Big data medicine is still largely unproven, but that's not stopping several medical centers and analytics vendors from jumping in with both feet.
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Explorys, a Cleveland Clinic spinoff, offers a cloud-based performance management platform that taps into a healthcare provider's clinical, financial and operational data to look for previously undiscovered patterns. Among its clients are St. Joseph Health System, MedStar and Catholic Health Partners.
Unlike old-school analytics, which relies on relational databases, the company has enlisted the services of Cloudera, a Hadoop-based software and services firm, to help its engineers and informaticians do the heavy lifting.
The Explorys platform allows providers to do three things: Do searches across patient populations and care venues to help identify disease trends; coordinate rules-driven patient registries; and view performance metrics -- a key ingredient if an organization plans to meet ACO requirements.
Of course, all this firepower is meaningless if it doesn't generate the hard data to demonstrate better quality of care and lower costs.
Anil Jain, M.D., chief medical information officer at Explorys, explained that because the company is relatively young, it has yet to generate those kinds of results. Put another way, there's no proof that it can reduce the number of foot amputations in diabetics or reduce the number of myocardial infarctions in patients with pre-existing heart disease.
But some of the data generated by Explorys suggests it is approaching that target. Working with Catholic Health Partners in Cincinnati, for instance, the analytics platform helped increase pneumonia vaccination rates by 14%, breast cancer screenings by 13% and increased HbA1c testing of diabetics -- a measure of long-term blood glucose control -- by 3%.
A recent report in the Journal of the American Medical Informatics Association (JAMIA) outlined an Explorys project that looked at EHR-generated patient data from nearly 1 million patients from several different healthcare systems. The analysis helped clinicians pinpoint those most at risk for blood clots in the extremities and lungs. The analysis took only 125 hours and required minimal manpower for a project that would typically take years to perform using traditional research methods.
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