Next Steps In Making Healthcare AI Practical

In recent times, AI has joined blockchain on the list of technologies that just sort of crept into the health IT toolkit.

After all, blockchain was borne out of the development of bitcoin, and not so long ago the idea that it was good for anything else wasn’t out there. I doubt its creators ever contemplated using it for secure medical data exchange, though the notion seems obvious in retrospect.

And until fairly recently, artificial intelligence was largely a plaything for advanced computing researchers. I’m sure some AI researchers gave thought to cyborg doctors that could diagnose patients while beating them at chess and serving them lunch, but few practical applications existed.

Today, blockchain is at the core of countless health IT initiatives, many by vendors but an increasing number by providers as well. Healthcare AI projects, for their part, seem likely to represent the next wave of “new stuff” adoption. It’s at the stage blockchain was a year or two ago.

Before AI becomes more widely adopted in healthcare circles, though, the industry needs to tackle some practical issues with AI, and the list of “to-dos” keeps expanding. Only a few months ago, I wrote an item citing a few obstacles to healthcare AI deployment, which included:

  • The need to make sure clinicians understand how the AI draws its conclusions
  • Integrating AI applications with existing clinical workflow
  • Selecting, cleaning and normalizing healthcare data used to “train” the AI

Since then, other tough challenges to the use of healthcare AI have emerged as the healthcare leaders think things over, such as:

Agreeing on best practices

Sure, hospitals would be interested in rolling out machine learning if they could, say, decrease the length of hospital stays for pneumonia and save millions. The thing is, how would they get going? At present, there’s no real playbook as to how these kinds of applications should be conceptualized, developed and maintained. Until healthcare leaders reach a consensus position on how healthcare AI projects should generally work, such projects may be too risky and/or prohibitively expensive for providers to consider.

Identifying use cases

As an editor, I see a few interesting healthcare AI case studies trickle into my email inbox every week, which keeps me intrigued. The thing is, if I were a healthcare CIO this probably wouldn’t be enough information to help me decide whether it’s time to take up the healthcare AI torch. Until we’ve identified some solid use cases for healthcare AI, almost anything providers do with it is likely to be highly experimental. Yes, there are some organizations that can afford to research new tech but many just don’t have the staff or resources to invest. Until some well-documented standard use cases for healthcare AI emerge, they’re likely to hang back.

The healthcare AI discussion is clearly at a relatively early stage, and more obstacles are likely to show up as providers grapple with the technology. In the meantime, getting these handled is certainly enough of a challenge.

About the author

Anne Zieger

Anne Zieger

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.