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Dr. Tanuj Gupta, vice president of Cerner Intelligence, sees electronic health record companies as assisting with five main jobs in the healthcare world.  

There’s capturing healthcare information; summarizing that information quickly; coordinating care; securing reimbursement; and making critical decisions.

In turn, he explained during a HIMSS21 “view from the top” session on Tuesday, Cerner has a set of tools it’s been offering to accomplish those tasks.

Five years from now, or even 50, Gupta predicted, those jobs will still have to be done. But already, he said, “people are asking for different technology to do those jobs” already.  

In the panel, moderated by Amazon Web Services’ Dr. Taha Kass-Hout, Gupta and other industry leaders explained how artificial intelligence and machine learning are helping to take healthcare to the next level – and how COVID-19 has shifted priorities for that technology.  

Before the pandemic, Rush University Medical Center’s priorities were typical of an academic medical center, said its chief analytics officer Dr. Bala Hota.  

Essentially overnight, he said, the whole organization went through an agility-focused transformation.   

COVID-19 marked the first time the whole of Rush “craved numbers from the top to the bottom.”   

“Our dashboard” tracking the effects of the virus “became something distributed across our whole system,” he explained.  

Now, he said, the challenge will be elegantly introducing even more changes to a staff who is beginning to feel fatigued by the shifts.   

“There’s a lot of skepticism, I think, at the health system level about innovation that goes a little too far beyond where we are currently,” he said. If a technology isn’t ready to be deployed, he said, it can be time-consuming to get it up to speed for people who just want to care for patients.  

“Getting over that skepticism and bringing mature solutions to our providers and to our systems – that’s really key,” he continued.  

Hota is excited by the fact that AI and ML tools that used to be custom solutions are now mature. Rush can build predictive models, for example, to try and bolster its screening program and connect patients in need with social services.   

Gupta, too, said Cerner has experienced a big demand for machine learning.

“But it’s not as simple as developing a model, throwing it in there and then letting it go,” he said. The models need to be frequently calibrated, which takes time and resources.   

For eight years, he said, the vendor deployed “maybe three or four” clinical models to clients. In the last year, after shifting investments and building necessary infrastructure, they deployed two models 35 times.  

“These sound like small numbers, but they were big for us,” he said.   

This year, the team is on track to deploy 10 models, 350 times.   

“We’re starting to solve that deployment problem, with the help of AWS solutions,” he said.

Meanwhile, Anthem Chief Data and Analytics Officer Ashok Chennuru said the managed care company has “embraced AI.”  

“We have a lot of data – structured and unstructured,” he went on. “We’ve embraced digital-first and are building a healthcare platform … that really leverages the power of AI and being able to transmit that data into proactive, predictive and personalized insights.”   

As far as future use cases go, Gupta predicted that telehealth and remote patient monitoring will likely loom large, especially when it comes to using machine learning to interpret patient-provided data.

Another demand, he said, will involve forecasting.  

“Health systems need to predict catastrophes that are going to overwhelm both labor and non-labor supply, in advance,” he said.   

“We should be able to do it with reasonable certainty. We do it with earthquakes, we do it with forest fires, we do it with weather,” he said.

Kat Jercich is senior editor of Healthcare IT News.
Twitter: @kjercich
Email: [email protected]
Healthcare IT News is a HIMSS Media publication.

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