The following is excerpted from the question-and-answer section of the transcript.
(Questions from industry analysts are provided in full, but answers are omitted - download the transcript to see the full question-and-answer session)
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: Great. Welcome, Will. So maybe we could kick it off here with a discussion on some of the risks and perils of general purpose models in health care.
So we've heard a lot about large language models. Are these a good fit in health care?
William Manidis
Sure. It's undeniable that chat GPT was kind of a break glass moment for industry, right? We went from having 20 years of AI promise to having a
technology that is in everyone's hands that is intelligent and broadly applicable. The issue is we haven't benchmarked that technology or built in
a way that is responsible for health care. The models that we serve to customers are a reflection of the data that they're trained on. And as we move
from prototypes to production, to patients, it's important that we reflect on the data these models are trained on, how we deploy them in the
safeguards we build.
At Science, we focus on building the world's safest large language models like we used in health care. And our collaboration with Veradigm is
focused around getting access to high-quality data across disparate patient populations to ensure these models can be used safely and well across
industry.
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: And can we discuss maybe some of the pitfalls of using these as it relates to health care space. I think something that we've heard from others as
they think about building out AI models and health care is hallucinations, things can appear differently than they are or there's a real problem with
health equity and making sure that you're getting information across a really wide spectrum.
William Manidis
Sure. I would say as we move into prototype across industry, many folks are deploying generalist models and seeing very strong results because
they're piloting on small patient populations that are largely homogeneous. As we move into health care broadly, and we see patients that are as
diverse as the folks in this room and more, having these models be safely deployed across that context, is incredibly difficult. And being able to
benchmark where the shortfalls of these models are is largely a problem that I don't think the industry has taken seriously enough, right?
Hallucinations step 1, but step 2 is the bias in the underlying data, the bias and the kind of observations we make and the patients we see and the
results these models put out.
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JANUARY 10, 2024 / 7:15PM, JPM.N - Veradigm Inc. Presents at J.P. Morgan 42nd Annual Healthcare Conference
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: And maybe another one is, how is how -- maybe both of your companies kind of positioned for the future of AI and Health Care? And also, how
are you prepared to maybe close the gaps in health care for some of these underserved populations?
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: And maybe we could spend a little bit of time addressing some of the revenue models for Applied Healthcare AI.
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: You talked a little bit about -- before about kind of providers being slow moving, sometimes resistant to change. Can you maybe spend some time
talking about what are the biggest hurdles to creating some of these intelligent healthcare tools.
William Manidis
Yes. I don't think it's provider excitement. Honestly, if you go on TikTok today and type in chat GPT doctor, you will see physicians using chat GPT
for their daily practice, from submitting prior authorizations to filling out documents. Providers want this tooling.
The issue is getting that tolling in their hands in a way that is safe and reasonable for their actual use. I think a really funny example is if you search
that on TikTok, the most prominent video was a provider filling out a claim denial form. And the citations that ChatGPT references are actually
entirely hallucinated. They're not real insurance policies. So it's not that providers are skeptical. The tooling are difficult to take it up, but it here
going outside of the tooling they already use and often doing so in unsafe ways rather than be able to serve them responsible tooling and the
infrastructure they already use.
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: And maybe just one more on AI. How do you envision the future impact of AI on health care?
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JANUARY 10, 2024 / 7:15PM, JPM.N - Veradigm Inc. Presents at J.P. Morgan 42nd Annual Healthcare Conference
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: I mean it's funny to think -- I mean, the EMR is such a source of friction and burden for the provider in the health care ecosystem, it's kind of an
interesting perspective to maybe fix that problem.
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: Maybe just one more for you, Dr. Ho on just kind of Veradigm specifically. What are you most excited for as you look into 2024 now sitting in the
seat where you sit?
Question: Anne Elizabeth Samuel - JPMorgan Chase & Co, Research Division - Analyst
: Terrific. Well, thank you so much for joining us today, and thanks, everyone, for attending.
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JANUARY 10, 2024 / 7:15PM, JPM.N - Veradigm Inc. Presents at J.P. Morgan 42nd Annual Healthcare Conference
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