Making sense of big data: Data projects spur progress
Providing a more complete picture
The Montefiore Medical Center in Bronx, New York, has partnered with Franz, Inc., Intel, Cloudera, and Cisco to transform statistical databases, such as spreadsheets, into interactive graph databases that can be used to make better informed and predictive healthcare decisions.
“If you are in a hospital and have millions of patients, you will need to do analytics in many ways—for more personalized medicine, for predictive modeling, and for better business intelligence,” says Jans Aasman, PhD, CEO of Franz, Inc., which specializes in semantic web technologies. “This system allows you to get all the data together from these different silos for analytics.”
Semantic data lakes (SDLs) enable healthcare providers to use multiple types of data sets congruently to get a more comprehensive picture of population health trends, says Parsa Mirhaji MD, PhD, associate professor of Systems and Computational Biology and director of Clinical Research Informatics at the Albert Einstein College of Medicine and Montefiore Medical Center-Institute for Clinical Translational Research.
"The ability to conduct real-time analysis over new combinations of data such as patient information, genetic data, medical device data, clinical trials, drug information and public health data—will fuel discoveries, significantly improve efficiencies and personalize care," Mirhaji says. “Smarter and more intelligent data and analytics opens the door for building analytic machines that can behave as if they understand the data and can think cohesively about the problem at hand using the data. This is called cognitive computing, similar to IBM Watson.”
The multidimensional data project used at Montefiore is expected to grow by trillions of data points in 2016. The hospital uses SDLs to bring together EHR data with data about health trends in a particular region and other clinic information so that practitioners can see links between health conditions. The researchers say that the platform could be available wide-scale in healthcare in the next five years.
"Making sense out of big data is a challenge, particularly in the healthcare industry where information comes from a variety of sources and in different forms including structured, unstructured, images, temporal, geo-location and signal data,” Aasman says. “With the SDL for healthcare we quickly ingest many types of data into a single system and apply artificial intelligence, machine learning and visual data exploration to discover new relationships between data that can save lives and improve care.”