About 25% of hospitals use some form of data analytics to mine traditional databases to learn more about past treatments and about how future treatments can be improved. But, what is contained in the columns and rows of databases represents an almost insignificant portion of the information about patients that's been collected; the most important information lies in unstructured data - the physicians' notes, radiological images and lifestyle information gathered from patients using mobile devices.
"That's the real renaissance that's going to happen in health care," Walker said. "With big data, what happens in a doctor's office is going to be vastly different from what we see today. The top five or 10 things that people die from in America are life-style induced. That's absurd. Maybe instead of vital signs, I'm just going to look at what you buy in a grocery store."
Today, data analytics in most hospitals is used to manage costs and increase the quality of care. The more promising use for big data, however, is the ability to discover treatment-and-outcome correlations using physician and nurse notes and data driven by genetic profiles.
By combining big data and genetics analytics, scientists today can determine how a patient will react to a medication and may someday even be able to predict who may become ill and -- if they do -- what customized medications can best treat diseases.
"When I look at the historical growth rate, [big data] is definitely a hot application in the marketplace," said James Gaston, senior director of clinical and business intelligence at the Healthcare Information and Management Systems Society (HIMSS).
Currently, one of the more promising areas of big data analytics involves drug therapies devised through the study of genomics, also known as personalized medicine.
Genetic diseases are akin to buggy code in software; the key to finding the cause of an illness is to uncover that error in the code, according to Alexis Borisy, co-founder of Foundation Medicine, a cancer diagnostics company.
"Cancer, for example, is a disease of the genome where something has gone wrong with the programming code and a mutation occurred. There are actual errors in the code and that's a core reason why cancer develops," Borisy said.
While sequencing the first human genome took eight years and cost about $1 billion, genetic sequencing costs have fallen dramatically in the last decade. It now costs from $5,000 to $10,000 per human genome, and companies are working hard to cut that cost to $1,000 in the next few years. Sequencing a DNA strand is becoming so inexpensive that hospitals will soon be able to do it for on most patients and add the data to an EHR, according to according to Nigam Shah, an assistant professor of Medicine at Stanford University's School of Medicine.
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