Understanding Healthcare’s New Life Savers, Data Scientists

30Jun - by aiuniverse - 0 - In Big Data Data Science Machine Learning

Source – hcanews.com

When a patient is in the intensive care unit (ICU), every second counts. And the decisions healthcare professionals make can have life-or-death consequences.

Gal Salomon, MBA, is intimately involved in those decisions, working hard each day to ensure patients get the right treatment at the right time, every time. But Salomon is not a doctor. He doesn’t have a medical degree. And his place of work isn’t even a hospital or health clinic.

Salomon is the co-founder and CEO of CLEW Medical, a 3-year-old Israeli tech startup that’s focused on leveraging the power of data analytics to improve patient care in the ICU. For Salomon, who’s had a long career in other business sectors, the turn toward healthcare was about more than capitalizing on a growing market.

“This is my first attempt in healthcare,” he said. “And to me, the most important thing is to work in something that is going to be meaningful.”

There was a time when the idea of “saving lives” while sitting at a desk was almost laughable. “Saving lives” meant taking action, racing down a hospital corridor or weaving through traffic in an ambulance. However, in 2018, people who can transform meaningful clinical and epidemiological data into actionable insights might also help save and improve patients’ lives, at a scale never before possible.

CLEW’s platform uses big data and machine learning to create predictive analytics that can be used to guide patient care in the ICU. Instead of relying on gut instinct, personal experience, or defensive medicine, physicians can use real-world clinical data to zero in on what might work best for a particular patient.

“The models that we’re creating are ones which basically will be able to come and say, ‘This patient has an X, and this is why you need to do: 1, 2, 3, 4,’” Salomon told Healthcare Analytics News. “‘And this patient has a Y, and you need to do something completely different.’”

Such information can be used not only to improve patient care, but also to maximize efficiency, ensuring hospitals deploy tests and staff members in the manner most likely to help the patient the first time.

Saving lives is very much at the forefront of Salomon’s mind. He trained in electrical engineering but changed course after his mother died from what he believes was a preventable medical error.

“The fact that they had a problem [being able to] to read between the lines and come up with a solution that could save her,” he said, “that was one of the main triggers. That was the alarm that I got.”

A New Era in Analytics

CLEW is part of a wave of new companies and healthcare organizations trying to bring the power of big data into the clinic. But although the technology is new, the concept of using analytics in healthcare is not, according to Evan Carey, MS, an associate professor at St. Louis University’s Center for Health Outcomes Research.

“Lots of people have been using large amounts of collected healthcare information to do research,” he said. “It’s not such a new thing.”

hat is new, he noted, is the scale and computing power healthcare organizations can leverage. To take advantage of those capabilities, organizations need expertise in managing large-scale data and developing accurate mathematical models. Someone with a background in traditional epidemiology or biostatistics likely won’t be able to manage such big data troves without additional training, Carey said.

“You’re going to have to learn to be a really darn good computer programmer,” he said.

That’s why St. Louis University launched a health data science master’s program three years ago. The program mixes classes in computer programming and data visualization with healthcare-focused coursework, such as “Foundations of Medical Diagnosis and Treatment” and “Healthcare Organization, Management, and Policy.”

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