John Quackenbush at MedCity CONVERGE
The healthcare ecosystem has clearly caught on to the idea of gathering information. But simply collecting it doesn’t equate to tackling the industry’s biggest hurdles — like cancer, for one.
At MedCity CONVERGE, John Quackenbush, professor and director of the Dana-Farber Cancer Institute’s Center for Cancer Computational Biology, put a finger on how we can sort through the massive amounts of data and utilize it to find solutions.
Over the past decade, the costs of generating data have plummeted. In fact, the cost per genome decreased from $100,000 in 2001 to less than $10,000 in 2014, according to a slide in Quackenbush’s presentation.
Another slide showed a 2013 infographic, which estimated that the average hospital would generate 665 terabytes of data annually by 2015. But Quackenbush said Dana-Farber now dwarfs this amount each year.
And with that lowered financial burden comes an explosion of data. “We have access to an ever larger store of data,” he said, which is collected through everything from mobile apps to claims data.
Yet all this information isn’t easily usable. Instead, it’s fragmented and convoluted.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
“It’s not a big data problem — it’s a messy data problem,” Quackenbush said.
Part of the reason behind the messiness is the variety of consumers who want to make use of the data. A patient wants to use it for one reason. A physician treating an individual at the point of care has different needs. And a scientist in a lab has still another idea of how it should be used. To eliminate this problem, access needs to be intuitive and easy, he said.
Despite these challenges, Quackenbush, who’s also the co-founder of precision medicine software company Genospace, decided to dig a little deeper into the intricacies of data.
By utilizing the PANDA (Passing Attributes between Networks for Data Assimilation) method, the company was able to recognize that the networks regulating biological processes differed between existing phenotypes.
Though big data remains complicated, if handled correctly, it will undoubtedly play a role in the future of medicine. To the delight of the audience, Quackenbush thereby concluded his presentation with a fitting quote from physicist Enrico Fermi: “Before I came here, I was confused about this subject. Having listened to your lecture, I am still confused — but on a higher level.”
Photo: Justin Lawrence
