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But medicine is competing with every other industry for these valuable employees.
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.”
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.”
What 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.”
Eda Zullo, a pharmaceutical commercial analytics recruiter at the recruiting firm Smith Hanley Associates, said healthcare was somewhat late to the game in terms of hiring data scientists.
“It’s the conservative nature of the industry,” she said. Before hiring new data scientists, hospitals wanted to wait and see how to do it right, learning from other industries.
“Now, healthcare and pharma are jumping on the bandwagon because they see the value,” she said.
Zullo often sees a trickle-to-wave phenomenon. At first, a client will ask her to fill a single opening for a data scientist, but once they see the power of analytics, they’ll come back seeking more new hires.
“Once I get one job in for a data scientist or a forecasting role, I get four of them coming in,” she said.
The specific demand changes over time. Job candidates generally need a degree in a science, technology, engineering, or math (STEM) field, and more pharma firms are looking for workers who specialize in particular therapeutic areas.
“The data is different for each of the different therapeutic areas, so oncology data is different from, say, rare disease data, as opposed to diabetes,” she said.
As visas have become more difficult to come by, some companies are pulling back on the number of foreign workers they hire. That can be a challenge, since much of the labor market in STEM fields comes from overseas.
But healthcare organizations aren’t only after technical expertise; they also want employers dedicated to the company’s mission.
Izik Itzhakov, vice president of business development at CLEW, said his company looks for employees who want to use analytics to save lives.
“We’re looking for the people that can combine their ability and their passion about what we’re doing,” he said, adding that the company isn’t interested in hiring people who view the job as a generic tech gig.
Like Salomon, SLU’s Carey also came from a different field: finance. But Carey said he came to a point where he grew tired of having a job where the main objective was to try to make more money.
“A lot of people work in healthcare because they do have a sense in mission, and I’m one of them,” he said.
Different employers dream of different ideal candidates, and a sense of superhero idealism isn’t necessarily most important.
What’s more significant, both for the business and the worker’s career prospects, is the ability to communicate and present information clearly, Zullo said.
“A lot of [data scientists] only understand the numbers and the data,” she said. “They don’t understand what the data means and, taking it to the next level, how that data is going to improve the business.”
Job candidates who can communicate the real-world, bottom-line effects of the data in client-facing presentations have the best opportunity to move up within their companies.
Data scientists with analytics skills have wide marketability in healthcare and beyond. Carey said that can sometimes be a challenge for some healthcare organizations—particularly nonprofits—who have trouble keeping up with the pay scales of other tech and financial firms.
To wit, Carey said the first class of SLU health data science graduates found a very pleasant job market.
“We have huge demand. Every one of our graduates so far has had multiples job offers,” he said.
In fact, the university itself wanted to hire one of the graduates, Carey said.
“And we were unsuccessful because we couldn’t pay them enough money,” he said.
Zullo, who focuses on commercial clients in the pharma, biotech, and medical device spaces, said pay usually isn’t an issue for her clients. Most of the positions she fills have comparable or better salaries than those in other industries.
But healthcare offers something not every industry can: job security.
“A lot of candidates see healthcare as a really good career path, to be quite honest with you,” she said. “Healthcare is mostly recession-proof.”
Another important trait—at least for the moment—is the ability to build from the ground up. That’s because many organizations are still dipping their toes into data science and analytics, and thus don’t have the expertise to fully understand what a data scientist will bring and where they will fit into the organization. He said graduates of the health data science program need to have something of an entrepreneurial spirit.
“That’s absolutely true, and that’s rapidly changing,” he said. “In five years, that will be different. In three years, that will be different, once medium-sized companies have more of an analytics staff in place.”
Like Zullo, Carey said he’s found that when organizations understand the potential of analytics, they’re eager to grow the department as quickly as possible.
“Actually, it’s pretty amazing what you can do for most organizations,” he said. “And when I come into an organization to do consulting or help them out a bit, invariably the kind of reaction is they were blown away.”
And even the relatively basic things he can do with analytics earn that “blown away” reaction.
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