Nearly 6 million Americans are afflicted with congestive heart failure, which is when the heart doesn’t pump enough blood and oxygen resulting in serious problems in other parts of the body. More than half of people who develop the condition pass away within five years of their diagnosis.

But because congestive heart failure (CHF) has certain characteristics that lends itself to big data analysis, it’s become a moneymaker for tech companies–who are also saving lives in the process.

Heart failure is usually caused by pre-existing conditions, and figuring out what those conditions are can save a lot of lives. But by the time patients are in the hospital, complications related to the disease can make it difficult to parse the cause from effect–at least for humans. Smart medical devices recording thousands of data points per second, and digitized medical records which build network connection maps of all sorts of health care factors, are being deployed to unravel the mystery.

Atlanta’s Emory University Hospital is a busy, high-profile medical center whose intensive care unit sees considerable traffic. The hospital has partnered with IBM and Excel Medical Electronics for an ambitious project: Smart medical equipment which records between 1,000-2,000 data points from each patient per second, multiplied by 100 patients.

Dr. Tim Buchman, the hospital’s director of critical care, is a tech-savvy physician who works with vendors like these to test out potentially lifesaving technologies. Excel Medical’s bedside monitors are plugged into an IBM analytics platform which parses the data as it comes in, and–hopefully–finds patterns which predict CHF, sepsis, or pneumonia before they happen.

In an interview with Co.Labs, Buchman compared the experimental analytics system to a GPS for care providers. Although he explained that no analytics system could ever replace “a well-trained critical care nurse,” they can help medical professionals make better decisions in high-stress situations, and anticipate changes to the patient’s health.

“If you speak to a critical care physician and ask how many decisions you make on a patient in a given day, it could easily be 30,” Buchman said. “You multiply that by 20 patients in the ICU and you’ll see that we make 600 decisions daily, all of which are based on situational awareness and a great grasp of information. Currently all of that is based on making sure you didn’t miss the right data elements and remembering what happened five minutes ago. This is the problem that we’re taking on. It is a big data problem, but even more important it’s a data in motion problem.”