Diving Into the Business of Big Data in Radiology

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 - Big Data

A patient goes in for a CT angiography exam. In addition to any pulmonary embolisms, physicians receive his calcium scores, bone density measurements and cardiac chamber size. The patient’s numbers are then compared against millions of similar patients, and an algorithm is used to determine that he has triple the risk for deep vein thrombosis. Although this perfection of personalized medicine is not yet a true story, big data may soon make it reality.

“Medicine is no longer encapsulated in one thing. We need more information than is encapsulated in a population, and have reached the conclusion that medicine needs to be personalized for appropriate decision making,” says Carl Jaffe, MD, professor of radiology at Boston University School of Medicine. Ultimately, the value of big data is tied to its ability to personalize recommendations for an individual. The holy grail of big data would be achieved by assessing a patient’s images, cross referencing findings, correlating them with genomic or histological data and them comparing the whole picture with similar cases to make predictions about treatment and outcomes.

The 3 V’s of Big Data

The amount of data

The speed that data is acquired

The different sources of disparate data

At its core, big data offers the opportunity to identify areas for improvement in any given field by making huge scores of data accessible, usable and understandable. When applied to healthcare, big data seeks a lofty goal: to revolutionize clinical care so that it is more effective, efficient, precise and personalized. In its simplest terms, big data is millions of terabytes of data. The buzzword, however, encompasses much more.

“When I’m talking about big data, I’m not just talking about the number of bytes and bits, I’m talking really about the incredible complexity associated with the image,” according to Eliot Siegel, MD, of the University of Maryland School of Medicine and VA Maryland Health Care System, who spoke at the 2014 annual meeting of the Society for Imaging Informatics in Medicine (SIIM) in Long Beach, Calif.

Big data is particularly important to radiology, a specialty in the business of big information and a key specialty in individualized decision making. Given the changing healthcare climate, radiology departments have to watch their spending due to heightened competition, declining reimbursement and pressure from payers to eradicate unnecessary imaging. It will become increasingly difficult for radiologists to obtain reimbursement and funding if people can’t see the data, and thereby value, in scans and reports. This is where big data comes into play.

“Today, we store images as DICOM files with some metadata, but there are a large amount of visually extractable data within these images, such as location of organs, measurements, annotations and location of pathology,” says Khan Siddiqui, MD, visiting associate professor of radiology at Johns Hopkins University School of Medicine and president and CTO of higi, llc, a healthcare startup based in Chicago aimed at helping consumers keep track of their health. “We’re only touching upon the tip of machine-enabled analysis of medical images.”

This hidden data, also known as pixel data, was likened to dark matter in space by Siegel at the SIIM meeting. Dark matter cannot be directly observed with current technology even though it’s thought to comprise more of the universe than observable matter. A standard CT pulmonary angiogram, he noted, theoretically contains thousands of typically unreported parameters that providers don’t yet have the capacity to parse out.

Data analysis has already led to some personalization of diagnosis and treatment protocols, explained Katherine P. Andriole, PhD, director of imaging informatics at Brigham & Women’s Hospital in Boston and professor at Harvard Medical School, during the honorary Dwyer Lecture at the SIIM. MIT researchers John Guttag, PhD, and Collin Stultz, PhD, built a computer model to analyze EKG measures of heart attack patients that normally would have been discarded. By using data mining and machine learning, the researchers analyzed massive amounts of data to reveal three electrical abnormalities that identified which patients had a double or triple risk of dying from a second heart attack within one year.

Although the use of clinical analytics in healthcare is just beginning, many radiology providers are investing in business analytics to streamline operations and reveal