How big data will save your life

Even as patient information moves to electronic records, important data is often siloed

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The informatics platform is used by more than 100 academic health centers around the world. It has been used to pinpoint genetic predictors for diseases such as rheumatoid arthritis and to identify harmful drugs.

For example, the informatics engine revealed that there was a higher risk of heart attack from the drug Avandia than from other drugs in the same class.

When the i2b2 software was deployed in hospital emergency rooms, it was able to predict, on average, two years in advance of the typical healthcare system whether a patient was suffering from domestic abuse by detecting physical traits, Kohane said.

"At the same time, this is almost like a back door. The data is being offloaded and analyzed [after the fact]. What about real-time care of patients across healthcare systems?" he said.

In chronic care, what matters most is that a doctor be able to access clinical data warehouses that contain information on thousands similar patients.

"What matters is the ability for the doctor to say you have these four diseases and you're taking these four drugs, here are the results of treating these other similar patients," Shah said. "There is no clinical trial that has every looked at these four diseases and the effect of these four drugs."

When data from EHRs can be exchanged seamlessly, a physician will be able to query what thousands of other doctors did in the same situation.

"Then I want to ask myself, 'What am I worried about with this person: Am I worried about blood clots or heart attack?" Shah said. "Then I can query what happened to the 1,000 other people who suffered a blood clot and determine ... that outcome in those people very similar to you."

"It's sort of like doing a clinical trial in silicon," Shah continued. "I refer to this whole process as practice-based medicine."

Historically, medicine has relied on published guidelines for treatment or the results of clinical trials for drug prescriptions, which always focus on one disease and most often use only younger, healthier patients as subjects for tests.

Data pigeon holes

For example, more than 60% of cancer patients are over the age of 65 and have anywhere from two to five other chronic illnesses, such as congestive heart failure or high blood pressure. Trials with younger patients would not involve the same mix of health problems.

"You get a younger adult, in the age range of 50..., that doesn't have any diseases other than cancer," said Robert Hauser, senior director of the American Society of Clinical Oncology's (ASCO) Quality Department. "So, once a drug is developed from a trial, it ends up being used on a population that wasn't evaluated on a large scale. Right now, we only learn from 3% of all adult oncology patients because only 3% of them participate in clinical trials for drug development."

And, once a clinical trial ends, patients are no longer tracked, Hauser added.

Also hindering advances in personalized medicine is the compartmentalization of healthcare data at hospitals, private practices and even clinical trials.

Additionally, EHRs use proprietary software, meaning they don't communicate with other systems. An EHR from Meditech, for example, doesn't natively share data with one from Cerner, McKesson or Epic Systems - the four largest EHR makers in the world.

"We realize the data standards wars and interoperability issues that go on amongst EHR vendors is not something that's going to be overcome in the near future," said Josh Mann, assistant director of Oncology Technology Solutions for the ASCO.

There is, however, an industry-wide effort under way to break the logjam.

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