Dr Robert Green Explains Challenges, Promise of Big Data in Oncology

The challenge with data in oncology is making sense of it and connecting it in a way that clinicians can make insights that inform the care they provide patients in real time, said Robert J. Green, MD, vice president of clinical strategy and senior medical director at Flatiron Health.
Published Online: December 08, 2015

 
The challenge with data in oncology is making sense of it and connecting it in a way that clinicians can make insights that inform the care they provide patients in real time, said Robert J. Green, MD, vice president of clinical strategy and senior medical director at Flatiron Health.
 
Transcript (slightly modified)
What are some of the ways that big data in oncology has evolved in the last 1-2 years?
The first thing I would say is that the data problem in oncology is more complicated data than big data. And it’s the difficulty in getting information out of the electronic medical record and connecting it with other sources of information. And I think what we’ve seen over the course of the last year is that we, and others, have really started to move toward figuring out how do you process these data, make sense of the data, and connect it all together in a way that you can make useful insights that you can take back and inform clinical care.
 
How can we increase enrollment in clinical trials to get better data, and what is the biggest barrier there?
So only about 4% of adult cancer patients go on clinical trials, which is a big problem. We think there are various reasons for that, a lot of which have to do with inefficiencies that occur within practices and barriers that physicians face in trying to enroll patients in clinical trials. So one of the things we think we can do, and that we’re trying to do, is to leverage data analytics to help identify patients ahead of time who might be eligible for clinical trials and to provide that information back to clinicians in a way that patients can be identified before they go on therapy, when they’ll still be eligible for trials.
 
How can what you learn from big data analysis be incorporated into everyday cancer care?
That’s one of the really tough questions: how do we take the learning and incorporate it? And there are a bunch of different ways you can do that. Probably, we think, the most useful way is by providing real-time feedback to clinicians who are actually in the clinic taking care of patients. So that can take the form of quality metrics: letting physicians know how they’re treating a certain population of patients and with what therapies; how those patients are doing; and the outcomes. It’s the idea of a learning healthcare system, where we actually give feedback to clinicians as they’re taking care of patients, based on data from their patients.

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