“We’re going to find connections in the data that we just didn’t understand or think about,” Jacob says. “We’re going from the theoretical to the possible.”

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