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Health-IT early adopters well-poised for big-data advances in clinical medicine

Fred O'Connor | April 3, 2013
But the majority of US health providers are in smaller practices that have been slow to embrace electronic medical records

The final build will include natural language processing, machine learning algorithms and distributed computing, among other technologies, he said. The final role of open-source software is still being considered, although open-source applications were mostly used in the prototype, according to an ASCO webcast. Hadoop, an open-source program used to distribute data processing loads, is a staple in large-scale data analysis.

Because the medical community is slow to incorporate IT into its workflow, these technologies were selected to make the adoption process easier.

"Typically, health care is slow to adopt IT due to the high cost associated with implementations and competing priorities," Dr. Hudis said. "Many of our goals around the architecture chosen was to reduce implementation burdens for practices and physicians."

With 85 percent to 90 percent of ASCO members using EHRs, oncology seems like a good match for a large data-analysis project, Hudis said. Additionally, patients and providers are very willing to volunteer their health data, he added. ASCO, whose 30,000 members are physicians and health care professionals representing all fields of cancer treatment and research, received 130,000 EHRs to populate the prototype after initially setting a goal of 30,000 patients.

Regardless of government mandates on EHR use, data analysis will lead to tangible patient benefits.

"We've got all this stuff in the health care guidelines and things that are reimbursable and they end up being a little bit divorced from how our patients actually end up feeling," said Beth Israel's Dr. Arnaout. "[Data analytics] is actually going to help how our patients actually end up feeling."

 

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