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In this episode, we sit down with Kyna Fong, a health care executive, to explore the evolution of health technology and its challenges. From the rapid adoption of electronic health records (EHRs) to the current usability crisis, Kyna shares insights on how AI can transform the health care landscape—if we learn from past mistakes. We discuss the potential of AI-powered tools to reduce administrative burdens and improve patient care.

Kyna Fong is a health care executive.

She discusses the KevinMD article, “What if AI focused on respecting the craft of medicine?”

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Transcript

Kevin Pho: Hi, and welcome to the show. Subscribe at KevinMD.com/podcast. Today we welcome Kyna Fong. She is a health care executive. Today’s KevinMD article is “What if AI focused on respecting the craft of medicine?” Kyna, welcome to the show.

Kyna Fong: Thanks so much, Kevin. Thanks for having me.

Kevin Pho: So let’s start by briefly sharing your story and journey.

Kyna Fong: Oh, my story and journey. Well, it goes back to, I guess, when I was a kid. So my dad is a family physician, and I grew up in Canada. And one day when I was about to start high school, my dad came home and told me and my siblings that we’re moving to the U.S., because he felt he could be more true to the practice of medicine there.

And so that’s when I kind of encountered health care directly, firsthand. My brother and I, my older brother, who I started the company with, ended up helping him start his practice, run it, do the back office for about a decade. And it was in our journey of trying to put an EHR into his practice that we ended up doing a side project to see if we could build something that actually focused on the physician patient relationship, as opposed to what we saw as sort of glorified billing and administrative tools that were asking physicians to use for clinical needs as well.

And so yeah, we did that, and one thing led to another and started the company, and here we are. So that’s a short form version of my journey professionally. Prior to starting Elation, I was trained as an economist. I did my PhD in game theory and health economics, and was actually a professor at Stanford for a short bit before diving deep into Elation.

Kevin Pho: All right, so you’ve been in that kind of health IT space for a while now. You’ve seen the evolution. What would you say is the current status of health IT in general, growing from its roots from glorified billing systems to what it is now? What would your assessment be of the health IT space in terms of facilitating or impeding that doctor patient relationship in the exam room?

Kyna Fong: Yeah, that’s a great question. I mean, I think in terms of the health IT landscape, I think it is still the case, as it relates to clinician technology, that EHRs are still sort of a massive presence in health IT. And of course, many of the incumbent systems and legacy systems have existed for a very long time, decades. The technology was built multiple decades ago. They’re not the latest and greatest technology that’s available.

And so what I’ve seen really over the last decade or so is a strong desire within health IT for innovation, and a proliferation of tools that kind of try to work around the EHR. They try to work on top, they try to work alongside, they kind of find every which way to interact with the EHR, but they find the EHR sort of a barrier to their innovation. And that’s sort of the push and pull that I think has really existed for the last decade.

And few of the EHRs have actually really stepped up and said, hey, we are the platform, we can enable this innovation, these much more powerful, productive, and quality of patient care enhancing capabilities. Like we can do this, we are the best positioned to do it. I think that opportunity has not really been jumped upon, and I think it’s a huge opportunity.

And it segues a little bit into the article that we’re talking about today, because I think there’s a really unique opportunity in the industry today with the advent of AI and LLMs to sort of renew that call and say, hey, can we do better and really help the clinicians where they are in their workflows, as opposed to in add-on or kind of adjacent but not fully integrated ways.

Kevin Pho: All right, so let’s talk about that article, “What if AI focused on respecting the craft of medicine?” And AI has really come into focus over the last two years or so, and it’s been pretty disruptive to the health IT space. Just on my Facebook feed are just so many companies with these AI tools trying to make things easier for the physician to put things into the EHR. And I just see so much potential with AI in terms of integrating what’s already in the EHR, searching for tools, decision support, just so many applications. It’s a little bit of a Wild West, people are trying to figure out what the best use of AI is. So I’m interested in hearing your perspective. Tell us how your article came together, what led you to write it, and for those of you who didn’t get a chance to read your article, just tell us what it’s about.

Kyna Fong: Yeah, absolutely. So obviously when large language models kind of came to the forefront over a year and a half, two years ago, there’s a lot of excitement about those, what can it accomplish, how might it transform the way in which we can deliver value to our physicians, their patients, and really transform that interaction and that value coming from technology.

And so we’ve done a bunch of work, and in lead up to the article, one of the things that we had done was we surveyed our customers. We said, hey, what are you excited about in terms of AI, where do you see the biggest potential? And it was really interesting what came back. And one of the very interesting things is the degree to which, I think over a third, physicians aren’t known for rapid adoption of technology, but over a third of those we surveyed had already been trying AI in some various forms related to their practice. And there was a lot of really positive feedback around both what they were experiencing but also potential they saw on the administrative burden front, as well as the clinical effectiveness and productivity.

And so that survey was part of the journey. And ultimately what led to the article is just thinking about, tying back to what I was saying earlier, EHRs, there’s sort of a very well-known, well documented, well understood set of usability issues with incumbent legacy EHRs. I mean, they were built for billing, they were built for administrative compliance purposes. Clinical was an afterthought to the billing aspects, and so that clinical usability has really been an afterthought.

And if you think about AI and LLM function in terms of available technology, I think it behooves us to think about, well, it’s just a technology. You have to take it, you have to apply it, you have to decide what problems you’re going to solve, where are you going to take these powerful capabilities to make somebody’s life easier.

And I think there’s just a huge potential and opportunity here if we choose to apply it in the places to help ultimately frontline clinicians who are experiencing never before seen levels of burnout, frustration, leaving the workforce. And ultimately who suffers when that happens is the patients, right?

And a big part of why Conan and I started the company is seeing the quality of care, the strength of relationships that we saw in our dad’s practice, frontline, firsthand, and that we know happen in so many other practices as well. And we want that for not only our family and our loved ones, but everybody should have access to something like that. And I think this technology innovation has a real opportunity to be transformative as it relates to these challenges, and ultimately making the quality of care better.

So that’s kind of where the article came about, because I do see there are real decisions that need to be made about where we prioritize and apply this powerful technology. And just based on what we’ve heard from our users, and also now rolling out our own AI tools, I think we have the potential of a really transformative era over the next couple years.

Kevin Pho: So right now we’re seeing a lot of ambient AI technology, where a lot of these systems would listen to the interaction between the doctor and patient in the exam room and transcribe, and then using AI would turn that into a note that goes straight into the EHR and save time from the physician standpoint. What do you think about that approach, and what do you think are some of the other AI approaches and tools that we could take going forward?

Kyna Fong: Yeah, I mean, I think ambient scribes, at this point there’s a ton of data that speaks to the degree to which it can save physicians time, allow them to focus on the patient. At the same time, we recently launched our own ambient scribe called Note Assist that’s embedded into Elation, and what we’ve learned through that is that there is devil in the details too.

So things like, hey, physicians are heavily reliant on things like templates, to reduce documentation, to simplify, streamline, on things like templates to set their agenda for a patient, to make sure they cover the key things that they want, make sure they do for certain patients with certain conditions. And so in kind of embedding AI directly into the EHR, we have opportunities like, it’s template aware, so it pre-populates the templates. It’s aware of all of the chart context in terms of patient history, conditions, etc., and can pull that in.

And so I think that ambient scribes, just sort of the third party ambient scribes, are really just touching the surface of what is possible. They are already making a big impact, no question. And what’s so exciting is, even as you look at them, there’s so many opportunities to do more, especially with AI that is context aware, physician aware, aware of physician preferences, aware of sort of the broader population. It’s just exciting to see. So yeah, very, very bullish.

And also I think it speaks to, our experience has really also spoken to not just the opportunity but the importance of some of the challenges, like the importance of design, the importance of designing for trust, and other things that are completely new when AI enters the picture.

Kevin Pho: So give us an example of some of these context aware AI tools that you’re talking about. Maybe give a story in the exam room of what you see as the biggest potential AI has as the next generation of tools come to exist.

Kyna Fong: Yeah, well, it’s hard for me to predict what’s the greatest potential, because I think ultimately these AI LLMs are predictive. Really interesting is they’re very much not like software anymore, in the sense that they’re predictive models. They almost behave more like humans than they do technology.

Like when you’re building software, it is deterministic, it is consistent. You tell it what to do, what logic trees to follow, and the software does, without bugs presumably, it does what you’re telling it to do. Predictive LLMs are actually more like people. Like you tell a person, here’s what I’d like you to do, here’s how I’d like you to approach problem solving, and they do something, but it’s not always 100 percent what you told them to do, told it to do.

Now in some cases it’s better than what you would have told, exceeds your expectation for what you could have told them to do in a given situation. But at the same time, sometimes it can surprise you. And so I think thinking about that’s sort of one set of both challenges and opportunities. Because if you think about trained, knowledgeable assistants that are just available to you, helping you be able to care for your patients and do what you need day-to-day, the sky is really the limit. There’s a lot that can be done in that way.

But returning to some of the, I think, important challenges, are around, physicians have a really important responsibility in society, whether it’s through their training, the Hippocratic oath, or whether it’s truly through the liability that they take on as medical professionals. And AI in some sense is sort of like magic. Like it’s not entirely clear what’s happening underneath the hood.

So how do you bring the right amount of transparency, how do you bring the right amount of context to enable those professionals on the other side to be able to interact with the AI in a way that they are confident they can trust what’s coming out of it, and it ultimately brings in that productivity enhancement and that ability to spend more quality time with patients that I think everybody’s looking for at the end of the day.

Kevin Pho: I think one of the reasons why technology adoption among physicians is somewhat slow is that we’ve been promised a lot of hype when it comes to prior technology waves, only for that hype to fall flat, right? So with this AI wave and all the hype and news surrounding it, do you think this time will be different for physicians? Do you anticipate the adoption of AI tools into electronic medical records or into a daily clinician workflow? Do you expect this to be different compared to prior hype waves?

Kyna Fong: So my judgment is much more often on what have I actually seen that it can do, not what it might be able to do or what we might imagine it could do, but what have we actually seen and how transformative can that be.

And I would say that I’m bullish for sure. At the end of the day I think all of this still needs to prove itself, like prove itself through use, through engagement, through actual impact seen in the real world. So with that caveat, if you’re going to ask me if I think this is uniquely transformative, that this is hype, quote unquote, that we may actually point to in the future, look back on and say, hey, that was an inflection point in capabilities and technology, especially to enhance patient care, I think it’s a good shot.

But there’s a lot of work leading up to it. It’s not plug-and-play. You have to think hard about it, and you have to think in different paradigms about what you’re building, what you’re designing, and how it comes across to the end users.

Kevin Pho: We’re talking to Kyna Fong. She’s a health care executive. Today’s KevinMD article is “What if AI focused on respecting the craft of medicine?” Kyna, we’ll end with some of your take-home messages that you want to leave with the KevinMD audience.

Kyna Fong: Take-home messages. Well, I think along the themes of what we’ve been discussing today, I think technology and the building and design of technology is almost nothing without clinician input, clinician engagement, clinician partnership. And I would really encourage patience and thoughtfulness and engaging, because that is what is going to get us to that next level that we’ve talked about.

And there are a ton of lessons that we’ve learned at Elation in terms of how to best work with clinicians and get the best of what technologists bring and get the best of what clinicians bring. And I just want to continue to encourage that, because that is the only path to achieving kind of that next level of usability and value in these clinical technologies.

Kevin Pho: Kyna, thank you so much for sharing your perspective and insight, and thanks again for coming on the show.