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Healthcare industry sees big data as more than a bandage

Allen Bernard | Aug. 6, 2013
The promise of big data in healthcare—increased efficiencies, better outcomes, more personalized care—is undeniable. How the industry reaches the promise land will happen in many stages, but hospitals and insurers aren't wasting any time getting started.

This effort uses near real-time data from primary care physician's notes, imaging data, demographic data, social welfare data, lab work and other NHS databases, compiles it nightly and batch processes it to figure out what triggers those readmissions. This use of analytics has led to a 40 percent reduction in diabetic-related amputations and blindness for participating institutions.

"That has revolutionized how primary care physicians and community care nursing practitioners deal with their local populations," Aridhia CEO David Sibbald says. "It's about keeping people out of hospital and managing care proactively."

Similar initiatives are underway at the Ohio State University's Wexner Medical Center, where CIO Phyllis Teater is working with the Battelle Memorial Institute, a research and development organization, to cut readmissions rate and improve care through data mining and predictive analytics, and in Europe, where IBM's recently acquired Cúram Software is doing similar work with health systems in Denmark and Catalonia.

Data Mining's Not New, But 'Perfect Storm' for Healthcare Is
Of course, data mining has been going on a long time in healthcare. It's the foundation of evidence-based medicine, after all. What's moving organizations into the realm of big data is the shift to EHRs and the benefit of integrating information from usually cloistered sources such as social services or census data.

Unlike most healthcare providers, Wexner has been using EHRs since the early 2000s. And unlike most EHR early adopters, the medical center went beyond basic admissions data and collected patients' complete medical history. Multiply this by 1 million patients, seen every year over 13 years, and Wexner has lots of data to analyze.

"Healthcare has a history of being very non-automated. Most of the data about patients was in paper charts. You can't do predictive modeling on paper charts," Teater says. "You certainly can't call it big data, because you're wading through chart after chart trying to see the specific condition the patient had."

Meanwhile, Seattle Children's Hospital has upgraded to IBM PureSystems to cut the time its analysts take to deliver answers around quality of care and UPMC is creating a comprehensive data warehouse that integrate more than 200 internal and external data sources, including labs and pharmacies.

In the United States' fragmented healthcare delivery system, the next step for providers is taking those walled gardens of data, de-identifying it and combining it into larger pools that are accessible to everyone.

"We're in a perfect storm," says Karen Parrish, vice president of IBM Industry Solutions. "There's so much [data], and it's so fragmented and...siloed, that one of the big challenges is, how to do we package it all together to get meaning out of it?"

 

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