Clinical cognitive science is healthcare’s new tipping point

But the looming shortage of data scientists will likely slow down adoption.
By Tom Sullivan
01:13 PM

SAN FRANCISCO — Many of the technologies that healthcare and other industries have spent the last decade working on are coming together in new ways to enable the next-generation of care delivery.

These include not only the near-ubiquitous electronic health records platforms but also emerging technologies including artificial intelligence, big data analytics and machine learning tools.

“Clinical cognitive science is at a tipping point,” said Terry Sullivan, CMO of OnPointe. “It will be happening here in the next couple of years.”

[Also: How predictive analytics, telehealth helped one hospital make patients safer]

During a panel moderated by HIMSS innovation committee chair Santosh Mohan, Robert Taylor, global head of population health management and vice president of innovation at Siemens Healthineers, said there are plenty of big opportunities.

Taylor pointed to imaging, radiology and diagnostics as well as patient scheduling to ensure that follow-ups are not missed as areas where hospitals could do so much better.

[Also: Healthcare analytics leadership, it's complicated]

But there may be a hitch. Citing research that McKinsey Global Institute published last month, Bryan Bennett, executive director of the Healthcare Center for Excellence, said that the United States will face a 250,000 shortfall of data scientists by 2024.

“It’s going to take a few more years because there will be a shortage of data scientists in healthcare,” Bennett said.

That will include many industries but healthcare has its own challenges, notably that big banks and financial services firms, for instance, can afford to lure top talent with bigger salaries than hospitals can offer, Bennett added.

But with salaries ranging from $110,000 to $163,500 Bennett anticipated that the talent shortage will begin to ease up as colleges educate more and more data scientists.

[Also: Machine learning will replace human radiologists, pathologists, maybe soon]

When it comes to adopting anything new, such as smartphones, ICD-10 or processes, physician engagement is key, according to Leigh Williams, administrator of business systems at the University of Virginia Health System.

The upside when it comes to putting analytics to work is that physicians care most about patient care so they are susceptible to getting on board with the work and joining the mission.

“The tipping point is when it’s well-proven, it works, and everyone wants it because they don’t have to take a lot of risk,” Williams said.

Sullivan added that healthcare is poised to take that next step.

“Money was poured into the system and we have responded to the money,” Sullivan said of the federal government’s meaningful use EHR reimbursement and other programs. “These things we’ve worked on the last 10 years, EHRs and EMRs. We’re moving to 4G and 5G phones. All these different approaches have culminated in cognitive sciences, big data, machine learning and analytics.” 

Twitter: SullyHIT
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