EMC Says Big Data Is Essential to Improving Health Outcomes
Big data is going through a transformation, and health care IT will be a major beneficiary of its analytics capabilities, according to an executive at EMC, a major provider of cloud computing, data backup and big data infrastructure.
The health care industry can use big data analytics to better detect diseases and aid medical research.
"There's a fair amount of hype in big data in general," Dave Dimond, chief strategist for industry solutions at EMC, told eWEEK. "In health care it's coming together."
Although the concept of big data may not have been as well-understood a year ago, rather than just being aligned with research, it's getting more focused, said Dimond.
"Our position on health care is that big data is real," he said.
In fact, EMC predicted a threefold increase in health care data between the beginning of 2013 and the beginning of 2016, according to Dimond.
The amount of health care data will eventually be 15 zettabytes worth of information. One zettabyte is equal to about 15 million iPads of data, said Dimond. (A zettabyte is equal to 1,000 exabytes, and one exabyte is the equivalent of 1 million terabytes.)
As the health care industry shifts from pay for service—or pay per pill—to Medicare incentives for outcomes under the Affordable Care Act, big data analytics will play a role in helping doctors predict outcomes for patients they're responsible for monitoring.
Predictive analytics can also help a doctor determine if a patient would need to be readmitted into a hospital.
Big data will allow hospital systems to centralize information from their multiple facilities, Dimond noted. This will enable the health organizations to better keep track of data for a large patient population and monitor health outcomes.
"To win in this business model [of accountable care] and thrive, they need to be able to do analytics," said Dimond. "They need to be able to access data."
Some health organizations may have big data applications but haven't been able to put all the data together because they're all in incompatible, unstructured formats, Dimond suggested.
Enterprise data warehousing needs to mature for some health providers, though others are using this technology well, he said.
In addition, when researchers can analyze entire human genomes, they will be able to compare populations of individuals. Then scientists will examine analytics models to see how data from electronic health records correlates with the genome data to help determine the cause of various illnesses. Analytic models can also enable medical researchers to evaluate whether certain treatments will be effective, said Dimond.
Analytics applications also enable health care organizations to analyze financial data and billing, he said.
"The data scientist can look at accumulating all the data possible from a number of sources—clinical systems, financial systems, outside providers, skilled nursing homes and putting it together in a way where you can look at it as a sandbox point of view," Dimond explained.
Analytics software such as EMC's Greenplum Chorus can help by pulling disparate health care data all into one place. Chorus is a collaborative data science platform that allows for file sharing, versioning, change tracking and archiving.
The Greenplum analytics platform also incorporates Hadoop, and the open-source software framework will enable health organizations to reduce unstructured data and associate it with structured data, said Dimond.
EMC acquired Greenplum in July 2010.