CDI Miami | Wednesday April 20, 2016

US Medical Imaging Industry Makes Debut into Big Data

Big data has varying definitions, but in essence refers to the analysis of large volumes of data. The advantage of big data lies in its ability to analyze multiple patterns and arrive at a data-driven results; an attribute that can lead to more accurate medical diagnoses.

According to Kinso Ho, a product architect at Agfa Healthcare, Big data is characterized by variety (structure to unstructured data, textual or multimedia, data graphs), velocity (how fast data comes in) and volume (massive amounts of data points, new and historical data).”


“Not all of this is new, the increase in velocity is not outrageous for most of us, and the increase in volume is offset by the increase in storage and processing,” Ho said. “The real game changer of big medical imagingdata is starting something that’s specialized: special hardware with special processing, and now big data technology develops an opportunity to use common hardware. Accessibility is no longer an issue, the true power of big data is its ability to transform high end processing to a commodity,” Ho said.


According to Henri “Rik” Primo, director of strategic partnerships at Siemens, big data was practiced long before the term existed, citing corporations such as Google and Yahoo, who already practice data mining.


“When I think about big data, I think about data that is very difficult for people to consume and extract from…so let’s talk about how you extract data that’s not easily accessible,” said Khan Siddiqui, MD, CEO/CTO of higi llc.


There are three pillars of data, Ho explains, data management, which refers to managing the data in a scalable way; search, which should be built around a search engine with fast searching capabilities; and data processing, which allows for the advanced processing framework. The game changer in these pillars, Ho said, is that they are all open source.


The advantage of big data is the ability to access existing patient records, reducing procedural redundancy and insurance billing. However, the current records are largely unstructured and disorganized making it time consuming to accurately interpret the data.


Primo expects a strategic plan for big data in medical imaging, anticipating a move to dynamically integrate medical images, in vitro diagnostic information, genetic information, electronic health records and clinical notes into a patient’s profile. This provides the ability for personalized decision support by the analyses of data from large numbers of patients with similar conditions.

“Big data has potential to be a valuable tool, but implementation can pose a challenge. Build a medical report with context-specific and target group-specific information that requires access and analysis of big data,” Primo suggests.


“As time goes on, we are going to be having greater challenges in imaging informatics, even than anyone else in big data, our data is far more complex, far more high dimensional…but I think we’ll be able to take imaging and take medicine to the next generation,” suggests Eliot Siegel, MD, professor of diagnostic radiology at the University of Maryland School of Medicine.


According to Primo, structured reports are the future of bog data in medical imaging. “Structured reports are needed to improve quality and collaboration between radiology and other disciplines…this will facilitate indexing and data mining and create semantic interoperability with all departments.”