AI was a topic that dominated the debates this year. ViVE conference, which was held in Los Angeles in late February.
There’s certainly a lot of excitement around the technology — but it has yet to prove itself when it comes to solving the most pressing health care crises, like the clinical burnout crisis or that Major shortage of workers in the sector. From what experts told me at the conference, health systems aren’t seeing a ton of ROI when it comes to their AI investments, because the technology is still in an iterative phase.
That’s likely to change in the next two years, though, according to Mount Sinai CEO Brandon Carr.
During an interview at ViVE, he described a grid-like conceptual model for organizing his thinking around healthcare AI.
“I think about this 2×2 grid. In that, I think about the administrative things that AI is good for and the clinical things that AI is good for, from whether these patients are good for or good for the provider. [An AI use case] It doesn’t have to be in just one of those four boxes, but it helps me organize my thoughts,” explained Carr.
Using this organizational thinking, he has found that there appear to be many relatively low-risk administrative use cases for AI that are beneficial to patients.
For example, Carr pointed out that providers are starting to see success when using AI to steer patients in the right direction. That includes tools that remind patients to get screened, give them tips on their diet or encourage them to increase their daily steps, he said.
Things get more complicated when looking at administrative AI use cases that are focused on supporting clinician workflow.
Carr highlighted the EHR inbox as one of the biggest sources of stress for physicians. It would be great if doctors had tools to help them manage it better, but this enters an area where AI can be used to do more serious work, such as providing medical advice or Reading the imaging study, he noted.
Providers are starting to use AI to reduce friction in clinicians’ EHR experience, but they certainly don’t have fully automated processes like giving clinical advice or reading studies. For things like this, one needs to stay in the loop, Carr remarked.
And it’s the purely medical side of things where deploying AI is most dangerous, he declared.
“The clinical side of the home is where people are most scared — because you’re going to start using AI to either make clinical decisions, make diagnoses or recommend treatment pathways,” Carr said. “That’s where we have to be most careful.”
All that being said, he noted that health systems are “no more than two years away” from starting to see a substantial return on investment from their AI deployments. He predicts that radiology will be the first medical field to begin seeing significant improvements in AI-related ROI.
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