FEATURE · ANALYTICS/AI
Might ChatGPT Transform Healthcare? Pioneering Leaders Are Working Forward to Find Out
By Mark Hagland P
erhaps no other phenomenon in U.S. healthcare has been the subject of a greater amount of hype, expecta-
tion, and confusion, all mixed up together, than has the emergence of artificial intel- ligence (AI). When it comes to the famous “Gartner hype cycle,” the development of AI for clinical and clinical-operational uses in patient care organizations might at this moment be anywhere between the “Peak of Inflated Expectations,” the “Trough of Disillusionment,” or the “Slope of Enlightenment,” depending on one’s perceptions. Further complicating per- ceptions has been the emergence in the past several months of ChatGPT, a large language model developed by OpenAI launched late last year. That launch has intensified the complexity of a scenario in which needs, expectations, early advances, and yes, of course, many stalls and out- right failures, are adding up to the current landscape, one that couldn’t possibly be more layered and complicated. Yet amid all the hype, the expectations,
and the confusion, some organizations are making real headway. One example? Michael Hasselberg, R.N., Ph.D., the chief
digital health officer at the University of Rochester Medical Center in Rochester, N.Y., has been leading a highly promising effort to streamline messaging intended for clinicians and other staffers in the health system. Speaking to the origin of the ini- tiative, Hasselberg explains that, “The problem has been MyChart messages [generated inside the patient-facing communications platform inside the
Michael Hasselberg, R.N., Ph.D.
Epic Systems Corporation electronic health record system] coming into our clinicians’ in-baskets. We have not had a good system to triage those messages, going to a staff member, nurse, or provider. We’ve pretty much been sending all those patient-gen- erated messages to providers [physicians], and that’s caused chaos.” Given that situation, Hasselberg
reports, “Three or four years ago, we decided to focus on this to build natural language processing models to reliably
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and accurately triage messages, in order to send them to the right individuals.” Fast-forwarding to just a few months ago, he reports, the emergence of ChatGPT has turbocharged work on that project. “We’re excited because we’re one of the health systems that have access to GPT4 in Azure; we have our own instance of Azure. And because we have access to GPT4 on that instance of Azure, it’s secure and private.” And the exciting develop- ment has been that Hasselberg and his colleagues have been able to test GPT4 inside Azure, tuning the large language model to very reliably and accurately triage those messages, and, he reports, “It worked within two days.” Indeed, he reports, “Once we had tuned
the model, and prompted it, we ran it multiple times on our data. We looked at reliability: did it consistently send the same exact message to the same people? We got high 90s-level reliability back. And then we pulled random samples out of each of those buckets and sent them to random PCPs and asked them, should that message have gone to a physician, a nurse, a staff member? And the accuracy
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