Data Analytics Driving Medical Breakthroughs: Using Big Data to Save Lives- Computerworld.com

Fewer sick babies

Back in Toronto, the hospital is processing its data in real time using IBM's InfoSphere Streams, software that can correlate and analyze thousands of real-time data sources. The University of Ontario Institute of Technology (UOIT) is using the software to collect streaming data from electronic devices that monitor the premature babies.

The technology is giving UOIT the ability to make sense of the data and analyze it in ways that include, they hope, discovering the onset of sepsis and various other conditions before these problems occur, says Dr. Carolyn McGregor, the Canada Research Chair in Health Informatics at UOIT.

This test has been running in parallel with current clinical practice so doctors and scientists can compare the two approaches, which they are currently in the process of doing. One day's worth of data is copied and sent back to UOIT for the offline analytics component.

The platform, known as Artemis, or "data baby," has been input with a set of clinical rules that serve as a layer of analytics to help it make predictions for the first time, says McGregor, who is also a professor and associate dean at UOIT. Today, medical devices at the bedside give broad information, she explains. Devices provide readings at a very high frequency, but "a human has to be able to analyze" the results, which are "constantly changing," McGregor says.

Final results have not yet been released -- they're expected sometime in late April for peer review and should be public by year-end. But initial results have proven Artemis's "robustness as an approach," McGregor says. The study, of over 400 patients in three sites, has collected "the equivalent of two decades of patient years" worth of data, she explains.

While medical personnel have some traditional indicators for the onset of infection -- such as body temperature -- Artemis will provide "a much richer environment," McGregor says, to analyze a range of different signals for a variety of various conditions that babies can develop.


Toronto's Hospital for Sick Children is testing a data analytics system to more accurately predict which premature babies, like this one, are at highest risk for infection. Photo credit: Carlos Garcia Rawlins / Reuters

"This system was designed to predict the onset of sepsis 24 hours before it became clinically apparent," says IBM's Chief Medical Scientist Marty Kohn. "It is using structured data to look for patterns that allowed [the hospital] to predict the onset of serious disease based on clinical observation. In a case like this, if you can intervene an hour earlier you can often improve outcomes dramatically."

"This has the potential to reduce a baby's average length of stay in the hospital, as well as to save lives," McGregor says.

UOIT and hospital staffers also plan to use the data to do more clinical research "to look beyond what people already know from watching a heart rate for example; what else we can find from looking at physiology," McGregor explains. By running new algorithms, she says Artemis would be able to tell clinical staff with high probability that a baby's behavior change might be correlated to infection.

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