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Artificial Intelligence: The Hope Beyond the Hype

By John Commins  
   August 29, 2017

"There was no way to piecemeal an entry into the EHR space. That type of approach to technology can be very painful. The lesson learned is that if you can avoid that type of faith-based investment, try to avoid it," he says. "Approaching machine learning as finding the biggest, baddest single vendor out there and betting the farm—that would make me very uncomfortable. The alternative is more cautiously step by step, with a smaller financial outlay, the focus being on early results and learning."

While acknowledging the snares and pitfalls of the HITECH Act, Mostashari says it's important to remember that today's promise of machine learning would not be possible without data created by EHRs. "Ten years ago the stuff of healthcare was not electronic. It was trapped in dead trees. What we were trying to do was really jump-start the transition that might have taken decades and compressed it into a four- to five-year period," he says. "To this day, I believe that you couldn't have had that without a strong role for government. In the case of AI, I don't see that same parallel. The private sector is fully capable of using these tools on this data platform that has already been built to solve market problems." 

Blum says the EHR rollout demonstrated a need to anticipate what new workflows would look like, and that's an important lesson for AI. "You can't build the EHR as a stand-alone that doesn't talk to anyone else. There needs to be data moving in and out of the EHR to other applications, and the AI algorithms are a class of those applications," he says. "You can't think that the whole process is done once you've implemented the EHR because there are many pieces that aren't touched. Advanced analytics like AI are not going to be intrinsically delivered by an EHR vendor. They're going to be powered by the EHRs' data, and they need to interact with the providers who are using the EHR. So you have to think carefully about how that is going to work out."

Metrics & ROI

For all the hope and promise of AI, at some point there has to be a return on investment, and so far that's been difficult to ascertain. How do you project costs and potential savings around an unproven technology? For that matter, how do you measure results to ensure you're on the right track? 

John Commins is a content specialist and online news editor for HealthLeaders, a Simplify Compliance brand.

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