Health-e Law Podcast Ep. 26, Pt. 2
AI Adoption in Healthcare: Opportunities, Risks and the Future of Care Delivery
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Listen to the podcast released June 11, 2026, here:
Welcome to Health-e Law, Sheppard’s podcast exploring the fascinating health tech topics and trends of the day. In the second part of this two-part episode, Cora Han, Chief Health Data Officer for University of California Health, joins partner and host Michael Orlando to discuss the current state of AI adoption across healthcare systems, including deployment, governance challenges, regulatory developments and the future of AI-enabled care delivery.
About Cora Han
Cora Han is Chief Health Data Officer for University of California Health and Executive Director of the Center for Data-driven Insights and Innovation. She also serves as Co-Chair of the Health System and Provider Advisory Board for the Coalition for Health AI (CHAI).
Drawing on her extensive experience in AI strategy, regulatory advocacy, and data privacy, Cora leads efforts to establish consistent guardrails for the use of health data with AI vendors and third-party collaborators. Her work spans the full spectrum of health data challenges, from de-identification of clinical data to navigating HIPAA compliance and AI vendor relationships, making her a leading voice on responsible AI adoption in academic health systems.
Before joining UC Health, Cora spent over ten years at the Federal Trade Commission, most recently as Senior Attorney in the Division of Privacy and Identity Protection, where she focused on data privacy and consumer protection, including a term as Counsel to the Director of the Bureau of Consumer Protection. Prior to her tenure at the FTC, she practiced at a leading international law firm, where she counseled clients on copyright and trademark matters. Cora also served as an Adjunct Professor of Consumer Protection Law at George Mason University School of Law for five years.
Cora holds a BA in Government from Harvard University and a JD from the University of Chicago Law School.
About Michael Orlando
Michael Orlando is a partner in Sheppard’s San Diego (Del Mar) office. He is team leader of the firm’s Technology Transactions team, a member of the Life Sciences, Healthcare and Artificial Intelligence teams, and co-leader of the firm’s Digital Health & Innovation team. Michael has more than 20 years of experience advising health technology companies, insurers, healthcare systems and providers, academic medical centers and research institutions, medical device manufacturers, and pharmaceutical and wellness companies on intellectual property and business transactions in key strategic areas, including EHR systems procurement and integration, telehealth, mobile health applications, clinical decision support technologies, artificial intelligence, data use, wearable devices, remote patient monitoring, and other medical devices, research and collaborations, patent licenses, software licenses, joint ventures, mergers and acquisitions, revenue cycle management, and other outsourcing transactions.
Michael founded a software-as-a-service company before entering private practice and completed an in-house secondment at a publicly traded biotechnology company, an experience that informs his practical and business-focused approach to client engagements.
Transcript
Michael Orlando:
From hospital boardrooms to startup war rooms, this is Health-e Law. Powered by Sheppard’s digital health and innovation team, we bring you quick and candid conversations with industry leaders, bringing sharp analysis and critical insights into what’s next.
This is the second of our two-part episode with Cora Han. In this episode, we will explore the use of AI tools in the UC Health system, and the opportunities and challenges Cora sees in her roles as Chief Health Data Officer for University of California Health and Executive Director of the Center for Data Driven Insights and Innovation.
So, for listeners who haven’t worked inside a large academic health system like UC, how would you describe the current state of AI adoption across health systems and large providers, and what are the most significant opportunities and risks around AI adoption that you’re seeing right now?
Cora Han:
Thanks, Michael.
So, I will start by saying that AI in healthcare is not new. For many years, health systems have been utilizing AI for improving administrative workflows, for imaging, for predictive analytics.
But what really is new, I think, is the pace and the excitement around adoption.
And with the advent of the generative AI tools, there is also much increased, I would say, accessibility of AI tools, and so what we’re really seeing is interest across all aspects of the healthcare delivery system.
And also a shift from there being more pilots and experiments going on in research labs across academic health systems to a desire to do full-scale, enterprise-wide deployment.
Michael Orlando:
So let’s shift to actual use cases. Where do you see realistic opportunities for AI in healthcare?
Cora Han:
So, on the opportunity side, reducing clinician and administrative burden, I would say, is very high on the list.
Tools like ambient scribes, inbox management, coding help, all of those things are things that we are indeed working on and have great potential to both save time and effort. It also has the chance, and we’re seeing this in some of the reading and articles coming out about the ambient scribes, to change the nature of the clinician interaction with the patient, and have clinicians focus less on their screens when they’re interacting with a patient, and instead be able to devote more of their time to patients.
So another area that has always been an area of focus, and that we continue to see a lot of use cases across our health system on, is predictive analytics. And that can be anything from predicting different types of diseases to how do you predict which kinds of patients in your primary care population, for example, are at greater risk of being readmitted to the hospital or to the emergency room. And how do you do outreach to those patients in advance to provide additional care management, so they might be able to avoid those visits.
I’ll mention one more kind of immediate use case, and that’s quality reporting. We do quality reporting from our center, and I know there was a study out of UCSD, maybe in late 2024, that was published, where they found that LLM tools could be helpful to save time with that quality reporting while still maintaining the efficacy of that reporting. And as those efforts improve, I think that will also be a big area of focus.
Michael Orlando:
Okay, now that you’ve identified some use cases, I’d really love to hear which of those use cases for AI you’re most excited about.
Cora Han:
So, let me then step back and say, I think in addition to all of the opportunity around being able to improve and make ROI and immediate workflows more efficient, one of the things that I’m excited about is that there is the potential, I think, to remake some of the care models to solve some of the intractable challenges that I think exist in healthcare.
So, for example, how do we get care into healthcare deserts? Much-needed care. How do we really alleviate the clinician shortage and burnout problems that are generally recognized as problematic and really challenging for the industry?
How do we take the expert care that’s provided in tertiary care centers and make it available to those who don’t live near a tertiary care center? Those are some of, I think, the big opportunities that these tools may be able to help with, and I think that is really exciting.
Michael Orlando:
I totally agree with all that, but it obviously doesn’t come without challenges. Can you take a minute to talk about the difficulties you’re seeing in implementing these types of AI solutions?
Cora Han:
So, the first is, we are just being inundated with solutions across all aspects of the healthcare delivery system. And I think it’s exciting that there are many tools being developed, but also it makes it hard for existing governance processes to keep up with that kind of velocity.
And that is a challenge. And I’ll say that we are, I think, lucky to have, across our academic health systems, resources that other health systems may not have.
So, we have data science resources, we are able to put together multidisciplinary teams to examine and vet use cases.
We have processes for how to prioritize incoming requests and to take in ideas from a wide range of stakeholders.
And still, it is very challenging to keep up with all of the desires that people have to really implement these tools very quickly. So that is a challenge.
A second one I’ll mention is that the regulatory and the policy environment is really evolving very quickly. So, for example, on the federal side, I see a definite push in a deregulatory way, I would say. And that has impacts on how we react.
And then a very, very active set of state legislatures and proposals coming through year after year. California definitely a standout, I think, in how active its legislature is. And so there is a lot to react to, and a need, I think, to be nimble and evolving in how governance structures work.
So, another challenge is the need for increased transparency. So, when you’re talking about AI tools, I think clinicians and those who are implementing the tools in the healthcare delivery system really need to know what the tools are, how they work, how they were trained and how they are performing.
Patients want to know how these tools are being used in their care delivery, and how they might be impacting them, I think, in material ways.
And because one of our goals in governance is to try and establish transparent, replicable processes, institutions, I think, want to know so they can be able to make appropriate decisions about risk. And so that is something that we’re always putting in effort to do.
And then the final, but this is by no means the least significant, I would say, are complex implementation workflows. So, what we have noticed is that even though a tool may be vetted, how it is actually implemented has a pretty significant impact on whether its utilization will be successful.
To give maybe another way of looking at it, the FDA, I think at the end of 2025, had authorized about 1,400 tools. So 1,400 FDA-approved tools, but only a small subset of those, I think, are really being shown to be utilized in an effective way on a day-to-day basis.
And a lot of that is because deploying it into workflows can be challenging. And so I think what we’re seeing is recognition of that, and a focus, even on the funding and research side, of how these tools are actually working in the healthcare delivery system. Are they improving care? Are they delivering the ROI that we wanted them to?
Michael Orlando:
That’s a lot of great info on how you’re using the AI tools and the ways to look at it.
Well, Cora, I really appreciate the time you took today from your busy schedule to talk with us on the podcast, share your knowledge and wisdom. I think it’s really insightful, and a lot of people are going to really appreciate all you had to say today, so thank you.
Cora Han:
Thank you, Michael. It was a pleasure to be on this podcast.
Michael Orlando:
That’s a wrap on this episode of Health-e Law, powered by Sheppard’s Digital Health and Innovation team, where health innovation meets legal expertise. Until next time, stay healthy and stay informed.
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