Clinicians Are Going to Become Partners With AI

Q: David, welcome to the 150th episode of The Big Unlock Podcast. Padmanabhan had done a lot of the previous episodes of this podcast and I’m carrying on his legacy.

So, David, would you take a few minutes and like to introduce yourself to the audience and talk about yourself and the health system as well where you have been working? 

David: Yeah, sure. Thanks a lot for hosting me. It’s an honor to be here with you this afternoon.

I’m David Berger. I started out as an academic surgeon and surgical oncologist. I did my general surgery training at SUNY Downstate and a fellowship in surgical oncology at MD Anderson Cancer Center in Houston, Texas. I initially thought I would become an academic surgeon—maybe a surgical oncology division chief, chair of surgery, or even a dean someday—but my career trajectory shifted due to unique opportunities.

Early in my career, I undertook a management role as the Chief of the Operative Care Line at the Houston VA, the Michael E. DeBakey Medical Center. It’s the largest, most complex, and busiest VA medical system in the country. Around that time, as part of the sandwich generation, I was also taking care of both my children and my parents. I realized how difficult it was to navigate the healthcare system. If it was challenging for me—someone who knows the system and has contacts—I couldn’t imagine how hard it must be for those without the same resources.

This experience shaped my career goal: to make healthcare simpler. The healthcare system is extremely complex, and I saw the potential of digital tools to transform care delivery, making it more accessible and effective. While at the VA, I implemented sophisticated digital health tools. One notable project involved real-time location systems (RTLS) for managing our operating rooms. We reduced turnover time between cases by nearly 50% and achieved a 95% on-time first case start rate—remarkable for any healthcare system, not just the VA.

Building on this success, I moved to the private sector as Chief Operating Officer at Baylor St. Luke’s Medical Center. There, we implemented machine learning tools to predict which patients were at risk of sepsis-related decompensation. By intervening early, we reduced sepsis mortality by almost 50%. This was back in 2014, well before the recent wave of AI publicity, but it reinforced my belief that digital health, AI, and machine learning are poised to revolutionize healthcare.

After Baylor St. Luke’s, I returned to SUNY Downstate, where I had completed my training, to serve as the Chief Executive Officer of University Hospital at Downstate. I started in September 2020 and recently left to focus on my passion: digital health and artificial intelligence in healthcare.

Now, I advise early-stage startups, participate in advisory boards and venture studios, and am about to become an LP in early-stage venture funds supporting digital health companies. That’s a little about me and my journey.

Q: That’s really great to know, David. It’s fantastic that physicians like yourself are taking on this journey because your deep domain knowledge can drive meaningful change in digital health.

I also learned that you’ve been an entrepreneur. Could you share a bit about your entrepreneurial journey and the kinds of software or digital solutions you worked with?

David:  So, about 10–12 years ago, I was approached by an early-stage startup company based in Canada. They had come across an article we published on early warning signs for readmission after colon cancer surgery.

We had identified at least 10 early warning signs and developed a machine learning algorithm to predict who was at risk of being readmitted. This software company wanted to integrate our findings into their tool. We conducted a couple of successful pilots and started discussing a long-term relationship. The idea was that we, as clinicians and researchers, would provide the intellectual property to power their software. 

But we thought they were undervaluing what we were bringing to the table. So we actually went to Baylor College of Medicine, and they spun us off as a company, as our own company, from Baylor Technologies.

I learned a lot from trying to start my first company. Not all the learnings were good, but they were important.

What I learned is, one, if you’re going to start a startup, you have to devote all your time to it. You can’t do it on the side. I was heavily involved as a clinician and an administrator, and I was trying to do this on the side.

Second, you must have alignment of your board. We had five board members—me, two from Baylor, and two from the company that was helping us on the IT side. Well, the people on the IT side and the people at Baylor College of Medicine were diametrically opposed on the direction, and we got stuck and couldn’t move forward.

So after about running through a million bucks, the company died. But what’s interesting is that there are a couple of startups just getting going with the same idea we proposed and were trying to move forward 10 years ago.

So, the idea was the right one, but the alignment was poor, and I wasn’t committing full-time to it. Those are really important lessons for anyone trying to start. 

Q: Great lessons learned there. Yeah. And then, David, you were talking about how AI is, you know, going to be changing the landscape, and you have a certain way of looking at it, especially for healthcare systems. So we’d love to learn your thoughts about that—about AI, GenAI, and where you think it is going, and how one should, you know, kind of embrace it and adopt it to get more value. 

David:  So I am extremely excited about the potential for artificial intelligence to help us provide healthcare because there are so many repetitive processes in healthcare that are being done by people that probably can be done by technology.

When I think about a health system—and, you know, I was running a hospital—about the different places where artificial intelligence could have a major impact, I break it down really into four different areas. This is general; there’s overlap.

So the first one is obviously back-office functions—things like revenue cycle, coding, billing, documentation, pre-authorization. All those things that have to do with revenue cycle.

Then I look at the front end or access—giving patients the opportunity to interact with a provider, giving them the opportunity to schedule, how they’re going to pay for their healthcare, et cetera.

And then on the care delivery side, there are things that help make care easier to deliver, and then there are things that actually help provide care.

So let’s break down those two things. When I say make it easier to deliver care, I’m talking about things like artificial intelligence scribes or ambient listening, ambient dictation.

When I talk about things that help actually deliver care, I’m looking at adjuncts to help read X-rays. So one use case that’s fairly well-developed is the issue around mammography. An important fact and something that radiologists look at in mammograms is breast density.

Breast density sometimes predicts further cancer down the road. Radiologists are really bad at quantitating breast density, and there’s a total lack of correlation from one radiologist to another. But using artificial intelligence, you can improve the ability to determine breast density to almost 95 percent accuracy.

So that is a place where artificial intelligence could augment care and improve how care is delivered and the diagnostic accuracy. That’s what I mean when I say help facilitate care versus actually help deliver care.

Q: Understood. And David, in particular, just for a moment to segue into all the new generative AI technologies that are coming our way. It’s not just about text; it’s also about so many different forms of care and media that it can transform. So any thoughts on where you think generative AI might play a larger role or be more conducive in the healthcare setting?

David: Yeah, so in terms of generative AI, one of the solutions we implemented is something called AVO. And what AVO does is ambient listening—it helps physicians to write the note. But that’s just part of it; that’s not really the full generative AI component.

What it then does is pull relevant information from the electronic medical record to create a real functioning note. It puts in front of the clinician a differential diagnosis, as well as the clinical pathways related to that diagnosis. So yes, it’s ambient listening, which is important, but it’s also natural language processing. It helps to generate feedback, a differential diagnosis, and then the proper treatment pathway for that patient.

It also presents the note in a way that facilitates coding, billing, etc., for the hospital or system to get reimbursed. That has been a game changer for our patients.

Thinking about implementation, though, the implementation of any new tool is really, really challenging. What we found with the ambient listening and ambient dictation is that there was a different level of adoption between specialists like surgeons and primary care doctors. Why do you think that is? I’ll throw it back at you.

Q: I couldn’t even guess that. First of all, I’m thinking to myself, is it the primary care physicians who adopt it more, or is it the—yeah?

David: It’s the specialists and the surgeons who adopt it more readily. For them, a note is something they have to do; it’s not really what they want to do. They want to focus on their operation, their procedure, etc. So, for them, the note is more of a barrier to doing what they really want to do.

For a primary care doctor, one of their primary goals is to create a detailed note. So, for them, the nuances around creating the note are really important. Whereas for a surgeon, they don’t care as much if it’s perfect—they just want to get it done. So, the adoption was much quicker with surgeons than with primary care physicians.

We also implemented it in the emergency room, which is a tight space with a lot going on. Some of the ambient listening got distracted because of other noises in the environment. We had to figure out ways to ensure that, during the interaction between the clinician and the patient, there was as little ambient noise as possible. But we worked through that, and the adoption was incredible.

People have talked about AI replacing physicians. Five years ago, someone came out and said that within five years, there wouldn’t be any need for radiologists. Well, clearly, that hasn’t happened. But I see AI being used as an adjunct to clinicians—helping them to be better at what they do, not just in terms of efficiency, but also in improving accuracy in care and diagnoses.

I think clinicians are going to become partners with artificial intelligence. Another area where I think AI, generative AI, augmented reality, and virtual reality are going to have a huge role is in education.

Even in teaching within the operating room. Right now, the way you teach in the operating room is by working with a resident across from you. Over time, you give them more and more experience and freedom to conduct the procedure. But think about this: if you have really good augmented reality, you could have them work with a headset in an environment outside of the operating room to hone their skills in a space where there’s no risk to patients.

So, there are amazing opportunities for augmented reality and virtual reality, not just in education, but also in surgery. For instance, I used to do a lot of liver surgery. The liver is a three-dimensional structure, but the inputs—like where the vessels are or where the tumor is—come from 2D images. These are from CT scans, MRIs, or ultrasounds.

Then, once you’re in the operating room, you’re trying to reconstruct that 2D image in your mind into a 3D picture, relating it to the organ in front of you. It’s challenging, but you get better at it with experience. Now, with augmented reality, you can actually see, right in front of you, where the tumor is in relation to other structures within the organ.

This has been really well-developed in neurosurgery, but it’s an exciting opportunity for AI, virtual reality, and augmented reality in other types of surgery as well.

Q: Yeah, great example, David. So, my last question is about your new venture or the new role you’re going to be taking on soon, as you described earlier in the podcast. Which aspect are you more excited about? Because you’ve seen the startup side of things, and now you’ve been in a large health system. Where do you think you’ll be spending more of your time and adding value?

David: So, I think healthcare is slow to change from the inside. A number of the bigger systems have developed innovation labs and are trying to move the field forward, but I think a lot of the change is going to come from outside—from companies outside of healthcare.

What I’ve decided to do is move away from running a healthcare system or hospital to being more involved in the digital health and AI ecosystems. I think the way I can best contribute is as an advisor to early-stage companies within this space, but also as an advisor to investors and venture companies looking at which technologies are actually going to make a difference.

Since I have the perspective of both a clinician and an operator within the health system, I think I bring a unique lens to identifying which digital health solutions are really going to make an impact.

Q: Awesome opportunity, David. I’m wishing you all the best in this role? And then would you have any other closing thoughts for our audience? 

David: I think we are at an inflection point, between technology and the desperate needs within the health system. The costs have risen dramatically, people do not have adequate access and there are huge problems within our health system. I think we have opportunity right now to make a difference. 

To move healthcare forward and to improve the healthcare system within this country, I encourage people who are thinking about careers within healthcare to think about the innovation side and the digital health side, because that is really going to have a huge effect and move the needle. 

We hope you enjoyed this podcast. Subscribe to our podcast series at www.thebigunlock.com and write to us at info@thebigunlock.com   

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.