Q. Please talk about a little bit about your business and your journey in the healthcare sector. 

Zafar: Thank you for having me. Pleasure to be here. I am Zafar Chaudry, I’m the Chief Digital AI and Information Officer at Seattle Children’s. I have been here for seven years. Seattle Children’s is a pediatric health system in the Pacific Northwest in the United States. We have 47 sites in Washington, Alaska, Montana, Idaho. 

In terms of my journey to where I’m at now, it’s been a very convoluted one. So, I’m a recovering physician. I started my career as a doctor. A doc practiced for a X number of years and then very quickly defected to the dark side.  Joined technology in the early 90s when clinical people weren’t necessarily doing IT stuff and I thought this might be a good idea and then went through that journey, did some dot com before it went dot bomb and then leadership roles in health IT and I’ve had many over the last 38 years, uh, in different parts of the world, UK, US, Middle East, Asia pack. 

So I’ve, I’ve seen a lot of healthcare delivered in different ways. And I work now in pediatrics because I have worked in pediatrics prior to this. And I like working in pediatrics because There isn’t a day you come to work where you have to think about why you do this job because protecting kids and helping them get healthy is an easy job to do. 

So that’s why I’ve been here such a long time, hope to continue the good work. So that’s sort of my very quick high level on how I got here. 

Q. So Zafar, tell us more about how do you think about innovation at your system and how do you involve other stakeholders and people in that journey?

Zafar:  Innovation is always an interesting one, right? Because I always say to people that first thing you have to do in any healthcare environment is make sure that the basics work really well. 

Because doctors, nurses, allied health professionals, patients, parents want your basics to be good. Your infrastructure has to be solid, your end user compute experience has to be solid. So it’s a parallel journey. So you do the, what I like to call the lights on, doors open very well, focus on that. The innovation piece comes in parallel from what you learn in that journey. So a lot of times you can take what you learn from the basics and then figure out, well, how do I make this better? How do I automate? How do I apply technologies that would change the way in which we do things? At Children’s, our digital strategy isn’t a strategy that I come up with. 

I have a group of parents and patients. We meet every month and they tell me. What we should build, why we should build it, what they need, because fundamentally, when you’re doing innovation and new developments, you can’t do those in isolation. You have to have stakeholder engagement. So whether you’re talking to a group of doctors about what is a problem they need solving to patients who tell you, They need a way to find locations in your facility. 

Those ideas then come together for you to decide what your next level of innovation is going to be. Right. Whether it’s building an app, whether it’s using an automation tool, or whether it’s using, you know, the dreaded AI, which everybody talks about. Of course, of course. Those are all things that you have to figure out. 

Yeah, by listening, learning, teaching and collaborating, and that’s what we do very well.  

Q. That’s great to know. This is very interesting that you have a group of parents and even, you know, patients. meeting frequently to kind of brainstorm on what the changes or the ideas might be. So Zafar, would you like to talk about anyone or one in particular, let’s say that you think was a good example of such an innovation project?

Zafar:  So I’ll give you a very good example. So in one of the discussions two years ago, actually, one of the patients, and what’s interesting about kids. Is the average 12 year old kid is a lot smarter than me when it comes to IT, right? They understand it. They grew up in it. I was having a conversation with a group in that group and the conversation came around. 

One of the kids said to me, well, he didn’t think we were doing a very good job because when he’s here for a really long period of time, there’s nothing to keep him engaged, right? And I said, well, that’s true. Okay, so what do you need? And his idea was pediatric hospitals should have a form of gaming to keep kids engaged. 

And so from that simple idea, yeah. We are the world’s first organization to have our own Minecraft server, and we built our entire hospital in Minecraft. And we just launched that eight weeks ago on Twitch. So you can come to our hospital as a child, you can become a character in our Minefield Minecraft experience. 

And you can do everything that you can do in Minecraft, and we partnered with Microsoft and others to make that happen. And that’s all live. So that’s an example of innovation that I didn’t come up with. Yeah. A child suggested. Yes. We took the idea and we built upon it. I made something amazing. And you actually can see this whole launch on YouTube. 

You’ll be able to see it. So that’s awesome. That’s to me is real innovation. Don’t come up with an idea yourself and think it’s amazing. Let somebody tell you what you need. And because it took us about two years to build it and get it ready. Lots of kids have been involved in helping us design, build, test. 

Oh, and I’m not a gamer because I’m old, but I can tell you that when I play Minecraft with the kids on launch, you know, I was killed in seconds to do this stuff, right? But they know it. I mean, they live and breathe it. They’re building things down. So I think that’s how you innovate. You find out what people really are passionate about. 

And Put some really smart people together, figure out whether that has real value. Now, of course, when I go and now talk to parents and patients about this innovation, we’re getting huge amounts of positive feedback, right? I talked to parents who kids have had serious surgeries who are telling me that by using this environment, their kid was able to manage through the pain, right? 

That’s huge. That’s huge. Absolutely. And gaming therapy is the whole area. So my eyes were open to that. We jumped on it. So that’s just one example of how stakeholder engagement. Yeah. Get you to an idea. You can get you to something workable, but you can pass through to a go live. That’s hugely successful. 

Q. That’s awesome. That’s awesome. What I’m also curious to learn from you Zafar is that when these ideas bubble up and you know that you have to spend basically time, resources, money behind these initiatives to make them successful, how do you filter or how do you allocate the funds? 

Zafar:  So we obviously nobody has unlimited funds including us. You do have to do a prioritization exercise, right? But when you’re looking at prioritization and how you allocate funds, you look at key things. Does it affect patient safety? Does it improve patient experience? Is it a quality issue? Is it just an operational issue that needs to be solved? So you set up a set of criteria, but you don’t use those criteria against the list of priorities by yourself in isolation, that’s also a stakeholder engagement process. 

So we have to include our clinical teams and our non clinical teams and ask them, would they rank these things higher? In terms of this gaming initiative, for instance, this was kind of different. We do assign some funds to what I describe as RD. So because it was something that hadn’t been done before. I didn’t know if it would actually work, but that fell under experimental R& D. 

And I do allocate some funds every year for experimental, because I think it’s very important to be agile and it’s very important to be able to do something well or fail fast. And learn from that and then improve. So this type of project would have fell into that category. Other basic projects would fall into the bigger prioritization matrix, patient safety, all of those things. 

And then based on the money you have, you get your list and then there’s a cutoff point, right? So if you have a list, if I look at AI as an example, we have a list of 300 things that people want to do with AI. And we only have funding for maybe the top three. So we had to do a big prioritization piece of work to make that viable. 

And then we’d pick the top three items. to fund and test in that sort of particular process when it comes to AI initiatives as an example.  

Q. That’s great. So talking about AI, uh, Zafar, I know that you have probably been on the AI journey for some time now. And of course, the Gen AI wave is coming at us fast and furious. Things are changing at a very, uh, So walk us through how you’re dealing with this change and how you have kind of set up the infrastructure for success.  

Zafar: So everybody has to look at AI. It’s not going away. So you have to put some focus on it. The way we looked at it was the first thing in any step is to educate people. 

So we have made AI training mandatory. So every year there is a module that you have to take that explains AI to you. It’s pros, it’s cons. It’s pitfalls and people then take a test and they pass the test and then they, that’s their mandatory training for the year. In addition, we’ve put together an AI policy that has been ratified by the board. 

So everybody knows what the do’s and don’ts are of this technology. Third step is we have an AI review board that has multiple stakeholders from different domains. And that panel meets to review any AI request that comes in. Not only for funding, but it also reviews it for safety. It reviews it for the technology that somebody wants to use to make sure it’s safe to make sure that data is not lost in any way. 

Yes. And AI is an evolution, I think, not a revolution. I think we’ve been using AI, most people have for a long time. It’s gone from automation processes to, you know, You have a small data language model that you’ve applied algorithms to, and now as you get to Gen AI. You have a much larger language model, a much larger database, and therefore, the bigger the data feed, the better the outcome will be. 

And so we’ve evolved through all of those stages. And yes, we’re now looking at, you know, what does gen AI, we have lots of access to more data points. But you have to protect the data, you have to do all these things, you have to make sure nothing gets out. So that’s the review board’s process. If it’s approved, then we will build it or partner. 

To build it to get to prototype because I think with every AI you have to prototype and then you have to test heavily. So to give examples, Seattle Children’s is very well known. You know, we’ve been a top 10 Children’s Hospital for 30 plus years and we’re very well known for our clinical standard pathways of treating certain conditions with kids and we’ve documented all those pathways and over time those pathways are now disseminated to different organizations even across the world and they use them as the standard way to treat conditions. The problem is, you know, It’s hundreds and hundreds, hundreds of PDFs. 

Yes. So the old way of looking at how to treat a patient using a standard pathway was to A, find the relevant PDF, then read it, and then figure out what are you going to do. Right. Really long winded process. So we decided, well, how can you make this, uh, a process where you can just use natural language and ask the question, how should I treat a patient with these symptoms and see if we could get the answer. 

So we did, we took all of our material and we put it into the AI and the AI obviously read it and tried to understand it. Then we put a series of clinicians To test it. And so now we’re at a point where you can actually ask the A. I. The question. I have a kid with cough with these symptoms. What should the treatment protocol be? 

And it will find the answer. It will tell you what to do, and it will reference it back to the document. So it’s instantaneous, right? It’s not, look at me, I’ll come back to this in 30 minutes after I read a whole bunch of documents, right? And it’s evidence based because it’s only based on our evidence, which is all validated evidence. 

So we’re in beta phase and it’s working very well. We’ve had 15 teams of people look at this and that’s going to revolutionize how quickly care can be provided. It’s also revolutionary for training new docs because they’re learning and they want to ask a lot of questions so they can learn about different conditions and how to treat them. 

It will avoid them reading thousands of pages of documents. But just going to what they need to know, so that’s just one example of how you can, but that was through the review board, built a prototype model, and that’s with Google on Gemini, and then we are testing it once it’s finalized and baked to be safe. 

Yeah, it will launch it for all of our clinicians. And that will be a huge win because it will give us speed of treatment whilst it’s still safe, right? Of course. Those are all the keys of biggest worry you have in May Isaac to hallucinate. Yeah. But in this scenario, because the language that you’re feeding into it, the data is restricted to our validated data only. Yes. It has less likelihood to hallucinate because it’s pointing back to the reference document.  

Q. Great. Yeah. That is, I think the way to go. In fact, at BigRio, we have an accelerator of which does such a thing using, I’m using a technical term here, Zafar, but I think in the audience, many people know this already, the RAG  techniques.

So this is a fantastic use case that you have there, you know, congratulations on this one. So Zafar share with us any other. Key initiatives on AI or Gen AI that either you might be looking at in the near future or you think other healthcare organizations might benefit from if you’re looking in the crystal ball, you know, in the near future, short term and long term future. What do you see?  

Zafar: I think in this particular model of Gen AI, the sky’s the limit. So for us, the next thing we’re testing already is. How do you, so when a patient comes, we send the patient home, we give them discharge instructions. This is how you’re supposed to take care of yourself after you leave. The challenge is in a multicultural society, not everybody is, primary language is English and what we found here in Seattle is. 

Yes, English is predominant, but so is Spanish, so is Somali, so is Ukrainian. So we were still giving people discharge instructions in English. So we’ve done the same thing. We’ve built a model in AI that can take these instructions, accurately translate them into the language that you want, and then we’re able to give the patient the right instructions in the right language. 

That will be a huge win because many patients aren’t primarily English speaking. Yes, it seems like a simple use case, but we were failing because we were giving people instructions in English. They weren’t following the instructions. The problem is if you don’t follow the instructions, there’s a high likelihood that within the first 30 days, you’ll come back and come back. 

You could be readmitted. That’s not a good outcome. It’s not safe. So we’ve done that same process design prototype test. It’s in beta, and it’s working very well. And once we fully tested it, that’s another one that we could launch. And actually, that’s not even a specific use case for us. Anybody Yeah, could do that because we are unique in the fact that we are treating patients who have multiple languages, right? 

Every region in the country has that. So that’s just another way of looking at it. But these are all prioritized things. Everything, of course, in AI is limited by the cash that you have. Yeah, some initiatives. Can be inexpensive, but many are very expensive to launch more of an invest to save scheme. Yes, can’t immediately get gratification and savings and AI, although many people tried to tell you that’s the case. 

It’s not the case. That’s true. But you have to be very careful in what you invest in and then you see what that return could be over a period of time. So you need, you need a lot of patience. Yes. Obviously some tech companies will want to tell you that launch this and immediately you will see benefits. 

You don’t always immediately get the cash benefits you need to pay for ongoing. So what I like to say to people is AI is definitely the way forwards. It is. Revolutionary and how it will assist people to work in health care in the future and scale faster our services in health care, but it is easy to launch is very hard to sustain. And that’s the big takeaway for me is I can do 15 more AI launches. Right. Within weeks, but they won’t sustain because what everybody fails to remember in any technology world, it’s about the people and process and change management is really important. The key actually. You can’t have an AI because think about the fear of it. 

So I want to do an AI that translates discharge instructions. I think it’s a good idea. We’ve tested it. It’s a good idea. But the change management for that is assuring the people that we have that somehow it’s not just going to take your job. So even though we will give you a printout if that’s what you want in the language of your choice, that doesn’t take away from the nurse Who still needs to tell the parents and the kid what they need to do to take care of themselves at home. 

Exactly. It’s not that we’ll miss that step altogether and just hand you this paper and say, good luck. Of course. But people immediately think, Oh my God, my job’s going away. My job is going to be minimalized and they’re going to reduce all these people. But we wouldn’t do that because in nursing, for example, we have a shortage. 

Yeah. So to actually allow nurses to do their job and not be as stressed. Burnout is real, right? In the clinical side of medicine, then these tools, you still have to educate the user on why this tool is good for you and how you can sustain it versus, oh, here’s just another great tech idea. Of course, of course. 

Q. So one thing which I would like to touch upon before we wrap up Zafar is around the idea of what would be your suggestions and advice for startups in healthcare. There are so many startups, some well funded, some less funded at various stages from seed to series E sometimes. There’s so many. So many. So what would you say to someone who was wanting to do startups in healthcare?

Zafar:  So my best advice is, is that if you want to understand how healthcare functions, there’s a couple of points. One is, healthcare is the long game. It’s not the short game. If you build a startup in healthcare, You really need to be ready to go the distance. This is not instant gratification. The margins in healthcare IT are much lower. 

So if you build an AI startup in healthcare, A, it’s going to take you some years to build traction. So you’re going to have to have funding to do that. B, If you think you’re going to make triple and double digit margins from a health system, you’re fooling yourself because you’re not. It’s always single digit margins. 

The benefit in healthcare though, is once you do good work and you have a relationship, you will get those single digit margins for a really, really long time. It’s a relationship business, right? Yes. So if you’re one of those entrepreneurs who says, I just want to build the next company and cash out, I wouldn’t recommend health it based companies because they don’t happen that quickly. 

If you tend to follow that model, they flame out. I would certainly, certainly say that. The other thing I would advise startups is if you’re in this new space of AI. And you really want to make an impact and build partnerships and get the logos you need to grow your business, build real use cases that people can test. 

Yes, don’t come in and say you can deliver the world if somebody pays you a whole bunch of professional services, because that’s too risky for people like me. But if you came to me and said, I actually have built five use cases. That we’ve tested with sample data, but we would like you to try with your data, then I would probably try it. 

What I don’t want to be is be a software development house, right? I don’t want to spend a year and a million dollars in professional services building an AI model that actually ends up failing. Because that’s not a good use of our money. Our money traditionally should go into getting patients better. 

Yes. And healthy, not, of course, we did a partnership with you. We lost 500 K and didn’t get any output. So if you are brand new and you’re funded, build three good use cases. How would you build those use cases? Make sure you have an advisory group of clinical people, people with healthcare backgrounds who can guide you on how to build those use cases three use cases, then bring those use cases to your sales cycle, and you’ll have more success.  

That’s awesome advice, Zafar. And with that, I would like to thank you for sparing your time for this podcast. Really appreciate it.  

Thanks for having me. 

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.

Q. Would you like to start with some introductions, Joel, and talk about a little bit about your journey so far?

Joel: Sure, thanks very much for having me, Rohit. I’m Joel Klein, the Chief Information Officer at the University of Maryland Medical System. We’re a 14-hospital system here in Maryland and one of the largest nonprofit employers in the state. I think we might actually be the largest.

We’re one of the dominant health systems in the DMV area. Our academic health system in downtown Baltimore provides everything from multi-organ transplants to high-acuity trauma care, bone marrow transplants, and other quaternary care services.

In addition, we have many community hospitals, rural healthcare facilities, and ambulatory sites across the state. It’s an integrated delivery system that operates in close partnership with the University of Maryland’s School of Medicine, School of Pharmacy, School of Nursing, and others.

Although we’re a private company established decades ago and not formally part of the university system, we work very closely with them. We operate very much as an academic health system with strong community components.

Q. That’s awesome. Joel, I understand you studied to be a physician. I’d be very curious to hear how you moved from being a physician, and perhaps a practicing one, to this side of things.

Joel: Sure. I’m an emergency doctor. I went to medical school back home in Texas at the University of Texas Southwestern Medical School and completed my emergency medicine training at Johns Hopkins. After residency, I was hired at one of our community hospitals in the system, where I still work.

I was the “night guy” for many years, working night shifts and taking care of everyone. It was a very busy place—at the time, it was one of the biggest emergency departments on the East Coast, with over 100,000 visits a year, which is massive by ER standards. The hospital was surrounded by nursing homes and assisted living facilities, so I saw a lot of elderly patients with sepsis, hip fractures, and other “bread and butter” community emergency cases.

I was eventually elected to lead my group of 60 doctors, nurse practitioners, and PAs. In that role, I oversaw incentive compensation, which was tied to performance metrics. To manage that effectively, I needed data—something that didn’t really exist at the time, about 20 years ago. Performance was mostly judged subjectively.

To get the data I needed, I began collaborating with the IT team. I worked with them to track metrics like door-to-doctor time, throughput time, admission rates, and compliance with treatment protocols, such as administering the right antibiotics for pneumonia or sepsis. This partnership with IT helped me access the data I needed to objectively evaluate and improve performance.

Around 2012, our organization embarked on a digital transformation journey, implementing a single electronic medical record (EMR) platform across the entire system. This unified platform gave us all the same situational awareness of our patients and allowed us to operate more seamlessly. I played a leadership role in that project early on.

By 2015, I had officially joined the IT leadership team, and in 2019, I became the CIO. This is now my sixth year in the role.

I still maintain my medical license—I just renewed it, actually—and I remain on the medical staff at that community hospital. During the first half of COVID, I split shifts with my old partners, working early mornings, from 5 a.m. to 9 a.m.

However, balancing shifts with family life and the demands of being a CIO eventually became overwhelming. I stopped practicing about three years ago. While I could still go back to it if needed, my focus is now entirely on my CIO responsibilities.

Q: I’m very, very impressed by your early decision in 2012 to go for the single medical record and also start your digital transformation journey. With over five years in the CIO role, could you share with the audience some lessons learned, roadblocks, or difficulties you found along the way? Where do you think you are in the journey? Do you think it’s completed, or is there still a lot more to do?

Joel: There’s a whole bunch to talk about here, obviously, but if I were to pick out one key thread, it’s this: the thing we’re really trying to do in medicine is reduce unnecessary variation in how we care for patients. There is a best way, for instance, to manage things like pneumonia, sepsis, hip fractures, prenatal screening, or how a delivery in obstetrics should be handled. There are best practices in all of these areas.

One of the biggest tools that any healthcare organization has to drive consistency and encourage all members of the care team—doctors, nurses, therapists, and others—to practice consistently is by having everyone on the same platform. A common set of tools and visibility into patients allows us to use data to address that variation.

If you’re all on different platforms—if, for example, the eastern half of your organization uses one system and the northern half uses another—you’re always going to be fighting against that variation. You’ve got to get everyone on the same playing field, not just to have a common situational awareness of a given patient or part of your organization but also because it’s the way to get to consistency.

There are so many tools within electronic medical record systems that can help drive that consistency. If we don’t move towards reducing unwanted, unnecessary variation, we’re never going to get costs under control, and we’re never going to see best practices done everywhere as they should be.

We’ve gotten onto a common platform and are very far along in driving towards consistency, but this is a never-ending journey. It’s still something we’re actively working on as a system.

Q. It is very interesting that you mentioned about reducing variation and driving consistency. How would that come into play, Joel, if there was any situation of, let’s say, acquisitions where you now have to deal with a hospital system that came into the same portfolio or a new set of physicians and they are not used to the same platforms or the tools or the systems that you are driving?

Joel: Yeah. A lot of health systems are dealing with this right now. I mean, this is true for any acquisition, any MNA activity. So I think you could generally speaking, divided up into two categories. One is an acquired entity or an entity that joins, you know, a health system or, you know, a larger company, um, that is smaller and less sophisticated and less mature.

And in that case, usually they welcome the help, you know, part of that’s the whole rationale for why, you know, we joined up in the first place. You know, I, I know. Um, some of the organizations that joined our health system, you know, it’s change and change is always hard, but the opportunity to to benefit from all the hard work we’ve done over the last, you know, 12 plus years in the journey that I just described, you know, they get to benefit from that without having to put in all that sweat equity, uh, that we’ve developed.

So that’s one scenario. The other scenario is harder, and that’s when you have two kind of like sized organizations that join forces to try to get some economies of scale. And the question is, how are you going to navigate, you know, the digital side of that merger? And it’s hard. Um, typically speaking, you’ve got to pick, right?

You’ve got to say, well, let’s go with this one and let’s extend that umbrella out, right? And the challenge is having governance to say, well, how do you do it? How do we do it? And I think the hardest part is, you know, getting that, um, that pattern getting into a groove of compromise. And, you know, it starts with trust.

It starts with dialogue. It starts with the leaders. Uh, not just the I. T. Leaders, but the clinical leaders and the operational leaders, you know, having relationships with each other, um, and realizing that, you know, it doesn’t really matter, uh, necessarily whether we do it, you know, whether the wallpaper is this wallpaper or that wallpaper, because they’re both okay, let’s pick some wallpaper.

Um, and, and that way, you know, we don’t have to spend a bunch of energy maintaining both kinds of wallpaper. Yeah, and you know, that’s some of the hardest work there is. Um, and, um, but it’s, it’s part of what makes the job fun and exciting.

Q: I’d like to shift gears a little bit, Joel, and ask about how you approach innovation. What’s your thought process? In a large system, innovation is usually difficult to do. How do you gather innovative ideas from patients, your nursing staff, physicians, or any other constituents or stakeholders in the organization?

Joel: Yeah, right. I think you have to approach it in two ways. One, you have to, right? Everyone knows you have to water the plants or they won’t grow. So you need a way to listen for innovative ideas, collect them, and then find a way to put some gasoline behind them and get them the resources they need to try to run with them.

Sometimes that involves identifying people out in the organization who are not part of IT and bringing them into IT, connecting them to larger teams that can help them. Clinical engineering is a great example in healthcare organizations, where people are out there repairing things and dealing with biomedical devices. They’re not always part of IT. You need to find talent in the organization that can benefit from the resources IT typically provides, whether that’s project management or infrastructure that isn’t intrinsic to clinical engineering. That’s just one example. So, you’ve got to be listening, and you’ve got to find a way to run with the ideas.

On the other hand, we have the “good idea fairy,” right? When the good idea fairy shows up, each hospital might have two to three projects or pilots they want to run. All of a sudden, you have 50 new projects, and that’s just not possible. So, you’ve got to have some governance.

Some of that governance requires command and control. You need people who have the right eye for success. If you have a big committee vote on ideas, you’ll get into groupthink, and sometimes real opportunities will be missed. You need people who can smell something good when they see it, and you need a process that’s not a dictatorship or some backroom deal. It’s a tricky balance, but you have to balance all of these things for innovation to be successful.

Q: Can you share some examples with us or anything that pops into your mind of something that you and your team have done that is innovative and that you are thinking of taking further along?

Joel: Yep. So, you know, there’s a ton of examples, some of which we’re really excited about in terms of products that we’ve developed that we feel have a serious future on the marketplace. But speaking generally, and running with the theme of clinical engineering, we believe that, just like all of us run around with our phones and use them for so much of our daily tasks, we’ve been spending a lot of time in our organization thinking about the devices in our nurses’ pockets.

How do we get more and more of the technology that patients depend on—everything from clinical monitors, cardiac monitors, fetal monitors, ventilators, nurse call systems, telemedicine, and, of course, the electronic medical record system—into a clinical device? The goal is to make it indestructible, vomit-proof, and in a form factor that our nurses really love using.

A lot of the devices we have to choose from, like an iPhone, are consumer devices, right? They’re not designed for healthcare. Whether it’s from a form factor point of view or user infrastructure point of view, iPhones are built to be used by individual users, not shared devices. That’s not what we’re going for.

So, you’ve got to wrap all of that together. It’s a lot of engineering and it has to unify a ton of vendors. What I just described isn’t the work of just one vendor, right? You need a team capable of putting that engineering together from so many different platforms. That’s an area where we’re really working hard to create something that our nurses will love to use.

Q: So, you mentioned something very interesting that ties into the environment many of us are facing right now, where you have to do more with less, in terms of budgets and resources. You mentioned throwing gasoline on a project, and to do that, you need money, resources, time, and energy to pursue these initiatives. How do you make that happen in an environment where budgets may not be available?

Joel: Yeah, so it’s a bunch of tactics. One, you have to look inside. In any IT shop, there’s probably stuff you’re doing that you don’t really need to be doing. You’ve got to be really thoughtful about that. I’m not saying stop keeping the lights on; I’m not saying that at all, but you’ve really got to be thoughtful about whether the work you’re doing is adding value. There are always people who will try to hide behind work because it’s easy, so you’ve just got to pay a lot of attention to that.

The second thing is, you’ve got to make it clear to your organization the value of what you’re doing. You need to talk about what you’re doing and what’s possible because, you know, we assume the budget is X. In my organization, the narrative from our finance community is that we need to find a way to get IT more resources. Yes, there’s the narrative of doing more with less, but remember, from a capital point of view, we’ve got to build new hospitals and buy new medical equipment. It’s a very capital-intensive industry. So how you frame all of that is key. Do they know what you’re working on? You’ve got to be able not just to tell the story, but also to demonstrate the art of the possible. Some of that involves making sure that everyday users feel the impact of what you’re doing.

And then, the last thing is good governance. You can’t just pull stuff out and say, “We’re not going to do this anymore.” A much better choice is to give the organization options. You know, say, “We want to pull X million dollars in costs out of the organization. Here are five ways to do that. They’re all terrible, but what do you think is the least terrible?” And you want the right people around the table making that decision rather than me making a unilateral one. As a doctor, I could do that, and it might be more credible, but it’s better if I do it in lockstep with my peers.

Q: Since we’re talking technology, Joel, there has been such a rapid change in new technologies like Gen AI and large language models that have really changed the AI landscape. Everyone is experimenting with Gen AI in some form or another, whether they like it or not. So, what are some of your thoughts around AI and Gen AI? How do you see these technologies accelerating innovation or improving patient care and healthcare delivery?

Joel: You’ve got to think about cybersecurity and privacy. You can’t dump a bunch of protected health information into some Gen AI platform and risk that data becoming public or leaked through a malformed or malicious prompt. That’s a real issue we need to address as an industry, and our vendors will have to help us if we want to really lean into these technologies.

That said, there’s a ton that the industry is looking at, and we certainly are, too. Ambient technology is a big piece of this. We’re piloting a number of solutions, and it’s promising, just like many new technologies are. The challenge is whether every doctor is going to appreciate ambient documentation the same way. This involves having a microphone in the room, typically on the doctor’s phone, that records the dialogue between the doctor and the patient. The output is documentation of the visit, which can take 5 to 10 minutes per patient if the doctor’s typing, or maybe only 60 seconds to review if they’re looking over what the AI generated.

It’s promising, but a third to half of providers love it, some aren’t sure, and others aren’t convinced. So, how do we get these providers to adopt it? How do we coach them? It’s like autopilots in airplanes—there will always be those who say, “Back in my day, we didn’t have that.” But it’s a tool for safe and efficient flight, so how do we get people to embrace it? That’s one example.

Another big area is idea generation. Whether it’s for an individual patient or hospital-wide strategies like patient flow, how can we use AI to come up with new solutions? What are we missing? AI excels at thinking about things differently, and that’s incredibly valuable in healthcare. Those are just a few of the thoughts we’re considering.

Q: So, you’re already looking at possibly using GenAI, and it’s crossing functional areas now, so you can actually use it in many different functional areas at the hospital system. You mentioned that you were at a VC conference and that you might want to share some thoughts and ideas from your visit at the conference. So what are some of your thoughts there?

Joel: Yes. Well, I’d say two things. It was an amazing meeting. There were about 250 or so CIOs from all different industries, including some of the biggest companies in the country. I think what’s fascinating is how all of us in IT leadership are fighting the same battles. We’re all dealing with vendors that are engaging in really punitive price increases. They’ll acquire something and then jack the price 500%. It’s not going to work. We will find a way out of that, and it’s a huge problem.

We’re all dealing with AI, productivity vendors, and capital challenges. Whether it’s an ERP vendor or CRM or anything IT-related, we’re all doing the same stuff no matter what the industry is. It’s just fascinating to hear that.

The other idea that I heard was using AI for idea generation. So, you know, here’s a business problem we have—give me five unorthodox solutions that I didn’t think about, or even some really crazy prompts, like, “Imagine the following business problem—how would the Middle Ages have dealt with this?” And it sounds ridiculous, but it’s not, because it gives you a spark, like a little way of connecting things.

What I heard from a lot of my colleagues at big companies is that that’s what they’re doing to try to get an edge on their competitors. I love it, because it sounds wild and fun. It’s a great way to keep us all collectively engaged at the highest levels, solving some of the toughest problems we’ve got today.

Q: I would love to get your thoughts on value-based care, Joel. Are you already in the midst of that? Are you planning for that? And what are some of your suggestions and advice for people who are thinking about that?

Joel: Yeah, so value-based care is not new. This is something that I think the whole industry has been trying to figure out, in some cases very successfully, and in some cases not so successfully. It’s this whole idea of having skin in the game, for some block of covered lives.

I think it comes back to how much we can focus on primary prevention. As long as we have contracts—this is Joel’s opinion— as long as we’re talking in terms of single-year, time periods of responsibility, primary care is always going to take a back seat. So, in other words, if I’m really accountable for getting patients to lose 20 pounds because of the health upside of that from heart disease or hip replacement, there’s just so many ways. But if I’m only responsible for, you know, between now and the end of the fiscal year, I’m not going to worry too much about that. It’s too short a time period.

So, how do we think about that? To me, that’s something that the whole value-based care space needs to think more about. Then, how can we partner better with payers? Who are the intermediaries here? A lot of people say the payers just suck money out of the system and they’re just bureaucracy, skimming 10-15%. But I think it’s more complicated and nuanced than that. They do have the ability to help providers be more strategic about how they apply resources. I think there’s more than just, “We’ll give you some analytics or a list of things to do,” but I think there’s opportunity there.

Q: Would you like to share some final thoughts on any new initiatives around digital transformation that you’re thinking of in the next few months or years? What do you see coming your way or our way as an industry?

Joel: Part of why I love my job is the whiplash. I’m going from this conversation to a meeting about how we can support philanthropy more aggressively, and then my meeting after that is with our CTO to talk about all the projects we have going on there. And then my afternoon is focused on our informatics team and how it’s, I mean, that’s like typical.

So, it’s great because there’s so much to do in our industry. I don’t think I’m alone. And, you know, to sort of point to any one thing would be saying, well, I love this child best. There’s just so much. And to me, that’s what’s so much fun about this job.

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.  

Welcome to the Big Unlock Podcast, Piyush. It is great to have you as our guest today. I’m Rohit Mahajan, Managing Partner and CEO at BigRio and Damo Consulting. I would request you to please introduce yourself.

Piyush: I’m Piyush Mathur. I’m a physician, an Anesthesiologist, and work in ICU too. Originally did my MBBS in India from Armed Forces Medical College, Pune, and then subsequently through my journey came to Cleveland Clinic where I have been for more than 21 years now. Did further training in quality and education and for the last many years have been very focused on how can we impact the quality of patient care using artificial intelligence, which is a data driven science.

Q. Great to know that you started your journey as a physician. So, what motivated you? What choices you had at that point in time when you started your journey and how did you come to US?

Piyush:  The motivation was very simple. My dad, actually wanted to be in the armed forces and he always wanted me to be a doctor and always encouraged me to focus on that. I happen to be good in biology too, throughout my high school. So, it was a natural fit. I ended up at armed forces medical college for that reason, and then decided to further pursue learning and training in the US.

I didn’t know about Cleveland Clinic at all, being in India over two decades ago, and it was actually my last application for a residency program. When I matched here, I was really excited. Since then, I’ve pursued my career at Cleveland Clinic, training in anesthesiology and later in critical care. Working here with people from all over the world and learning from them has been an excellent journey.

Q. Piyush, I know that you’re pretty busy as a physician, but you still find time to do other business ventures. Could you tell us about those?

Piyush: Yeah, I always like to learn and grow. Being a clinician is important, but as we thought about applying AI in healthcare, we wanted to innovate and make it something sustainable—beyond a hobby or pure research. That’s when, along with Frank Pepe, chair of plastic surgery at Cleveland Clinic, we founded BrainX, an AI and healthcare company.

We were both very excited about this scientific opportunity for applying AI in healthcare. But there were a lot of challenges and there were a lot of misunderstandings somewhere around six, seven years ago and still continue to be. But that’s what we wanted to demystify, that how can we apply this exciting, rapidly growing science into healthcare to make our patient’s journey, a better journey, how do we empower our clinicians with this wonderful tools that the science represents so that they can be enabled to give better care to our patients.

How can we make the healthcare system more efficient using this data driven science so that the delivery becomes better? So those were all the questions that we were trying to trying to address. And we did believe together, collectively, that AI represented a great opportunity to solve that rather than just trying the tools that had been tried in the past.

And subsequently, we had other founding partners like Dr. Szymanski, Dr. Maeshwari, and Dr. Khanna join us. And we started working on exploring this, and over a time period, we had Raghav and Shreya join us as our technical leads and our research leads now. They are both PhDs in AI. And we have just subsequently grown leaps and bounds in this exploration.

Q. That’s great to know, Piyush. How many years has it been, and which are your focus areas? Tell us about work by giving perhaps one or two examples.

Piyush: We never anticipated that we’ll grow that rapidly. So, it’s just been six, seven years now since we started. Initially started with just our core team, the BrainX team. Eventually, there were so many other people who were asking the same questions what we were asking, and everybody was so excited. So, we ended up forming a community around us called the – BrainX community – so we can engage people from across the world. That’s a 6,000 plus member international community. Also, very active LinkedIn group with the same name called BrainX community. We have a website, a whole host of resources that are available over there. Free to join. All the content is free. As we grew, we also realized that, you know, we need to expand our research team.

We created one of the verticals that we have now within BrainX, our own lab called BrainX AI Research. Now we have people from across the world participating in our research work with all different backgrounds, all the way from undergraduates to PhDs. Everybody working together, trying to research different aspects of AI application in healthcare. All the way from basic data science, we are very focused on all those.

Q. We were very fortunate to have you as our keynote and spotlight speaker when we did our GenAI workshop near Cleveland Clinic. You spoke very deeply about the applications of AI in healthcare and GenAI in healthcare. Could you share with the audience some of the insights perhaps from either that talk or any others that you very frequently do otherwise as well?

Piyush: So, generative AI—or for those who may not know the term, AI like ChatGPT—is an application people are now familiar with. Tools like this have become widely available for public use, and there’s a lot of excitement around them. Our team was already focused on the predecessors of these tools, particularly in natural language processing during the pre-GPT era. So, as these fascinating tools became available, it was a natural transition for us.

It’s like being in a toy shop where developers are putting out all these different algorithms—we’re fascinated and excited to trial them. But it’s also a pivotal moment. Just like Steve Jobs made personal computers accessible to everyone, AI is now talking to people. People can interact with it directly, see how powerful it is, and integrate it into their daily lives.

This is generative AI’s “hello world” moment. But with all the excitement, we need to be careful, especially in healthcare. These tools need validation, testing, and possibly adaptation. One model might not fit all healthcare needs, so we and others worldwide are experimenting to understand the right approaches for applying these models.

What’s exciting is that these state-of-the-art tools allow people to interact in natural language, but we must ensure they are vetted and applied appropriately to help with patient care. That’s the exciting part.

Q. That’s great to know. And I am also super excited to learn that you have launched a new book, Piyush. I’m looking forward to reading it very soon. Would you like to give some insights to the audience about what is the contents of the book and what motivated you to write the book?

Piyush: Thank you, Rohit. We’re trying to put together our experience and expertise in that book. It’s called ‘Strategies for Artificial Intelligence and Healthcare.’ Y You can find it on Amazon. Frank Pepe, Jeff Mangus, and I co-authored this book. The idea was not to provide overly technical knowledge, as that already exists in other publications, but to focus on strategy.

Strategy is something I often find lacking across organizations or individuals—how to approach applying AI in healthcare. That’s why the sections of the book are laid out to address questions like why AI in healthcare, who, what, and so forth.

It’s an easy read and offers a framework for applying or learning about AI in healthcare, whether you’re a startup, hospital, or multinational company. We hope this book provides that key strategy element for mindful AI adoption in healthcare.

That’s great to know. So with that, I would like to finish our conversation for today. And I say this because we might have a follow-up podcast with you, as the landscape is changing so fast.

Q. What do you see in the crystal ball? What do you see in the next few months regarding healthcare adopting more AI and generative AI applications? If that’s what you’d like to talk about, or anything else you’re observing—whether it’s at the Cleveland Clinic or in your practice as a physician.

Piyush: The thing that excites me the most is the opportunity that AI brings. Look at this opportunity—right? You and I are talking about AI here, a physician and someone from the technological world. It’s brought all of us onto a common platform.

And what is it bringing us together for? It’s bringing us together for a common purpose, which is beautiful: to improve the delivery of patient care. That is what I’m most excited about, and that is what keeps me energized all day long about this opportunity.

I think we, on this platform, collectively working together, will clearly build great tools in the future, whether it’s with BigRio or through collaborations with many other companies we’re looking forward to working with.

The other part of this is the evolution of AI itself. If you see how fast-paced it is: we started with natural language processing a few years ago. It was slow for a while, and then suddenly, with the advent of generative AI, things accelerated.

Now, we are moving into multimodal AI. The excitement of a constantly evolving technology is exceptional. I haven’t seen such rapid growth in science or so many people engaged and involved in decades.

That’s what excites me and gives me hope. It keeps me optimistic that we are building a great future for our patients, our clinicians, and everyone involved.

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.  

Welcome to the Big Unlock Podcast. We are carrying on The Big Unlock podcast, which was started by Paddy Padmanabhan. This is 146th episode and we are very excited to have you as the guest and looking forward to our interaction. 

Shakeeb: Thanks, and appreciate you having me on. Glad to be the 146th. My name is Shakeeb Akhter. I’m the Chief Digital and Information Officer at Children’s Hospital of Philadelphia (CHOP), where I oversee digital and technology services, our overall enterprise digital transformation strategy, and all things ranging from infrastructure, applications, cybersecurity, AI, data and analytics. I’ve been in that role for almost three years now, and it’s been wonderful. I’m looking forward to speaking with you about the work we’re doing.  

Q. Shakeeb, what motivated you to start off in your healthcare journey? 

Shakeeb: That’s a great question. It takes me back about two years. My background is in finance and economics. I’ve had two career stories: about a decade in finance, starting with big four consulting in financial services, then moving into banking, specifically in derivatives quantitative modeling during the 2008 financial crisis. That’s when I really began to understand the intersection of technology, banking, and quantitative modeling. 

It was an interesting time, using data, analytics, and technology to drive things forward. During that period, I realized I wanted to do something more meaningful long-term—something aligned with my values. I looked around and found healthcare to be a great place to build a legacy, contribute to humanity, and drive improvement. So, I made a conscious and hard pivot from finance to healthcare technology. 

It’s been a rewarding journey, full of change and opportunities to give back. I’ve learned a lot, and I’m really happy with that decision.

Q. That’s great to know, Shakeeb. Would you like to tell us more about what drives you at job from delivering the services that you do and touch on your digital literacy and culture initiatives?

Shakeeb: Sure. Just to give a quick overview of the Children’s Hospital of Philadelphia (CHOP), we have a very special mission: to solve the most complex problems and give every child hope for a healthy future. CHOP was actually the first paediatric hospital in the U.S. 

Yeah, many people don’t. We focus on three major areas: providing excellent patient care, driving innovative research, and offering quality education to advance the field. That’s what drives us.  

In terms of scale, we have three hospital locations, one of which is dedicated exclusively to behavioral health. While we’re based in Philadelphia, we have over 50 care network sites, including urgent care, primary care, and specialty care. We see around 1.5 million outpatient visits and 30,000 to 35,000 admissions annually. Although we’re located in Philadelphia, we have a global footprint, serving children from all 50 states and over 65 countries. 

We have about 28,000 employees, including 2,000 credentialed physicians and dentists, and around 700 beds. We also have a large research institute focused to drive research based breakthroughs in pediatric care. Our clinical operations, research, and foundation work together to drive transformation in pediatric care. 

Q. That’s awesome. I understand you’ve focused on some special initiatives around digital literacy, especially with all the changes we’re seeing, like AI and Gen AI. But things are changing and care needs to be more accessible. You have some good initiatives around training and teaching people about all things digital and also building a digital culture in the organizations. Could you please talk with us about that? 

Shakeeb: Sure. To answer that, I’d like to take a step back. When I became CIO almost three years ago, we embarked on a journey to develop a digital transformation strategy. Before that, we had an information services and technology strategy, but we took the time as a team across CHOP to define what digital transformation truly means to us. 

What does it actually mean beyond just being a buzzword? For us, it’s a systematic and intentional redesign of everything we do through technology. The key to our digital transformation strategy is that it was developed in partnership with our chief strategy officer, chief medical officer, and one of our chief operations officers, with input from around 200 people across the organization. We asked ourselves: What does digital transformation mean to CHOP? Where should we focus, and how can we create a digital culture here? That’s how the work began a few years ago. 

It was really valuable to gather ideas on where to focus and what matters most to CHOP—what leverages our strengths and helps us transform ourselves and the industry. We landed on three focus areas. 

First is AI and automation. No meeting is complete without mentioning AI, right? So, what does AI and automation mean for us? How can we use it to make our work easier, give time back to clinicians, improve efficiency, and drive innovation in both care and research? 

Second is the total experience—improving patient, provider, employee, and partner experiences. This includes our digital door strategy, where we’re focused on improving access, creating a seamless experience for patients to access care at CHOP, and reducing time to care. For providers, we’re tackling burnout, especially with documentation and EMR. How can we use technology to ease that burden, reduce workload, and improve satisfaction? 

The third piece is digitally enabled care models. How can we design care models that use digital tools to help us meet patients where they are? We’ve launched several remote patient management programs to discharge children earlier and provide care at home without sacrificing quality. 

These are just some of our initiatives. Our strategy’s success is rooted in integrating digital into everything we already stand for—high quality, the best patient outcomes, excellent patient experiences, and driving research and innovation. That’s our philosophy: digital is woven into our business strategy. It’s not something separate; it’s about being digital, not just doing digital. That’s how we frame it.

Q. That’s a really interesting positioning, Shakeeb. I’m sure that your background in data and analytics has helped really propel it forward. 

Shakeeb: I’m a big proponent of data analytics—it’s been in my DNA for a long time. I believe it’s the cornerstone of driving digital transformation. It’s the foundation for understanding our current performance, figuring out how we can improve, and measuring progress moving forward. We use data analytics as much as possible to guide our digital transformation efforts and to measure the outcomes. It’s really important to us.

Q. Can you share some insights from your journey, especially on managing data from disparate systems? How do you prepare it for a 360-degree view of the patient, or any 360-degree view, in a large system like yours with millions of patients and thousands of physicians?

Shakeeb: Yeah, absolutely. From my perspective, our data analytics strategy, which we built five years ago, has six pillars. The first is creating an enterprise data hub—getting all of our data into a single data warehouse or lakehouse. We have over 100 source systems feeding into this, with both structured and unstructured data, allowing us to ingest everything into one repository. 

The second step is modeling the data appropriately. We integrate, standardize, normalize, deduplicate, and trust the data. Data integration is crucial to our strategy. The next step is ensuring data trust. Without trusted data, it can’t be used for decision-making. We’ve created a data trust office focused on governance, quality, and stewardship, applying these standards at the warehouse level to build a trusted data layer. Our enterprise-grade analytics solutions are based on this layer, eliminating most of the usual questions about data origin and meaning. 

About a year ago, we launched our enterprise data catalog—a single front door to all data assets at CHOP. This includes metric definitions, cohorts, and dashboards, making data more accessible. 

Next, with trusted and integrated data, we can create a data-literate workforce. We’ve invested heavily in data literacy through our Data Analytics University, which has trained thousands at CHOP. We teach people how to use data, tell stories with it, assess its quality, and make data-driven decisions. 

The final two pillars involve deploying self-service analytics solutions. Thanks to our work on literacy, trust, and integration, we’ve seen widespread adoption. Now, we’re focusing on predictive data science and advanced analytics—how to predict outcomes before they happen. That’s the advanced analytics component of our strategy.

Q. All this seems to play into Gen AI as well, right? It’s democratizing access—anyone can use it in plain English. Can you share your thoughts or initiatives around Gen AI?

Shakeeb: Sure. I think Gen AI along with every other type of AI is highly reliant on large data sets that are of high quality. When my team and I talk about it, we said that the real limiting step to deploying AI at scale is data quality. So, we’re highly focused on measuring, monitoring, and improving our data quality. 

We’re implementing tools to do that, in addition to the data trust work I talked about. That’s the key. In terms of AI, I remind folks Gen AI is the newest form of AI. We’ve had deep learning, machine learning, natural language processing, and computer vision for decades—studied, tried, and true applications of AI—and they all rely on high-quality data. We’re focused on being inclusive of those other branches of AI and seeing how we’ll apply them. 

Gen AI has a ton of potential; it’s the newest and brightest in terms of attention. We’re testing what we’re calling CHOP GPT, our own instance of CHOP GPT. Our vision for AI is AI as an ally to our workforce. We want to position AI to help us work smarter and care better for our patients. That’s our focus for AI. 

For Gen AI, we’re developing CHOP GPT and working on other AI uses. For example, we’re training a large language model on our policies and procedures, making them searchable and accessible to staff. On a broader AI scale, we’re using predictive AI to predict claims denials before they spread and to predict other clinical events. 

With Gen AI, we’ve partnered with Epic. We’re the first organization to scale augmented response technology (ART) with Epic for all clinicians. This uses LLMs to draft responses to patient messages. Our clinicians get millions of messages a year, and this saves time. We’re also working on use cases with Epic, like note summarization, to reduce the burden for clinicians.

Q. Very cool. So, Shakeeb, moving on to another topic of interest. In large systems, one would imagine it’s difficult to innovate. What’s your approach at CHOP around innovation? And along with that, how do you work with startups? They’re innovating all the time, coming out with new solutions. If you could touch on these two areas, that would be great.

Shakeeb: Sure. From an innovation perspective, innovation is in CHOP’s DNA. It’s been a hallmark of the organization from a clinical and research standpoint for a very long time. We’re trying to bring that same thinking into the technology landscape. 

We do this in a couple of ways. One is partnering with our research institute to identify research scientists working on AI, machine learning, and other techniques in healthcare in novel ways. We figure out how to support them in their work. 

Second, our starting point for digital innovation is always defining a problem or challenge that’s impactful to CHOP, then working backward to determine the technology solution. We don’t look for technology in search of a problem. We focus on problem statements, outcome metrics, and prioritizing where to spend our time and effort. Our Chief Medical Officer has also launched an Innovation Catalyst Program to identify large, challenging problems. Our Digital Technology Services (DTS) team is embedded in those conversations to determine if there’s a technology solution that can drive innovation in that space. 

Regarding partnering with startups, our strategy is to constantly scan the market for new solutions that add value and help solve our pain points. We have a platform play strategy, where we first turn to our large, strategic partners to see if they have an existing solution that meets our needs. If not, we’re open to partnering with others. 

Long term, I believe we’ll move in the direction where, when we see a unique solution, we’ll approach startups with key problems we’re trying to solve at CHOP and open that up to other startups in the market. If they’re working in this space and meet our requirements, we’d love to hear from them. As we work on the Center for Digital Innovation, this is part of our vision for the future. We’d love to co-develop products with others and potentially share in their success. That’s what we’re thinking about for how we’ll partner with the startup ecosystem.

Q. So, Shakeeb, I would like to wrap up the session with your thoughts or insights on what’s coming next. The professor at Wharton I used to work with, Professor Jerry, would say, “the next big thing.” What do you think is the next big thing or the next big innovation?

Shakeeb: I think the next big thing is really bringing all the disparate data sets in healthcare together to analyze and predict preventatively which patients are at risk for specific diseases. This concept of precision medicine is on the cusp of becoming a reality, thanks to advancements in technology, the availability of data sets, and interoperability standards led by the industry. 

I believe this is the moment when precision medicine will be a reality for us in the next few years, allowing us to offer more tailored care to our patients. This brings together many elements we discussed: data, analytics, and AI, along with clinical decision support and informatics. These will be essential to integrate these ideas into workflows and identify patients at risk for particular diseases. 

We’ll use genomic data and other forms of data that patients may not even be aware of, helping them improve their outcomes through this process. So, I think that’s the next big thing in healthcare, and we’re at the right time for this tipping point. 

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.  

Q: It is the 145^th episode of the Big Unlock podcast, which the previous founder of Damo Consulting started five years ago. It is very well received in industry circles, and it’s great to have you as a guest on this podcast. Would you like to start with a brief introduction of yourself?

Jawad: Thank you so much for having me. I’m the Chief Data Analytics Officer and Vice President at Akron Children’s Hospital. I worked at Tufts University Medical Center prior to my job here at Akron, and before that, I was at Rush University Medical Center in Chicago.

I have over 15 years of experience on the provider side, working in data and analytics at different institutions. It has been an amazing journey. I’ve seen the progress and transformation within data and data infrastructure for analytics. It’s been exciting, and I’m happy to share my thoughts with you today.

Q: Would you like to tell us a little more about how you started in the healthcare provider industry? What attracted you, and what keeps you motivated today? And can you also tell us about the organization you work for, Akron Children’s, as you mentioned?

Jawad: Sure. Let me first start with the organization, and then I’ll talk about my background and how I got into healthcare and specifically data analytics in healthcare. Akron Children’s Hospital is the largest children’s hospital in Northern Ohio. We focus exclusively on paediatrics—children are our specialty.

We are a two-hospital system with three ERs, four urgent cares, and about 44 to 50 specialty clinics across Northern Ohio. We are a fully integrated system and use a single EHR across our health system, which is unique and serves us well as we continue to grow in the region.

A bit about me: I’m a computer scientist by background with a degree in computer engineering, and I also studied electrical engineering in college. My first job out of college was in healthcare IT. Back in the day, we were developing serverless Java applications for handheld devices, or PDAs, as they were called. We created applications for checking formularies for providers as they prescribed medications, faxing prescriptions to pharmacies, and other functions. This was in the early 2000s, so it was ahead of its time, but a great learning experience.

Since then, I started in healthcare IT but moved to various industries, working in marketing, real estate, and capital markets. I was the managing director of a capital markets company in the Chicago area. I then moved into enterprise architecture, doing a lot of cloud transformation work for a large company. Eventually, I had the opportunity to join Rush University Medical Center in the data analytics space as a director, later becoming EVP. Now, here I am, 15 years into healthcare systems, with experience through pandemics and other challenges.

Q: So, Jawad, when you think about digital transformation and how it’s solving challenges for your patient population, which is essentially, like you said, paediatrics and children in the local area, and perhaps people coming from all over the world as well, what are some of your thoughts? How do you approach it with your team at Akron Children’s? Any other experiences from your past that you’d like to share with the audience?

Jawad: Our mission at Akron Children’s is to do everything for them that we would do for our own children. So, we don’t turn anybody away. That’s part of our core values and mission of the organization, and we’re very sincere and focused on that, on delivering value and fulfilling our mission. What that really means is our objective is always to provide the best possible care.

We always look at holistic care. We have several programs anchored in the community and in schools where we focus not just on sick care but also on wellness care. We want to make sure our patients stay healthy and remain healthy for the rest of their lives. And these are kids we’re dealing with, so we are very, very passionate about that.

Q: When you think about some of the challenges in the digital transformation space from your perspective as a children’s hospital, what are your thoughts? What are the initiatives or projects you’re currently working on, and what do you have in mind for the future?

Jawad: One of the challenges, and all challenges are opportunities eventually in healthcare, is that data is often locked in proprietary databases. At Akron Children’s, we’re fortunate because we’re an integrated system with a single EHR across our entire system. It’s a bit easier from the EHR perspective, but EHR is just one application among 300-odd applications that make up our entire IT ecosystem.

All of those systems collect data, and that data is critically important. It’s very difficult for any institution, including ours, to unlock all of that data, convert it into information, and eventually generate knowledge that can be leveraged. So that’s a very current and ongoing challenge. We’re looking at several ways to ensure we can extract data from all relevant applications needed for data analytics. We want to provide that data to the right people, who can then extract knowledge and make data-informed business decisions.

For the last 15 years, there’s been so much change in that space. We used to manage data in siloed databases. But now, we’re talking about integrating it into open platforms, like public cloud, using Lakehouse architectures to bring data together. The goal is to make data easier to access and remove as much friction as possible in making that data available to end users. It’s been a journey, and it continues to be a journey.

Q: When you say end users, are you also thinking end users in terms of the patients themselves for self-service applications? Or mostly when you’re thinking end users, you’re thinking of the application systems within the hospital that your colleagues, physicians, and nurses might be using?

Jawad: We are thinking all of the above. Today, patients can access their data within minutes of their visit. Labs become available to them on apps as soon as they get their labs done. So yes, we are providing access for our end users—patients—to the data in a timely fashion.

We are also talking about internal customers who are responsible for running the business. We are accountable for ensuring the system runs on data-informed decisions. So, all of that is part of our end users, and we strive to provide data to them by removing all obstacles and friction, organizing the data in a meaningful, usable way.

 Q: How do you wrestle with the challenge of when someone comes in from another hospital system that may not have the same EMR or EHR? Do they still bring their health record along with them, or do you have to request it from the other health system? How does the whole process work in terms of data platform interoperability?

Jawad: It has improved quite a bit. If folks come from hospital systems using one of the current EHRs, since we are an Epic customer, we have care everywhere in our backend to exchange data between Epic hospitals and even some non-Epic hospitals that participate in that network. We have backend capabilities that allow us to exchange information between systems.

Now, not everyone is part of that network or can share information programmatically in a timely fashion. When that falls short, we use older methods—calling on behalf of the patient, receiving data in CDC format, or even just a fax that gets scanned into our EHR, making the data available.

Yes, over time, it has improved, but we still rely on legacy methods that aren’t timely. There can also be compliance issues depending on the state, and patient consent is required, which can create obstacles. But we are able to exchange information using both advanced and older methods.

Q: Before I move on to more exciting technologies like AI and GenAI and how you’re thinking about those use cases, I want to touch upon innovation. How do you foster innovation in a large health system where clinical decisions impact patient care? Any thoughts or examples from either your current or previous experiences?

Jawad: Yeah, that also is an art and exercise in itself. There’s a lot of innovation happening now, and healthcare is at an influx point. Historically, healthcare has lagged in adopting innovation compared to other industries, but with GenAI and AI, it’s starting to leap ahead. I’m excited that healthcare is ready to accept innovation and address the challenges it can solve.

At Akron Children’s, we’re using GenAI for note summarization for physicians to reduce pajama time, meaning time spent outside clinical hours completing summaries of visits. GenAI was able to summarize them and even do other additional things to make it more time efficient for them to be able to document. Documentation is a big burden on healthcare and healthcare providers. Gen AI is obviously a very natural use case.

There are other areas, where automation and AI is becoming quite relevant and important, including pre-authorizations, where I’m seeing a lot of implementations on inbox management. Inbox management has been always been a burden for the providers. Automation of routing of messages, even auto replying some of those messages, obviously with human in loop to make sure that they are able to physically read the message that was generated, authorizes, and send it forward. These are the very low hanging fruit in terms of how GenAI is implemented. We are using data and analytics side also.

There is quite a bit of GenAI applications that I am seeing now. We haven’t implemented them yet, but we’re in the process of evaluating them and implementing them for folks to be able to use natural language process, like natural language to extract insights from the data.

You can come to the data and you can ask the question in your most comfortable language using your most comfortable phrases and the GenAI then provide the dashboard without anybody having to create a dashboard. Analytics is also becoming quite common now.

We are actually piloting it to implement it at our institution as well. GenAI is also being used in the background which front end users might not be able to see. To normalize the data, to curate the data, there’s a lot of solutions that we’re piloting and evaluating. We are working with various partners to see how it would make our processes or back-end processes effective.

This is a high interest of us right now because that directly translates to FTEs, that really translates to ROI, and things that we do, and even improved efficiency, integrity, and quality of the data as we look forward. So, I’m very, very excited about these solutions.

Q: That’s great to know. New technologies like GenAI often come with governance challenges. Could you share how you approach data governance in your organization, especially in the context of AI?

Jawad: Yeah, we should all remember that Gen AI is not an exception. Of course, we can discuss the risks of Gen AI and AI in general, but ensuring proper validation, data quality, and care quality has always been central to healthcare. We are a regulated industry, and we must make sure that since we are dealing with human lives, all healthcare solutions are validated, curated, secure, and have been thoroughly tested before they are implemented. This applies to any healthcare innovation.

With Gen AI, given that it’s artificial intelligence and involves machine learning, there are some added risks that require validation, like understanding the openness of the model and how interpretive it is. These aspects are critical. Regarding governance, when it comes to implementing AI or even non-AI innovations, we have established proper committees. We have an AI committee that reviews all proposed solutions. The team of informaticists carefully evaluates and validates these solutions to ensure they are feasible for implementation.

We also have a data governance structure in our organization with a steering committee and a data analytics committee. They oversee data curation processes, data definitions, and the validation of metrics. What I’m getting at is that we manage the entire end-to-end lifecycle of data—data and analytics. When I say analytics, I’m also referring to advanced analytics, including predictive modeling and machine learning, which all fall under the purview of our AI committee.

Members from various parts of the organization sit on this committee. Our Chief Informatics Officer leads the initiative from the Information Services Department. I am also a member, along with our CISO and key clinical staff, to ensure that new technologies are fully understood and vetted before they are implemented.

We have governance structures in place, and whether we use them effectively is something time will reveal. As new solutions and innovations are introduced, we will refine and adapt these processes as needed. But we do have a solid organization in place to implement AI innovations, and that’s reassuring.

Q: Any parting thoughts or suggestions for the audience on what you’re seeing for the future?

Jawad: If I summarize my 15 years in data analytics, especially in healthcare, the challenge is dealing with data and unlocking silos. Today, 80-90% of our analytics is generated using only 20% of the data. A lot of data is unused so as we look forward to sort of turn the curve on this, we really have to understand the best possible ways to be multi-modal in our approach to solutions.

So how do you not just do with discrete data? What do you do with unstructured data? What do you do with imaging data? All of these solutions, like we have to think through those things more carefully and not leave them behind, right? Not leave the 80 percent data behind to provide majority of the solutions.

We have to start becoming more data-savvy and multi-modal in our approach, considering not just discrete data but also unstructured data and imaging data. That’s really what we have been dealing with as we build warehouses, they cause us. The idea really becomes, how do you model the data?

My other recommendation to your listeners would be to adopt common data standards as much as possible. Whether you are good in FHIR schema or whether you want to go more on the research side and implement OMOP like, uh, schema to organize your data, all of those solutions are feasible and staying to the common data standards might help down the road as you exchange and become more interoperable with other institutions.

Also, think about how to present your data through APIs so that apps can consume it easily. Removing friction in data access will be key moving forward.

Today, most of the good innovations are happening on the app side, so apps are being developed that are more transactional with the data. So, how do you present the data as application programming interfaces or APIs so that those apps can consume your data? These are the things that we really have to think about and organize the data so that it becomes like we remove all the frictions for folks to access the data.

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

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

Q: Welcome to the Big Unlock Podcast, David. We’re really happy to have you here. This is the 144th episode and it’s been going on for a while and we’re happy to continue the legacy that Paddy left behind.  

David: Thank you for having me. First and foremost, I must say, I started recently listening to the past episodes and seeing the incredible lineup of guests that have been on it. I’ve been working for the Crystal Clinic for the past 15 years.  

Q. Tell us a little bit, about your journey, like how you came into healthcare and how you came to the Crystal Clinic and what has your experience been? How the healthcare field has evolved with digitization and the Affordable Care Act and how it’s impacted your work. Where do you see it going now with the trends in generative AI?

David: I’ll give you a brief story. So back when I was doing my first undergrad, I had a dream and a passion to get into healthcare, either be an anesthesiologist or a cardiologist. But then looking at my career, as it would extend and what age I would be at and when I would actually start being a real physician and getting paid that salary or that pay, it just wasn’t lining up for me. The rest of my family, almost the entire family is involved in nursing. I figured I would go that route because it does open doors for many opportunities. 

So, I got into nursing, and within the first week, in Nursing 101, they listed out all these opportunities. I got into nursing for two career paths: one was informatics, and the other was nursing law. Well, nursing law didn’t pan out. I had an opportunity developing with a woman who had a practice in town, and she was the consultant for all the major law firms in nursing law. The day I graduated from nursing school, I found out she had closed her practice. I was already working at one of the local hospitals in the ICU as a nurse tech, so I followed that path into ICU. Then, I got hurt on the job, which required surgery, and I was laid up for about 10 weeks. During that time, I started dabbling in website programming to pass the time.  

While I was sitting around, I got a call from a friend of my wife’s who was working with Cleveland Clinic to implement their EPIC system. This was about 16 or 18 years ago. I jumped at the opportunity to get into informatics. From there, I helped roll out EMAR, order entry, and worked with Children’s Hospitals and Cancer Centers, focusing on orthopedics and surgery.  

At the end of my contract, I was supporting all the ORs at the main campus of Cleveland Clinic, specifically in orthopedics. I started looking for a more stable position because I didn’t like not knowing if I had a job when the contract ended.  

Surprisingly, I got a call from Summa Health System, which Crystal Clinic used to partner with, about a clinical analyst position. I had applied for a similar position about a year earlier and heard nothing, so ghosting is not new. That was on a Friday. I went in for an interview on Monday, February 22nd, and they wanted me to start the next day. I asked for a day to think about it and started on Wednesday. By Thursday, I was neck-deep in programming the EHR, training on it, and more. Myself and one other person were responsible for it all. 

At that time, we had a deadline because Crystal Clinic Orthopedic Center is a physician-owned facility. There was a regulation, I believe from Congress, that physicians couldn’t own hospitals, with a hard stop deadline of June 30th, 2009. The project was about six months behind when I joined, but with a colleague’s help, we turned it around in three months and went live with our EHR, HMS (now Medhost), in May. That’s how my journey into informatics began. 

From there, I moved into management after a few years and, since 2015, have been working as a director. I’ve held that position ever since. Working on promoting interoperability and meaningful use allowed me to learn more about healthcare. 

Crystal Clinic is a physician-owned hospital system, with 16 clinic locations that are outpatient extensions of the hospital. We’re governed by the Joint Commission, unlike typical physician practices. We have a surgical hospital, an outpatient surgery center for orthopedic-specific urgent care, and as of last Friday, we have 51 physicians. We specialize in orthopedic and plastic reconstructive surgery, but also offer pain management, physical medicine, rehabilitation, and sports medicine, rounding it out with over 90 physical and occupational therapists. We cover the entire spectrum of orthopedic care, end-to-end. 

Q: Is this all in Ohio, David?  

David: Yes, it’s all in Ohio. We’re venturing out this year with something I’ll call “travel medicine.” It’s not exactly telehealth, but we’ve been ranked number one in Ohio and top 1% in the U.S. for orthopedic hospitals by CareChex Analytics. We have four certifications from the Joint Commission for disease-specific measures in total knee, total hip, total shoulder, and spine, making us one of only three in the nation. We do it better and cheaper.  

We decided to partner with local, regional, and now national organizations, many of them self-insured, because they find it’s cheaper to bring people here as a destination rather than have it done locally. For example, if you go to Texas for a total ankle replacement, it might cost $300k, while here, it’s less than half, including travel, room, and board. Plus, we have quicker turnaround times and better outcomes, so companies don’t lose key personnel for long periods. This is catching on with OrthoForum and others. It’s great because patients get the care they need faster with the outcomes they want. As we expand nationally, we still maintain our local presence. Rather than open facilities in other states, we bring people here. We couldn’t replicate our cost structure elsewhere like we have here in Ohio. 

Q: Yeah, that’s a really interesting model you’ve outlined because usually, when people talk about expansion, they think of opening new locations rather than bringing people to their existing ones. I think that makes digital health initiatives even more important. How do you see your vision for this, especially in terms of sharing everything back with other hospitals? Have you had to rework your systems, and what investments are you making to make this possible for Crystal Clinic? 

David: The nice thing about not expanding physically is that you avoid capital costs. If you have a platform like Zoom, you can use it no matter where you are, and it’s the same for us. We have availability with some of the new providers we’re bringing on board, and we have the capacity to fill up both our surgical and clinical schedules. 

We’re not doing anything different than if the patient were local. We have a care coordinator, or sometimes multiple ones, depending on the patient’s path. They handle all the details—booking travel and hotel stays—while we focus on the patient and the healthcare we provide. 

Q: I was asking more from the point of view of, because the patient isn’t coming to you pre-procedure, you need to get all their information into the system and make sure the pre-checks are done, maybe not locally. How does that impact your IT outlook, and how do you handle that in this model? 

David: Yeah, we manage that through the care navigator. Let’s say we have a patient in California who can’t follow the usual pathway. We have someone who coordinates with them ahead of time and arranges everything. We recently rolled out online scheduling. We’re probably a bit behind compared to larger organizations, but as a physician-owned facility, things move a little slower. We want to ensure physician buy-in because the last thing we want is to launch something that impacts them, and they don’t accept it—that’s just throwing money away. 

So, we started with a few interested providers, taking baby steps. We had them pilot the system, and from there, we went live with a beta test group. Within a few days to a week, we had over 100 people schedule online. As providers see their schedules filling up, they’re getting on board. Some providers are still a little hesitant, feeling like they’re losing control of their schedules, but we’re getting more people in here. 

The data shows these patients are scheduling in the middle of the day or even at night—times when they can’t call the office. That’s encouraging because we were missing these people before. So, that’s how people outside of the travel medicine pathway are accessing our care. We also learned this through patient feedback, seeing why calls were being dropped. Since opening up online scheduling, those numbers have declined. 

Q: So what were the major learnings? What do you think patients or consumers are looking for today in the digital health experience? What insights did you get from that, which you then used to improve the systems and make it work? 

David: That’s a good question. I’d say we weren’t really meeting the patients where they are. We weren’t providing the opportunities that newer generations—like millennials and Gen Z—are asking for. We were stuck in the mindset that our core population is Medicare. It was a bit of a misnomer on our part, thinking we shouldn’t roll out too much technology because older patients may not have it. 

We found out that almost everyone now has access to technology, or they have a caregiver who does. That realization was one of the driving factors. We also started listening more to our patients—reaching out to those who passed on scheduling or cancelled, and understanding why. Since implementing changes, we’ve seen a decline in cancellation rates in a short time. It’s not necessarily tailored medicine, but we’re definitely tailoring the experience to meet patient expectations, and we have to evolve with that. 

Q: So this leads nicely to the next question. I think it’s a valuable insight where you’re saying that you can’t just wait for things to happen. You can’t push back and say, “This group is not going to use the technology,” because it’s coming, and you have to be ready for it. That’s similar to how we feel about AI and generative AI. Every organization needs to at least recognize that it’s out there and consider the use cases within their own organization. How do you feel about that? I saw one of the questions you sent about AI said, “Just because we can, doesn’t mean we should,” but you also mentioned that you had to get on the technology bandwagon. How do you feel about something similar now applying to AI? 

David: Our short-term initiative right now is getting into ambient generative AI. We have physicians willing to participate, but we’re taking our time. It’s about starting small and not rushing, similar to how we approached online scheduling. We made sure not to rush through it or go big bang. We also had to ensure it integrates with our current EHR. 

What’s been great for us is that over the years, we’ve been able to customize and program our EHR to our specific specialties—orthopedics, plastics, and now pain management, among others. A lot of large organizations try to fit everyone into the same mold, but we’re customizing ours for our needs. 

The company we’re piloting with—though I won’t name them as we haven’t signed a contract—works well with our system. It outputs the ambient AI into specific observation terms, which go into our EHR’s fields. It’s just another means of getting data into our custom-programmed EHR. 

Q: So, this is during the patient consultation? 

David: Yes, during the patient consultation. As the patient is speaking, the ambient AI generates a note and segments it into different sections, like HPI, health history, past medical problems, and so on. The assessment and plan go into their own sections. The AI-generated text is then assigned to specific fields in the EHR. It’s almost like having a scribe, but it’s working ambiently in the background. 

What’s nice about our current system is that it’s all structured data. If we want to report on it, we can run a SQL query and pull it all out. That’s the beauty of it. Instead of having just a note or a searchable PDF, we have structured data we can run analytics on. This is where we’re at now and what we’re looking forward to—it’s a nice, easy transition. 

Some providers using scribes are excited about this. Scribing is a transitional role, often filled by those waiting for med school. With this AI, they don’t have to worry about things like a scribe calling in sick. It replaces the need for a human scribe and does all the documentation for the provider. It’s also exciting because the company we’re working with can control the EHR via voice, which makes it a great blend of both. 

Q: When we did our CHIME focus group, we found that a lot of work was happening in this area, and in our generative AI workshops, we presented ambient listening as one of the success stories. However, the CHIME survey showed that people still want a human in the loop. How does that work here? Do physicians still need to review everything done by the scribe and then click an approval button? Or does it just go into the system? How are you handling that? 

David: They do have to approve it. Nothing is final, even with a physical scribe in the room. The provider still needs to attest to what’s in the documentation. If you look at regulations, especially in Ohio, but also nationally, a scribe can’t place orders. So the physician still has to enter and sign off on documentation and orders. 

There’s also a need to handle anomalies. AI has come a long way, and it’s not just a transcription service anymore. But anomalies can still happen, and that’s why a provider must sign off on it. It’s human nature that sometimes a tired provider might just glance at the note and approve it without fully reviewing it, which can lead to mistakes. At the end of the day, no matter how the information gets into the system, the provider is responsible for it. They need to read it, agree with it, and sign it. 

Q: This leads to another question—do you think AI is contributing to the decline of critical thinking? For example, we see cases where LLMs generate care plans, and nurses go into review mode, just signing off if the plan looks okay. Do you think having everything pre-filled for you leads to a loss of core skills that professionals have spent years developing? 

David: That’s a great question. In our current setup, I would say no. We’re still having conversations with the patient, doing exams, and verbally documenting findings. We don’t yet have clinical decision support in place, and that’s where the line between AI and critical thinking can blur. 

I’ve seen this evolution in healthcare, from handwritten notes to electronic systems. Nurses in particular have lost some basic skills—like even how to sign their name—because they rely so much on the system. Some newer nurses, who have never worked with paper records, might say, “The system didn’t tell me to do this,” when it should be part of their critical thinking. 

One example from my time as a nurse involved a stroke patient. They passed all the cognitive tests, but they made an offhand comment that their loved one was in the basement of their house, which was actually the hospital floor below their room. AI probably wouldn’t flag something like that as an issue because it seems irrelevant. However, as a nurse, I alerted the attending because it was unusual for this patient. 

Unfortunately, while attending to another crashing patient, the stroke patient had a large hemorrhage, and the outcome was unfavorable. This is a case where critical thinking made a difference, and AI might not have picked up on it. 

That’s my fear with AI in healthcare. Just because we can use AI doesn’t always mean we should. AI can help with documentation, but we risk losing those small pieces of critical thinking if we rely too heavily on it. It’s a slippery slope—if the system doesn’t prompt you, does that mean you shouldn’t do something? Critical thinking should always be at the forefront. 

Q: Yeah, that’s a really interesting point of discussion. You hear that it can ace all the medical exams, learn everything, and you have these AI companions trained for mental health. But it’s paradoxical because the main problem in mental health is loneliness. If you’re substituting it with a computer that doesn’t have feelings but can talk to you, then it’s a paradox. We have to wait and see where this technology is going to take us, but it’s good to be aware of the pitfalls, limitations, and what to watch out for. That’s why we want to take it slow. We know it’s there; we don’t need to rush into it. We’re not being forced to yet. Because you’re dealing with patients and have a really interesting background coming from nursing, which is a great motivational story. Whenever we talk to people in healthcare, almost everyone has a special story about how they got here and what made them enter healthcare. 

So, from your perspective within your organization, what do you think could be a good use case for AI? Where would it directly benefit your patients and be a real win for your organization? If you had to choose a field, where do you think this implementation could be? 

David: That’s an interesting question. Honestly, I don’t know if I can answer that. I’ve never really thought about it. I don’t want to just say something specific without having a solid plan. For orthopedics, I think one area where we could potentially use AI is in radiology or imaging. AI could analyze digital images and create reports with findings, with a radiologist signing off to agree with it. We haven’t implemented that yet, but I believe it would be a huge win for us. 

It would improve accuracy in what we’re finding and help fine-tune what needs to be done. From a surgeon’s perspective, we have radiologists review our X-rays as a backup, but they may miss an anomaly that doesn’t seem right. They’re still held responsible if it shows up in the image, but with an AI layer, we could flag potential tumors or other concerns for further evaluation. I see that being ortho-specific and really helping us take better care of our patients. 

More issues are surfacing in patients, and I think we can provide more holistic care, even if we’re not directly treating those issues. We can refer them and ensure the patient is well taken care of. I also have another use case involving custom implants. We have a program that depends on MRI or CT scans to create custom implants for our patients. I see that improving and becoming more precise. For example, my mother had her knee replaced this year. After her CT scan, they sent the data to the vendor to design a custom implant based on her specific anatomy. It’s no longer a trial-and-error process. Now, it’s almost like plug and play. I believe we’ll see an increase in AI usage there, tailoring everything to each patient’s anatomy. 

Q: Personalization. Those were very good examples, David. Thank you for sharing. Regarding radiology, I have an interesting story. During a generative AI program at Harvard Chan School of Medicine, one participant, the head of NHS from England, discussed how every radiology report must have two radiologists sign off on it. This led to significant backlogs, causing people to wait a long time for reports. 

One AI solution they found was allowing AI to generate the initial report, with the second radiologist reviewing and signing off. This resulted in a significant reduction in wait times. 

So, your points about radiology and the custom implants are solid examples of AI’s potential. AI can also enhance personalized medicine. For instance, when treating a fever, the recommendation for Tylenol is based on an average weight. However, AI could tailor the dosage specifically to an individual’s body. This concept relates to digital twins, where AI could tailor treatments based on a digital model before applying them in real life. These are all interesting examples. 

This brings us to our next topic of AI governance. I’ve heard both pros and cons about having a chief AI officer. Some argue that with AI’s applications across many departments, having a dedicated officer could limit its scope. What are your thoughts? Is your organization considering hiring a chief AI officer, or is AI spread across different departments? 

David: Our system is set up as a small organization with big problems. We limit our chiefs, and I haven’t seen more than four during my time here, with each wearing multiple hats. I don’t foresee a chief AI officer; that could be both good and bad. Each department has unique needs or wants related to AI, but it ultimately ties back to IT. 

While we may not govern AI usage, we should support it. We have a physician director for IT who acts as a liaison to ensure we meet providers’ and patients’ needs and maintain appropriate communication for any system changes. He essentially fulfills the role of a chief medical information officer without the title. I don’t see a chief AI officer because we’re still early in our AI journey. Maybe a CIO could oversee it, or perhaps a chief informatics officer, depending on how we expand our leadership. 

I don’t think it should be governed by one person; it needs to be collective because AI touches all departments. A committee might be beneficial to ensure that while AI can be effective, we also consider fiscal responsibility. Just because we can implement something doesn’t mean we should if the ROI isn’t there. 

Q: So, do you have that committee in place? 

David: We don’t have an AI committee yet, but we have a technology committee that looks at these areas. 

So what do you think is your timeline for, you know, like, are you thinking of implementing this in the next six to nine months or maybe like a year? 

David: Me personally, it has to be the next six to nine months. And part of that committee  once formed is definitely we had to educate people. That’s the biggest thing I’m finding. In our organization and all of these conferences I’m going to and people I speak with,  when you say AI, everybody thinks chatGPT. 

Yes. It goes beyond chat GPT. I know we started writing AI policies and we’re really being vague. Um, but the first AI policy is it must be approved to go through these steps before we can. Start implementing it, but it was, it started out as a blanket AI policy and it’s like, well, wait a minute back when we started programming our EHR and yes, 2000, 2012, I think we sort of did AI there, you might not think it, but you look at how we programmed it, there is AI involved, granted, the algorithms and what we use are extremely small and not really large, but it was, we took point and clicks and And we created a narrative from that point and click based on everything that all the data was being fed into it. 

So that is definitely on my radar to make sure that we need to educate. I know one of the workshops this year in the spring that I was at, someone mentioned the difference between people Who have a job in the future and those who don’t are those who are AI and light. Yes, exactly. Yeah, you gotta be educated and you have to have a familiarity. 

You don’t need to know LLMs and everything else that goes along with AI and how is all there, but you need to be aware of it and we need to educate the end users. Not to be afraid of it, of replacing you, it’s going to help you do your job better and be more efficient. It’s a power tool and it’s, it should be in your, and you should be able to have that conversation. 

That’s how we budget, you know. And you should apply it to the right areas too. I mean, do the ROI on it. See how much time is being invested. And just project management is another one that I’m passionate about, that there’s a lot of stuff Where AI can really help out there and just doing the mundane task and helping up with the communication plan. 

Making sure everybody has all the information and it’s  nicely tailored and it looks pretty and the product manager didn’t have to spend the time to go through and redo all the stuff and make sure that the system does it for you. Let the system work for you where it can, but still have the checks and balances in place that need to be there so it can be, the anomalies can be addressed. 

Ritu: Yeah, we’ve seen a very good implementation of that with our client, like I was telling you about. They’ve been able to generate these comprehensive care plans, and then it goes into the nurses’ admin panel. Only once they hit publish does it go out, and that’s the beauty of it. It can read and scan a lot more data, understand, and remember everything, especially for people with multiple chronic conditions, and come up with quite a comprehensive care plan. They’ve launched it in a couple of hospitals already and have received really good feedback, but as you said, it’s all evolving and it’s very new, so we have to kind of wait and watch to see where it takes us. 

David: I just had a recent conversation at the beginning of last month, actually, it was the day I came back from holiday. I had a broken car in the middle of the highway, so I was doing a Zoom call with this woman discussing AI education. A lot of universities, like many healthcare systems, are struggling with budgets, but they know they have to have something AI-related to stay competitive. Otherwise, they risk falling behind, and students will choose organizations that offer these programs over those that do not. It’s not a choice, but it’s changing so rapidly that universities lack the financial backing or budget to keep up. They’re working with companies that specialize in AI education and courses. Ideally, it would be nice for universities to handle it all, but… 

Ritu: This was a New York Times article talking about synthetic data. I read it too. We’ve incorporated it into our workshop for the next session because it’s really interesting. Sam Altman said that billions of pieces of data are generated every day. Just like you said, these bots are crawling the web looking for new content to read and ingest, and they can’t tell the difference between human and AI-generated content. They’re reading their own content and building upon it. Once you go through several generations of this, it all starts evening out and becoming monotonic. A lot of the content that bots generate is very impersonal; you can’t add those little human stories. The example you gave about the patient won’t be there in ChatGPT-generated content. So everything starts becoming very vanilla and indistinguishable. That’s a big pitfall right now because they’ve run out of human-generated data. They’ve already read everything on Reddit, and now they’re moving to generated data, which is going to be a huge problem. Even in image generation, I saw an example, maybe in the same article or a different one, where all the faces of people start looking alike after about 10 generations of reading their own content. The differences and diversity get wiped out, and everything ends up looking the same. There are definitely a lot of concerns that need to be addressed, and regulations need to be put in place. But yeah, those are topics for another podcast. 

David: One thing I find interesting is realistic expectations. A couple of weeks ago, I spoke with a company that scrapped their AI initiative for social determinants of health. They were aiming for perfection—100% accuracy—saying, “We ingest all this data, and here is the guarantee that this patient will have no sugar for their next visit.” They found that the timing of when that data is ingested and documented affects accuracy. The algorithm runs only based on the data at that particular time. They didn’t want to sell something that might only be 75% accurate because life-changing events can happen after that initial visit, like losing a job or a family member. Well, you lose that AI data because it happened outside of when the algorithm was run. They were trying to gather everything to produce a very high percentage of accuracy but struggled to get even close to 90%. All those external factors come into play, and garbage in, garbage out—you have to document appropriately. So yeah, it’s a tough problem to solve. 

Ritu: The no-shows are a huge issue, like you said. There are so many variables and factors. In fact, we show that as one of our case studies of where things could go wrong because those no-shows are biased towards people who already have problems. That’s why they don’t show up. Maybe someone has to look after their kids or something, and when you build these algorithms, it kind of reinforces that and makes it worse for those people. It’s really a difficult problem to solve. 

David: Then you take that data and have an insurance company use it, designed specifically for one area but applied elsewhere, which it wasn’t designed for. It doesn’t support the decisions being made. It can be good and bad. I find myself sitting on the fringes, waiting to see where we’re going with this and whether it will ever truly be 100% effective. We’re heading in one direction while I’m not quite sure if that’s the way we should go or if we should just wait and see what others do and learn from their mistakes. 

Ritu: Thank you, David. It’s been great having this conversation. Any last thoughts before we conclude? 

David: Not really. I’m just excited about where AI is taking us. I’m excited about what Big Rio and Damo are doing and where this podcast has gone and is going. I thank you for having me on and allowing me to share my story and experiences. Hopefully, it benefits others out there. 

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

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

Q: Welcome to Season 5 of The Big Unlock. I’m Rohit Mahajan, Managing Partner and CEO at BigRio and now, Damo. This season carries forward Paddy’s legacy, and joining me for an exciting discussion in this episode is Shahidul Manan, Chief Data Officer for Bon Secours Mercy Health, Cincinnati, Ohio. Tell us a little about yourself and your organization.

Shahidul: I’m Shahidul Manan, the Chief Data Officer at Bon Secours Mercy Health and Vice President, Digital Health at Nordic Partners. I’ve been in this space for almost 20+ years now— in financial, for over a decade, then in high-tech software space running analytic software, and finally, in healthcare for the last seven to eight years. It’s an exciting time to be in technology, especially given emerging innovations in data and AI. 

Currently, Bon Secours Mercy Health is an enterprise of about 70 hospitals—a hospital system and six subsidiaries—and about 70,000+ employees and 20 million patient lives. We’re in seven states and through an acquisition, in Ireland, as well. As the CDO, I manage pretty much anything and everything that touches data, including data platforms, the cloud, digital innovation, and focus on analytics while serving all the systems, data productization, and AI innovation. I’m also focused on our platform and data AI product commercialization to some extent. Commercialization entails sharing the goodies with other systems and in the healthcare space, so that our innovations can actually touch other systems and their patients while garnering revenue for us. 

Overall, healthcare, right now, is a great place to be in because of the innovation and all the direct impact that we can have on patients and our healthcare services. Historically, healthcare was or has lagged a little in terms of digital transformation. Our first true digital transformation began with the ACA—Affordable Care Act—where the EHR became almost like a mandate. The digital platform—the EHRs—started capturing all digital footprints for our patients and providers and everything we did to create the data that we needed. In the next phase, we will use that data to drive everything we can from building insights, data-driven decisions and operations, and data analytics-driven innovations for patient outcomes, quality of services, and various patient engagements. 

Q: Prior to Bon Secours Mercy Health you were at Mass General in Boston. Tell us what got you excited about getting into the healthcare industry? This was not where you started originally but this is where you are spending most of your time now.  

Shahidul: Absolutely. I was with Mass General before and in a similar role as now. I drove many innovations with data AI there, as well. Today, healthcare is becoming more and more interesting for two reasons.  

One, it has been a little bit of a greenfield compared to other industries as the focus is more on digitization. There are tons of greenfield innovation opportunities that are emerging, today.  

Two, healthcare is likely to show highest growth in data sets. The AI revolution we’re in is eventually going to come down to who has the most differentiating data and who has the highest quality of data and healthcare, I think, has an edge in this matter— having the highest growth and volume of data. That also places on us the burden of harvesting that data and enabling it. There are plenty of challenges to this, starting with the fact that 80 percent of healthcare data is actually unstructured data in the form of physicians’ nodes or X-ray images. Therefore, building the truth, getting insights, or even building predictive power from these, is more challenging than ever. In terms of challenges and opportunities, this is a great place to be in.  

Lastly, this is very much a mission-driven effort and a mission-driven space, which also motivates me for I know that anything that we do or our team does in terms of innovation actually touches human lives and makes a difference in patient outcomes and the cost of healthcare—that we, as a nation, and actually globally, are struggling with. That’s why I get excited about health care and find it extremely rewarding.  

Q: You spoke about the explosion of data, especially in healthcare and I couldn’t agree more. We are seeing this increasingly with remote patient monitoring and all the devices that help the patient’s journey. How do you think about two things—building a robust data engineering and data infrastructure and leveraging these and AI to generate use cases? 

Shahidul: This is a pretty complex one in terms of how we are going to tackle this big challenge. My strategy is—and that’s what I would suggest to the others—to think of large-scale enablement, first. How are you going to collate all your data in an efficient, consolidated, aggregated manner such that you can leverage high quality or better processes to technically enable your enterprise to generate all kinds of use cases?  

No longer are we in an era of daily and weekly reporting. We’re getting into real-time analytics and insights to build that infrastructure, platform, and enable data aggregation with proper quality measurements. With quality oversight, proper data dictionary and governance, and AI comes a larger responsibility of using data governance for AI governance. For all these to come into play, there has to be some enablement. That’s what I call the foundation. Anyone, thinking about this space then, needs to start with that.  

The foundation needs to be bigger than what your EHRs offer. It needs to be a more independent and innovative workspace with these various bells and whistles of governance and innovation tools so that it can move from real-time analytics to AI-driven applications, productization, and building workflows so physicians, nurses, and hospital administrators can use it. The more you operationalize it, the better benefit you get. Even on the Gen AI front, it’s no longer a science project of some kind where you try it on an experimentation level for that’s just the beginning. You want to think about how to operationalize it and actually put it in production—in the hands of the field players—so you can actually see tangible value coming out of it. Therefore, my suggestion is start with that foundation—think big, but start small.  

In the next phase, look at harvesting various types of value use cases. You want to pick the use cases that actually provide that tangible value and start with all these field stakeholders so you get a pragmatic use case you can build, validate, and then, actually put to use because the value comes out only when you use it. Keep that in mind, start the innovation there, and put these in the hands of the field level players—physicians to hospital administrators—and that’s exactly what I’d call innovation space of data and AI.  

The final one is never ending continuous improvement geared toward value extraction. This will enhance operational maturity levels of your data and AI. When you are at a stage where you have built your foundation and proven your value through various high value use cases, you can aim at operationally efficient levels and make it a part of your business or enterprise strategy. Your strategy and your AI initiatives cannot be separate—all of this needs to be at a space where systems, healthcare providers, payers, or any stakeholder in healthcare, when they build their next level of strategy for service, or product, or improve what they do, need to think of the additional value services or products they can provide with AI.  

Therefore, it needs to be embedded with their strategy and they need to feel confident that they have the capability and the value use case or value proposition to connect them to and build their next level of strategy together. That’s how I would approach this.  

Q: You’ve mentioned strategy and innovation. You must be participating in many C-suite meetings at your organization and across the healthcare segment. What are the business problems that you hear of from stakeholders and senior management teams? How are chatGPT and Gen AI influencing these?  

Shahidul: Everyone’s excited about Gen AI and its potential but in many cases, they don’t know where to start. In many cases, they’re worried that they will be left behind. So, it’s an interesting time actually for the C-suite to be challenged with this opportunity presented by AI. What I hear most from my colleagues and the leadership that I work with is that it is several fold. 

One, everyone’s worried about the enablement. Many are wondering if they are ready for undertaking this type of innovation or even taking advantage of Gen AI, machine learning, or even advanced analytics type of activities. Honestly, most of them feel they’re not ready. I don’t want to put an exact number to this, but it would easily be between 70 to 85 percent that think they’re not fully there with their data technology and AI capabilities to even explore to the extent that the right value can be extracted for their enterprise.  

Two, the interesting part is that 90 percent or more—and this is also from various studies—feel compelled to do something with this. They are committed to some extent with funding or with their strategy to do more with data AI from platform to innovation. So that’s the good news.  

Three, I have also been observing aspects to do with governance and rightly so, because we’re in a highly regulated environment. Being a part of the healthcare domain means we are the custodians of patient data so we have to be extremely careful not just from the regulatory perspective, but also from the ethical and clinical usage standpoints of what we do. All of this needs to be orchestrated with the strategy and then, only must tactical approaches be undertaken as many healthcare organizations are doing now.  

Lastly, on the governance front, I must add that we will see a huge change in the next six to 18 months in the regulatory landscape. The AI Act is already in place and when it gets executed, it will start to demonstrate new opportunities and new challenges for businesses and organizations such as ours in healthcare. The White House has set up various task forces to come up with new regulations for managing AI better and these will start hitting the ground in this time horizon. As technologists, practitioners, and leaders in the innovation space, we must comply with these regulations and leverage the opportunity while ensuring we are prepared and have fortified our organizations to manage it appropriately. 

I think that the next six to 18 months will be more like a warmup. We will need to see how we can better prepare ourselves and enhance our capabilities to manage the regulatory environment better for what we want to do and what we see as the opportunities with Gen AI in the next couple of years.  

That said, we already are tinkering with a lot of Gen AI type innovative use cases. Many organizations are actually trying these out, which is a good thing. It means, sometimes you have to get your feet wet just to understand where things are. It’s at that stage. However, with these maturing, there’ll be—even though it may sound overestimated in some cases—some slowness in the healthcare innovation space. But in the next three to five years, there may be a tsunami of products and services in this space.  

Q: With regard to regulatory oversight, how do you innovate in your space? How do you encourage your team and yourself?  

Shahidul: Besides the technology hat, we have to also wear the leadership hat. In addition, our evangelism hat has to help build that influence with the senior leadership and drive that influence to motivate our organizations.  

Starting with the evangelism, I have in place major programs to educate and train across the enterprise so that everyone gets on board and understands what this is and how it can be utilized. We focus on democratizing the data and AI capabilities so everyone can try them out, get onto the field, and benefit from access. That is also an enablement that we constantly work on—how to democratize data and give it to the data scientists, any department, or any business executive so they can see the data and make data-driven decisions.  

Another big part is about building the strategy, showing the value incrementally, and convincing the leadership about the value proposition of this whole digitization with AI. All these need to be orchestrated and so I also work closely with the team to help them comprehend the vision, understand the value proposition, and get the inputs and feedback so they feel excited about what we work with. It has to come from all sides—top down, bottom up, and sideways. We have to talk to the stakeholders, understand their pain points and how we can help them better. So, an upscaling of the organization is another focus area because the technology is exciting and constantly changing. It’s important to ensure that the team is excited and focused on constant learning—a state we are in since everything is coming together.  

Lastly, I have actually established a pretty robust governance model, even though I think there’s more to work on and do in terms of maturity. I work very closely and am lock in step with my Chief Privacy Officer, the Chief Security Officer, the legal counsel, and the IT teams so everyone feels they have a voice in this. We need everyone’s voice because it’s complicated and we are in a highly regulated environment. It is important to balance the compliance with innovation and the best way to do that is build that partnership, education, and understanding across the board. 

Q: We have launched a new offering recently in the market— it’s an Gen AI workshop that we have actually conducted at one of our client locations in Houston and given the positive response, we conducted another one at the Harvard Club of Boston. That’s our way of bringing some upskilling and education opportunities to our clients as well. In that context, can you elaborate on the data platform that you have built and are now commercializing? What does that product offering do? How can it benefit other healthcare systems and perhaps digital health startups?  

Shahidul: Thank you for bringing that up and for evangelizing on Gen AI. I’d love to hear more about the workshop because that’s exactly what we need. We need those words out and get everyone to understand what this means, the challenges it entails, the opportunities involved, and how one can get started, better. I certainly look forward to that.  

In terms of our platform, we have taken it to the next level by making it multi-channel capable and building into it greater de-identification and anonymization capabilities. Those are our custodian responsibilities—to make sure we are, as guardians, doing everything right to protect the data. 

That said, we are also looking at opportunities and partnering others—pharma companies to data vendors—to look for opportunities where we can help them innovate and share this anonymized or de-identified data for greater good while garnering revenue for the system. We almost have such a partnership with six vendors and partners. Not only is that garnering revenue for us but we want to build it as a more robust platform. We have more interested parties approaching us from across startups and pharma companies and system organizations because, I think, the more we can share data in this way, the higher our chance to become a larger ecosystem that can help solve the larger healthcare problems.  

Sharing innovations will also help those who do not have the budget or the capability to do everything in-house. For us as a system, we want to diversify our revenue and become a revenue center instead of a cost center.  

Lastly, we are also looking at several AI products that we have built and proven in-house from general health to COPD, patient risk stratification, hospital stay prediction or various readmission prediction models, all of which can help drive value-based care. We can certainly help bring those to market. 

The goal is to start looking at opportunities to bring to the market, provide that product and service to others, and garner more revenue. That’s a very, very strategic step and definitely, we’d be interested in learning more from Damo Consulting. 

Q: Would you like to peek into the future and tell us what is coming our way in terms of some disruptions and changes?  

Shahidul: I don’t have a crystal ball, but looking at the trends and the exciting movements, it’s a good time to be in data AI, healthcare, and technology as a whole. The next few years will be more exciting because we are going to see the outcomes of all the initial innovations that we have seen with Gen AI, LLMs, machine learning, natural language processing, data platforms, and Cloud driven analytics capabilities. There’s a plethora of technology capabilities that we have set up and are innovating with, today. So, stay tuned.  

I can see that personalized medicine, for example, will become big and that will actually make patients’ lives better. It will actually impact our day-to-day lives. I can see that healthcare cost and efficiencies will become more and more robust with the predictive power of data. That will help us manage our healthcare costs, better. We do need systemic improvement to solve the entire problem, but these are going to help tremendously. We are the biggest spenders on healthcare, but our return is unarguably one of the median or lower than median. We need to solve for that and this is a great innovation space that I believe is going to have significant impact; tangible and real impact in the coming days. I’m looking forward to it and hope you also get to test it, see the benefits, and participate actively in it.

Welcome to Season 5 of The Big Unlock. I’m Rohit Mahajan, Managing Partner and C.E.O. at BigRio and now, Damo. This season carries forward Paddy’s legacy and joining me for an exciting discussion, in this episode, is Vineela Yanamreddy, Chief Information Officer for United Medical Center also known as Not-For-Profit Hospital Corporation in Washington, D.C.

Q: Tell us, Vineela, what interested you in the health care industry segment? How did you become the C.I.O. of a hospital system? 

Vineela: For this, I have to tell you a small story of how and where it all began. 

Right about when I was in the seventh or eighth grade back in India, my grandfather was diagnosed with some blood clots in his brain and we were seeking treatment for this. Around 30 years ago, when this happened in India, MRI technology or the CT technology was not as advanced as it is right now. Given that, some of the blood clots were missed. He could have lived longer, I think, and because I’m very attached to my family, losing him was the saddest thing ever for me. 

That’s when I decided that we needed to get into this field of technology where we could help not just the clinical world but a lot of people. That’s where it all began. I went on to pursue Biomedical Engineering from J.N.T.U., India and then, did my Master’s in Biomedical Informatics from Rutgers School of Health Professions, New Jersey. 

Q: How long have you been in the leadership position at this hospital system? Where is it located? What kind of population does it serve? 

Vineela: I’ve been in this leadership position for about six years. I joined as Director and then, became the C.I.O. 

This hospital is located east of the Anacostia River in D.C. We currently serve 200,000 residents in our primary focus area and then, about two million in our secondary population area. While it’s not a very diverse population, we do serve a majority of Medicaid patients at our facility. 

Q: How do you feel, from the technology perspective, about supporting the business needs of the hospital? You’ve done over 200 applications and transformed the EMR system as well. Would you like to share some of the thought processes that drove those changes? Do share some successful implementations.

Vineela: Upon my arrival, the hospital underwent some changes in management. Every year, there’d been new management so, it had been a very rough road for the hospital and the population itself. There were about 50 projects that were on hold or in progress that, because of the changes in the leadership, every team would want to pursue a certain way. 

That being said, there were many incomplete projects as well—some of them could not be integrated with our EMR since the hospital was so old or because the EMR and the applications were in fact legacy systems so, new technology wasn’t compatible for integrations. 

What we had to do was, determine the overall goal for the management and prioritize all the projects that would immediately bring significant success to the management team. I learned somewhere that such arrangements—the silo I.T.s where every department would purchase their own application without consulting the centralized I.T.—existed here, and the teams would want to just implement it or use the many web-based applications. In fact, the departments could procure these themselves and start putting the information everywhere. 

What we did therefore, was, we worked closely with all the leaders, the stakeholders, and the end users themselves, because at that time, the leaders also didn’t have the complete picture of what was happening in their department. We had to collectively form a task force and reconcile all applications. So, we reached out to every single end user to know their workflow and prioritize these projects that were on hold or in progress. We tightened all role-based access and worked on all data sharing mechanisms. 

The next step was to revise old and the ongoing policies and implement any new ones if we thought we needed them. We got our security risk analysis done because of the plethora of applications we had and got an external entity to fix our security issues. 

I mentioned some projects that were in progress earlier, right? We couldn’t attain completion there so they couldn’t be integrated further in our EMR. For those though, we put together our creative heads and built the bridge solutions because the ones in the market wouldn’t be compatible with these. 

We worked out different kinds of solutions to bring something functionable—what we thought at that time would be standardized and customized—to our hospital.

Q: That’s pretty significant—bridging the gap across a very diverse set of applications and technologies—and being able to bring all the stakeholders together. What do you think about your digital transformation efforts? Can you talk about a few of these which have impacted the patient population?

Vineela: I found these efforts very useful to measure against our peers or basically the industry standards to see if we were on the right path or not. That self-check is always important. 

To answer your question better, I would like to go back to the point where I started. There were some things like morning huddles, afternoon huddles, interdisciplinary action and a lot of other measures that were ongoing in the hospital—every hospital has these. 

In the morning when I attended my first interdisciplinary meeting here, the meeting was run by that shift supervisor and they were presenting all the numbers to the team that attended the huddle. I noticed them jotting down all those numbers in a book and then, emailing those numbers to a bigger group after some time. 

This continued on the second day and the third. I couldn’t stop myself asking the whole team—“How are you going to analyze this data? How are you going to make some informed decisions? How are you going to predict something if you are going this way?” 

I didn’t mean to criticize the way it was being done, but they were doing the best at that time with what they had and with all the changes that were happening in the hospital. That day, we just decided on how to proceed—and I had huge support from each and every one over here. I presented the idea, and it is not new because rest of the world was already on it. The way forward was to have live dashboards, robust and advanced data analytics. 

This was the biggest challenge because we were using so many island applications and a legacy EMR. Again, the readily available market tools were not going to work for us so we had to customize and build our own analytics platform. We put together all our skills, people, and we built ourselves an analytics platform. It took us about a year and a half, but we were really into it. I saw that hunger in all the physicians and the leadership because they wanted to take informed decisions. We were able to get that. 

We now have analytics throughout the hospital, and it provides real-time updates, alerts, and on-demand information, while enhancing the patient care and ensuring operational efficiency. Every department now relies on it.

Q: How did you manage to change the mindset of the people to adapt to this new technology—AI and Gen AI, for instance? How did you bring about a change in behavior and culture?

Vineela: I’m sure changing cultures and behaviors is very challenging. It’s challenging and time consuming, but I must say that the leaders and the directors, in envisioning the goals of the hospital, knew what they needed to do. The only thing they did not know was how to approach the changing landscape—and that’s where my specialty lay. 

My expertise comes in here because my specialization lies in EMR and health care applications. I had a lot of cooperation from everyone and despite the delays, once we had proper planning, could visualize the big picture, and saw some samples, we knew it would be easy.

Q: Can you share one example of how these technologies you’ve implemented are accessible and usable by a variety of users within the hospital and outside it?

Vineela: My focus has always been to identify and prioritize critical technology needs for immediate applications that can bring significant benefits to the hospital and both, clinicians and patients. I give them equal weightage. 

The more time the clinicians can spend with the patients, the better it is for us. Right now, with all these regulations, I know the clinicians are always complaining about the time they spend on documentation and going into different types of applications rather than being there with the patient. 

I tried to engage myself and get my team to evaluate how we could ease things for the clinician. I always look for that because it’s basically about balancing the need and preparing the staff. That is crucial. So, we developed a clear strategy, outlined the goals, timelines, and the expected outcomes. I put more time into that because I knew the team had the skills to take this ahead. 

We started off with a small notion in our team—we do whatever it takes to get the outcome. We were open minded enough to not limit ourselves to our roles, sit there, and do just that. We cross-trained ourselves. We became continuous learners. I enrolled myself into a number of courses that I probably couldn’t even finish, but I kept on learning. I kept encouraging my team to do the same thing. We assessed the current skill sets, identified the areas that required improvement, provided training and if we required specialized skill sets, we always had the option to hire seasoned professionals for that role. 

Our main focus has always been the end users. We prioritized user friendly designs, making it easier for the users to adopt these. We would constantly think about how we could improve their workflows. In this context, one of the most important items we implemented was a robust Change Management System. We asked—“What is the current workflow? What are the changes that we are going to bring? What will it impact and how? Is it simplifying it? Is it making it complex?” If we felt it was making things complex anywhere, we just cut back because it was not worth it. The goal was to try to simplify the end user’s workflow. 

Q: What are your thoughts on innovation—Do you involve external parties? Do you have an innovation-focused department? Or is it something everyone contributes to?

Vineela: The main idea here is to address two things. First, I am always in contact with the C-Suite leaders—the directors and the managers. I ensure that I am aware of all their application needs. 

Second, we’ve implemented the IT Steering Committee. I’ll explain what it does through an example. If the Anesthesia Department wants to go completely electronic and they bring up an application and say, “Hey! I picked that because some of the Anesthesiologists have experience in one application, and they would want to go into that”, we ask them to put it through the formal Project Request Process. Here, they have to enlist all their requirements including the technical specifications. The whole team—the entire hospital and all the leaders—will then look into that requisition and vote on whether to move further or not based on the budget. The group may decide, “Okay, we have this budget but what is our dire need?” The prioritization happens at that level. 

Once we all agree that this department needs this application, the C-Suite level work gets done. When that is complete, we go to the stakeholders and the end users because up above, they may have the overall idea, but when it comes to the day-to-day activities, I’m a 100 percent sure even I will not have that kind of a hands-on experience. 

What I would love to do and have always implemented is to go back to the end user and observe. It’s not only just about you being somewhere 1000 miles away but also that we don’t know what’s physically happening. So, we observe their workflow—the physical workflow. We calculate and take account of the amount of time the complete workflow takes. And then, we see how putting this application can enhance not only their day-to-day activities but their digital activity, as well. That is the level of thinking and the thought process we adopt.

Q: What are your thoughts on these new technologies, especially AI and Gen AI? Have you been experimenting with any predictive analytics or large language models? What would your advice be to other health care leaders on how to go about this journey of exploration?

Vineela: AI and advanced analytics are indeed shaping the future of care delivery and treatment plans. There are several ways in which these technologies are making significant impact, such as, through personalized medicine. 

Analyzing vast amount of data allows for the development of personalized treatment plans tailored to the individual patients and again, the use of predictive analytics is widespread for disease prevention. That’s what we use over here. 

We analyze historical patient data and patterns, so that the algorithms can predict the likelihood of certain diseases or health events, way ahead of time. That’s one thing that helps lowering the health care costs. 

I also always look towards efficient resource allocation. Advanced analytics can, in fact, optimize resource allocation within health care organizations by analyzing patient flow, bed occupancy, and staffing schedules. That’s definitely another way to reduce wait times and improve overall patient experience. 

Everyone knows remote patient monitoring and Telehealth especially given how it proliferated during the COVID pandemic. We are into Telemedicine as well and AI-driven analytics enables continuous remote monitoring of patients with chronic conditions which allows health care providers to detect early signs of deterioration. This enables prompt intervention and swift adjustments to treatment plans without the need for frequent visits. 

Another thing I have been looking into is fraud detection and prevention, where you can use AI algorithms to analyze claims data and identify patterns, projects, and activities in health care. Not only these, but advanced analytics may also be utilized for population health management, clinical trials, natural language processing for data extraction, enhanced imaging and to reduce costs and contribute to more patient centric and proactive approaches in health care.

Q: Lots of uses and applications for all these new technologies. In hindsight, everything is always 20-20. So, standing here and looking back, if you could change one thing or do it differently, what would you do?

Vineela: It may not be doing it differently, but one of the best examples in recent times that I can cite is from during the COVID 19 pandemic. Our patients and clinicians definitely stepped up and went full scale on Telemedicine for the care they needed. 

Not only were we trying to expand Telemedicine back then, but we had adaptation issues faced both by clinicians and patients. Switching to Telemedicine from traditional methods was difficult but you know how the saying goes, necessity is the mother of invention. The COVID pandemic definitely brought about a different kind of reality but since our Telemedicine platform was ready and embedded in our EMR with minimal infrastructure, we were able to serve our patients. It has been tremendous collaborative work by both our physicians and the patients to utilize the digital platforms. 

One thing I am working towards is, given the population I described earlier, we are trying to implement a tech bar where when the patients are in the waiting room, we can try to educate them about our patient portal, how they can access it, look up their summary on their dashboards and the like. We’ve already started doing this upon patient registration itself but we want to take one step ahead and show them, while they are waiting, that there’s help available. We try to encourage them to take steps toward the digital advancements and show them how this can help them better. 

We also have the Help Desk for our employees and our internal communication system which we have implemented within all of these projects. This system tells us our response times and it’s tremendous. If you text me in that, for instance, my response time is two seconds. That’s how far we have come in catering to the patient’s needs.

 Welcome to Season 5 of The Big Unlock. This season aims to carry forward Paddy’s legacy. I’m Rohit Mahajan, Managing Partner and C.E.O. at BigRio and now, Damo. Joining me for an exciting discussion in this episode, are my colleagues from Harvard Medical School. 

N. J. Udochi: I’m J. Udochi, a Board-certified family physician, Geriatrician, HIV specialist, and Addiction specialist. I’m also C.E.O. of the Summit Medical Group, a multi-specialty group in Columbia, Maryland.

As a physician who’s been practising for 35 years and a physician leader, I’m in the trenches, daily. While I’m knowledgeable about the pain points that hamper the practice of medicine, I’m also really passionate about finding solutions that will make our lives easier as we deliver the best care to our patients. I’m also obsessed with technology and how it can be used to improve access to and quality of care for everyone, especially underserved populations here in the United States and in my home continent of Africa. In fact, my entire project during our training at Harvard was focused on this subject matter, so, I sought other physician leaders whose goals aligned with mine. And voila, here we are! I’m really excited to be part of this first recording focused on AI and its importance in healthcare. 

Craig McLeod: Thank you, for having me today! I’m ecstatic to be a part of this podcast with my classmates from Harvard Medical School. I’m Craig McLeod, Founding Member of the McLeod Law Group, a healthcare attorney, and Founding Principal along with Tarul at Ellipsis Healthcare Leadership. I reside in Lexington, Kentucky. I have  20 plus years of experience operating in the healthcare space, delivering healthcare-related services to the elderly and the physically and mentally disabled. Our healthcare enterprise currently employs around 1,100 people on a daily basis and serves approximately 1,050 patients per day around the state of Kentucky. I’m here to offer legal input on AI and Machine Learning. 

Tarul Kode Tripathi: I’m Tarul. I’m a Pharmacist. I received my Doctorate in Pharmacy in 2002, but over the last ten years or so, I’ve been very passionate about and interested in healthcare leaderships. What brought us together was the Harvard Medical School Global Healthcare Leaders Program. It was around that time that I decided to leave my job of 15 years to focus on not only leadership, but effecting meaningful change in the healthcare ecosystem. So, we’re really excited to talk about the implications of AI from clinical, legal, ethical, and design standpoints, and why this matters in this moment. 

Q: I’d like to start with N.J.’s standpoint as a practicing MD. There are a series of questions that I will go through on why this matters in current practice. My first question for you, N.J., is how is the healthcare landscape changing for primary care provider practices? What are the drivers? How is value-based care impacting things as they stand? What motivated you to start your practice? 

N.J.: Thank you, Tarul! In recent years, the U.S. healthcare industry has been transitioning to a value-based healthcare system. That’s one that puts people and their health first, much before profits and shareholders. 

Largely, this change has been driven by patients being more proactive in their approach to healthcare. Also, it’s led a lot of health providers now to begin making changes to the way they deliver care and the platforms they work with. This is especially important in the chronic care space. 

CMS has seen the value of a collaborative approach to healthcare delivery, and they have pilots and demonstration programs in several states encouraging this approach to care delivery. In fact, in Maryland, we have the MDPCP program—the one we are participating in. We’ve just applied for that.  

These trends have been further accelerated by COVID-19. More recently, the issues of workforce shortage, mental health, physician and helpers’ workforce burnouts have sparked a lot of interest in trying to make sure that care delivery is modified such that we not only enhance it but also decrease physician burnout. With technology and innovation, especially Gen AI, ChatGPT, and LNMs, the future is in blending technology as an augmentor in healthcare provision. These are the drivers in healthcare today.  

For me, what motivated me to form my group was what I saw coming—the administrative burdens already placed already on primary care practices and the dying out of solo practices. We would want to use technology to improve healthcare, ease administrative burdens, and enable collaborations to provide and deliver the best care for patients. 

Q: Thank you, N.J.! What are the top concerns of primary care providers, physicians, and business owners in this landscape? Would they rather be acquired by hospital systems or private equity players? What does that look like for you?  

N.J.: I think that primary care physicians and business owners really are very focused on trying to survive the administrative burdens that have been posed now when you deliver healthcare. The big problems for us are not just burnout but also, as I mentioned earlier—workforce shortage. Many of us had to look to outsource to help deliver care that we needed and also, profitability. We were having a very difficult time trying to survive with these payers and what had been delivered in terms of payments. So, a lot of the providers, health groups, and even solo physicians were looking to find a way to be acquired by hospitals or private equity (PE) groups just to help them in terms of delivering the best care possible because of these administrative burdens.  

Q: From a place of values-based leadership, how do you think this impacts patients and the quality of care?  

N.J.: This is something that is really dear to me. When I talk to other physician leaders in the space and patients, what really stands out is that the quality of care that they feel is being delivered is not where it used to be. This is where it’s really important to blend technology with care delivery in a way that will be a win-win situation for both, providers and patients. This is where the value or the value proposition in this lies—finding a solution that will help decrease the administrative burdens that occur while improving the quality of and access to care for patients. 

If you have a solution that really meets those needs, then, that’s truly what providers are looking for. In the short run, it may seem more expensive. However, in the long run, this sort of a solution is what will lead to profitability. It’s all about really taking a taking a long-term look at it.  

Right now, a lot of physicians and providers in the health space are really burnt out because of all the administrative burdens that we face. This workforce burnout impacts the quality of care delivered—which is less than you would want. Trying to find or use technology that will help solve these pain points is where we need to be. 

Q: You’ve actually answered my last question on how technology could help reduce the administrative burden and reduce burnout. Would you like to add anything else? 

N.J.: I would like to add a few things.  

It’s really exciting because what we have now is Gen AI and these Large Language Models. You’ve also heard about ChatGPT. I remember being in a conference in San Francisco early on this year, and I was able to test drive the Doximity GPT and its application in solving authorization problems for physicians. It was wonderful. A lot of the physicians there were very excited because this has been one of the pain points that in care delivery. Using technology to solve these administrative burdens is one way a solution can help improve care delivery—it will leave physicians and other health workers more time to actually deliver the care that patients need. 

There are so many ways in which technology can even add value at the front-end or the back-end, in terms of scheduling appointments or even enabling patient engagement. Rohit was speaking about trying to help with care management but there are just so many ways, even in virtual care—we’re all very familiar with telemedicine, whether it’s in audio or in video format—with reimbursement from the government and other payers. This has really taken off, and it’s just about finding ways in which to improve access to care for all populations, especially those living with chronic diseases.  

Remote Patient Monitoring and Chronic Care Management are all areas that really lend themselves to improvements in technology and care delivery. So I’m really excited about this. It is solutions that can actually combine both, that will be the best for the future. Any company that can bridge these two gaps will be the companies that really make it in future. 

Q: There are many providers who are trying to talk about how AI and technology can help benefit care delivery. What are the ethical standards? What should we be watching out for? What makes for a good program? 

Tarul: Where I’ll start, especially after you’ve given us some very thoughtful comments from the clinical lens is, from the high level with what is the current state of artificial intelligence in healthcare innovation and why is it such a big topic.  

We are definitely in a significant moment when it comes to healthcare technology specifically powered by AI. By  being aware of and up-to-date with the implications—clinically, ethically, and legally—we can ensure that we’re putting patients at the centre of our care which is obviously the MVP for all of us.  

AI is a term that’s applied to machine or software, and it refers to the technology’s capability of simulating intelligent human behavior, which includes instantaneous calculations, problem solving, and evaluation of new data based on previously assessed data. That is what we mean by Generative AI. 

As you mentioned, AI applications in healthcare have literally changed the medical field, including Imaging, Electronic Medical Records (EMR), lab diagnoses, treatment, augmenting the intelligence of physicians and providers in a variety of different settings, new drug discovery, providing preventative and precision medicine, biological extensive data analyses, speeding up processes, data storage, and enabling access for health organizations, at large. So AI and Generative AI are creating massive efficiencies for providers and patients, alike.  

With that, I’m going to turn it over to Craig for some of his perspectives, not only from a legal standpoint, but why we should all be sitting up and paying attention to this topic. 

Craig: I’m going to start from a 10,000 foot perspective and a historical perspective.  

Historically, the evolution of technology has been based on a concept or principle called Moore’s Law. In 1965, Moore’s Law was put forth by Gordon Moore, who was Co-founder of Fairchild Semiconductor and Intel. He was the C.E.O. of Intel—and everyone that has a computer, cell phone, PDA, knows or should know who Intel is.  

Mr. Moore observed that the number of chips would double each year and that would turn into double processing power. This was on a yearly basis from 1965. This law stayed pretty intact until 1975 when it doubled to reflect that the doubling of chips per integrated circuit and doubling of the resulting processing power would move to every other year. Moore also established his second law, also known as Rocks Law, which stated that, even though the size of the chips on the relevant integrated circuit was reduced and the processing power doubled, the cost of the physical plant and the necessary R& D related to the advancement would increase exponentially over the same time period.  

The reason why this is important is, it creates pricing issues which are inevitably passed on to the ultimate consumer which is either a business model or the personal consumer. Staying with Moore, his laws are based fundamentally on the historical norms of manufacturing, supply chain principles, as well as basics of economics and supply and demand considerations. There’s always physical work necessary to maintain the underlying basis of Moore’s law. However, the explosion of AI—and I use AI to include Generative AI and machine learning—over the past year or so, has made these traditional considerations less of a factor to predict and forecast the increase of computing power and evolving technologies. 

As AI and machine learning are not customarily limited by these physical factors as the processing manufacturing principles due to the advancement of self-learning that’s done internally and within the relevant AI machine learning systems, their elimination allows for the exponential increase in advancement of the capacity of the relevant AI and ML systems that’s assuming that the information fed into the AI and ML systems are accurate and factual. This requirement to effectively process information is being interfered with intentionally, specifically by the artistic community—and this is with regard to the Nightshade and Glaze programs that are being implemented by the University of Chicago. More about this coming up on my next statement and probably, future podcasts.  

Q: What are the best practices in AI innovation and implementation?  

Tarul: So AI is not going to replace clinicians, but it will advance caregiving capabilities. Clinicians must increasingly rely on human skills such as, empathy and culturally-relevant care. For example, we have to train our providers— MDs, nurses, all caregivers—in all different modalities in meeting patients where they are. This real world data feeds the algorithms that AI will influence and ultimately use to improve care and efficiencies. For example, in marginalized populations, we have to do a better job of understanding the factors and interventions that will close gaps in healthcare disparities.  

Another example is trauma informed care which needs unique ways to speak with and treat patients who have lived through gender-based or other forms of violence. There are some really interesting discussions going on all over the country on how to carry out this care. We all must sit up and ensure that we’re paying attention. These intentional practices will definitely influence health equity as AI technology advances. Otherwise, we’re at risk of leaving the same communities who have historically been underserved behind, again and again. 

If AI is viewed as critical to healthcare’s future, then, a diverse group of stakeholders must be engaged in the dialogue. So a group like ours—data scientists, engineers, clinicians, patient advocates, ethicists, economists, and policy makers—have to come together and hold honest and wider discussions around how to implement ethical and equitable AI. 

Q: What are the legal considerations and why it matters in terms of risk avoidance, management, aversion etc.?  

Craig: AI and machine learning are evolving very quickly but as everyone knows, the legal field and the resulting legal decisions and lawsuits are so slow. This is a very new topic that we as attorneys are trying to figure out how to speed our pace of evaluation and bring these to light to expedite these type of decisions. The key here in understanding AI and machine learning legal perspectives is comprehending where and how the protected and confidential information and or the data is stored, utilized, and integrated into local or and/or hosted on external systems or platforms. 

In law, usually confidential or protected information is just that until it’s not. Where you get to the “it’s not,” is voluntary disclosure, negligent disclosure, or for an authorization by an agent or principal of a business, or him or herself donating or allowing this information to be imported into an AI or ML system. That brings us to confidentiality and trade secrets. I’m using this generically to mean “any confidential protected information” includes protected health information under the U.S. Code. This states that an owner of a trade secret must take reasonable steps and measures to keep such information secret. What that means is, you can’t go out and claim you have a trade secret or protected or confidential information and then, put it on a billboard somewhere. That’s the historical viewpoint. But we’re not in the 1950s. We’re not in Cleaverville, anymore. This is 2023 where everything happens over a computer. 

Information is stored everywhere. What happens when an employee or an agent of this information—a steward or a custodian of a company or an individual—just happens to link an information source or a depository containing this great secret or protected health information or any other confidential information? It doesn’t even have to be intentional. It could be that they unintentionally and even negligently link this information or this platform to an AI or machine learning system. Typically, when you feed information into the AI and machine learning system, it doesn’t really care where the information comes from because it’s utilizing it. But once those processes become active, there’s no going back. Once the trade secret is input into this AI and machine learning system, it’s more than likely your trade secret will be considered public domain. Anyone can use it for any reason at all. There’s about four or five big ticket lawsuits going on right now around the United States. If you read the initial briefs and decisions, they’re pretty much in lockstep with each other. The courts are saying, “Hey! You gave it up. You fed this into and made it part of the public domain.” You’ve lost any protection that the courts can give  you.  

That leads us to patient and data privacy. So everyone knows how important the Protected Health Information (PHI) is from a patient’s healthcare and societal positions. However, one problem immediately arises when the AI systems become aware of this data. As I just brought to light, how does the AI system know that this information is still confidential? How does it know that some of this information that was confidential hasn’t been made un-confidential or public by release or by a court order? It doesn’t. This is why it’s so critical to safeguard information from an AI or machine learning environment.  

Another issue that I want to bring to light is the facial recognition technology that may be hijacked for improper, immoral, or unethical purposes. The first thing that came to my mind is it’s akin to the past concerns of DNA information and the sharing of the same on Ancestry.com and 23andMe because of the notion that this information really is the last line that police agencies can utilize to find and crack cold cases etc.  Ultimately, the jury is still out on how to utilize this DNA information and how it is being utilized after being deposited on a third party site.  

I mentioned earlier about the bad actors and the poison pills. There are some participants in the space now that are attempting to mask or thwart AI machine learning’s ability to learn to do just that. Specifically, I’ve been reading and studying the Glaze and the Nightshade programs from the University of Chicago. The baseline notion here is, these artists that are seeking protection of their work. They’re artistic and into music and things like that. They shouldn’t have their work stolen, which is serious and valid concern. I think, we can all agree on that. But AI doesn’t seem to steal this information. They’re just utilizing it to learn for future endeavors. My initial observation on this position by the artists utilizing Glaze and Nightshade was, “Did they or did they not go to art school? Did they or they not learn from Van Gogh and Renoir? Didn’t they, weren’t they influenced by those artists?” I doubt that they gave credit or anything to those artists, but they learned from them. They created their own style. They didn’t steal it. They just were influenced by their artwork. So it seems to me that the artists utilizing these two programs sort of want their cake and eat it too. 

Q: How does all of these that we’re discussing influences the design best practices.

Rohit: As N.J., Craig, and you mentioned, this is a revolution. Today, AI is becoming household name with ChatGPT and that has reached inflection point. Everyone’s using it for some purpose or the other. It took a long time. And AI has been around. In fact, I’ve been playing with AI since my engineering school days way back when, but it has now become ubiquitous to the point where it’s easily available. One can log in and use it especially those Large Language Models and the applications which are immense, very broad, and across industry segments. 

However, in the healthcare industry segment, there are obviously privacy concerns and patient lives at stake. So, when we design AI systems, we do have to keep in mind all the various aspects that we discussed here, today—the ethical, the moral, the patient-centric etc. 

I’ll illustrate it by an example of a project that we are currently doing for one of our clients. Hopefully, that will bring to life some of the design approaches. We are working with this very innovative digital health company that N.J. mentioned I’d shared with her. We are working on building a care planning tool and platform applicable to chronic diseases and patients with multiple disease conditions.  

The challenge was—how do you build a wall so that the Large Language Model is not infringing on any private data of the patients?  

There are now possibilities, especially with large tech companies which will allow you to create these walls so you can stay in the swim lane where you’re building a Large Language Model, which is custom and learning from your proprietary data. It is staying in that silo. It’s not getting externally released, at least outside of that wall. These are some of the design conditions when we approach projects.  

Typically, you look for a POC—a proof of concept—and then, as you build it out, you put it in front of the stakeholders so they can test it. Usually we’ve also found that even in this project, there is a clinician in the loop. So, we still need that certified, qualified human being—a nursing staff or a physician—in the loop to look at the output of this Large Language Model and like sign off on it before it goes into the next step of being shared with the patient.  

This whole landscape is changing very fast. There is literally innovation happening almost every day. It’s explosive. I don’t know which law—Moore’s law or AI law or what we should call it—but things are changing very fast. In the company, we have some very qualified data scientists who keep up-to-date with all the current literature. That is how we are able to bring to bear our expertise and technology to create these robust POCs and pilot projects for our clients. 

Q: As we wrap up our discussion, now is the time for the “so what?” What should our multidisciplinary stakeholders take away from this podcast? Where do we start? What actions should be taken? Rohit, I’ll start with you and then, N.J. can describe how advances in this space can improve the lives of providers and patients alike using a real world example. 

Rohit: I’ll chime in with a very small example. I think this stems from a recent discussion I was having with someone who’s at a very large healthcare provider system in New York. He was saying, “Rohit, I have so much data in my repository. I don’t know what to do with it.” I think, that is really the starting point.  

Organizations and providers have data sets and that is a gold mine, right? How do you leverage that data set is where AI and Gen AI comes into play because AI is good at making predictions. So, if you have data sets, especially those which are retrospective, longitudinal, and immensely powerful, these can be a base for building AI and Gen AI models which can truly help with patient care. In my mind, for any AI project, everyone knows 80 percent is data engineering and 20 percent is data science. That still remains true. 

Q: From a provider and a practicing MD’s standpoint, what do you think people in this space need to be doing in terms of action? 

N.J.: It’s really exciting because the healthcare industry and providers produce a lot of data like Rohit has said. These massive amounts of data from multiple sources is exactly where AI and machine learning can add value. This is especially in healthcare delivery. I’ve talked so many times about this that there’s no day I don’t wake up trying to figure out how to solve the administrative burdens and improve healthcare delivery here in the United States and around the world. So, it’s really about helping solve those pain points.  

In healthcare, AI can add value in healthcare diagnostics. It’s been very valuable for many of the providers in Radiology, Pathology, and inpatient monitoring. Also, we have AI that has been—and many tech companies have also helped in—improving care delivery in terms of predictive modeling. We talked about virtual care—telemedicine and wearable devices—there are so many areas in which AI can really add value.  

But really, the technological solution that we want is one that really can blend the pain points that we experience in care delivery. A solution that can solve those problems and at the same time improve patient outcomes, enhance access to care, and ensure profitability for health systems, provider groups, and organizations. That is what the healthcare community is looking forward to.   

Like I said earlier, in the conference I had attended in San Francisco, there was so much excitement with Doximity GPT because we could really use it right there and and solve the prior authorization problem on a daily basis. There are so many problems that we have, whether it’s at the front-end of care delivery or at the back-end. The value add will be in trying to use AI and LLMs to solve the problems that we have in care delivery when we see patients.  

We talked before about the back-end in terms of patient coding in the Revenue Cycle Management (RCM) process, too. That is another area where technological advancements are needed. Perhaps startups can focus on this in the healthcare space because when you combine both the front- and back-ends to solve those problems, there is where you’re going to see a lot of excitement.  

In the end, the problems that we’re experiencing as providers—the burnout, physician charting, in vitro scribing—are all areas where physicians are looking forward to using AI to try and augment the care that they provide on a daily basis. 

Rohit: Thank you for the excellent panel discussion, N.J., Tarul, and Craig! It is great to get together, virtually. I would like to end by giving a shout-out for a book, Quantum Care, that I recently wrote and which has been published. It is a deep dive into AI for health research and delivery.