Updates in Big Data for Oncology: What Are We Learning?

There are a number of issues concerning big data in the oncology world, the most prominent of which concerns the number of patients participating in clinical trials, Robert J. Green, MD, MSCE, vice president of clinical strategy at Flatiron Health, explained during his presentation at The American Journal of Managed Care’s 4th Annual Patient-Centered Oncology Meeting. He added that only 4% of adult cancer patients participate in a clinical trial.

“Clinical trials are a good thing,” Green said. “They’re often good for patients. They’re how we advance treatments. They’re how we make discoveries. It’s also important because what that means is that 96% of patients don’t go on clinical trials and for those patients, we actually lose an enormous amount of information and the way I like to refer to it as we essentially lose the patient experience. The voice of the patient, what they went through, becomes lost.”

Green explained that many oncology groups believed the implementation of electronic health records (EHRs) would help solve this issue, as EHRs could not only make it more efficient for patients to get into clinical trials, but they allow stakeholders to learn more about those patients who are not participating in the studies. However big data, as it’s usually referred to, has become very complicated, and Green said that his new focus is figuring out how to make sense of all of this data that is being generated by the care of a cancer patient.

The 2 main components impacting the understanding and implementation of big data are the way unstructured data is being used and where the information is being stored. Green explained that unstructured data is the information that is crucial to understanding populations and how individuals are being treating. While structured data like demographics and age are important in EHRs, so are the information that allows clinicians to better understand what the right treatments are, as well as patient experiences. Additionally, data is being stored in multiple different places, which Green said is making it difficult to find the needed data and integrating it into the EHR systems.

“We really believe that this problem, this data problem in oncology, is a technology problem but at the same time, in order to solve it, you need people who understand cancer and who understand data science,” he explained.

Green added that a program Flatiron Health has developed and implemented has allowed clinicians and technologists a faster, more accurate means of gathering, analyzing and understanding data concerning large populations of patients with cancer. The technology enabled abstraction process has allowed him and his team to more rapidly understand what exactly is working among populations and where improvements can be made.