How big data will save your life

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

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"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).

Personalized medicine

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.

Shah works in biomedical informatics, meaning he works toward making sense of the information in clinical data warehouses.

Sequencing of a human genome yields a massive amount of data, and storing one person's genetic code can require up to 1TB of data storage capacity, Shah said.

The human genome contains 3.2 billion lines of code, which means that finding a flaw in that code requires sophisticated computer algorithms and massive, clustered server farms. Adding to the complexity is that disease is often the result of multiple mutations, according to Shah.

While diseases such as Huntington's or Alzheimer's disease are caused by common genetic mutations, and are more easily spotted, most illnesses are caused by rare mutations. Diabetes, for example, is thought to be caused by a number of genetic mutations, which on their own confer a small amount of risk, but in combination can be more serious.

"If you genome type someone, and out of the 50 [mutations associated with diabetes] you have 10 of them, it's very hard to say what's going to happen to you," Shah said. "Part of the problem is that we just need to do more research and collect more data, and some of it we just need better methods."

But tremendous progress has been made. To date, scientists now know the genetic causes of about 5,000 rare diseases.

One of the most promising areas of genetic research is pharmacogenomics, which uses a person's genetic makeup to determine how they'll respond to drugs, tailoring treatments to specific mutations -- even mutations found in cancer tumors.

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