The machines are coming, and no, it's not a Terminator-like apocalypse. Artificial intelligence is poised to transform healthcare -- in some areas, it's already doing so -- and that means doctors and clinicians are wrapping their heads around automation, machine learning, advanced algorithms and predictive analytics. The future is here.
The technology has the potential to improve patient care, and provider performance, by helping clinicians make decisions based on reams of data and patterns. Automated tools are simplifying tasks and allowing doctors to focus more on the clinical side of things.
But to get the most out of AI, doctors and nurses need to be properly trained. Their already-important skill sets need to be expanded if the technology is to be leveraged properly. That's a challenge, and it requires a culture shift.
WHAT THE EXPERTS SAY
Dr. Clemens Suter-Crazzolara, vice president of product management for health and precision medicine at SAP, said any retraining efforts need to keep in mind the various stakeholders affected by AI. After all, the software should be there to support the person, not the other way around.
"That means you have to take this person on the entire journey with you," he said, "and make sure they understand, 'This is how I do things at the moment, this is how the algorithm is being built, this is the KPI that is being measured.' (With) a lot of the AI algorithms, you have to be very open about it."
Because of that, retraining efforts need to begin while the algorithm is being built, not after. The more closely management educates people and works with them throughout the transition, the more seamlessly it will be integrated into clinical practice.
PeriGen CEO Matthew Sappern sees retraining as a challenge that's largely predicated on the usability of the AI interface, particularly when it comes to things like alarms. And the designers of the software need to keep in mind that things have to fit relatively seamlessly into an existing workflow.
"That user interface is so important," said Sappern. "How intuitive can you make this information? … When we try to design tools to look at the patient, to us it's the degree of abnormality over time -- what's the trend?
"The ability to generate with great accuracy a readout on a patient is critical, but even more critical is how you communicate that information," he said. "It has to fit into the workflow, because you can't really change the workflow. It's like pushing a string uphill. You just can't do it. How do I put the alert where the eyeballs are? That's a real challenge."
John Showalter, MD, chief product officer at Jvion, said the crux of any successful retraining effort should be centered around retooling clinicians' collective mindset. Healthcare has moved to a very evidence-based approach to medicine, and with the advent of AI, there has emerged a more personalized approach to that evidence-based protocol. But it required a culture overhaul.
"There needs to be training around the fact that there needs to be a mind shift, and that this benefits patients," said Showalter. "If you get them to accept that and move forward then there's more nuanced training that says, 'For the patient, this is what this means.'
"We find that this thought process takes three to six months," he said, "and after that time they really get it, and in an expandable way, have a much better idea of how to incorporate that information into their clinical decisions."
PREPARING FOR THE FUTURE
First thing's first: AI probably isn't coming for clinicians' jobs. It's being used as an augmentative tool, focusing on areas in which doctors and nurses typically struggle, such as the processing of radiological images.
According to Recondo Chief Technology Officer Eldon Richards, AI may even create new jobs, although how things play out will be difficult to predict. They usually are when technology is involved.
He called AI the fourth wave of the industrial revolution, following the steam engine, mass production engines and computers.
"My job didn't exist before the industrial revolution, so it's been a little hard to predict what it looks like," said Richards. "There will be changes, some of them due to the fact that we have this new technology -- people gathering data in 2D models, that's something that didn't exist before AI.
"Let's say I work in the revenue cycle or I"m a doctor. Those changes can be unpredictable. The things I'm doing today may be automated or may be augmented. I don't think AI will be used to replace them, but maybe I'll split time between working with patients and reviewing downstream claims that are being denied. … Breakthroughs in the space are happening every day."