Q. Hi, Rob I’m Rohit Mahajan, Managing Partner and CEO at BigRio and Damo Consulting, and host of The Big Unlock podcast. It’s been a popular show for many years, and we’re continuing the tradition started by Patty Padmanabhan, the founder of Damo Consulting. Many healthcare leaders have been on this podcast, and it’s great to welcome you. Over to you for your introduction, Rob.

Rob: Terrific, Rohit. It’s a pleasure to spend time with you and with your audience. I’m Rob Posner. I’m the Chief Technology Officer for AbsoluteCare. I’ve been with AbsoluteCare for about two and a half years now. I joined because of the game-changing mission of this organization. I’ll get into that in a moment, but first, a little background.

Prior to AbsoluteCare, I spent the last decade in similar private equity–backed, provider-centric organizations that are changing healthcare. My passion for healthcare has really been about the transformation that’s needed—not just for those organizations, but for the country and the world. I truly believe in that mission and the role technology plays in achieving it. That’s why I decided to move into the healthcare industry.

Before that, I was in hospitality and entertainment. I live in South Florida, and the cruise lines are big here. I’ve worked with major cruise lines and Disney Parks in particular. I built a team and worked backstage at Disney Parks where we developed the MyMagic+ experience and led major aspects of that global rollout—transitioning to a managed guest experience. It was one of the early efforts in what’s now known as the experience economy, leading products and services by experience for consumers and guests.

Q. That’s awesome, Rob. I’m actually a fan, and I don’t think I mentioned this to you last time—Disney has a university where they run programs on leadership, quality, service, and a few other topics. I’ve been through all four of those. So great to know that background. Now that you’re at a healthcare organization, could you tell us more about AbsoluteCare—what the business model is, and how it aligns with your experience in provider-centric organizations that are really changing the healthcare industry?

Rob: Absolutely. AbsoluteCare’s mission is to improve the healthcare of the nation’s most vulnerable populations. We do this by improving outcomes through holistic care and care management.

We refer to those we serve as members—not patients—because we contract with payers for a panel of their members. So they become our members, and we take full responsibility for their care. Our tagline is “Beyond Medicine” because we deliver primary care, care management, pharmacy, behavioral health, and address social determinants of health in a comprehensive way. That’s what differentiates us from traditional care models.

We operate under a full-risk, value-based care model that demonstrates improved outcomes. We’re fully accountable—clinically and financially—for delivering on the triple aim of healthcare. We don’t just talk about it; we live it. That accountability is central to being a sustainable and impactful organization.

Q. And, and I understand you are in several markets and tens of thousands of patients now, so there must be some early learnings. 

Rob: We are rapidly growing. We were in seven markets at the end of 2024, and we’re about to be in 11 markets over the next couple of months. We serve tens of thousands of high-acuity, high-utilization managed Medicaid members. That’s important to understand—these are some of the nation’s most vulnerable individuals. They often have multiple chronic conditions, compounded by social determinants of health. We need to address all of those factors to be successful. 

Q. That’s awesome. And your delivery model is partly center-based and partly community-based, right? That seems like something unique AbsoluteCare brings to the table. 

Rob: Absolutely. We deliver about half of our care in our centers and the other half in the community. In each of our markets, we have a center located in the urban core to serve our members. But the reality is, it’s often difficult for our members to reach us. Since we’re responsible for their outcomes, it’s on us to go to them—wherever they are—to ensure we’re delivering care and care management that drives better outcomes.

Q. That’s amazing. I have a curious question—from my perspective. You mentioned that you get the cohort of patients from the payer side, right? Because they obviously want better outcomes. So, do you work with providers at all?

Rob: Our organization includes providers. In other words, we deliver the actual care. We have our own employed providers who deliver primary care services. We also employ care management teams, behavioral health specialists, and pharmacists who work in our pharmacies. So we provide comprehensive care and care management in our centers. Additionally, our providers and care teams go into the community to deliver care in members’ homes and other facilities. 

Q. So I understand that there’s a lot of technology at play here. Obviously, we are all leveraging all kinds of technologies to help better outcomes for these members and for these patient populations. So, would you please talk to us about your journey? Like how was Absolute care and then the how did you fast track? I think you were talking about fast tracking transformation. 

Rob: Oh, absolutely. Yeah. So I’m the first C-level IT executive in the organization. And not surprisingly, that means that usually there was—coming into it, I think the teams were doing the best they could with what they had, but they didn’t have a seat at the executive table. And so we found that the state of technology was not where it needed to be. And of course, that’s why they brought in a Chief Technology Officer.

So nothing was really surprising from that perspective. But systems were not configured for our mission. It’s not unusual because systems are not really made or designed—typical EMRs are not designed—for value-based care, full-risk provider models, right? So it’s not surprising they weren’t configured correctly.

The technology team was following the business rather than leading. There was a lack of appropriate governance. And like I mentioned, the teams were working really hard, and I’m proud of the work they did to get to that point. But clearly the organization recognized that more needed to be done.

Just to explain the point—when I got there, within a month or so, we had a terrible situation where we had a significant outage of our EMR that went on for more than a day. And no one in the organization thought to tell me. Our EMR—which all of our providers and care management count on to do their jobs every day—was down. And she let me know that we were down. So it was the kind of thing where we had to change the culture, change expectations, and deal with accountability.

That was kind of the start of that story—saying, okay, that’s where we were. I installed new leadership within my department, got people who understood where we need to go, how to set expectations for our teams, reset what operational excellence means, and establish that culture of accountability.

We worked very rapidly to start reconfiguring our EMR. Even in terms of the governance—we had a steering committee for our EMR, but what we found was a whole bunch of leaders were sitting on calls that should be about strategy, and they were dealing with day-to-day issues. That pointed to the fact that we didn’t structure our governance well. We didn’t have core teams and the right people dealing with the day-to-day so we could address those, and then separately deal with things like: should we be moving to the cloud? Should we be on one instance of the EMR?

These things have to run in parallel, and you have to have the right people engaged in those conversations—and the right cadence. Those are some of the things we had to do very quickly to start dealing with the rapid transformation that we needed to make as an organization.

Q. Right. And you really had to do groundbreaking—or from the grassroots—you had to build a care management system, because the EMR really isn’t suited for that purpose, right? So please talk to us about that. It’s something very different, I guess, that you’ve done in the organization, and it’s like the bedrock for engagement.

Rob: Absolutely. And just about all EMRs you could categorize as being focused on delivering care in an office, in an ambulatory environment. What does that mean? For a patient visit, a member visit, in a center. That’s really what it’s designed to do.

But when you talk about all the work we need to do to provide longitudinal care for that member—it’s the things happening 99% of the time when they’re not in the doctor’s office. That’s care management, and that’s where a lot of the outcomes for our patients happen. But it’s not really addressed in EMRs. EMRs look at things like decision support for the doctor and care gap closures, but care management is its own thing. It has its own workflows, and we need to make sure they’re focused on what produces improved outcomes.

So, we determined that EMRs don’t really do that, and we brought in a full-fledged care management system. We implemented that and went live about a year ago. We also integrated it with our EMR, which at the time was a fairly significant step, because the objective was to let people work from one pane of glass.

If they’re in the EMR, they shouldn’t need to jump into the care management system to check on something—and vice versa. That’s been a journey. We’ve done the first couple of iterations to make it work. There’s more to do, but we’ve gotten our MVP product into the hands of our markets, and it’s being used successfully. It’s been a great success story, showing how we can integrate care and care management in a way that reflects our model. We have a care model, and we need to make sure our systems are aligned with that—not force people to work within systems that don’t fit.

Q. Absolutely. That’s significant. And it’ll always need to be taken to the next level, based on user feedback and the people who engage with it. So we talked about care management, Rob, and it’s fantastic that you were able to build a product to engage members and support all the stakeholders. How did you think about the tech stack? Because you’re operating at two levels, right—like we discussed before, also out in the communities?

Rob: Absolutely. And from what I’ve seen in the industry, these systems are typically designed to work in a facility—an office or a hospital—or maybe a hospital-at-home type setting. The assumption is that the technology stays in one place: someone is a remote worker, someone is working in a hospital, etc.

But in our integrated model, that’s not the case. We’re delivering half of our care and care management in people’s homes, which means our workforce is mobile. They’re in the field, moving between members’ homes. We have transitional care managers who go out into facilities when we get an alert that a member’s been admitted to the hospital or has visited the ED.

So we need systems that can work across a variety of environments. Our original tech stack wasn’t built for that—it was built for more stationary settings. So we had to completely rethink it. We tested different laptops, connectivity solutions, and carriers until we found a solution that worked. We looked at each market individually, since different carriers perform differently depending on the location.

We landed on a solution that includes new high-powered laptops, MiFi devices, and iPhone 15s. It turned out that the 5G technology—and specifically the antennas on that hardware—allowed us to overcome many earlier challenges. 5G really opened up the capability and gave us the bandwidth we needed to connect to an EMR in the field.

And once we did that, it really unlocked the power of our solution. I saw that firsthand when I did a round with our transitional care managers. One of them was at the bedside of a member who had been admitted to the hospital. Their job is to coordinate care, make sure follow-up appointments are scheduled so we can continue to support the member.

And right there at the bedside, the care manager was able to open the EMR, schedule the appointment, confirm it with the member, and ensure continuity of care. That’s exactly what we need to see—technology working in the field, making a real difference in the care and care management we deliver.

Q. That’s awesome. Yeah. Being able to schedule appointments at the bedside is, is fantastic. Even today to reschedule my appointment with my physician is a big task.

Tell us, you know, uh, Rob, that in building all these solutions to solve the problems that you saw, how did you go about the governance aspect of it? 

Rob: Yeah, governance is a really important aspect here. It’s really easy to focus on what system you use—there are great enterprise solutions for most of the challenges we face in healthcare, broadly speaking. We have our in-house pharmacy, so we implemented a pharmacy system. We have a care management system, our primary EMR—so we have the big pillars, if you will, of our clinical applications.

But what’s really important to unlock the value of those systems is establishing product teams, having effective steering committees, and creating a proper intake management process. It’s so easy to get lost in all the new requests that come in. You need a way to manage that, making sure we stay aligned with the business—both in terms of the strategic plan and the day-to-day changes happening.

So you have to manage between the strategic and the tactical. You can’t just do one or the other or you won’t be successful. Keeping that executive alignment—engaging the appropriate executives at the right times—and having an overall change management and governance approach are all key building blocks. If you don’t have these things, it doesn’t matter what systems you put in place—you’re not going to be successful.

And I think another point—not governance in particular, but related—is not forgetting the small things. Like I mentioned earlier about the community tech stack—one of the things we didn’t think about was the equipment itself. You’ve got to pilot everything. Don’t go live with anything without piloting it, because that’s where you learn the small things that make a big difference.

For example, we provided all this technology, but we didn’t have the right type of briefcases for the care management team. We eventually got them rolling bags—and they had to be locking bags—because of PHI. We’re a HITRUST-certified organization, which is business-critical to us. So we take the handling of patient information very seriously.

Those are the kinds of things that make a difference. If you don’t take care of the little things, you don’t get the adoption—and then you don’t see the business results. Which is why, at the end of the day, you’ve got to think about the big things and the little things. And together, that’s what makes a solution really work for the organization.

Q. That’s great. So Rob, no podcast would be complete without touching on AI and innovation. So, would love to get your thoughts on innovation, AI, and now GenAI. What are you thinking, and what are some of the use cases you might be coming up with? 

Rob: Sure. Look, AI is a really exciting topic in the industry, and for us in particular, I’m really excited about what we’ve been able to accomplish recently, and equally excited about the opportunities in the future.

So on the clinical side, rolling out ambient experience—ambient listening for clinical notes—is the critical use case. We’ve been able to implement that successfully. We went from pilot very rapidly to full rollout. We saw the results very quickly. And look, there’s a lot of change management to do with the providers—to get them used to the fact that this ambient listening device is there, making sure they’re talking to their patients about it and what it means, and how to leverage it effectively. And so that’s a learning process, and we’re definitely still going through that, but we’re already seeing results.

One of the immediate results is that our providers can engage our members more effectively, right? At the end of the day, they’re spending less time hands on keyboard, and more time engaging with our members, having the important conversations that they need to have to deliver care. And so that’s really exciting in terms of the impact on care delivery and outcomes.

Another piece on the clinical side—we implemented a system that detects diabetic retinopathy with fundus cameras. The solution takes the images in the office, sends them immediately to our partner in the cloud, they do a read of those images, and send back—within 30 seconds—a result and a recommendation for referral. What we’re seeing is that because we get that instantaneous result, our patients—our members—are actually going forward and getting that referral and follow-up appointment. And that’s really what we’re talking about: we’re changing the behavior of our members so they get better outcomes, address diabetic retinopathy early, and take care of it before it leads to something as serious as blindness. It’s a really urgent issue, and here’s an example where technology is really motivating our members to take care of their health. That’s amazing and exciting to see.

Additionally, we’re starting to see AI throughout all of our systems—it’s just almost happening organically, I would say, just by vendors providing it. So, certainly Microsoft Copilot, which has become pretty ubiquitous—we’ve rolled that out. We piloted it, saw great results, saw great adoption. I’d say of all the technologies that we’ve released, Copilot was one where we just put it out there, gave some training and tips and tricks, and the uptake was amazing. Unlike an EMR, where providers are required to use it and follow detailed workflows, with Copilot there was no requirement—and still the adoption was high. We’re seeing great productivity results and people learning how they can use AI in their day-to-day work. And that just rolled out recently. We expect to do a lot more with it. We’re doing workshops to enhance learning and help staff understand what’s possible with AI.

So that’s what we’ve implemented so far—those are active in our organization at the enterprise level. In terms of where we go from here, there are a couple of areas that are very exciting to us. Robotic process automation—though it’s not new—is an area where we can continue to refine the EMR experience for our providers and frontline staff. We’ll continue to look at automation opportunities and other AI capabilities within the EMR, like documentation and note summarization, translating into different languages to communicate better with patients, and reducing the multi-click environment of the EMR by automating routine tasks. The next area is really AI agents—looking at what’s happening outside our core platforms that could be managed with more automation and integration. That way, we can free up our team members to focus on what really matters—our members.

Q. That’s awesome, We actually did a seminar today, Rob, on Agentic AI in healthcare, and there was a great response to the webinar. It’s such an interesting topic—people are actively looking for use cases. We’ve worked on several use cases with our clients, and we definitely see Agentic AI as one of the key options to explore.

So I think we’re toward the end of our podcast, Rob. Any final thoughts or remarks you’d like to share with the audience?

Rob: Yeah, I would just say that it’s a really exciting time to be in healthcare technology. I believe we’re at a point of inflection—where not only do we as healthcare technologists see the opportunity, but the business side clearly sees it as well and is relying on the technology function to step up.

And I think that’s happening—as executive leaders begin to expect more from their technologists, but also expect the business side to think about how to leverage technology. That’s where we’ll really start to see the tech and business teams working together to solve meaningful problems and drive real impact.

Subscribe to our podcast series at www.thebigunlock.com and write 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. Hi Siva. How are you doing today? Welcome to The Big Unlock podcast. Very happy to have you as our guest today. For our audience, as you might be aware, this was started by Paddy Padmanabhan, and I’m building on his legacy. We’ve done many episodes. Let’s do some quick introductions. I’ll start. I’m Rohit Mahajan, Managing Partner and CEO at BigRio and Damo Consulting, and also the host of The Big Unlock podcast. Over to you.

Siva: Wonderful. Appreciate you having me on the podcast, Rohit.
I’m the CEO and co-founder of a company called Cohere Health. We started in late 2019, and we are solving the burdensome issues related to prior authorization.

I’ve been in the healthcare industry for more than 25 years. Prior to Cohere Health, I founded another company called Coda Health Analytics in 2007, built it over 10 years, and sold it in 2018 to EXL. It was a successful analytics company in the healthcare market, funded by VCs including Sequoia Capital. It was a good exit for everyone. We were serving health plans at the time.

Before Coda Health, I started a small company in the provider space and sold it to Perot Systems. I also started my career at Intel, then went to business school at the University of Michigan, got my MBA, and moved to Connecticut. I live in Connecticut now and came here to work for Gartner Group. From there, I wanted to start something on my own, and that’s how I began my healthcare career.

Q. That’s amazing, Siva. Thank you for that introduction. With such successful exits, I’m sure Cohere will also be on a great footing. Could you share how the idea for Cohere came about? You’re so familiar with the healthcare space—I’m sure you saw a big problem to solve. How did it start, and what’s your journey been like?

Siva: As I indicated, in my previous company, I had been working closely with the health plans—health insurance companies. We were doing analytics, and at that time, we were focused a lot more on payments, population health management, care management, etc.

During that process, I came across the prior authorization process, which the health plans were involved with. I’ll get into more detail about prior auth later. So I got involved in that, and it was always in the back of my mind that the process was highly manual and caused a lot of operational issues for both providers and patients.

It was a major pain point for the health plans. After I sold the company, I had to work there for a bit. And then I started thinking: how can we apply AI and other advanced technologies to solve this problem in the healthcare ecosystem? That was my angle.

While I was working with the health plans earlier, some of my clients had indicated they’d be willing to work on this problem—if I could come up with a better solution. That’s how I kind of got into this.

Q. That’s amazing to know that you were able to discover a gap and then build a new business enterprise to fill that gap. So tell us, Siva, a little bit more about prior auth. Most people in healthcare know what prior auth is, but tell us some of the intricacies and more details about it. You’re the expert on it, right?  

Siva: Sure. Prior authorization is, you know—as a patient, for example—when you go to a specialist, they’ll usually examine you. Say you go there for knee pain. Depending on the severity, they might just take an X-ray first to see what the problem is. If it seems more acute, they might order an MRI.

Now, the moment a physician orders something more expensive—like an MRI, which can cost between $1,500 and $2,000—the insurance company wants to know why that test is being ordered. So the physician’s office has to fax or submit information explaining why I need the MRI.

Then, the health plan looks at their policy and decides whether to approve it. So that’s the process. Anytime there’s a costly or potentially unnecessary procedure being considered, this process acts as a check and balance to ensure it’s appropriate.

On the insurance company side, the submission process itself can be confusing. It might happen through a portal, a fax, or a phone call. Then, the health plan assigns it to a nurse, who reviews the information and determines if it aligns with policy. If it does, they approve it.

If the nurse thinks it doesn’t meet the criteria, it goes to an MD for review. The MD might then say, “You don’t need it,” and deny it—or they might approve it. If it’s denied, it’s usually by a specialist on the insurance side who gives a reason—like saying based on the X-ray, the issue doesn’t look serious, so physical therapy might be enough.

My physician might not agree with that, but that’s the process. So then they might send me for conservative therapy, etc. That’s the prior authorization process.

Q. Okay. And Siva, in the first part of what you were explaining, you used the word abrasion, right? I’m very curious—what is this abrasion that’s happening? And second, how long does this process take? Because now the person needs to apply, right? 

Siva: Yes. Right, exactly.

So the process hasn’t always been very clear in terms of what information needs to be submitted. What happens is there can be a lot of back and forth between the physician’s office and the insurance company. For example, the office sends some information, and the insurer says, “No, no, we’re looking for an indication of something else.” Then it gets sent back. The provider looks at the documentation and says, “No, we actually did provide that—here’s where it is,” and they send it again.

So that back and forth adds time and creates more administrative work on both sides.

All this paperwork, documentation, back-and-forth communication, and waiting can take anywhere from five to 14 days. For very complex procedures, it could even take 13 to 14 days. For example, if someone needs surgery, they might have to wait while going through multiple rounds of paperwork and approvals.

Meanwhile, the patient is the one who suffers. The final decision—whether it’s approved or denied—won’t be known until that whole process is complete, and only then can the surgery be scheduled.

That’s what causes the abrasion: the administrative burden, the delays, the unclear requirements, and the possibility of denial at the end of a long process. And that’s still the case in many areas today.

Q. So because of your prior experience with payers, in this particular case insurance companies, you chose to focus on prior auth with them. And there is the healthcare system in the loop, which is the physicians and the providers. How do you distinguish between the two? Because prior auth is important from both perspectives, right?

Siva: For the health plans, it’s a cost. The main reason why there is prior authorization is because, as we all know, healthcare costs have been going through the roof.
There is quite a bit of waste, and a lot of it is due to unnecessary procedures, unnecessary imaging. For example, there’s no need to do imaging if it’s sufficient to have just an X-ray, which costs like 50 bucks instead of a $1,500 or $2,000 scan.
Because of the excessive use of high-cost items, there’s waste in the system. Health plans, being the intermediaries, manage the dollars for employers or the government, like Medicare.
So one of their tasks is to control for this. The health plan’s viewpoint is to prevent unnecessary things.

Obviously, the physician thinks something is very important for the patient, and that’s where the tension is.
The reason we decided to go with the payer side is that payers have the volume, and a lot of things can be controlled from the health plan side using technology.

There’s no point in just speeding up the process on the physician side. There are benefits to it, but you’d have to do it for every physician office.
If you go to the health plan, you can address all of this in one shot.

Q. Awesome. So Shiva, you mentioned that you started in late 2019. So that’s actually before COVID, right? 

Siva: Yeah. That’s like three months before COVID. 

Q. And then COVID hit. It must have impacted your go-to-market and your plans. But you stuck to the mission. You have very good investors who’ve supported you in your journey.
Tell us a little about the bumps on the road, how you overcame them, and where you are today. How many employees, and how are you going about this?

Siva: One of the things is that we actually managed to partner with a large health plan—okay, Humana—it’s on our website. What happened was that I had hired like four people or so. We were actually working in a WeWork office in Boston in February and were in the process of finding an office and recruiting people, etc.

I remember in early March 2020, while working in the office, they called all of us down and said, “Hey, we found somebody with COVID today in the offices. So you guys have to go home. We will call you, and we’ll see when you can come back.”

That was the last time we all saw each other—the four of us. And we came home, and after that, we didn’t see each other for more than a year.

But then we changed our entire plan—worked remotely—and built the product out. They had a deadline of January 1, a client. So January 1st, 2021. We said, “We can’t just sit at home and wait for COVID to go. We need to develop the product and everything else.”

We actually took advantage of the remote situation because initially our office was going to be in Boston, and we were going to recruit engineers in Boston—everybody in Boston. But because of COVID, we said, “You know what? We can hire people anywhere in the country.” And so that actually opened up the pool for us. We went around the country and recruited people from all over.

Q. That’s amazing. And I understand you’re still fully remote, which is very different from many companies shifting to hybrid or back to the office.
So tell us—what’s the secret sauce for keeping people engaged? You’re up to several hundred people now, so how do you keep such a large team engaged remotely?

Siva: It’s not easy. By the beginning of 2023, when things were becoming more normal, we were already up to 400 people across the country.
We didn’t have a choice. A substantial number were in Boston, but that was only about 35%.

So we continued with the remote model but tried to make it more efficient. There are pros and cons. We manage it by making sure management and teams meet regularly.

Our travel budget is high, but since we save on office space, we spend on getting people together. From a management team perspective, we meet once a quarter.

We also have regular team meetings—sales, clinicians, operations, technology, AI, product—each meets in different parts of the country throughout the year. That’s important for building camaraderie.

Q. That’s amazing. And from a time zone perspective, since everyone is in the U.S., that works well. We’ll talk about expansion plans in a bit, but you just mentioned AI. Tell us how you’re applying AI, GenAI, and agents in your product development. Things are moving fast with GenAI.

Siva: In fact, from day one, our goal was—let’s try to provide real-time approvals instead of the usual five to seven days. At the end of those five or six days, if it’s going to be approved anyway, why not do it immediately if the information is there? So we focused on how to approve things faster.

We found that at the end of the prior authorization process, 80 to 85% of requests are usually approved. So we said, let’s focus on that and use AI to approve—not deny—because denial still needs to be reviewed by a nurse or MD. So we focused first on solving that piece.

Today, we approve about 80 to 85% of requests in real time. That’s where AI comes in. We use AI in six or seven different ways on our platform. One of the main ones is this: we get the EMR or medical record from the provider’s office and ask what service is needed. Then, we analyze the unstructured data—diagnosis, patient history, etc.—and determine whether the treatment is clinically appropriate based on certain policies.

For that, our physicians review the algorithms to ensure they’re clinically sound. We have about 50 physicians in the company across multiple specialties. They review the information and help us encode that into the algorithms. It’s a painstaking process, but that’s how we reached 80%, and we’re still improving.

If there’s any doubt about a request, it goes to an MD. We never use AI to deny care—we leave that decision to physicians, who then communicate with other physicians. That’s one big area where we use AI.

We also use GenAI for scheduling patients, retrieving missing information, and automating tasks like converting faxes into structured data. We have intelligent agents that complete entire workflows. Summarization is another area—we use GenAI for documentation and generating letters. We’ve been an AI-native company from day one.

This has helped reduce abrasion because users know that 85–90% of the time, they’ll get an answer immediately. That’s a huge win—they don’t have to wait or reschedule.

We do quarterly user surveys. Our NPS is between 65 and 67—very high. Providers are saying, “Okay, someone is finally solving prior auth,” and that’s one of our biggest outcomes.

For the remaining 15% of requests that still need more review, we’re now working to bring that timeline down to one or two days using AI. We’re able to summarize and present all necessary information so physicians can quickly review and approve it—or reach out to another doctor for a quick consult. So AI is helping us shrink that review time, too.

That’s how we’re deploying AI across the board.

Q. Very interesting. Siva. So that brings me to my next question actually, that when you consider the benchmark of companies or your landscape in which you are doing your competitive positioning, are there any other large players that are in the same space and different and unique and how do you position yourself?  

Siva: The process has been there for more than 30 years. So there are legacy companies that have been doing this for health plans. Yeah. So this is not a new process, right? We didn’t invent this process.

They’ve been doing it, and they are the ones with seven-day, 40-day turnarounds, paperwork, old technology—you’re seeing all of that. So we are completely disintermediating them. We’re creating a completely new category, where we’re actually differentiating ourselves from them.

We’re kind of coming in and changing the way things are being done in this industry.

Q. That is great to know. So, I think we have covered a lot of ground Siva. Any other closing thoughts or any other information or news that you would like to share with the audience? 

Siva: I know that there is a lot of press around prior authorization. To listeners—especially providers and patients—almost everyone goes through this. Just know that companies like Cohere are now using AI to solve the problem. Relief is on the way.

Subscribe to our podcast series at www.thebigunlock.com and write 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. Hi Alvin, welcome to The Big Unlock Podcast. It’s a pleasure to have you on board. As you might be aware, this was started by my colleague Paddy Padmanabhan from Damo Consulting, and we’re building upon what he left us as his legacy. I’m Rohit Mahajan, Managing Partner and CEO at BigRio and Damo Consulting, and I’m also the host of The Big Unlock Podcast.

Alvin: Rohit, thank you so much for having me on this podcast. I’m excited about the interesting topics we’ll be chatting about today.
So yes, happy to give you an introduction and some sense of where I came from and what I’m interested in.

My name is Alvin Liu. I was born and raised in Hong Kong. I came to the U.S. as a teenager to attend a boarding school in New Hampshire. After that, I did most of my schooling on the East Coast. I’m a practicing retinal surgeon. I did my ophthalmology residency and retinal fellowship at Johns Hopkins Medicine, and I stayed on as faculty.

I actively practice and take care of patients with a variety of retinal problems. Outside of my clinical work at Hopkins, I’m focused on artificial intelligence in several areas.

Within Johns Hopkins Medicine, I wear several hats. First, I’m the inaugural Director of the Gills AI Center at the Wilmer Eye Institute—this is the first endowed AI center at the Johns Hopkins School of Medicine, made possible by a generous $10 million donation by Dr. Gills.

Second, I’m a clinician-scientist involved in the development of clinical AI tools.

Third, in recent years, my focus has been on the implementation of AI tools for both clinical and operational purposes at the health system level. I’m sure we’ll dive into specific examples later today.

And fourth, I’m involved in AI governance. As you can imagine, there are many developments in AI in healthcare. In response, Johns Hopkins Medicine recently established a leadership team to oversee AI efforts across the entire health system, and I’m part of that team. I’ll be happy to talk about the AI governance work we’re doing at Johns Hopkins.

 Q. That’s amazing, Alvin. I wonder—with so many responsibilities, how do you even find time? Do you sleep at all? 

Alvin: I do sleep and try to get seven to eight hours sleep every day. I think that’s extremely important because, I myself cannot think very well if I don’t get enough sleep. So, I do put a premium on the amounts of sleep that I end up getting.

 Q. That’s amazing. So tell us, Alvin—you studied here on the East Coast and you’re a practicing physician. What attracted you to technology, especially emerging technologies, and when did you get involved with it? Also, talk to us about some of the work you’ve done in this area.

And even before that, if you’d like to talk about the health system itself, the geography, and the kind of patient population it serves, feel free to do that as well.

Alvin: Sure, I can start by talking about how I got involved in AI.
Near the end of my clinical training, around 2017–2018, I first got started with artificial intelligence. That was when a specific kind of AI technique called deep learning really started gaining traction.

Deep learning is the underlying architecture that powers much of what we know as AI today in 2025. It’s especially good at two things: image or video analysis, and more recently, natural language processing through large language models.

Back in 2019, most deep learning applications in healthcare focused on image analysis. As a retina specialist, I’ve always worked closely with images. If you look across medical specialties, radiology and ophthalmology are the most image-intensive, both in research and clinical care.

That’s why, when you look at AI research and real-world implementation today, the two medical fields leading the way—in the U.S. and globally—are radiology and ophthalmology.

What really got me interested in deep learning’s application to ophthalmology, and to medicine more broadly, was a study published by Google a few years ago. They showed you could train an AI model to predict someone’s age, sex, blood pressure, and smoking status just by looking at a retinal photograph.

That’s a superhuman capability—no doctor can do that. That one paper convinced me that AI would change medicine and society as we know it. And that’s something I want to dedicate the rest of my life to.

 Q. That’s amazing. So, tell us a little more, Alvin, about Johns Hopkins as an organization and the kind of patient population you serve. And then we can dive into some of the use cases you’re seeing or currently working on. 

Alvin: I’ll start by giving a sense of what Johns Hopkins Medicine is about, and then we can dive into specific examples.

Johns Hopkins Medicine is headquartered in Baltimore, Maryland. As an integrated health system, we operate six hospitals and around 50 outpatient sites. We serve a wide range of patients, most of whom are urban residents. Over the past several years, we’ve been working on a variety of AI initiatives. I’ll give you two specific examples.

The first is a clinical one—the deployment of autonomous AI for diabetic retinopathy screening, which we started in 2020. This is a significant application. When this technology was first approved by the FDA in 2018, it was the first-ever fully autonomous AI system in any medical field to get FDA approval. So my field, retina, actually made history. A recent study published in the New England Journal of Medicine AI showed that this technology is now the second most widely used clinical AI tool in the U.S. I think it’s a great gateway example to explain the broader medical AI ecosystem.

The idea is simple: everyone with diabetes should get an eye exam once a year. Diabetic retinopathy is the leading cause of blindness in the working-age population globally, and it’s expected to worsen with rising diabetes rates. It’s also well studied—we know that annual screenings, early detection, and timely treatment are effective and cost-efficient in preventing blindness. However, the challenge is that even in the U.S., only about 50% of patients with diabetes undergo these recommended screenings each year. The rate is even lower in many other countries.

Autonomous AI changes that. Traditionally, a primary care doctor would prescribe medication and manage diabetes, but eye screening required a separate appointment with an eye specialist, which creates friction. With autonomous AI, screening can now happen right in the primary care office. Imagine going in for a routine visit—your vitals are checked, medications refilled, and now, photos of your retina are taken. These images are analyzed in real time by an AI model in the cloud. Within a minute, the AI autonomously determines whether or not you have diabetic retinopathy.

If the answer is yes, you’re referred to an ophthalmologist. If no, you’re done with your screening for the year. We started using this at Johns Hopkins in 2020 and reviewed the data to evaluate its impact. The result? Yes, it worked. We saw improved adherence to the annual screening guidelines.

When we looked closer, the greatest improvements were seen among historically underserved groups—African Americans and Medicaid patients. The positive impact was outsized for these communities, and we published our findings in Nature Digital Medicine about a year ago. The second example is operational—using generative AI for revenue cycle management.

For those unfamiliar, revenue cycle management is how health systems like Johns Hopkins get reimbursed for the care we provide. It’s complex and involves many steps and a lot of paperwork. Traditionally, automation efforts have relied on older machine learning approaches like robotic process automation (RPA), which require a lot of rule writing and don’t handle exceptions well. This is where generative AI, particularly large language models, shine. They are adaptive, understand text and unstructured data, and can handle edge cases much better.

We’ve used GenAI specifically for prior authorization. It has significantly reduced the time needed to complete and submit each case, making the process more efficient overall. So, these are two real-life examples—one clinical and one operational—where we’re currently using AI at Johns Hopkins Medicine.

 Q. That’s very interesting. So, I have just some curious questions. Alvin, on the first example I. That you talked about in the primary care physician setting, that a patient can go and get their eyes checked. So, does it need specialized equipment at this time, do you think? At some point in time, it may be that I can just use my iPhone camera and or look into some kind of a kiosk. And, you know, kind of get it done at the airport or, you know, I always look into this when I do the security clearance. 

Alvin: That’s a great question. You’re touching on a really important point—the nuts and bolts of implementation. Implementation is key when it comes to scaling any kind of technology, including AI.

The short answer is yes, it does require some specialized equipment, but these are very common. In short, you need a way to take a picture of the back of the eye, which we call a fundus camera. These are already widely used by ophthalmologists, and there are many different brands and models. So, if you step back, there’s already an existing supply chain and industrial process in place for producing these cameras.

Now, the traditional cameras are desktop-based. They’re not very portable—they’re a bit heavy, and you can’t easily carry them yourself. But their footprint is relatively small—about two feet by two feet—and they can sit on a mobile table. So they’re easily accessible, and the image quality is quite good.

Of course, there’s been work on developing more portable cameras, and many of those already exist. You can even use an adapter with a smartphone to capture retinal images. So the technology is there.

However, in real-world settings, most of the AI models for diabetic retinopathy—especially the ones used in clinical deployment—are designed for use with the more common desktop-based fundus cameras. While they’re larger, they typically deliver better image quality, which is why they’re still preferred.

 Q. And then a curious question on the prior auth side—are you implementing and experimenting with prior auth across the board, or is it for a certain set of disease conditions, CPT codes? And then, is that a software that the team has developed, or is it something you’re using from the outside in? 

Alvin: That’s a great question. So, what you’re getting at is the nuances between the different service lines—who would benefit from prior authorization or not.

Broadly speaking, there are certain fields that require a lot of prior authorization, and that’s how insurance payers do utilization management. And I’m painting with very broad strokes here.

Typically, the service lines or medical specialties that require prior auth tend to give out more expensive treatments—things like infusion medications in oncology or dermatology, or in our case, retina. We do a lot of injections into the eye—what we call intravitreal injections—for diabetes and age-related macular degeneration. These are examples where, because the treatments are expensive, they’re more likely to require prior authorization.

So when we did our pilot at Hopkins, we focused more on those specialties that require a lot of prior auths, versus ones where the care typically just goes straight through without it.

But that’s a great question, and you’re absolutely right—the devil is in the details. Even for a relatively specific step in revenue cycle management like prior auth, designing a pilot that makes sense, that demonstrates ROI, and establishes relevant KPIs—requires a very deep understanding of how medicine works and operates. And not in a vacuum.

 Q. So shifting gears a bit, Alvin – with the macroeconomic factors now impacting the whole ecosystem, including digital health (which is a very large part of the U.S. economy, as we all know) – what are some of the things that you feel are coming in the near future?

Alvin: I’ll answer your question from two opposite ends of the spectrum. First, from the startup angle—because in my role at Hopkins, I end up interacting a lot with startup companies in the AI space. And then I’ll speak from the enterprise perspective.

So on the startup side, I think one of the common mistakes startups make in the healthcare AI space is not considering—or not understanding—the reimbursement issue from day one. And I think that’s the most important thing.

One could argue that healthcare AI is still a very new field, so the payment mechanisms in the market aren’t yet mature enough to handle an influx of new products. It’s a tough situation, honestly, for healthcare AI startups. If you’re on a founding team that doesn’t have a deep understanding of how medicine works, you probably don’t know what a CPT code is, or how that’s how services get paid for. If you want to get a CPT code, very likely—especially if you’re in the AI and medical device space—you fall under the FDA’s purview.

And if you want FDA approval, we’re talking about $3 to $5 million off the bat. One mistake I see is startups being hyper-focused on building the product—both in terms of execution and how they spend their funding—without accounting for or budgeting for that FDA process. And even if you’re lucky enough to get FDA clearance, then you have to think: are there existing CPT codes that will reimburse you for the AI service? Very often, there are not. So then you have to go to the AMA to negotiate a new applicable CPT code.

That process takes a long time. And even if you succeed in getting a new CPT code, there’s no guarantee the payers will reimburse you. And even if they do, the rate might not be financially sustainable.

So from the startup side, you really have to think long and hard about your reimbursement pathway. Of course, there are other ways to get paid—not just through CPT codes—but that requires a deep understanding of healthcare business models. And in some cases, you may need to invent a new one.

Now, on the enterprise side: AI is here to stay. But for health systems, it’s chaotic. We—as an integrated health system—get many, many sales calls from AI companies every day. It’s a crowded, noisy space. That’s why having a robust AI governance structure that looks at multiple aspects—clinical, operational, ethical, financial—is absolutely necessary. And I think Johns Hopkins Medicine is one of the first major integrated health systems to give this serious thought.

It’s still evolving. We’re learning. But building a thoughtful and industry-friendly governance system is critical. And if you zoom out even more—on a very macro level—the billion-dollar question is: how will value-based care and AI come together? These are two very big trends that will intersect soon. What that intersection looks like is going to be very interesting.

 Q. That’s very good insight, Alvin. So could you talk to us about any digital health programs that have been implemented and that you’ve been involved with, which improve access to care—or any other examples you’d like to share from the digital health side? 

Alvin: The example I would give is the autonomous AI for diabetic retinopathy screening program. Yeah, that’s a good example. We already talked a little bit about it. What we learned is that, again, like 80% of a successful program is all about implementation and how you execute things.

So, for example, even if you have a successful screening program at the level of primary care, you still have to figure out how to get the patients who screen positive to ophthalmologists. That’s a different line of work.

You can extend this analogy to other areas as well—for example, in omics. Just to set the stage, omics is a relatively new field that connects biomarkers found in the eye—mostly retinal biomarkers—with systemic health conditions. I’ll give you a couple of examples. Right now, we can already use retinal images paired with AI to predict someone’s future cardiovascular risk, risk of kidney damage, or even dementia.

So, I think diabetic retinopathy is just an early example. We’re going to see an explosion in the adoption of omics. But the question is: even if you have an AI-based omics screening program in a community or primary care setting, and you identify patients at risk for various systemic conditions like Alzheimer’s or cardiovascular disease—what do you do next?

How do you set up a workflow to get these people to the subspecialists they need to see downstream? That’s still in the works. It’s very fluid. But I think that kind of thinking—being able to implement and execute things efficiently at scale—is going to determine the success of a lot of AI programs, especially when it comes to AI in omics.

 Q. Yes, that’s amazing. So, Alvin, we talked about governance a bit. How do you structure prioritization and funding, and what kind of operating models do you look at? 

Alvin: Sure. I’m happy to talk about that. I’ll take a step back and give you a brief background on how this all came about.

Back in 2024, the executive leadership at Johns Hopkins Medicine started a task force to develop an implementation strategy that ensures Hopkins becomes a global leader in the responsible use of AI.

One of the key tenets the task force identified was that they wanted this to be a clinically led responsible AI program—meaning physicians like myself would and should play a major role.

The task force then identified seven core principles that are critical for responsible AI: fairness, transparency, accountability, ethical data use, safety, evidence-based effectiveness, and so on.

From these, we identified several implementation plans. A key one was to establish a governance process and framework that would integrate with existing governance structures. As a result, an AI oversight team was created. It’s an eight-person leadership team drawn from across the health system. I’m one of the eight, and we have purview over all things AI-related across clinical, imaging, and operational domains.

In a nutshell, what we’ve developed is a standardized framework for how AI vendors should interact with Johns Hopkins Medicine. So, for example, if you have a clinical AI product and want to engage with us, there’s a standardized intake process. First, you need to find an internal partner at Johns Hopkins who will advocate for you.

We then have standardized questionnaires—what is the tool used for? Do you have data cards? Model cards? What’s the expected ROI? How do you demonstrate it’s safe? And so on.

Based on the nature of the tool—whether it’s clinical, operational, or imaging—the application gets routed to different sub-teams. Then there’s an internal review committee that dives deep into the responses. We grade them, bring them back to the committee, debate, and often go back to the vendors with follow-up questions.

Ultimately, the committee does an up-or-down vote based on a variety of criteria and decides whether the tool can be implemented at scale across the enterprise—or not, and why.

 Q. That’s very robust process Alvin. Thank you for sharing such good examples, thoughts and advice so far. Any final closing comments? I think we are coming to our end of our conversation. Any other things that you would like to bring up? Any announcements, news items? Or anything else that you would like to share that’s upcoming on your horizon?

Alvin: What I’d say is that, at a high level, most people agree that AI is going to change medicine—and society—as we know it. The train has left the station. It’s no longer a question of whether we’ll adopt AI, but what the future will actually look like.

When it comes to healthcare specifically, it’s one of the most heavily regulated industries—and also one of the most personal. At the end of the day, we’re in the business of taking care of people and reducing suffering, and there’s a deeply human, emotional component to that.

I do believe that, for the good of humanity, we need much more collaboration in this space. And in particular, I see venture capital and startups as major engines of innovation.

What’s been missing—but is starting to improve—is a strong connection between the VC/startup world and integrated health systems. I think that relationship needs to get better. In the U.S., integrated health systems deliver the majority of care. So whether startups like it or not, their products will ultimately have to go through these enterprises.

That said, health systems don’t move as quickly as the tech industry. And that’s understandable—but I also think there’s room for improvement, particularly in how quickly decisions are made. Technology is evolving at an exponential rate, and AI is no exception. Things move fast—and for good reason.

So, there’s work to be done on both sides. I’m hopeful that we’ll see much stronger and closer collaboration between startups and health systems in the near future. If that happens, I think a lot of good will come out of it.

Subscribe to our podcast series at www.thebigunlock.com and write 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.  Hi Patrick, welcome to the Big Unlock podcast. Great to have you with us today. I’m Rohit Mahajan, Managing Partner and CEO of BigRio and Damo Consulting. The Big Unlock podcast was started by Paddy Padmanabhan, founder of Damo Consulting, and it’s been a successful series with many healthcare leaders. Great to have you here.

Would you like to introduce yourself to the audience? 

Patrick:  Sure. I’m Patrick Mobley, CEO and founder of Vivid Health. A bit about my background—I’m speaking to you today from Raleigh, North Carolina. I grew up around healthcare; my dad was a physician. I eventually found my way into the startup world. I was an early employee at a company called Evolent Health, where I did a bit of everything. I started on the clinical side, ran their Virginia market, and oversaw value-based care operations.

Then I moved to Aledade, where we aggregated independent providers into shared savings arrangements. We grew from about 20 practices to 220 while I was there. Next, I joined Bright Health and ran a large health plan in the Mid-Atlantic. Across all three companies, I saw what high growth looks like—but I always wanted to build something myself.

Eventually, I connected with the leadership at Redesign Health. I had hired a company that spun out of them. For those who don’t know, Redesign is a venture studio based in New York, backed by General Catalyst, UPMC, CVS, and Aetna, among others. We came up with the idea for Vivid, which really reflects a lot of that experience. I know we’ll get into the details, so I won’t spoil it all now—but it’s really focused on making clinicians’ lives better. 

Q. That’s really interesting, Patrick. You’ve had such a deep career in healthcare. What was the early catalyst that set you on this path?

 Patrick: As I mentioned, I grew up around my dad’s clinic. It was a very familiar environment.
I saw how hard he worked—the long hours, the behind-the-scenes frustration with paperwork and administrative work. It was inspiring to watch, and it made me want to solve some of those pain points. Thinking I could build something that might help with the things that gave him headaches was definitely exciting. 

Q. That is awesome to know. So, you mentioned Vivid already and it came out redesign. So Patrick, please tell us more that. What is your thesis at Vivid Healthcare and and what kind of problems are you solving and what are you working on?

 Patrick: In my prior roles before Vivid, I worked closely with nurses or had them report to me. What stood out was how much time it took to call a patient, assess them, build a care plan, and follow up. These are four essential steps in any risk-bearing organization looking to manage cost, high-risk individuals, close care gaps, and improve risk adjustment. I kept thinking—how do we leave the nurses with nothing left to do but provide care? And when I say “nurses,” I also mean LCSWs, community health workers, the whole care team.

So, in partnership with Redesign, we started exploring what generative AI could do—specifically, how we could automate tactical workflows already used in care organizations. That’s exactly what we’ve built: from intake and referral, where an AI agent can call, text, or email a patient, to assessment, care plan generation via a large language model, clinician approval, and automated follow-ups. Whether the patient is with the practice for a day or six months, our platform supports it all.

Q. That’s impressive. What makes the platform so unique? You were also one of the earlier adopters of GenAI in clinical workflows. Was it hard to incorporate? And what results are you seeing now? 

 Patrick: It definitely wasn’t easy—but we had great partners, like your team at BigRio, to help build it out. We focused on covering a wide range of specialties and conditions. For nurses, it’s about understanding both mental and physical health needs, gathering that data, and turning it into a care plan—while always keeping the provider in control.

In fact, we actually own the trademark for “Provider-Led AI.” We strongly believe that no matter how helpful AI is, the final say should always rest with the clinician. Our platform lets AI agents handle tasks like calling patients, enrolling them in chronic care management, or conducting assessments like OASIS in home health. It builds care plans and allows nurses to focus on care coordination.

One of the most important aspects was making sure we could scale. If we were effective at engaging, assessing, and managing patients up front, then scaling the backend workload was critical. We wanted to amplify our nurses and care teams—individually—but also make sure they weren’t overwhelmed. That’s why we designed the platform to automate follow-ups. The agent can call, text, or email patients. Nurses don’t need to do anything manually. They simply turn on the platform, see patient stratification, view notes from calls, and take action from there. It delivers scale and reach—whether you’re a risk-bearing or home health organization—that you just can’t achieve with any other platform.

Q. So, is that where the positioning of the platform is also Patrick? Tell us a little bit more about who are the kind of potential customers or clients or users of the software platform.

 Patrick: Yes. There are a few distinct markets, though I often describe our platform as the perfect puzzle piece for any organization.

One key market is the primary care space. It’s ideal for risk-bearing organizations, but even those that aren’t can still use it to deliver extra care and generate additional revenue.
We can deploy our AI agent to enroll patients in chronic care management, conduct assessments, and complete all required documentation for chronic care, annual wellness visits, and transitional care management. We’re seeing a 100% reduction in outreach specialist labor using our voice AI tool and over 50% cost reduction compared to competitors.

We also target post-acute care—specifically home health and hospice. These settings have some of the most burdensome documentation requirements. In home health, for instance, the OASIS form is 27 pages with 200+ questions. Nurses typically can only see two patients a day because of this.

We’ve automated that entire process. When the nurse enters the home, the OASIS answers are already received, the care plan is generated, and the visit can focus on actual care—not paperwork. We’re seeing over 50% reduction in documentation time, which directly impacts revenue. If a nurse can see even one more patient per day, that’s a significant gain.

Hospice is also going through a big shift to a form called HOPE, which is like a shorter version of OASIS. We’re applying the same technology there and expect similar results.

We currently cover 100 conditions across 16 specialties. That’s generated interest from palliative care providers, non-skilled nursing organizations, and even some specialty groups. Once they see the platform in action, it really resonates. We’re proud of what we’ve built.

Q. That’s amazing, Patrick. And I know you’ve built a robust chronic care management capability across many disease conditions. You also mentioned a bunch of surveys—that’s your proprietary IP, right? So, that is what you built early on the core of the system. So, could you describe that a little bit more in detail on how it adds value and what it actually does?

 Patrick: Yes—and there’s a bit of a story there. Many organizations, rightfully, focus on five big conditions like CHF, COPD, diabetes, depression, etc. But I always felt that if a patient has COPD and also a kidney disorder, that second condition could significantly affect their overall health—physically and mentally.

So, we designed our system to evaluate patients across a broad set of conditions, not just a narrow few. We also wanted our surveys to assess both mental and physical health. So we ask questions like, “Do you have chest pain or swelling?” but also, “Do you have anxiety about paying for your meds?” or “Do you have transportation and social support?”

This creates a holistic view of the patient. The feedback we’ve received is that when patients interact with a clinician directly, they may feel pressure to answer a certain way. But when we deliver the assessments through text, email, or voice, patients respond more honestly. Nurses tell us the responses they get are clearer, more detailed, and more accurate than before. That’s been very cool to see.

Q. So when you are thinking of this in a larger setting, Patrick, obviously you might need to think about how it integrates with the systems organizations might already have in place. So what is your approach to that? How do you make it easy for your customers to use it?

 Patrick: Yeah, I think there are three paths there. One, just straight out of the box, the platform works really well as a standalone. It can do everything we’ve talked about so far—it does really well.

Second part of that answer is we use FHIR server—that creates a data standard for us to push and pull information from and integrate, frankly, with most EMRs. So we’re fully capable of integrating with just about every EMR in the market.

The third, and it’s the most interesting, is we partnered with an organization called NO2, which is part of something called a QHIN. And I know I’m getting kind of technical, but QHIN is a Qualified Health Information Network. What that is, is an interoperability layer to our platform that allows us to push and pull data from just about every EMR instance in the country.

For example, every single hospital in the states of Washington and Oregon is on this QHIN. So today, while we may not be directly integrated into OHSU in Portland, we can access their EMR—we can push and pull data, we can make requests. And I really think, like bigger picture beyond Vivid, that capability is going to be table stakes for any organization entering the digital health space.

Q. That is great to know. So tell us where you think the future is, Patrick, and what are some of the early findings that you have from your client implementations, and where are you headed in the next few weeks or months? 

 Patrick: Well, hopefully a lot of growth. We’re certainly seeing a lot of interest. There’s no limit of folks that want to talk to us, engage with us, and understand what the platform does.

I think what we’ve seen—and it’s been kind of entertaining to watch—is when we go through our demos and what the platform can do on the voice side, we’ll have them call the agent—her name is Sage—and just the faces light up. Their creative juices start flowing. They start thinking, what are the many different places they can deploy this agent?

You’ve gotta think of her as like an employee that doesn’t get tired, that can work 24 hours a day, that you only have to train once. And it’s incredibly powerful. So I think all phone calls that don’t require clinical decision-making will ultimately be done by agents like this long term—and across specialties, it doesn’t really matter.

Then the third thing—more going way out in the future—I think that at least within our platform, we’ll start to deploy different types of agents. So we talked about voice agent, which is one type. Another is one that will take all the data we’ve acquired—and maybe we’re not directly integrated with the EMR—but we are using the agent to go into that EMR and deploy data into specific sections.

I was at a conference last week and part of the conversation was, does the EMR just become the file cabinet for everything, but all the action happens in applications like Vivid? The idea is that because of these QHINs that I referenced earlier, it’s going to allow pushing and pulling data, and the agents can take it and put it into the specific spots it needs to be.

So maybe the EMR stays the source of truth or system of record, but the actual technical capabilities and advancement—and frankly, the efficiencies that AI will bring—will live in a layer above that. And that’s where the clinician does a lot of their work. I could see that definitely happening, because I think agents will be able to operate everyone’s computer at some point.

Q. Yeah, that’s true. Patrick, just shifting gears a bit—because you mentioned value-based care, and that is something you’ve been very closely associated with—what are the macroeconomic or other trends that you’re seeing, whether it’s value-based care becoming more prevalent or anything else coming our way?

Patrick: Yeah, so I think, having lived in that world for many years, I don’t think it’s going to entirely go away. We’re not at 80% market saturation with primary care being in a risk-based arrangement. I think the MSSP numbers are around 40 to 45%, somewhere in that range.

What’s interesting is, pre-AI, the way organizations worked with independent primary care—or even a health system—was that you’d deploy a lot of nurses and staff to find the sickest of the sick, manage them well, try to bend the cost curve, and then make money based on how much you saved.

A lot of those organizations had deal terms where, for every dollar, 50 cents went to the company and 50 cents back to the provider. I think the revenue opportunity for the provider is going to go up because the cost of providing those services is going to go way down, thanks to AI.

So instead of deploying an army of people into an ACO, the agent can make all the same phone calls, do all the same engagement, at a fraction of the cost. And now it’s going to look a lot more appealing to a primary care doctor who, instead of making 50 cents on the dollar, can make 80 cents.

As those models mature and the technology merges with them, it may accelerate—but we’ll see. There are contingencies, but there’s definitely a path that could be really interesting.

Q. Right. Any other changes that you’re seeing that might impact the business model—or anything else coming up in the future—that you’d like to share as part of your closing remarks? We’re getting to the end of the podcast here. 

Patrick: I think it’ll be interesting to see whether it’s federal or state governments that ultimately dictate individual AI regulations in healthcare. You’d prefer it to be more federal than state, or else you end up with 50 sets of rules that every company has to manage.

Had this discussion last week—you want a standard. You want everyone playing by the same rules. And if it doesn’t happen fast enough on the federal side, states are going to figure it out themselves, which could lead to unintended consequences for organizations wanting to operate in multiple states.

That’s not just true for us—that’s true for OpenAI too, who may have different rules in 50 different states. So, having a set of standards and regulations to help manage what’s coming—not just for Vivid, but AI in general—is probably something we should all be keeping an eye on.

Subscribe to our podcast series at www.thebigunlock.com and write us at info@thebigunlock.com 

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

Ritu: Hi Shekar, welcome to The Big Unlock podcast. It’s really nice to have you on the show. We’re now in our sixth season, with over 150 episodes and a great listener base. We’re excited to have you here and look forward to a lively discussion.

I’m Ritu M. Uberoy, Managing Partner at BigRio and Damo Consulting, and also a co-host of The Big Unlock podcast. I’d like Rohit to say a few words before we hand it over to you.

Rohit: Short intro from my side as well, Shekar. I’m Rohit Mahajan, CEO and Managing Partner at BigRio and Damo Consulting, based in Boston. As I mentioned, we’re very happy to have you on the podcast.

Shekar: It’s a pleasure to be here, and I’m looking forward to a great conversation with both of you.

I’m Shekar Ramanathan, Executive Director of Digital Transformation at Atlantic Health System. We’re a seven-hospital—soon to be eight—health system based in New Jersey, with over 20,000 employees and about half a million patients in our ACO.

We’re a fairly large organization, and I’m responsible for the integration of digital, AI, and data—essentially, using technology to solve business and clinical problems.

We’re really focused on working outcomes backward: identifying what we want to accomplish, the metrics we aim to achieve, and then developing solutions to meet those needs.

Ritu: Great. Great. We all know generative AI is the buzzword right now. I just attended two conferences—HIMSS and Human X—both heavily focused on AI, generative AI, and now the latest: agentic AI.

We’d love to hear your thoughts on these technologies, your AI maturity model at Atlantic Health, and where you feel the organization stands in terms of AI maturity. Also, tell us about some initiatives you’ve led or are currently working on.

Shekar:  Sure. I don’t think I’ve been at a conference or had a talk in the past two years that hasn’t been about AI. Even if it starts with something else, it ends with AI. It’s definitely the hot topic no matter where you go.

For us, it’s very much focused on the “what are we still like…” I think our strategy hasn’t changed in terms of what we’re trying to do from a digital or organizational perspective. What we’re really trying to do is see how we align that with the new capabilities that are emerging—continuing the same business strategy, which is always somewhat challenging because people keep asking, “What is our AI strategy?”

I used to say our AI strategy is our business strategy—it’s not any different. But I’ve somewhat changed that over the past two years. Now, our AI strategy is really about building the framework. It’s about enabling the business and understanding where the technology is going so we can be in a position to fully leverage it.

That means setting up the right governance and the right processes to monitor AI—to ensure it’s the right solution and at the right cost. I think that’s been a challenge for many organizations. You see a lot of piloting—we started there too, with plenty of pilots. But scalability becomes a challenge.

Then comes the question: who are the right people to use these tools? How do we extract value and not just get excited by the art of the possible?

We’ve done a lot of what other health systems are doing—ambient voice, note summarization, routing of messages, and so on. But we’ve also done some novel things, like focusing on a virtual MA where we use a quasi-agentic approach. Not fully agentic, but using some of those tools for outreach, patient communication, helping manage care—with escalation to a clinician or care provider whenever necessary.

We’ve seen a lot of success. At the same time, we know things are changing rapidly. That’s probably one of the biggest challenges—not just for us, but for healthcare in general. We’re used to a fairly slow process—just selecting a vendor, signing a contract, going through implementation—it’s usually a long timeline.

Now, by the time you select a vendor, the next one is already out, doing it better. So how do we shift to being truly agile in our thinking? Solving problems in smaller pieces, being more iterative—those have been some of our key focus areas and challenges.

Ritu: Great answer, Shekar. It’s amazing that you mentioned the top three use cases—ambient, scribing, and message in boxing. You mentioned that going from pilot to scalability is a challenge. Could you pick one of these initiatives and talk a bit more about your experience—specifically, the timeline and what that looked like?

To give some context, I attended a talk at HIMSS about innovation using GenAI, and one of the takeaways was that culture can be both an enabler and a barrier. You have to be open-minded and ready for these accelerated timelines, but if there isn’t buy-in across the organization, change becomes difficult. Would love to hear your thoughts on that.

Shekar: Absolutely. I think one of the key things is that people often start by piloting with a highly engaged, super excited group—folks who really want to leverage the technology. They get great results in that small setting. But when it’s time to scale, it becomes difficult to replicate the same level of adoption, utilization, and value.

We’ve had more success when we focus on narrow workflows—being very intentional about what problem we’re trying to solve. That allows us to have the bandwidth to do proper education, integrate the technology into the workflow, and not just introduce a tool that people are playing with.

With a lot of the GenAI tools, what we’ve seen is a burst of initial excitement—people want to try it, see what it can do, maybe generate a song about COPD, and that’s great. But then reality kicks in. When people are back to caring for patients, they ask: Is this actually saving me time? Do I know how to write a good prompt? Do I understand when it’s useful—or not?

We’ve had more benefit by being very prescriptive: “Here’s the use case, here’s the prompt, here’s the button to click.” That helps users adopt the tool more effectively and ensures they see real value.

It also helps with cost control. These tools can get expensive as they scale, much like how cloud costs were a challenge to predict a few years ago—but AI takes that challenge to another level. So we want to manage rollout carefully, ensure users understand the benefits, and then scale in a controlled, thoughtful way.

Ritu:  Okay. Thank you. Next, we would like to talk about the role of a Chief AI officer and if Atlantic Health has a Chief AI officer, and what do you think would be the pros and cons of, you know, that role and what are your viewpoints about that role?

Shekar:  So, we don’t have a Chief AI Officer per se. We have a lot of people who kind of wear the Chief AI Officer hat—myself included—where part of my role is to drive what our AI strategy is. And that means different things to different people, right?

For us, it’s really about how we lay the infrastructure so we can support the different ideas and initiatives that are coming in. It’s also about identifying what’s truly different between an AI project versus a digital project or a regular technology project—what do we need to think about differently?

Then, depending on whether it’s a business use case or a clinical use case, we need to make sure we’re bringing in the right stakeholders. Especially on the clinical end, we need to have the right clinicians involved and really understand the potential impact—and make sure we have the right processes around that.

So it ends up being a group effort. I definitely see the role evolving, but the question is whether it becomes a purely dedicated position or if it stays tied into roles like data leadership or digital transformation. I think it really depends on the organization—what makes sense for them, and the size and scale of their AI ambitions.

That said, I do think we’re going to see a lot more Chief AI Officers emerge, especially as the space grows, the opportunities expand, and there’s a greater need for structure and oversight.

Ritu:  Yeah. What we’ve seen with other folks we’ve been talking to is that the real need for a Chief AI Officer, like you said, is around strategy. Multiple people can wear those hats and do the work, but the real need they felt was around governance, ethics, bias, and some of the other thorny problems that crop up.

Would you like to talk about any challenges you’ve faced—primarily in terms of AI implementation—like hallucination, bias, or data integrity? And how you’ve overcome those challenges?

Shekar:  Yeah, and I think one of the biggest challenges is really understanding what a lot of these vendors are doing, especially given the pace at which innovation is happening. And then there’s the challenge around black box AI, right? I mean, that’s the so-called “vendor secret sauce.”

But at the same time—are they really doing something truly innovative? Are they actually getting results? How is it working? Do we know what data they trained it on? Patients in a rural area may be very different from those in an urban area. Or maybe the model was only trained on adults and not pediatric patients. There are so many variables that can introduce bias.

There are also a lot of things that can make a model either work for you or not. So how do you really evaluate that? Right now, a lot of these companies are coming to us without the level of research and documentation we’re used to—things like clear evidence of efficacy or quality outcomes.

It’s sometimes hard to get that information, because this is a new, shiny object and people are excited about the art of the possible. That’s especially challenging for us on the operational side. People come in with a great idea, and they’re promised big results that maybe they don’t fully understand.

It’s like—someone says this tool is 99% accurate. But then, when you look at the positive predictive rate, you realize that nine out of ten times, it throws a false positive. So now you’re getting ten alerts for every one useful one.

Is that really helpful? Sure, it’s 99% accurate—but it shows up all the time, and that affects how people experience it. So we have to interpret that correctly and make sure the business is fully aware of what the actual experience will be—before we sign a contract, implement the tool, and then find out later that it doesn’t meet expectations.

Rohit:  Shekar, I’m very interested about your journey in healthcare so far. The audience always likes to know what got you started, what interests you. What are you thinking about the future as well? So if you can share with us what motivated you to take on this role and how you walked into the healthcare industry segment, and where are you headed?

Shekar:  It would be great. Yeah, no, absolutely. I’ve always been in the healthcare technology space. I went to grad school for medical informatics—back when nobody really knew what that meant. And now it feels like everything is kind of coming together.

I started more on the development and consulting side, working with a number of state governments to develop syndromic surveillance systems and similar initiatives. I also did a lot of research around patient experience during grad school.

Eventually, I ended up at Epic and spent some time there. That gave me a lot of exposure to electronic health records and large health systems. After that, I worked across several large healthcare systems—BayCare Clinic, Kettering Health Network, Wake Forest Baptist Health—and eventually landed here at Atlantic Health System.

Over time, I’ve been focused on clinical applications, digital transformation, generating value from data, and process optimization. And now I’m at a point where I can pull all those different pieces together and apply them more broadly.

I’m really excited about the potential of AI right now. I’ve been talking about the future of healthcare technology for a long time—what we could do with EHRs, how to collect and use data—and now it feels like we’re finally at a tipping point. We’ve spent years burdening our clinicians with documentation, all with the hope that it would one day lead to better care, and I think AI is finally enabling that transformation.

That’s why there’s so much excitement in the space. People are energized—maybe even tripping over themselves a bit—trying to figure out the right solutions. I share in that excitement, but I also want to make sure we do it right. We’ve moved at a glacial pace for a while, and now we’re ready to sprint—but we need to do it ethically and consciously, always focused on outcomes.

I feel really fortunate to be at this point—where everything in my background is converging, and I get to be part of pushing healthcare forward.

Rohit:  Absolutely. I’ll add one more thing here, Shekar—I think you’re right we are at an inflection point. I completely agree with you there. A lot of changes are coming at us fast, and we have to adapt and adopt the technologies that are actually useful.

From a patient perspective—since you mentioned Atlantic Health System is based in New Jersey and you serve around half a million patients—can you tell us more about that geography? Is your patient population very diverse? And is there anything you’d like to share about patient engagement?

Shekar:  Yeah, absolutely. So, we’re primarily based in New Jersey, with a bit of presence in New York and Pennsylvania, but mainly focused in northern and central New Jersey. We have 500,000 patients in the ACO, and even more overall.

New Jersey is very diverse, and that diversity really comes into play when we start talking about things like health equity and digital engagement. One of the challenges is figuring out how to reach patients with varying levels of digital literacy.

Interestingly, the patients who could benefit the most from digital tools often face the most barriers to accessing care in general. So, the question becomes: how do we make it easy and accessible for everyone? There’s a portion of the population that will be really excited that there’s an app for everything, but the ones who really need it may not be thinking in those terms. So, it’s about how we reach out, educate them, and truly enable them to be partners in their own care.

Ritu:  Yeah, I think we’re almost at time. Shekar, would you like to share any closing thoughts? It’s been a really engaging discussion. Maybe you could tell us what you see as the top three future trends with generative AI?

Shekar:  I think the next big thing’s gonna be agentic AI. It’s the next evolution as things become more and more “autonomous.” I think we’re gonna see a lot of kind of a hybrid, kind of a mix of agent traditional generative AI solutions.

I think a lot of this comes down to how do we start removing a lot of kind of that burden of healthcare. We’ve spent a lot of time asking providers to do more and more, and there’s where people are excited is that, maybe more of that can get offloaded so clinicians can really focus on direct patient care and a lot of those other things that may be more administrative or tangential.

That’s really where I think we’re gonna see a lot of technology that’s going to be able to kind of help solve some of those problems. 

Subscribe to our podcast series at www.thebigunlock.com and write 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: Hi Priti, this is Rohit Mahajan. Thank you for joining us on The Big Unlock podcast. We are in season 6. The audience is diverse and broad. So, we are looking forward to an exciting interaction today with you, and with that, would you like to introduce yourself?

Priti: Sure. Well, that is amazing, the number of podcasts you have done. I enjoyed listening to some of your former guests. Son and I appreciate the opportunity to be here. So, my name is Priti Patel. I am a family physician and clinical informaticist.

I am serving as the Chief Medical Information Officer at John Muir Health, where I have been for 23 years now. John Muir Health is a community-based health system located in The San Francisco Bay area. We serve Contra Costa County and some of the surrounding areas. We have a two-hospital system, one behavioral health center, and over a thousand providers.

When I started 23 years ago, I started off as a primary care physician and quickly got into the administrative side of medicine and medical directorships somewhere along the journey. The electronic health record came to be, and so I started off as a super user, and then one thing led to the other. I was very involved with our enterprise-wide implementation of Epic, so that was in 2012. From there, I just continued to keep doing the same and took the position of associate CMIO, and then for the past three years, I’ve been in the CMIO role, so I am very fortunate that I get to spend most of my days at the intersection of clinical care and technology.

Q: That’s amazing, Priti. You spend so much time in this field. Tell us a little bit about how you work with your colleagues. If you are the CMIO, then you might have other colleagues in your organization who are also involved with technology. So, how do you work together as a team, and what do you think is the evolving role of the CMIO in health IT and its adoption?

Priti: I am fortunate that I have had two other CMIOs before me, and so they were critical in laying the foundation for our informatics structure. Our first CMIO was responsible for our EHR implementation. The second came in and really established a lot of committees, the governance, structures really helped optimize those, and through that entire journey we continue to grow year by year. Just the number of clinicians and physicians that have been involved in informatics is really astounding. When I started, no one even knew what the term informatics was, and we were just a part of IT now. When we add people to our team, many of them have master’s degrees in informatics. Many have done board certifications in informatics, and some have even gone through fellowships. So, we continue to grow, and IT is now part of every part of the health system. I think there’s informatics that is part of our team. Formally, our physician staff includes many informatics representatives. And then our nursing staff now has really kind of come to the table and joined us in this journey. More recently, we brought on a nursing director of informatics, and I continue to see these types of roles growing as time goes on.

Q: So, could you talk to us a little bit about how you, at your community-based health system, how do you think about aligning the digital strategies with your priorities, especially keeping your patients and employees in mind?

Priti: As a community-based health system, our focus is really on our patients, our workforce, and the quality of care that we deliver.

So those are our founding principles, and so when we think about what type of digital tools we would want to implement, we look to see, you know, how does that make them better at what they do? How does it support them? How do we elevate the care with these various tools? We are an epic organization, and so we do have an epic strategy, and that’s true of most of our core applications.

We focus on leveraging what is available to us through our major applications. And then the other key component is really driving the adoption. So, it’s not enough. To really have the application but really trying to leverage it fully is one of the things that my team does is that we identify where perhaps people aren’t really leveraging it in their workflows.

Maybe the patients could really come to know a little bit more about this. So, we have a whole team that goes out to the clinics, to the hospital, and rounds through the floors to really share a lot of that knowledge of what is available through all of our core applications. When we get to a place where our core applications cannot serve the need. Let’s say we have some special strategic initiatives, and you know, ambient AI is a perfect example of this. This is not something that was part of our Epic application. So we looked at other vendors and found one that we thought would be the best fit. This is something that we have integrated with Epic.

However, it is a freestanding application, and we do that with a number of different solutions where we are looking to align it with what we’re the outcome that we have in mind. So, we do add innovation on top of our basic core application structure.

Q: I heard you say before, when we were talking earlier, that you have a Gartner report, which got published around the Ambient Listening initiatives. And that, of course, is a business application, which a lot of health systems are embracing, and they are finding a lot of value in that. But I think you have a lot more to share with the audience on this specific implementation. So, could you talk to us about some of the three aspects I would like to bring out with you, if we can? What were some of the challenges that you faced? What do you think were some of the key success factors, and what were, I think, you measured? Results in this particular case. So, do you have any quantitative results that you could share with the audience? 

Priti: We started our journey very early. This is the technology I was waiting for, as a primary care physician. I really wanted to spend more time with patients instead of interacting with the EHR and spending time on documentation. And so, for a number of years, we were looking at the early ambient solutions that were out there. And then a couple of years ago when large language models came to be. We really focused on that, and so it was early 2003 when we started to look at a variety of different vendors, and we ultimately settled on ambiance, and really, our providers had the opportunity to test it.

We did a lot of role-playing with physicians and complicated patients’ situations, and so we landed on a tool called ambiance. We implemented that very early, in July 2023. I would say that it was a very exciting time, and I think everyone was very interested in utilizing this technology. So, the adoption was easier than most technologies that we’ve tried to implement before because we had that enthusiasm and eagerness from our physician population.

The challenges were that no one had done this before and this was new territory. We were co-developing. A lot of the technology that we have today is things that have evolved over the last year and a half. And so, I think really the exciting part of this was giving people this technology and within four hours, most physicians adopted and start seeing really the benefit of it, really enhancing that human connection.

Finishing their notes on time, being able to go home on time, and not having to spend time, documenting the electronic health record. Patients have also shared with us that they enjoy the interaction that they have with their physicians because now they are face-to-face, and they’re not distracted by any technology.

So, it’s been a really positive experience for us. When we first started, we were in a non-integrated state, so the applications were side by side, and we were copy-pasting notes from the ambiance application into the electronic health record. So, the adoption had slowed because of that lack of integration.

And then once we integrated, all of a sudden, a hundred providers just came out and signed up, and we are ready to go. And you know, at this point, we have 60% of our users utilizing it. And every week our adoption continues to grow, not just with the number of people using it, but just how often they are.

When you look at certain users, some physicians use it a hundred percent of the time. We had one provider that hit the all-time record of 10,000 encounters. So, this is how we deliver care at, John Muir Health. It’s been a really exciting journey. We’ve got lots of qualitative feedback from our physicians, saying that this is something that would allow them to practice for a lot longer.

How it’s given them a light work-life balance backport of the quantitative side. So, we’ve been tracking a number of different outcomes. There are efficiency gains for sure. We’ve seen about 30 minutes of time savings in documentation. And then, when you ask our providers – how much time do you think you’re saving? They will say, we are saving two hours. And so, what that tells you is that they’re feeling less fatigued. There is a tremendous reduction in cognitive load. And so, I think there’s just so many benefits with this technology, and we’re just starting to really realize what it can do. And I foresee this continuing to improve and expand as time goes on.

Q: Yeah, it’s very interesting Priti that you said that the inflection point came when it got integrated with your, in this particular case, Epic system. So that was kind of like a good learning point. So, how did you go about building a data-driven culture? Also, talk to us a little bit about your enterprise data strategy.

Priti: In addition to AI, we have also been focusing a lot on our data strategy. About a year and a half ago, we really focused on a number of different strategic initiatives, and we wanted to really measure outcomes at all levels to be able to drive continuous improvement.

So, we have also implemented lean methodology and a daily continuous performance improvement program that everyone is doing at all levels of the organization, from leadership to the frontline. So, there was this incredible need for data to see how we are driving our operational success. And so that really laid that sort of foundational need for data.

One of the things that my team did was really try to figure out how we can support each of the users in their need for data. So, about a year ago, we launched a data literacy program. We have lots of dashboards, lots of reports, and self-serving tools but unfortunately, people don’t know how to use these tools; they’re not able to access the data that they need.

So we, starting with our C-Suite, did one-to-one training with on a variety of different reporting and analytics tools. From there, we moved to the directors and the managers. We have webinars, recorded self-service, and self-paced learning. We have open office hours now so that people can drop in.

What’s astounding is that you see the increase in the reporting tool usage, and then when we are doing our weekly report outs on all of the variety of the various strategic initiatives, everyone is now speaking with data and really sharing their outcomes and tracking that. So, it has been a really exciting journey where, a number of different initiatives came together.

And then the State of Literacy program was there to support everyone’s need for data to support, the work that they have been doing.

Q: I am sure in this journey, when you try to do cultural changes there is always change management, which comes into play, and then I’m sure you are adept at balancing your innovation efforts with the clinician or the patient design. So, talk to us a little bit about how do you drive innovation, change management, and what are some of the things that you are seeing are working or not working in that space?

Priti: Yeah, change management is by far the most important component. When I think about what I do every day, even as a position, I was a change management agent.

And then on the IT and informatics side that skill comes in very handy. Even if you have the best technology, the technology that you think is really going to support the clinician or the business owner, I think what happens is not everyone approaches that technology the same way, and they need support in different ways.

They need to understand why they should use it and how it will help them. That is one of the things that we do as informaticists is we really try to bridge the workflow with technology. If technology is designed well, it is very easy to do. If it is not really designed with the end user in mind, then that’s where, the change management becomes even more challenging.

So, I think that change management is really key to adoption. Adoption is really key to seeing the benefits of technology so that connection is really key.

Q: As we move on to more discussions around AI and GenAI, what are you thinking could be the next initiatives? You have a very successful one already underway, and things are changing fast around us. As you know, everyone is talking about Gen AI. In fact, we have a webinar coming up in the next two weeks on Agentic AI, and I was surprised by the number of registrations for that. It seems to be very topical and of great interest. So, how are you thinking about new AI initiatives and Gen AI in your organization?

And I know you might be early on how you are thinking about the policies and the governance aspects as well.

Priti: Yeah, this is a very exciting time, and we, too, are excited about the AI agents where we have started with GenAI. I mean, our first application was the ambient scribe. But we have also been utilizing Gen AI to help draft responses back to messages that come from patients.

So, it really helps reduce the documentation burden. We are thinking about leveraging it on the inpatient side for nurses to help create care plans. In about a month, we are going to start a pilot to really look at how GenAI can and natural language processing can really summarize the medical record.

So, our inpatient physicians have to spend a lot of time looking through the chart to really understand why the patient might be in the hospital. And so, there are some great tools that might help summarize and really raise up some very pertinent points to care that’s a pilot that we’re really excited about.

There are a number of different applications on the business side, so when you think about doing prior authorization and letters for responses to denials. Those are really appealing use cases where I think a lot of people spend time in this paperwork, administrative back and forth.

And this is where gen AI really has a great application.  As I mentioned, we’re on this data-driven journey and teaching people how to leverage these self-service tools. There is quite the learning curve, you know, on how to sort of set up your query, right? And that’s where natural language processing and gen AI may be very helpful.

So, there are some tools that we’ve been looking at to say, can the user just speak out their query. So that then the data analysis is done for them and then they can more easily utilize it. So that is really exciting. We have been doing a lot in predictive analytics, so that’s kind of the next level. I mean, having the data and the EHR is one thing, but now, doing things with it, that is where the magic really happens. So, we have a number of different predictive analytics tools live today. One that helps predict readmissions. We have another one that has been in play for a very long time, really predicting the high-risk patients, those who are at risk for clinical deterioration in the hospital.

So, that’s been great at identifying those who may be developing sepsis or may need a higher level of care. We have a great tool that helps us detect steps stroke early and really mobilize the team. And that has really improved our stroke care. So, I think there’s so many applications and tools.

It is almost like we have so many solutions that how do we implement these fast enough in order to, You know, really take advantage of everything that is out there. And then, of course agentic AI is coming, and that is something that we’re very excited about, too. So, I’ve had a chance to see a few demos, and it was very compelling. So yeah, we’ll have to see what happens over the next six months.

Q: That’s amazing. So, I think as we are heading to the close of the podcast, Priti, are there any other thoughts or information you would like to share with the audience before we close?

Priti: Yeah, this is a very exciting time to be part of this. I think we’ve all kind of noticed that there’s something different in the last year. And I would say that I am really interested in everything that’s out there and trying to find a solution that will fit our problems. That is always a challenge and when I think about what would really make a big difference for us is finding solutions that really solve problems that we have. I am someone who really enjoys technology, and so everything’s exciting, but how do you figure out what’s the one that’s really going to make a big difference and really improve patient care and experience for our clinicians? 

Subscribe to our podcast series at www.thebigunlock.com and write us at info@thebigunlock.com 

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

Rohit: Hi Crystal, great to see you. Thank you for joining us. As you might be aware, Crystal, the show—is called The Big Unlock. It was started by Paddy Padmanabhan of Damo Consulting, and we are building on the foundation that he had laid. And it is great to have you here. I’m Rohit Mahajan, I’m the Managing Partner and CEO at Damo Consulting and BigRio. Would you like to please introduce yourself to the audience?

Crystal: Hi Rohit. Thanks for having me on the show. Hi everybody. I am Crystal Broj. I’m the Chief Digital Transformation Officer at the Medical University of South Carolina. I am based in Charleston, South Carolina, but our system spread the entire state—all 46 counties of South Carolina. We are one of the oldest medical universities in the country, and we’re celebrating our 200th year this year. So, we’ve been around a long time. We have 2.4 million patient encounters annually, 16 hospitals, and about 2,700 licensed beds. But besides being a health system, we’re also one of two national telehealth centres in the country, recognized as Centres of Excellence.

We have a university with six colleges, about 884 residents and fellows, and 42-degree programs. We’re the number one rated hospital in South Carolina according to U.S. News & World Report. We’re also a research organization with about $300 million in research funding annually, 1,200 clinical trials, and about 55 active startups. There are a lot of exciting things happening here, and I’m just so blessed to be here, bringing digital transformation to the organization in a variety of ways. 

Q: Can you please tell us what inspired you and attracted you to this space? How did you get started in healthcare? Tell us a little bit about your journey, where you’re at, and what you might be thinking about for the future.

Crystal: I started out as a COBOL programmer when I came out of school. I was in IT, doing COBOL and programming. Then I jumped around to a few different jobs—back in the day, if you didn’t move, you’d get stuck in the system. So, I moved around, went into consulting, then back to big business, then back into consulting. I did that for a while, but with two young children at home, working 60 hours a week in consulting wasn’t sustainable. So, I took some time off and had the opportunity to be a Director of Christian Education at a church for a couple of years. It was totally different—a passion project.

I don’t have the typical IT trajectory. I bounced around quite a bit. Eventually, I went back to management roles, working across various industries—manufacturing, health clubs, insurance, human resources companies. So, I gained a broad range of experience. I didn’t grow up in healthcare. My first step into the healthcare space was as Chief Technology Officer for the American Association of Diabetes Educators. It was healthcare-adjacent—we trained diabetes educators to help patients. That was my first real jump into healthcare.

Then COVID happened, and like many, I found myself out of work for a while. During that time, I actually wrote children’s books.

Yeah, that’s the fun fact—I saved it for now. I put them on Amazon. I probably made a hundred dollars, but it was fun. I wrote and illustrated them, and it gave me something creative to focus on. Afterward, I ran a business analyst office, and we started teaching Agile. Then I went to NorthShore University HealthSystem in Chicago, where I was Vice President for Digital. I built a digital team from scratch—there wasn’t one before—and we grew it to eight people, supported by some consulting services.

Then my dream job came up in Charleston. I wasn’t actively looking, but my daughter was attending school here—she’s earning her doctorate in Nurse Anaesthesiology and graduates next month. We started spending more time here and fell in love with Charleston. When this job opportunity appeared, I knew it was exactly what I wanted.

I started as a team of one. This organization is very innovative—they truly believe in innovation. What we’ve done in just two and a half years is incredible. My team has grown from one to eight, and I’m lucky to work with amazing people—not just my staff, but also the senior leadership who constantly ask, “What can we do next?”

The team that works for me puts their whole heart into everything they do, and it shows. When we roll out projects, they’re rolled out carefully and thoughtfully. One of the things I always say is: We won’t roll it out until it’s right, because we only get one chance to make a good first impression. We don’t just throw something out there and hope it works. We started with a small project. We were, we were using Notable is the, is the key name, right? You, you’ve heard of them, obviously. They’re well known in the marketplace.  We started with a pre-engagement process. So, if you have an appointment, we send you a note three days before— “Hey, here’s a reminder. Do you want to confirm or cancel your appointment?”

We launched this with just five offices to see how it would go. We wanted people to try it, to prepare the offices, and to understand how patients would react. It began as a pilot, and then we started to grow from there. One of the biggest lessons learned was: start small, then scale. From five offices, we moved to 100, then 500, and now it’s implemented across all offices in the organization. We send out pre-reminders, and we receive confirmations and cancellations in advance. Then we added another layer: validating demographic information. We pulled data from Epic, so patients could quickly review— “Yes, I still live at the same address,” “Yes, I’m still married,”—all the things providers need to know before you walk in the door.

After that, we integrated bill pay. Patients could pay their copay or any outstanding balance in advance. So, you can see, we added small features step-by-step. We didn’t try to perfect everything at once, but each step was rolled out thoughtfully and intentionally.

We also listened to patient feedback. At the end of each interaction, patients could give a thumbs up or thumbs down. And so patients would say, thumbs up, and then they’d say, this was great, or this is this. We get some thumbs down and we read every single one from our customers. We make sure that we’re touching the patients and find out what they want. And so we got things like, Hey, I come to the doctor three times a week. Why are you sending me the same information all at once?

So, we worked with the vendor and adjusted it. Now, at the beginning of the week, patients receive a single message listing all appointments: “You have three appointments this week. Do you want to check in for all of them now?” They can fill out all their information at once and then simply walk in and say, “Hi, I’m Crystal. I’m here.” We also added Spanish support. Spanish-speaking patients now receive the messages in Spanish, and the feedback has been fantastic—things like, “This is easy,” “Thank you,” “I can do this from home.” It’s been a huge success.

But one key thing we learned was the importance of change management. Our front desk staff were used to handing out clipboards. So when we rolled out digital check-in, some staff were unsure—“How do I know it really worked?” Some still wanted to give patients the clipboard. That, in turn, frustrated patients who had already checked in online. They’d say, “Wait a minute—I already did this!” So we had to step back and think through how the technology impacts everyone, not just the patients. We retrained front desk staff to trust the system and support the new process.

Of course, there were a few glitches—nothing is ever perfect. Sometimes the system didn’t work, and staff would say, “Just come to the front desk,” which undermines the experience. But we’ve been learning and improving every step of the way. It’s okay. Sometimes we’d get someone new at the front desk who had no idea what this was. They hadn’t been part of the rollout, so they didn’t know, they didn’t understand it, and it just wasn’t on their radar. So, we created training materials for new hires. Now, whenever someone joins, they receive the necessary training. We also make sure to communicate updates—like, “By the way, patients can now get this in Spanish,” or “Here’s a new feature we’ve added”—so that everyone is on the same page. Those were some really valuable lessons for us: start small, communicate—and over-communicate—and grow alongside the vendor to create something truly impactful.

Q: That’s great to know Crystal. Lot of great experience there. In terms of scaling the solution, I did read somewhere that you mentioned that innovation is in in our DNA. Could you talk any other of you Digital transformation projects or innovation projects where you’ve had success and you, I think you are very good at tracking metrics as well, is what I understood. So how, how do you track those metrics and kind of look at the return on investment?

Crystal: Sure. I’m happy to go into a bunch of those things. Once we started with Notable doing that, then we started looking at how we could do other outreaches to patients. In Covid, people didn’t go to the doctor, right? So, women especially don’t take care of themselves. We went back to close the care gap for mammograms because most women didn’t go during Covid, and then they got busy and forgot about it. So, we did outreach. If you haven’t had a mammogram in a year, we send you a reminder saying, “Hey, we care about your health. You’re overdue for a mammogram. Would you like to schedule it now?” Automatically schedule it. You didn’t have to call in. You didn’t have to do anything. We turned it on at five o’clock at night on a Thursday, and by the next morning, we had 129 women that had scheduled their mammograms. That’s a KPI. And then we started tracking those women to see. It’s still in progress today. We do it monthly and go, “Who hasn’t gotten it in a year?” Every month. We send out these notifications. Up to date, I think we’ve had 18,000 mammograms that have been scheduled. What we found is that about 180 of those women needed to come back because they had abnormal results. So, we got them the care that they needed before it became too late. That’s where you’re like, okay, this is digital. And there might be KPIs, but these are KPIs where we could potentially be saving people’s lives. And there’s no dollar amount that you can put on that, right?

And so, with that being very successful, we started hitting up well-child to make sure people came in for their visits, for their kids getting the shots they need before school. We send notifications about flu shots to remind people to come in and schedule. We’re going to start doing uncontrolled hypertension and getting readings on that so people can do it—diabetes as well—start checking on that. We’re just continuing to build on that platform and get those kinds of things.We know that we have sent out, since June last year, 1.7 million reminders to our patients. We’ve had about 50,000 cancellations. That seems like a lot of canceled appointments. But at the same time, if you cancel, it’s not a no show. The doctor can refill it. We have an automated way to do that through Epic. It’s called Fast Pass. You can get somebody else in. That helps with access because then people can get in sooner. Helps doctors’ schedules. Makes everything flow better for both patients and providers, which is really what transformation should do, right? We don’t ever want to burden providers. We want to make things easier every month. Easier for patients as well. With Notable, I can’t say enough about it. We have a 98% satisfaction rate from our patients, and that’s another KPI that we track. We just started with a new product with Notable where we’re actually working with revenue cycle—we’re doing prior authorizations.

We all know that you might get a referral for an MRI, but you have to wait for your insurance company to say it’s okay before you get it and make sure they’ll pay for it, all that kind of stuff. Well, to do that, somebody has to take your referral, type it in Epic, and then go to the payer site and type in all the information again. And play back and forth with the payer, then put it back into Epic. That can take anywhere from 15 to 30 minutes. But we have an automated AI agent that does that now, and they can do it in about 30 seconds.

If you think about how much faster that is for the back of the house—not just that number alone—but how much quicker patients can get in. And tracking how many are automatically approved right away—we have about a 37% accuracy on this agent, and it keeps learning all the time. That means almost 40% of the ones we send through are done and the human doesn’t have to touch it.

Coming up next, we’ll go, “Okay, Rohit, you’ve been approved. Here’s a link. Schedule.” We’re going next with that. So those are some things that we’re doing. Great success with that really helped our senior staff know that this is a team that can take big things on and do it well. We brought a voice bot into our patient access center. We have 42 phone lines, about 150 people that answer phones every day. If you ever call the access center and you press one for this, press two for that, and wait on hold for who knows how long—we’ve got an automated natural language processing agent.

We call her Emily. She answers the phone: “Hi, I’m Emily, your digital assistant. How can I help you?” You say, “I’m checking on my appointment.” She says, “Okay, give me some information to validate who you are.” It goes into Epic. “Yes, Crystal, I see that you have an appointment next Tuesday and next Friday. Which one do you want to know about?” “The one with Dr. E.” “Okay, sure. That’s two o’clock next Tuesday. Do you want to confirm, cancel it, or do you need to reschedule?” “Actually, I just needed to know where to park.” She can do all of that, and it doesn’t take up an agent’s time.

We deflect—and that’s another thing we track—we deflect about 17% of the calls that come into the patient access center. We’re not getting rid of jobs, not by any means, but it means that our patient access reps can handle more complex questions. Something that’s harder to do. Our hold times have gone down. The number of people that get frustrated and just hang up has gone down. So those are the types of things that we track to prove that the software—these digital tools—are doing what we really want them to do.

Q. That’s interesting implementation. So, the handoff between Emily and the real live agent is seamless?

Crystal: Yep. The thing about Emily is she can answer the phones 24/7. So if the center was only open, I don’t know, eight to five or whatever. So, she can answer after hours and answer questions and maybe sometimes it’s a more complex thing.

So the agent can go like – sorry, I can’t, you know, have you call back to talk to an agent tomorrow to say you call kind of thing. And we’re just now changing it so that she can reschedule because      rescheduling is much harder than just scheduling. And, within the next month she’ll speak Spanish too.  But that comes with another thing where we really had to train the bot. Right. So, it’s, it had to learn. It must learn. Accents. We’re in the south. I come from Chicago, so I don’t sound like I’m Southern except when I say y’all. But we had to train it so it understands the accents. It would understand certain words, understand that appointment may mean visit may mean something else. And do that training of the model. And take the time to do that. So, testing is important. Yes. Really going through and testing and validating that to make sure that it works. Getting the people that are going to use the software to use it, so they understand what patients are hearing so that when it comes to me, I’ll know that, oh, Emily already asked this and this, and I’ll have some of that information for me.

Things like that. But it’s wildly successful. All 42 lines on it. Now we’re going to roll it out to the rest of the state because Right. Oh great. Just in Charleston, we’re going to roll it out for our revenue cycle. We’re going to roll it out for our pharmacy.  So that we can take some work off the back of the house. So again, they can work at a higher level of license, whatever that license is. So that growth stuff that that things that we just want to get out stupid stuff and then start to do, you know, those higher level things that provide more value to both our system as well as our patients. 

Q. So, I heard you say, I think early on in our conversation, maybe even before the podcast started, that you, you did look at a lot of KPIs and you, you have a certain way of looking at return on investment and there’s certain benchmarks that you kind of track. So, could you tell us a little bit more about that piece?

Crystal: Some of it is based on how many patients encounters we’re facilitating or the financial impact we’re seeing. For example, with pre-experience copays, we’ve collected about $1.4 million through the technology—completely automated, no human involvement.

Front desk staff often don’t like asking patients for money, but if you collect ahead of time or send a reminder afterward, patients can just use their credit card—done. For open balances, we’ve collected about $1.9 million. For voice, we track how many calls are deflected, how many are completed, and how much labor time we’re saving. A phone call can take three to five minutes. If an employee doesn’t have to handle that, they can be more productive elsewhere.

We also track bot performance—how many calls it picks up, how many referrals and authorizations it handles. We compile all of this into a monthly report for our stakeholders, including business leaders and senior staff, showing how many appointments were scheduled, how many tasks were completed, and the overall impact of the technology. It clearly shows the value and why we continue investing in it.

We just launched new scheduling software last week. If you visit our website, uchealth.org, and click on “Find a Doctor,” you’ll see the new interface powered by a product called DAX Care. You can search by specialty—primary care, ortho, etc.—or use natural language, like “I have an elbow strain,” and it’ll pull up orthopaedics with first available appointments, both for new and returning patients. It pulls directly from Epic and displays real-time availability, making appointment access completely transparent.

In just one week, with no advertising, we’ve had over 200 appointments scheduled through the new system. People are finding it on their own, and satisfaction is high. We started with primary care and are rolling out specialties next—orthopaedics tomorrow, then new specialties every couple of weeks.

We expect strong results from this rollout because patients everywhere are asking for easier access. Now, they can see exactly when a doctor is available. And if someone searches something like “I have a cold,” we surface not just primary care, but also options for virtual care. So if they want to be seen now, they can click and do a virtual visit—something many forgot about after COVID.

Virtual care helps alleviate scheduling pressure since we don’t have enough doctors to meet demand in-person. If your child gets sick at 7 p.m., this gives you quick access and guidance on what to do next. Speaking from experience as a busy mom, having that option is incredibly valuable.

Q: That’s awesome. So, what are some of the challenges, crystal, like you go about implementing all these solutions? What are some of the big challenges that are being faced by, you know, people who are implementing these solutions?

Crystal: I think, across the country, you know, we’re short staffed. Our IT departments are short-staffed, so you know, they’re busy keeping the lights on and they do an amazing job making sure epic, workday and all those things are working and going plus. All the technology from the laptop I have to the network and all that kind of stuff they must handle as well.

So, you know, they’re doing that. And then they’re busy full-time. It’s not like they’re sitting around. Right. And then here comes digital transformation and they have a new toy that they want to have. Right. And they want to put it in. And it’s like, it’s not like I can go do that on my own. Like I do all the work around it but eventually tie into a main system. So, it’s, you know, sharing with. It why this is important, what we’re trying to do, and getting on their schedule so that they can connect me to a Cadence analyst or an API analyst who you know, get cooked up together. But it’s not like the projects they’ve done in the past. You know, if you do an upgrade for a major system, you know, on June 30th, I’m going to, you know, implement the system.

So, 30 days before, I should be done with testing and 30 days before I should do something else. When you start using these new technologies, like I said, you start small. Five offices. So okay, I need a connection for the five offices, then I’m going to go in in two months to a hundred offices. Well then I need a new report that gives me a hundred offices, and then six weeks later I need another report added on for these many things. So, I’m like the gift that keeps on giving to IT. Which does not always make me popular. Exactly. And yet, you know, you know, we found a way to kind of work together and we’re still working out some of the fine tuning on that to make it really sing because it’s hard for them to figure out what does digital transformation means now, right? An API person, you know, the vendors will tell me, oh, this is easy. It’s four hours, you’ll be done. Yeah. Four hours to get the connection going. And then, you know what? When it doesn’t work, I’ve got to give you a call so you can talk to my analyst to go, why does troubleshoot. That’s it. They can’t plan for that.

Right. And so, we don’t know. Well, we might have problems, but you know, they say they won’t, but we probably will. So, let’s see if we can figure out, let you know that this might happen. But there’s no way I can predict what the problem’s going to be or how many hours I’m going to need. And that’s, that makes it, I think a lot of people are struggling with that across the country. And that whole agile concept of, we’re going to add more. It’s sort of, it’s never done. It makes it hard to budget resources. You know, my team’s just constantly churning through it, but it’s got like a whole other job to keep the lights on. That’s a whole different. So that’s a little bit of a challenge.

And then working with our user side, our business side. You know, we’re changing the way they do things. And change is hard. And so, there’s a lot of change management. Why are we doing it? What’s in it for me? Why should I do this? And some people resist that. We’re using ambient technology as well here. Not with a pilot of 125 doctors that are using, nuanced stacks and they’re talking in their phones and saying, okay, Rohit, I’m the doctor. You’re my patient. So this is going to record my notes. So, if you’re okay with that, and we always ask. And so, press play and then you and I talk and like we did before, we were talking about my daughter’s graduation, we’re talking about other stuff.

And then I’m going to ask you about, I don’t know, maybe you have asthma or something else. I’m going to talk to you about that. I’m going to talk about the medications, how it’s working, how it’s not, and then tell your dog I said hi, tell whatever. And all that gets translated into the note. Except the stuff about my kid, about your vacation, about your dog isn’t there? It just takes the medical stuff. Which is still amazing to me that AI can do that. And then, you know, you can say yes no, or adjust stuff. There’s always human that are just checking the ai cause it’s a consistent, it’s not, you know, an independent body. Right. And then you pick that and it’s that; well it saved 37% of pajama time for doctors.     

Yeah. Which is crazy good. Right? And it also closes charts faster. So, revenue cycle’s really happy about that. When we did our pilot, not every doctor loved it. Yeah. You’re like, uh, and of course they don’t want to look dumb in front of their patients. Yeah. So, if they aren’t comfortable with it, they’re not going to use it. And so, there are a lot of things that we learned from that, that you know, okay, we have to find out what the sweet spot is. How do we communicate? We wrote great emails, the most beautiful emails in the world, Dr. Crystal here, this is what you need to know, and here’s some information. And we’d send them out.

The doctors don’t read it. They don’t. So you, they weren’t responding, they weren’t asking questions, they just stopped using the software. So, we had to figure out ways to either text or have somebody in their office ask them how it’s going. And some doctors just didn’t take it right away, so then we gave it to somebody who wanted it. Overall, the amount of time savings and closing charts faster and doing those kinds of things to give doctor satisfaction. At the end of the year was great. It was so great that we’re giving an enterprise license. We have 600 doctors waiting to get the technology. Which we’re going to start rolling out this spring. So, the KPIs were important to show that this really was valid in the pilot to, there was a no go, and if it wasn’t working, we weren’t going to use it. We showed that it did work and so then we can add on to something else.

Q: That’s great initiatives, Crystal. So, as we are coming now to kind of close off the podcast, I would like to ask you for, you know, what do you see when you look into the future? What is your peek into the future? And any other closing remarks or thoughts that you would like to share with the audience? Crystal?

Crystal: I think we’re going to see more and more AI, but I don’t think we’ll ever see AI taking over our jobs. I think we’re going to have to find some sort of a sweet spot where integration becomes easier between our main systems and these add-on systems. I think go away from like, if you go to any show, you see, oh, there’s this for, I don’t know, brain cancer. Let’s say there’s this for heart, there’s this for whatever. And you know what? I can’t afford to buy this. Nor can I integrate those. Do that kind of stuff. So we have to have something that can kind of do everything at least somewhat well, but we can customize and make it so that it’s better for the whole system. I think we’ll see more of that coming along. The spot solutions, not that they’re not great ideas, but I can’t afford to do it. I don’t know many hospital systems that can take on that many things.

I think we’ll see more in patient navigation, and I don’t mean wayfinding per se, but I mean like I know I’m going to have to go in for an ortho appointment, so you’re in education before that, I’ll be able to maybe answer my questions through two-way text or something beforehand. I’ll get stuff a day before or three days before. Maybe my husband who’s going to support me will get something as well, and then I’ll come in, maybe there’s something in the hospital that does some education or whatever, and then when I’m discharged, I’ll get follow-up both from a nurse. Maybe it’s text first, and then I have a question. A nurse will call me back. Maybe I’ll get some more education. Then all of that will be seamless to me as the patient. It may be pointing solutions along the way, but for me it’s just seamless. It’s my hospital system caring about me. It’s my hospital system showing me the way to go. Those kinds of things we’ll see a lot more of that coming out as we get better at using the tools that are at our disposal.

Subscribe to our podcast series at www.thebigunlock.com and write 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. Hi Rita. Welcome to the Big Unlock podcast. We are really happy to have you on our show. This is the sixth season, and we look forward to a very engaging conversation with you. So welcome once again. Just to get started, I’ll give you a very brief introduction about BigRio and Damo Consulting.

I’m a managing partner here at BigRio and Damo, and I’m based out of Gurugram, India. BigRio and Damo are both heavily into technology consulting, and BigRio is mainly into strategic and advisory consulting. We have a proprietary DigiM maturity model that we had several very good clients — Geisinger, Intermountain, Montefiore — and we’ve had two versions of our DigiM awards for the last two years. We had very good participation and we also added an AI maturity model to that as well. BigRio is the technology implementation partner. So we have the entire offering because we do the strategy consulting and the advisory, and then we can also help with the technology implementations. So that’s a little bit about Big and Damo. Would love to hear about your background and about Pager Health and your journey into healthcare.

Rita: Yes, absolutely. So I am the Chief Product Officer for Pager Health. Previously I was at Salesforce, having led the development of Health Cloud, which is focused on patient relationship management. For my entire career, I’ve been dedicated to healthcare technology, really bringing solutions to the market to improve.

The experiences for patients and for providers. I had an opportunity to join this Pager team. Super exciting. Some of the work that was happening at Pager Health and continues to. We provide nurse triage, wellness care management for nearly 30 million lives for payers and for providers, and our NPS score is over 90. That’s exciting to me because I know that we’re making impact in the market for members and for patients.

We’re fortunate to work with leading health plans in the US and in Latin America, self-funded employers, and large health systems. I think we have the whole breadth of the offerings that are available. We’re white labelled, so a lot of people don’t necessarily know Pager Health, but they know our technologies and our services because they’re encountering them under the brand name of a health plan or a provider.

That’s really great to know and I was really interested to read the report, which Samantha shared with me. That had some brilliant insights about numbers in terms of metrics and how 67% of your audience feels that they trust the copilot. It was really amazing to see the level of Gen AI adoption from that survey.

Q. So how do you feel about the Gen AI implementation at Pager Health? What are some of the challenges that you’ve encountered? What are some of the big wins you’ve seen, and what is your overall view towards the next trends for Gen AI and for Pager Health?

Rita: Yeah, so I think that we’re right at the beginning of this revolution, right? So, I think we’re going to see some amazing transformation. It’s an exciting, exciting time to be here. I think we’re going to have meaningful impact on patients and providers. I think we can help providers with administrative functions, medical documentation. We, we just rolled out something that allows us to summarize a chat and actually submit it into an EHR. It takes five to 10 minutes out the process. We use it for triaging patients, doing intake with them, providing conversational chat bots and AI assistance. We do provider facing chats. We do patient facing chats and agents.

There’s a lot of ways we’re gonna be able to impact. I love to talk about this one example. In Columbia, we have a connected health platform for an integrated health system, which supports 14 million lives today. And so they use our platform to do 3000 per week consultations remotely. And because the technology is AI powered, there’s a lot of concurrencies, right? They can have six concurrent conversations at the same time, using the chat technology, using the AI technology, and then they can ramp that up extremely quickly. They can also get the healthcare providers onto the platform extremely quickly. So, whether that’s a nurse or that’s a physician, they go onto our Enterprise 360 platform. But all of that is enabled with ai. Other thing that’s super exciting to me when I look at our ai. Possibilities. We can use AI agents to navigate benefits or schedule appointments. We can use it to create really personalized health journeys based on people’s preferences, their lifestyle, their care needs.

We can help them find doctors, and these are all the things that consumers told us. That’s what’s gonna build trust for me in the health system.  And you talked about. Consumer survey and they said, they said, I trust AI to help my healthcare professional. I trust AI to help me navigate the system. I help a, I trust AI to be able to give me a personalized journey for my healthcare. So, what I think is so exciting is that the consumer has said, I trust ai. It’s partly because it’s all around them. So, it’s not so novel anymore, but they’re starting to build more and more trust. And the consumer survey showed us. I do think another component of it that’s very important to Pager Health and I think is important to the healthcare industry to be frank, is we have to keep the humans in the loop. So I think AI is going to create efficiency. I think it’s going to help us. I think it’s going to help our healthcare professionals with clinical decision support reviewing vast amounts of data. We know 20% of the world’s data is in gen ai, right? So that’s a lot of data to be able to consume and to be able to use for healthcare decision making. But I think we’ve, we’re going to have to keep humans in the loop for the time being. I think with consumers, we’re going to be able to do a lot for them, to help them better navigate providers and the system in general, and their benefits, and figuring out what access they have to healthcare professionals and to services.

Q. That’s a really good answer. Thank you. I was at a couple of healthcare conferences, and the number one issue or challenge or barrier was when they talked about trustworthy AI and responsible AI. They felt that, because of all the hallucinations, deep fakes, and the problems encountered with Gen AI, the trust of the consumers in Gen AI is not as high as what your survey indicates. This was really heartening to see and also hear about your Columbia implementation—that it’s going well and that people have a high level of trust in Gen AI.

Rita: Yeah, and it’s creating efficiency in the system, right? So that’s really important for the time being. If we keep humans in the loop when it comes to healthcare decision-making, it’s going to be super helpful because we can start to build more and more trust with the end consumer. It’s not a new technology, but it’s relatively new in the way we’re using it. So, if we keep humans in the loop and focus on efficiency, we’re going to see amazing inroads with Gen AI and AI in general.

Q. Yeah, that might be an interesting one because with GenTech, we are sort of moving away from human-in-the-loop and going into autonomous systems, letting them do things on their own. It’ll be interesting to see how this plays out and at what level we still need to have humans in the loop versus autonomous systems.

Rita: That’s right. Agentic is very important. When I talked about benefit navigation, finding a provider, or scheduling within a provider, we have AI agents today that can deliver that. We help with intake, with clinical intake, but delivering the care—humans do that. We continue to make the humans more efficient with their consumption of data and how they provide the best care to a particular patient.

So, it’s this hybrid mix. We’ve rolled out several agents into the market, but they’re focusing on data consumption—benefits, providers, and helping consumers get to the right resources. Consumers told us they trust that. When it comes to the clinical piece, we have this hybrid approach.

Q. I think that’s a fair resolution for now because that’s where we’re seeing a lot of success, even in other healthcare implementations. With ambient and scribing, taking away the burden of documentation and letting clinicians focus on patient care, with Gen AI taking care of administrative tasks. Also, I wanted to ask you about your “Really  Well” product, because that also uses agentic tech and seemed like an interesting implementation. Can you tell us a little more about that?

Rita: Yes. “Really Well” is our wellness program, our product, I should say our platform. It has the ability to take a health assessment, gather data from the patient or member, and then give them specific journeys around self-guided programs or other programs they have access to—challenges, things like that. It gets very personalized. We’ve introduced a Wellness Companion that now lives within that platform. For a particular consumer, it guides them through their experience. It’s not just 100% self-serve—they’ve got the companionship of their AI agent, their Wellness Companion, to help guide them to specific solutions. For instance, if someone takes their health assessment and says they have high levels of stress or that their job is stressful, the Wellness Companion can guide them to a stress management or mindfulness program. In order to deliver the N-of-one experience for every person, Gen AI is incredibly powerful. It gives that personalized experience within the wellness platform. That’s how we’re leveraging AI and the Wellness Companion within “Really Well.”

Q. So in terms of adoption of “Really Well,” have you seen any challenges? Or as your survey mentioned, did people trust it and engage with it? What were the success metrics for that program?

Rita: So, our success metrics are how many people engage in the wellness program. We built this platform for health plans to use with employers. They use the wellness program and sell it into employers. What we’ve seen is adoption is over 50% in terms of filling out health assessments and engaging with the program and getting wellness.

We’ve seen results in terms of reduction of stress, reduction of weight within the program, smoking cessation increases. We’ve seen impact, which we know has clinical implications downstream and helps reduce costs with a wellness program. In some cases, we can integrate care navigation so people can access a nurse if they need specific help—say, mental health support. A nurse can be accessed within the same platform, and we’re starting to see more uptake because it’s an integrated platform. For a long time in the industry, wellness sat in one compartment and health in another. Our opportunity with Pager Health has been to bring those experiences together. people do not compartmentalize themselves, right? They don’t have a sense of like, this is my wellness compartment, this is my health compartment, it’s me, it’s my body, it’s my health. So, when you’re in a wellness experience and need access to a nurse, you can get that within the same platform. That integrated experience and whole-person approach has been very successful. We’re seeing great results and also ROI. For employers to keep investing, we have to deliver ROI, and we’ve seen it—sometimes as high as 7x within populations using Really Well and our care navigation solutions.

Q. Have you been using voice assistance or is it all just text-based?

Rita: They’re not using voice assistance today. There are the ones integrated into phones, and we have an omnichannel experience. So people can go on the web, they can go on their mobile phones, so they can leverage the voice technologies that are available to them in either one of those platforms.

Q. Okay, because that was one of another trends that we were seeing at, you know, the HIMSS conference and other conference. The voice has just exploded suddenly. And we were talking to a partner, Vivid Health, which does longitudinal care management plans, and they said that they were seeing like a flat demand, but then as soon as they added voice, it was like a hockey stick and just, you know, they haven’t been able to keep up with it.So, in your opinion, what do you think caregivers or clinicians are looking for in terms of digital enablement? What we’ve seen so far is mainly like their number one ask seems to be ambient, but in your opinion, what do you think? Like what have you come across and what do you feel?

Rita: I’m gonna reference that consumer experience survey where we actually heard from 2000 consumers about what their experience with health plans and the health system is today. We know that trust is broken, that what they said to us. Three out of four people said, Hey, customer service. That reduces my trust quite a bit. Lack of access that reduces my trust. So what they’re actually looking for, and this is the great news I thought from the consumer survey for me personally as a, a leader in healthcare, is that they are willing to put that trust back into the healthcare system. It’s, there’s an opening. So they haven’t shut themselves off, they haven’t given up. Maybe they have on communications, they have not done so on healthcare, which is great news. And so they’re frustrated by things like not being able to find information or get access. And what they said they would want is 24/7 access to a nurse.

Having one agent, one coordinator, answer all their questions. Think about your experience in healthcare being sent from one place to another place to another place. They don’t want that.  They want one experience and they would like to be able to be supported through navigating the system. The system is complicated. So whether it’s a health plan system or a provider system, or all the great digital programs that are available to people, just navigation, they want to be able to have some help with that and they would like the information and support to be timely. So they told us what they want and from a technology perspective, we can address all of this. And so if there’s the right technology partners, the right infrastructure in place. This is all very possible for us to deliver. And so we just have to prioritize it. Healthcare systems have to prioritize it. Health plans have to prioritize it, and if they do, and I’m seeing lots of health plans and, providers starting to do this, they’re able to get that better experience to the consumer.  That’s what they want and they want the, not only the convenience of it, but they want the ability to feel like they’re being cared for in the system because. They told us they’re telling you what they want and you’re delivering that. So that I think is what people are seeking.

Q. What were the kind of timelines that you were seeing on this implementation that you built for really well and you know, for these other products? Because what we were seeing with a lot of clients and companies that we were talking to, they all have FOMO. They know that they all have to do GenAI, but then they’re also like, we saw 70% of the POCs are not moving forward or not being able to translate or scale into production. So did you, you know, have a similar experience at Pager Health or was it a smooth implementation? What were your thoughts or feelings about that?

Rita:  In terms of implementation, it was very straightforward, right? Because we’ve trained the models, we’ve trained the agents, we’ve got it specific to a health plan or provider’s needs, right? So that part from a technology perspective. That did not have friction. Okay. I think all of that is well honed. I think the biggest issue we actually had, believe it or not, has been data use rights. So plans, providers, very sensitive to the use of data, sensitive to data breaches. They’re sensitive to that. So what we’ve done is created a data use rights memo. Our legal team has done that. And again. Just to ensure that the legal teams feel comfortable in terms of what’s going to happen with the data. Our goal is not to leverage the data to be able to do, to utilize it. It’s not to you put it into the LLM, we rely on the Google Vertex model and their LLMs to be able to build our agents. And so it’s not our LLM, it’s their LLM and the Google relationship says you don’t have to give me that data back into the LLM. Right. So some of those things, again, I could. It comes back to trust. That’s what’s created more barriers right now. Again, this is all new. The technology part of it has been very straightforward. For the most part, it’s been about building trust with the organizations themselves around data use.

Q. Yeah, I would agree with you there. I mean, people have had like a different you know, experience sometimes with the models also, but maybe because you locked in with a technology provider and you felt that the implementation went smoothly. In that case. That’s right. So we also heard at the same conference that company culture was another thing that frequently came up because they said that culture can be an enabler or it can be a barrier sometimes.  Because when people are not, you know, bought in to the entire concept of Gen AI or the education doesn’t happen across the company, then people are more reluctant to participate and then culture becomes a barrier. So what were your feelings about that at Pager? And did you like have a formal education program? How did you create that buy-in from all the stakeholders and ensure the smooth implementation, like you said.

Rita:  I think it’s a technology forward company. In terms of pager health, we were started by the founder, one of the founding members of Uber, so Oscar Alazar. And so it’s already always been this disruptive, innovative technologies. It’s, you know, it’s a relatively, it’s a young company, so it terms of new technologies, it. It’s very receptive to that. Like that is our, the crux of our organizing principles. So Gen AI was very welcome. We have a significant data science team, data engineering team, so it wasn’t a big change within Pager Health for sure. Okay. A big change has been at the health plan and provider level to help them feel comfortable. Like I was just talking to you about data use rights. People saying, okay, so what are you going to do with my data? How are you going to ensure that it’s safe? How are you going to ensure that it’s not going to end up in some LLM that I don’t have control with? These are the kinds of questions people are asking. That’s where there’s been more of a change of mindset.

But this is the great news. Many of them, many of our customers. Are building their own AI gen AI technologies. So they’re using them for their internal purposes to reduce some administrative costs to reduce just the navigation within their own system. It’s the piece of it when you have to introduce it back out to consumers where they have to be extra careful and they’re being extra careful. But I think the change has happened. I don’t know that people are resisting it anymore as much. Yeah. Because it’s here. It’s not like we can’t afford to sit on those sidelines, and the leaders of healthcare plans and professionals, they know that that’s the case. So they’re embracing it, they’re leaning it.

I would say that wasn’t true two years ago, but now I would say it’s, definitely true. Not so much change management.

Q. I think we can conclude with maybe some of the risk factors or security related questions or, you know, security considerations, cybersecurity or what in your mind would be the top risks and how at Pager do you address those? And on that note, I have another related question. In some of the companies we are seeing that this primarily falls under the role for a Chief AI officer. So does Pager have a Chief AI officer and you know, do you have a governance or ethics committee?  What are your thoughts about all of that?

Rita:  Yes, so we don’t have a chief AI officer.  I do have, very senior data scientists that work within our team, and they’ve issued memos and findings and policies about responsible use of ai and, and we’ve issued these, they’re available for consumption by any of our customers. So again, that’s the governance. Body essentially was our C-suite and the data scientist team that continues to reinforce for us. The partnership with Google also helps to reinforce the responsible use of AI because they’ve done made so many incredible investments in that area. So as a partner, we can leverage that. I think the security in governments, it’s, again, I come back to the data use rights. We just have to help people feel comfortable with the fact that we’re going to be very responsible with the use of the data and we’re going to be very responsible to the consumer with the use of their data. So we cannot breach privacy. HIPAA compliance continues to be a complete commitment from us as a technology company, and so we sign BAAs. We make sure that the data is not transferred between customers. It’s all for the customers, for the needs of the customer. All those kinds of things are very important, again, to building that trust with the organization. Security, privacy, all the things that we have been doing for a long time in healthcare. We just have to keep doing. Them and just keep doing them more and reinforcing the responsible use of ai powerful technology. But we have to be responsible with it. And that’s how I think we’re approaching it.

Q. I think it’s been a wonderful conversation and we look forward to going live with this. Rita, any other closing remarks that you would like to share or, you know, tell our viewers about?

Rita: I know I want to thank you for a great discussion. It’s, uh, fascinating to talk about this and hear about your perspectives as well. I think people are going to want access to that consumer experience survey. They can go to https://www.pagerhealth.com/cxsurvey and get access to the report, or they can reach out to me directly at our rsharma@pager.com and I’m happy to get. That report sent to them, so they should get access to it. We want as many people as they want to use that. I think it’s a seminal piece of work, and it’s going to help with a lot of decision making as we think about future initiatives. And there’s a lot of misbeliefs, if you will, within. With about AI You know, people are scared of it, but actually they’re not that scared of it. There’s also misinformation around elderly people not wanting to use technology or using text or it’s not true. It’s just not true. They’re using it, they’re embracing it, they’re not afraid of it. So we just need to dispel some of these, these issues, and so this report I think will be very help in doing helpful in doing that. I thank you for your time and this discussion. It’s been fantastic.

Subscribe to our podcast series at www.thebigunlock.com and write 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. Hi Yaa, welcome to the Big Unlock podcast. So, I’m Rohit Mahajan. I’m the CEO and managing partner at Big and Demo Consulting. And we’ve had the good fortune of having you on our podcast once before when Paddy was running it. So welcome back. And with that, would you like to introduce yourself to the audience?

Yaa: Absolutely. My name is Yaa Kumah-Crystal. I’m a paediatric endocrinologist and biomedical informaticist, and I serve as a clinical director of health IT at Vanderbilt University Medical Center here in Nashville, Tennessee.

Q. That’s awesome. Tell us a little bit more—how did you start in this field of being a physician, and what attracted you? How did you move from being a practicing physician into health IT, which is a dry field for many people?

Yaa: I think the luck of it all is that one of my features and bugs is that I’m a complainer. I’m always trying to improve systems around me, always finding opportunities. When I came to med school, we were transitioning from paper to the EHR, and as we started using some of the electronic health record systems, I had a bit of a computer science background. I saw that there were lots of ways we could be improving things—the way data flowed, to not have to repeat entry. I think I just complained, complained, complained to enough people that they said, “You should talk to Kevin Johnson.” He was the chair of our biomedical informatics department. He said, “You think you have some good ideas that can improve things?” And I said, “I think I do.” He was like, “You should consider informatics.” And through luck and happenstance, I got involved in a lot of really interesting projects in improving the EHR. Got my master’s in biomedical informatics and was able to get a position serving in health IT to improve the EHR.

Q. That’s awesome. So talk with us about some of these early experiments in improving EHR, because they’ve come a long way now—including, we were just talking about how Epic is integrating generative AI into the product itself and the platform itself. Share with us what you’ve seen so far, and where you think it’s going in terms of all these new developments.

Yaa: Sure thing. It’s funny—I almost want to break up the EHR into three eras: the paper era, the classic data entry era, and now the generative era In the classic era, there are a lot of great concepts and thinking to improve the way we’re. Capturing information because a lot of the problem was we had these paper records and people would just clone a paper form into a digital form and say, have at it. Go ahead and enter all this data in doctors, and please do a good job on the way and don’t miss anything and make sure you can get everything for our quality improvement and billing.” Doctors were getting so frustrated, and we tried different modalities to improve things. We had dictation, voice technology—we even worked on a project that was a voice assistant for the EHR, kind of like Alexa, early on. You could ask it things and get information back. We collaborated with Epic on a tool called “Hey Epic” to talk with the EHR, but architecturally, it was so complicated to plan for every single way someone might ask a question.

Then generative AI—large language models—entered the scene, and suddenly the concept of naturalistic interaction became feasible. You can now ask for things and get responses in the way we think about them as humans. Is just part of what we do now. So a huge aspect that has been an improvement that doctors have seen right away is ambiance workflows. So being able to just have a regular conversation with your patient, have the AI do all the work of the documenting for you. Yeah. And generally, note at the end so you can pay attention. So the person in front of you has been such a game changer. We literally get emails from providers saying, “This is such a game changer. I get my notes done on time. I can pay attention to my patients.” And it’s getting better every day.
So I think this new era, following the standard digital EHR era, is going to bring some of the most exciting advancements in patient care.

Q. Yeah, that’s good to know. One of the things you hit upon—ambient listening and integrating it into workflows—is resonating across the board. It’s quickly becoming a key technology that everyone is going to use. What others do you see on the horizon as you look around and evaluate developments in the generative AI space?

Yaa: What I see—and I’ve always been interested in—is voice technology and being able to converse with technology the way we’re conversing right now. I think everyone listening to this podcast has probably played with ChatGPT, Claude, and other similar tools. One interesting feature I find is voice mode, where you can ask questions and get responses back. Any of us who’ve played with, you know, the older Siri and all that stuff, know that. Sometimes you ask the question, it’s like, um, I’ll search the internet for you. And you’re like, ah, yeah. But with these tools now you ask it and you get something back, and the thing you get back is related in context to the thing you’re asking for, and you can ask follow up questions. My biggest question is when will we be able to integrate a workflow like That into the EHR, because many times when you are trying to put together the story of a patient to figure out what to do next or what’s been going on with them, you have the questions in your mind. You’re like, when did they last get this test? Or how many times have they, um, been to the hospital for this particular issue? These are questions you have in your mind. To find them, you have to navigate the EHR. You have to say, well, if I wanna figure out how many times they have an encounter, I need to go to the encounters tab and I need to filter for ED visits.

And there are these steps that you’re taking to answer this question, but what if you could just like ask the question and then have the EHR understand you because it’s , it has this transformer architecture in the background that. Understands that can inter interpolate what is you’re saying and feed you back the information, and that takes off so much of the cognitive burden of the work to find the things you need to care for your patient.

Q. That’s so interesting you say that because recently, I’ve been talking with several people—including at VIVE —and one of the leaders I speak with often, Dr. Ashish Atreja, described it as a “headless EMR EHR.” The voice interface comes to the front, allowing you to ask questions in natural language, while the EMR EHR itself fades into the background for the purpose of just gives you the answers. It’s fascinating that you’ve been following this for so long.  Let’s flip perspectives now—how does this look from the patient or caregiver perspective?  And we were chatting about patient portals a few minutes ago before we got on the podcast. How are your paediatric patients and their caregivers embracing this technology, and what are they asking for?

Yaa: This has been a very interesting time as a paediatric provider because our patients are young, and their parents are from an early tech-savvy generation. They’re more in tune with technology than, I think, some of the older patient populations in internal medicine, for example. I’ve actually had a patient say to me, Hey, I ran this through chat GPT, and this is what it told me. Do you agree? And what my relief was that they asked. Me for my opinion, yes, to see if I concurred with what chat GPT was giving them course, but I don’t think it’s gonna be a long time until we’re going to start seeing patients discover more and more that these tools are able to give responses. The only concern that I have is how reliable and accurate these responses are, and what responsibility do we have as a medical organization to work, to develop tools where that they can safely ask for questions and can give back more certified, approved answers  to answer some of their questions.

Concern that I have is how reliable and accurate these responses are, and what responsibility do we have as a medical organization to work, to develop tools where that they can safely ask for questions and can give back more certified, approved answers, uh, to answer some of their questions

Also, circling back to voice user interfaces on patient portals—patients are navigating these platforms trying to find information, but sometimes they just have a question. What would it look like if they could ask the portal: “When’s my next appointment with Dr. McChrystal?” or “Are my labs back yet?” or “Hey, I need a refill on my albuterol—can you send that in?” And then those things just happen as part of the process. Right now, the buzzword is “agents.” So we’re talking about smart agents that can make our patients’ lives easier.

Q. That’s awesome that to think that we are, because I would love to do that. Ask my a question and get an answer back instead of us trying to validate my, my user ID and password. 

Yaa:  And that’s the thing because um, there’s so many, there are really interesting ways to authenticate people like it would know your voice. There are other factors, like it knows that, oh, you’re nearby because it detects like your phone’s nearby. There are just. Many ways. It’s such a fun time to be a nerd right now because there’s so many things that are converging with a lot of technologies we’ve been thinking about for a long time. 

Q. That’s great. So that leads me to my next curious question: in a large system like yours, how do you approach innovation and new technologies? Do you measure return on investment? How do you decide your next projects for, say, this year or the next?

Yaa: Great question. Vanderbilt is a large academic organization. We have trainees and a lot of researchers. I think we’re all aware of the changes happening in the broader landscape around funding and priorities, so we have to be thoughtful about how we implement technology—making sure it serves our mission and benefits our providers. At the same time, we want to try new things and partner with vendors to say, “Hey, we’re experimenting with this, and we have opinions and suggestions for improvement. So I think part of our strategy is to do early pilots. So we can help to steer the direction that these tools are going to be materializing when they go to full production. At the same time, we aim to deeply understand what ROI even means in this context. Are we trying to improve turnaround time for notes? Reduce pyjama time for providers Do we want to decrease the number of messages that patients want to send back and forth? Once you can really define what is you’re trying to improve, then you can figure out how to measure it. And once you can measure it. Then you can really know if you’re making an impact. I think a common concern that people raise with looking at ROI is, well, sometimes the ROI is just that your providers are less miserable, that they’re less out, and how do you measure something like that?

There are interesting metrics that can help us assess whether the cognitive and emotional load on providers is being offset by the tools we provide. Some of these tools aren’t inexpensive, but I think we’ll start seeing providers expect a certain level of expectations that their EHR systems are working at a certain state of the art. EHR systems are working at a certain state of the art. I work with trainees and one of my trainees, she’s finishing up her training and she was looking for a new organization and she joked to me, she was like, well, I sure hope they have ambient there because I’m so spoil right now. I don’t know what I’d do without it.

Yeah, that was a joke that she made because it’s so early days, but I think it’s actually gonna become a truth where if your organization is not on the leading edge and not providing these tools to improve the way your providers work. People might decide that they want to go to a different organization. So, I think the people in the C-suite needs to be giving that consideration. 

Q. That’s great to know. So, you touched upon this, topic of one of your trainees. So, talk with us about education, training and learning initiatives in the community or at the. At the health system, how do you go about that? How do you level up and what are some of the things that you can do about it? 

Yaa:  Yeah, this is a really, really fascinating part. A lot of the new tools that we have help people become more proficient and help people be more efficient. You help you see patients faster, help you write notes faster, and that’s great. But on the other side of the coin. Trainees are not just supposed to learn how to be efficient. They’re supposed to learn how to practice medicine and how to balance logistics. So, we need to find a good balance of teaching them how to use the tools they’re gonna be using in the future, but not to the extent that we’re impeding on. Their actual learning process. In medical school, I took a course on how to write a good medical note because you had to think through the diagnosis. You had to figure out of all the things the patient told you, what things do you wanna keep in the note? What things do you leave out of it because it’d just be too much and that’s distracting. You have to do that synthesis and assimilation yourself. Well, we have tools to do that. Now, does that mean that we don’t teach students what a good note is anymore? Do they just solely rely on something that’s generated? These are decisions that we are going to be making and making in real time because no one knows the answer. cause we’re on the pioneering edge of it all.  But I think we need to not forget that trainees being a medical student, a resident, a fellow. It’s not just about learning how to be efficient. You need to reach a level of proficiency before you need to worry about efficiency. So, we need to make sure that we’re teaching them things they need to be good doctors so they can be efficient, good doctors eventually.

Q. Do you give that into your program over there? How does that? 

Yaa:  Absolutely. Yes. We have medical students and we all sorts of trainees that rotate through and I, I love, love working with trainees because they will ask me questions about things I have never thought about. I’m like. Let me go look that up. Yeah, just think of things in very, very new and interesting ways. 

Q. That’s very cool. So just curious that, you know, many times the focus is on the main system, which is the EMR EHR system. Let’s say a pick  Epic in this case, and then there’s patient portals. It could be MyChart or anything else from the same vendor or, or different providers as well. What other systems is a physician touching upon during their work? Which could be transformed digitally. Any thoughts on any other systems which come into play? 

Yaa:  So many, so many. So communication systems in general, a lot of what we do is communicating. Yeah. In the medical system, we’re still using like fax machines and pages. Yeah. Like we’re singly keeping those industries alive. There’s. So many ways, things could be improved about the way we communicate with other medical centers. Interoperability is a huge, huge challenge that we’ve been trying to tackle for years. Where I see a patient, they’ve been referred to me, their paediatrician has all their records and all their growth charts, and I’m like. Boy, I wish I had those. Yes, and they’ll fax me something and I can like pull up a very fuzzy PDF. I’m like, oh, there’s got to be a better way. And we know there is, we have standards like Smart on Fire where yeah, we were integrated appropriately. Everything that they had could magically show up on my growth chart, that so many opportunities there.

Q. I would like to ask you if there’s any other thoughts you have in closing, and if you were to look in the crystal wall, what you’d see, see coming our way in the next, let’s say, one to two years, and any other thoughts that you would like to share with the audience?

Yaa: I think my crystal ball is going to be really interested in how care can be delivered more at home and how we can get more insights from patients based on what they’re doing outside of the medical centre. So I’m a paediatrician, so I care a lot about kids and the fact that they spend most of their time just in school and the school nurse. That’s our main touch point to anything that’s going on with like our patient’s diabetes and what tools do we have that can help us. Better understand what’s going on with our patients when they’re outside of our medical center walls. So with diabetes, they have these things called continuous glucose monitors. That can help. Blood sugars are periodically. What are interesting ways for me to get the information in real time, to get alerts, to get, stratified risks of the patients that have the different technologies so I can know how to act. How useful would it be to have information like that available for. All sorts of patients for anyone on their watch, just so we can get a sense of which patients we need to make sure that we’re really engaging with so we can help improve their health and on the patient’s side, so they can have a sense of autonomy also, to say like, Hey, I really understand these details about my health. I’m getting these insights from my environment, and I can be proactive about how to stay well. 

Q: Great. So with that, yeah, I think thank you for the podcast being a guest on the podcast second time around like we, we talked about, and thank you for your time. 

Yaa: Thank you so much. I look forward to a third, fourth, and fifth time we’ll talk about Yeah, of course. What we got. Implement our, our new robot army to take care. We hope you enjoyed this podcast. 

Subscribe to our podcast series at www.thebigunlock.com and write 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: Hi Keith. I’m Rohit Mahajan, CEO and Managing Partner of Damo Consulting and BigRio, and also the host of the Big Unlock podcast. Welcome to the Big Unlock podcast. I’d love for you to introduce yourself, Keith—share your background, your role, and what motivates you on a daily basis.  

Keith: That sounds great. I’m Keith Morse. I am a pediatric hospitalist, which means I’m a physician who takes care of children admitted to Lucille Packard, Stanford Medicine’s Children’s Hospital. I’m a clinical informaticist, focused on studying and optimizing the use of technology and data systems for care delivery within a health system, specifically within informatics. My role is Medical Director for Enterprise AI, where I lead the team deploying and evaluating AI and large language models within care operations at the children’s hospital. I’m also an educator. I serve as the Program Director for our Clinical Informatics Fellowship Program, a two-year training program for physicians who have completed their specialty training and want to gain expertise in clinical informatics. 

The program prepares them for roles as CMIOs, Chief AI Officers, or positions in industry. Finally, I’m a researcher. I conduct research in AI and also serve as the Co-Site PI for Stanford’s participation in PEDSnet. PEDSnet is an 11-institution EHR data-sharing consortium that supports large-scale investigations to improve pediatric health.

Q: That’s awesome. I was reading about your background, and as we discussed previously, you did your MBA in healthcare administration and then chose to become a physician.
What motivated you in that direction? Do you still practice and see patients, and how does it work within the Stanford Medicine health system?

Keith: Certainly. I studied Econ and business as an undergrad, and where I was, they offered a combined MBA program. You add on an extra year to your undergrad training and you can start to get a sense of MBA-type training courses. I was doing my pre-med courses then as well, and I had a couple of years between finishing the MBA and starting med school. The job that I had was writing SaaS code for a consulting company, analyzing Medicare and Medicaid data for federal and state government agencies. 

That was essentially a data scientist role before the term “data scientist” was commonly used. And I loved it — understanding what data you have, how you can use it to answer questions, how you summarize it, and how you present it back to the requester. Those are such core things. I was in medical school in Philadelphia at Thomas Jefferson and then did a pediatrics residency in Phoenix, Arizona. During both of those times, I got involved in a couple of research and operational projects where I served as the data scientist. Writing R code, but also getting a sense of, instead of just being a data consumer as a data scientist, I was also a data producer. 

When you start delivering care, you are the person standing at the bedside, talking to the patient, making decisions, and also writing a note, ordering labs, and ordering follow-up. All of that is the beginning of the types of data that eventually get billed and trickle into databases that get used. In residency, I started working with our CMIO to analyze the data that was available through our EHR and really started enjoying it. 

Then I joined Stanford for their Clinical Informatics Fellowship and have been on faculty since then. We are fortunate here at Stanford Children’s to have a forward-looking CMIO, Dr. Natalie Pageler, who supported both myself and a small team to start building out our organization’s capabilities in operational AI back in 2020. We have been working at this for the last five years or so. Obviously, when ChatGPT hit, we got a whole lot busier, but it wasn’t the beginning for us. The processes we have in place now are built on that early investment.

Q: That’s pretty early to start in 2020. That’s a solid five years of experience with various kinds of AI implementations. How do you approach the business problem? Do you have specific use cases, and what do you do with them? How do you decide where to put your energy, time, and money?

Keith: I’ll answer this in three parts. First, I’ll talk about our process for identifying use cases, and then give a brief overview of two use cases that are relatively mature. First off, I love talking about use cases. In some ways, it’s a mythical concept. When operational AI folks get together at conferences or meetings, people sit around and whisper, “What are your use cases?” The reason is it’s such an important topic because we are, in essence, asking: In what ways have you found that AI is valuable? Where is the juice actually worth the squeeze? It’s easy to have research papers or proof-of-concept pilot projects showing that AI is theoretically useful. But when you have to make it work for an enterprise indefinitely, it’s a much different problem to solve.

We think about where AI can bring value without it costing $50 million or taking five years to implement. These are the types of considerations we focus on. This is actually a really hard question because three separate areas of expertise must align to arrive at good use cases.

The first is understanding AI technology in isolation. What is a large language model? What is a deep neural net? What is logistic regression? What can reasonably be expected of that technology? 

The second is understanding what infrastructure is available at my organization to support those tools.
It doesn’t help to have a sophisticated AI tool if you don’t have the data available, the compute power, or if the data isn’t available at the needed cadence for the algorithm. Another tricky part is that AI infrastructure is invisible. You can’t walk into a room and see where the AI lives. It exists in the ether. You have to be plugged into the organization’s IT structure to understand your current infrastructure. And it’s always evolving. We’re growing our infrastructure, making investments. It’s different now than it was two years ago, and it will be very different five years from now. 

The third and by far most important — and the hardest — is that workflow expertise does not solve nebulous, non-specific problems. It must solve a specific problem for a specific human, in a specific job, in a specific part of their workflow. We summarize all of that by saying “workflow,” where in a person’s workflow, AI can potentially be useful. The challenge is that healthcare is a diverse, complicated entity. My health system is different from the health system down the street, different from those in Cincinnati or Texas. 

Even within my organization, there are so many different workflows that no one person understands all of them. Our process for learning about these workflows — and this is something my team spends a lot of time doing — involves talking to people within our organization to help them tell us what problems exist and how AI could potentially help.
You would think that’s easy. You might think you can just talk to somebody and figure it out.
It turns out to be surprisingly difficult. The reason is this: if you imagine a standard organizational hierarchy — with director, supervisor, or executive oversight at the top, then managers, and then boots-on-the-ground staff — you find some patterns. This could be in Revenue Cycle, Supply Chain, Sanitation, or any other department. 

Usually, when you talk to the more senior folks, they are very excited about what AI can provide: speed, efficiency, safety, uniformity. They are generally onboard. Being a manager is different than being a boots-on-the-ground employee. Leadership often doesn’t understand what happens on a day-to-day basis in their teams. That’s not a dig on leadership; it’s just a fundamentally different job. People who oversee big teams — it’s too much to know. You can’t look to leadership to tell you where the problems are. Also, leadership is not the group whose jobs will be directly supported by AI. It’s going to be the people working on the ground. You have to talk to the folks whose workflows are potentially going to be impacted by AI to understand exactly what their workflows are and where AI could be helpful. Usually, the way we do this is to start at the top and work our way down. We get buy-in from leadership, then get passed to middle managers. They might suggest three or four areas that are worth exploring. We then meet with each of those teams and ask specifically what it is they do, what data they look at, what problems they encounter, and how AI could potentially support their work. Our hit rate in those meetings is relatively low. Most problems are not solved by sophisticated AI. There could be other, simpler solutions that work just as well. Often, our biggest takeaway is directing teams to existing data and reporting tools that can solve their problems without the need for advanced AI. But through this process, we do identify good use cases, and that informs our future efforts. 

The main takeaway is that no one outside your organization can credibly provide a guaranteed use case because they don’t know your AI infrastructure or your specific workflows. I don’t even know all the specific workflows within my own organization, so how could a third party or a hospital elsewhere know? There is a long-term role for internally identifying use cases, because they aren’t easily transferable from other institutions. One area where this is starting to shift is when AI is embedded within your electronic health record (EHR).

For example, our EHR is Epic. We use some tools they provide, like drafting responses to patient messages. This is a use case gaining traction nationally because Epic controls or manages all three areas I mentioned earlier: First, Epic understands that drafting patient messages is within the capabilities of a large language model. Second, all the infrastructure needed to use this drafting tool exists within Epic — no extra resources are needed. Third, the workflow for responding to patient messages is entirely within Epic, meaning they have a good understanding of the process. If the workflow required multiple steps outside the system — reading something in Epic, going to another machine, speaking to a patient, and coming back — it would introduce complexity and variability. But responding to a patient message is done entirely within Epic, so the workflow remains visible and consistent. That’s why use cases like this are more broadly transferable — because all the necessary components are self-contained.

Q:  Could you please share with the audience your experience with some of the large language models and where you have been successful in implementing them?

Keith: We have a couple of implementations, and we tend to publish most of the work we do, so a lot of it is available in the literature. One of our major use cases is using machine learning to help identify confidential content in teen notes. There are laws in the state of California and in most other states that protect teens seeking care for certain types of sensitive issues like mental health, sexual health, and substance use. In California, there are explicit laws that prohibit providers who care for adolescents from informing the teens’ parents that they are receiving this care. The reason for these laws is that there is strong evidence showing teens are more likely to seek care for sensitive issues if they are assured confidentiality. The challenge is that federal rules also require health systems to make patient records available to teens and their parents.

The 21st Century Cures Act requires that health information be available without undue effort. Basically, this means parents must be able to access and review records, usually through a patient portal within the health system. Anyone who takes care of adolescents faces a challenge: the federal government says you have to share all of the patient’s notes with the patient and their parents, but state laws say you can’t share portions related to three sensitive topics — mental health, sexual health, and substance use. We address this with what we call a “confidential note type,” where providers can document sensitive information. These confidential notes can be excluded from what’s shared with parents, and that system works well. Confidential information in these notes that we are theoretically sharing with the parents and families. This is a great application for large language models because it essentially processes large volumes of text to identify certain definable concepts. This is one of the projects we worked on before large language models came out. We developed a bespoke NLP model to help identify these concepts. We have replicated that analysis using large language models, and the way that we use it now is as a retrospective audit and feedback mechanism. We process the notes of different divisions, and then we can see who in the division is documenting in a way that’s potentially problematic. We can bring that back to the individual and also the leadership and say, “Hey, just FYI, we noticed that your notes contain a lot of information that would run afoul of the California state privacy laws.” 

We can then help providers improve their documentation. Often, what it is there is some sort of smart phrase or automatic pulling in of patient information that’s considered confidential. We just sort of edit or help update somebody’s note template, and the problem goes away . It’s those types of nudges that we think are helpful and really get at what we want, which is to be able to share patient information with the teens and their parents. We want this stuff to get out, we just need to do it in a way that is thoughtful and responsible.

Q: How about any other use cases?

Keith: Hospital operations. We have one that is not pediatric-specific, and it’s around tracking and quality metrics. This is work that we haven’t published quite yet, but health systems, as you well know, are on the hook to report on various quality metrics. One of the quality metrics is around surgical site infections—identifying what percentage of patients have an infection at the site of surgery within 30 or 90 days after the procedure. That’s a marker of surgical quality, infection control, and lots of really important things for health systems. We have teams within our hospital whose job it is to both track these metrics and then identify ways that we can intervene and identify bundles. That we can introduce to help improve these metrics. But there is a core function of tracking and identifying whether a surgical site infection occurred. The way that works is that there is a team of folks who essentially read the patient’s chart after a procedure to identify whether or not a surgical site infection occurred. Reading patient notes about how the patient came back to the ER with what looks like a surgical site infection, where they were prescribed antibiotics by their PCP—those types of things would flag as an identified infection. 

It’s a ton of work to have a human read every chart following every surgery for 30 to 90 days. So, we developed a process by which a large language model is used to review the notes in that relevant time window. We ask the model a relatively straightforward question: “Do you think this note contains evidence of a surgical site infection?” Very simply, that. We provide a few examples to help it understand what are some key indicators. We do that for every single note the patient has in that post-surgical time window and come up with a score, basically, what percentage of the notes the large language model thinks refer to a surgical site infection. When we run this on our historical data, we see that the large language model thinks somewhere between zero and 60% of the notes refer to a surgical site infection. What we can do then is draw a threshold, say 5%. What we do is have the human review everything above 5%. We still need a human to understand the nuances of what counts as a surgical site infection—reading the lab results, reading the imaging results. 

We need a human to identify the true positives, but there are many true negatives. Somebody who never has an infection, does great, doesn’t come back to the health system—those probably aren’t worth a person’s time to review. If we can identify the true negatives, the reviewer could spend more time on the true positives. What’s fun about this is that the numbers are exceptional. For our preliminary data, looking at 2023 and 2024, if we set the threshold at 5%, the reviewer would be reviewing 70 to 80% fewer cases. We would miss somewhere between two and five cases. It’s not perfect, but we are nudging toward a world where we can spend our time reviewing the cases that are problematic and positive, and less time filtering through the vast majority of surgeries that don’t have an infection or an issue, moving those to the back of the queue. We certainly aren’t replacing a person. We still need the person to be involved, but we are helping that person focus their energies on the cases where their expertise and their interpretation of what is actually happening is maximized.

Q: Yes, that’s great to know. As we all know, Gen AI and LLMs are becoming all-pervasive. You have a large workforce at your health system, people at various skill levels, and now they have to either use some of these systems that are going to be deployed or are already participating in what you described as a use case process. If there is more appreciation of Gen AI, LLMs, and AI, they would be in a better position to do their job and embrace it. So how do you go about learning and development and upskilling the workforce? What are your thoughts about that?

Keith: That’s a great question, and I think it is foremost on the minds of health system leaders who are hoping to use AI in any meaningful way. The reason it’s so important is that it is totally unrealistic to expect somebody to use a tool that they are unfamiliar with, particularly when that tool is something as amorphous and multifaceted as a large language model. If you think about it, if we use the analogy, large language models distilled down are basically like office productivity tools. Think Microsoft Excel—Microsoft Excel has been around for 40 years. I used it for middle school projects. My mom used it when she was working at a bank in the eighties. 

Most people, by the time they enter the workforce, have experience with using Excel. It’s not unreasonable to have that be a requirement of the job, or when Excel gets augmented in new ways, for people to be able to jump on it. It’s relatively straightforward. We have none of that with large language models. Expecting people to use these things without a ton of training is really unfair and unrealistic. That applies up and down the organizational chain. What I mean is that leaders, just because you are an executive VP, you have exactly the same two and a half years of awareness of these large language models as everyone else. Historically, people could turn to coworkers or get extra training, but that doesn’t exist here because it is so new throughout the organization. You make an excellent point about needing to upskill our workforce and attaching an experiential component to that training. 

Sometimes we hear about organizations using online modules for training, but I don’t think this is the sort of thing where watching some videos will really give you the insight you need to understand how these tools work. The reason it’s trickier in healthcare is that it’s one thing to tell somebody, “Hey, go play around with ChatGPT. See what you learn.” But we don’t want people to use ChatGPT to come up with recipes or poems in pirate. We want people to use large language models for their job because we suspect there are major productivity benefits that can come out of this. In a health system, most people’s jobs involve PHI—patient health information. You can’t put PHI into public models. So we need to make things available. We need to make a PHI-compliant large language model available to our employees. 

At Stanford Children’s and our adult hospital, we have made a large language model that is PHI-compliant available to our entire workforce. We’ve had it available for about a year now. Our IT department is called Digital Information Solutions, DigiIS, and our chatbot is called Ask Digi. The idea is to encourage people to start experimenting with an appropriately provisioned large language model to figure out how it can make their life better. I have no idea how somebody in a revenue cycle role could use large language models to make their life better. Most folks in that role don’t know yet either. We’re going to figure it out in a couple of years, but we have to let people experiment to learn that. 

We have three broad approaches for training: One is online modules and training. We have a couple of training tools, both generally about what large language models are and about Ask Digi and the local tools we have available. The second is prompt engineering workshops. Two of our clinical informatics fellows have developed a two-hour hands-on, in-person workshop on how to develop prompts and how to know that your prompts are doing what you want. Engaging with a large language model through a prompt is a totally new skill. Starting to get people to understand what is required in a prompt, lowering the fear factor, and giving people the confidence that you don’t have to be a computer scientist to prompt these models—it’s relatively accessible—and that comes through education sessions. The third is having a local champion or expert who gets involved in a pilot project and then brings that insight back to their teams. For example, in our work with surgical site infections, we are working with the folks in infection prevention and control. 

They are seeing the ways that large language models are helpful and not helpful. They are learning alongside us. Those folks will now become the experts in their team for how these tools can be used. Hopefully, that will propagate into more training. Engaging folks in these pilot use cases is helpful not only because you learn about the use case, but also because you train the person in that area, creating ripple effects across the organization.

Q: That’s great to know that there are so many initiatives for upskilling and learning, and development in the organization. One other topic that I thought I would touch upon—we talked about it a little—is PEDSnet. You mentioned it early on in the conversation today. I would love for you to share with the audience how this helps in terms of interoperability and data sharing, and any new aspects it might bring as well.

Keith: Definitely. Happy to. Just for a quick background, PEDSnet is a pediatric EHR data-sharing consortium that has been around since 2014–2015. It’s primarily run out of CHOP—Children’s Hospital of Philadelphia—which serves as the coordinating center.We have a relationship with them, as all members of the network do, and we send them a harmonized version of our EHR data four times a year. The harmonization is based on an OMOP model. The Odyssey community is an open-source EHR standardization initiative, and PEDSnet has adopted their common data model as the bedrock of what we use. 

We have some minor modifications that are specific for pediatrics and for care in the U.S., but it enable us to share data to a central location and conduct studies with a volume of patients that is unmatched elsewhere. About 15% of the children in this country are represented in our PEDSnet database. It’s a chance for us to do large-scale studies at a fraction of the cost it would otherwise take to develop these types of tools. As we move into the world of large language models, it’s not hard to envision a future where we are able to have a large language model help us process unstructured information from these different sites, extract relevant insights from notes, and conduct large-scale studies using unstructured data. That’s not here yet, but it’s in the near future. It’s really exciting to think about the potential.

Q: That’s awesome. Talking about the near-term future and potential, if you were to look in the crystal ball, what are some of the things that you see coming down the pipe? Agentic AI that people are talking about, virtual nursing, and ambient listening. There’s so much going on. What do you think are some of the big things coming our way?

Keith: Definitely. We are rolling out a pilot of ambient listening and have a similar cue as many organizations for things that we’re going to be adopting in the near future. Taking a slight step back from a regulatory and oversight perspective, it’s important to remember that those types of issues aren’t going away, regardless of how excited we are about the technology. Any investment in technology requires an understanding of the upsides and the downsides. I think we’re currently a little over-indexed on the upsides of this technology. We’re very excited. 

From a regulatory and oversight perspective, it’s important to remember that those types of issues aren’t going away, regardless of how excited we are about the technology. Any investment in technology requires an understanding of the upsides and the downsides. I think we are a little over-indexed right now in terms of what the upsides of this technology are. We’re very excited about what it can do now and what it can do in five years. We’re starting to see some efficiency gains. The feedback, particularly around DAX and other ambient listening, is generally very positive. We are less concrete about the downsides, and what I mean by that is there’s lots of talk about potential issues that come with AI. 

In the past, we have relied on federal or state agencies to provide oversight to make sure that those downsides aren’t present or are appropriately mitigated and recognized. AI, and particularly large language models, is proving very difficult to regulate because it’s such an amorphous entity. I think it is unrealistic that we’re going to see a robust regulatory system in the near future. What that means is that the burden for making sure this technology works is falling on the provider, and that’s great—until it’s not. What I mean by that is the burden is on providers to use these tools appropriately. At some point, we’re going to see a lawsuit from someone who claims they were harmed because of a large language model’s involvement in their care. Once that happens, we are going to get a very concrete piece of evidence about the types of downsides inherent in using these tools. We haven’t reached that point yet. 

Right now, the downsides are so amorphous that they’re easy to ignore. Once there is a price tag attached to the cost of mistakes, then things become different. If that price tag of a mistake is enormous, the overall value of these tools could change substantially. We know that, particularly early on, we are potentially introducing risks and mistakes with the use of these tools. Even if the output of a large language model is 99% accurate and you have a human in the loop who is reviewing it, and their review is 99% accurate, there are still errors that are present. Part of the reason I bring that up is that at Stanford, we take it very seriously. We are ultimately responsible for the use of these tools, how they impact our providers, and how they impact our patients. Nobody is going to take that responsibility from us. That is appropriate, but we are building systems and processes with that worst-case scenario in mind in order to prevent it from happening.

Q: That is very cautious. So with that, I think this is a great session. Any closing remarks?

Keith: Thank you so much for providing this opportunity. It’s exciting to be able to talk about the work that we’re doing here. I think there’s lot to discuss regarding pediatric implications, so maybe we’ll find some time in the future to talk again.

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.

Rohit: Hi Angelo, welcome to The Big Unlock podcast. We’re so happy to host you, and thank you for taking the time to join us today.

Angelo: Thank you, Rohit and Ritu. It’s an absolute pleasure. I was delighted to be invited. I’ve listened to some of your episodes—you have a fantastic program. It’s an honor to be part of what you’re doing here.

Rohit: Thank you! That’s awesome. I’m Rohit Mahajan, Managing Partner and CEO at Damo Consulting and BigRio, and also the co-host of The Big Unlock podcast.

Ritu: Angelo, welcome. Absolute pleasure having you here. I’m Ritu Roy, also a Managing Partner at BigRio and Damo Consulting, and co-host of the podcast. We’re looking forward to an invigorating discussion today.

Angelo:  Thank you again both for the invitation and the opportunity to speak with you today. I really like what you’re doing with this show. I think it is speaking to issues that are so important right now in healthcare, in innovation, in healthcare management.

So, it’s a tremendous pleasure. I can tell you a little bit about who I am and what I do, and then we can jump deeper into the conversation. But my name is Angelo Zo. I am trained as a pediatric cardiologist and still spend a little less than half of my time in clinical practice. And my clinical practice includes patients from prenatal life all the way through to adulthood who have primarily congenital forms of cardiac disease, but also acquired forms of cardiac disease.

And my clinical practice keeps me very busy. It’s a very interesting field. It’s a field where we have many applications of technology, which I think are really interesting. It’s a—pardon me—a field that includes some procedural medicine, some diagnostic medicine, and we work very closely with other specialists, including surgeons and anesthesiologists and clinical care physicians.

So, it really is a fantastic type of clinical practice, and it’s one that many people may not be familiar with, but is a really important part of what we do in the realm of children’s healthcare.

The other part of my work is as an administrator. So I am the Chief Medical Officer for what we call the Duke Health Integrated Practice. So I am here at Duke University Health System, which is based in Durham, North Carolina.

The Duke Health Integrated Practice—you can think of it as the faculty practice of our health system. It’s actually a bit more than that. We have about 4,200 physicians and advanced practice providers—so our nurse practitioners and physician assistants are counted within the faculty practice as an important part of what we do.

And we really are the specialty practice of the health system. We have another organization that manages primary care as their primary book of business—no pun intended. Primary care, primary book of business. So that is not part of my purview, but I work very closely with that organization. That organization has its own Chief Medical Officer, but I’m the Chief Medical Officer that interacts with our 18 clinical departments—everything from pediatrics to internal medicine to OB-GYN to radiology to surgery to dermatology, et cetera.

And I help to develop strategy—primarily for our ambulatory platform—and also implement and operationalize clinical innovation procedures that help us improve access, that help us open up our door, so to speak, to new patient populations, to new areas of practice—whether it’s medical or surgical, or diagnostic or procedural. So I have a broad responsibility here and a broad engagement.

And I think the final thing I would say is that our health system is associated with—obviously—Duke School of Medicine, which is a fantastic medical school, always considered a top-tier medical school. So we have a very significant mission around education, around training, around research, and around advocacy. And all those functions are directly connected to the clinical mission. So everywhere we practice medicine, we also want to innovate. We want to do research. We want to teach. We want to educate. And we want to advocate as well. So even though my role is primarily a clinical leadership role, I get to work with other leaders in all those other areas, which makes my job really multifaceted and really exciting.

Rohit:  That’s awesome. Thank you for that introduction, Angelo. You know, it’s been quite a journey for you. You’ve been with this health system for a long time, so would you like to share your journey with the audience? Like how did you get started? What interested you, and what are some of the new things that you are working on? And also, talk to us a little bit about the patient population in the local area.

Angelo:  Sure. No, I’d be happy to. So in terms of my journey into healthcare and healthcare administration, it actually started quite early when I was finishing up my fellowship training. I was offered the opportunity to build a branch of our academic pediatric cardiology practice away from the mothership, so to speak—to build essentially a large satellite office.

So very soon in my career, I got involved in practice building, practice management, and healthcare administration. Within about a year, I took over the medical directorship of this practice, and over the course of about 10 years, grew the practice from a very small operation with just a couple of physicians seeing a few dozen patients a week, to what is now a very large, multi-specialty practice that has about three dozen medical and surgical specialists coming in and out of our doors, seeing thousands of patients a week.

Very proud to have been part of this project from its initiation, from its conception, and through its phases of growth. Along the way, I had very significant hands-on training with executive leadership, with management, and also with day-to-day clinical operations, which became an area of interest to me.

I was eventually offered the opportunity to take on a new role, and that role was as the Vice Chairman for Clinical Practice for the Department of Pediatrics. And in that role, I took what I had learned at the local practice level and brought it to an entire department, and became the operational and strategic leader for everything we do in our Department of Pediatrics, which includes about 250 or so clinical faculty, another 50 or so research faculty—primary care, specialty care, diagnostic care, urgent care, emergent care, inpatient, outpatient—all different aspects of our care.

I did that work for a little less than a decade, and I’m actually moving away from that role now, because last year I stepped into yet another role as the Chief Medical Officer for the Duke Health Integrated Practice. So I’ve been in that role now for a few months, and I hit the ground running when I took on the role—immediately, as in my first day on the job—had to help with some challenges we were going through as an organization.

It’s been very exciting, in just these first few months, to begin to see the opportunities that I will have. In terms of—you know, you asked about some of the things that we’re working on—and we’re going to talk today about some clinical innovation, and that’s been extremely exciting to work on.

Rohit:  That’s awesome. Yeah, so that leads us right into our next topic. What are some of the clinical innovations or challenges you’ve faced that you were able to overcome? And what are some of the digital health programs you’re getting involved with? Anything you’d like to dive into and share more about?

Angelo: Yeah, fantastic question. So, my first day on the job, I had to address the audience of our entire practice—all the clinical leaders from across the health system—and I told them that I saw my role as Chief Medical Officer as one who is there to support clinicians in their day-to-day work.

My platform rested on the idea that I want to bring aid, tools, and tactics into the practice that actually help people do their work. In the last few years, we’ve been facing workforce challenges—people feeling burned out, disillusioned, like cogs in a machine. I want to bring the joy back into the practice. And I think the way to do that is to allow physicians and other clinical providers to spend as much time treating patients as they can.

So we’ve identified areas where we can innovate—where we can bring technology to bear to improve the experience of delivering care for our doctors, nurse practitioners, physician assistants, and other clinical personnel.

One thing we’ve done—something very concrete and in partnership with a very innovative healthcare technology organization—is apply what’s called ambient listening technology. And we’ve done this in a way that’s been thoughtful, in response to a specific problem: how can we alleviate our clinical providers of some of the burden? In this case, the burden of documentation.

We know that physicians and other clinical providers today spend a lot of time in front of a computer—often while they’re in the room with a patient. This can make people feel like their job is data entry. We don’t want the job to be data entry—we want the job to be managing patients. We want it to be engaging. Spending that face-to-face time matters, because we know so much of diagnosis comes from the history, from having an in-depth conversation and hearing the patient’s story.

Yes, we can do a lot with diagnostic testing, but at the end of the day, it’s about engagement with the patient. That’s how we build a differential diagnosis. That’s how we begin to form our assessment and plan.

So what we’ve done is bring this ambient listening technology into the clinical encounter. The tool records the conversation between the clinical provider and the patient, and from that back-and-forth, it creates the encounter documentation.

It may seem like a simple evolution of the standard tape recorder-to-human-scribe model, or even having a human scribe in the room. But this is truly the modern version. The conversation is filtered through a natural language processing algorithm, and the finished product is something that’s usable as clinical documentation.

This has been a very exciting opportunity for us to leverage technology at the point of care and remove some of that documentation burden.

Rohit:  That’s a great use case—one of the hottest we’re seeing in many health systems, Angelo. Tell us a bit more.
You mentioned some metrics before we started. How do you measure the success of this wide-scale implementation? And you also said it’s integrated with the EMR/EHR system. These are critical for those still thinking about this.
Many have already taken the plunge with varying degrees of success, but you’ve really embraced it and rolled it out broadly. Any key takeaways from your experience that were instrumental?

Angelo:  Yeah, fantastic questions—thank you.
I’d start by saying our pre-implementation data was both qualitative and quantitative.

From a qualitative perspective, we were literally hearing from our doctors, nurse practitioners, and clinical providers that they were losing the joy of practice. These are people who’ve invested so much time and energy into becoming clinical providers. They want to engage with patients—that interaction is a special opportunity.

But over the last several years, we’ve created systems that make them feel disconnected. These electronic health records require tons of digital input—typing, checking boxes, clicking through screens.
At the same time, we’ve opened the door to patient interactions outside the visit. They’re now answering after-hours messages, doing telemedicine and telehealth visits, which are great but can also add stress.

So it starts with qualitative data. Are our people enjoying their practice? If not, we have a problem. We saw workforce challenges—many physicians and nurses left during the pandemic. We need countermeasures.

On the quantitative side, we look at “signal” data—engagement with the EHR: login times, time spent in the system, responsiveness to messages, how quickly notes are closed, and whether they meet patient expectations.

We also track provider ratings. We ask questions like: Did your provider listen? Know your history? Speak clearly? Explain things well? These bridge qualitative and quantitative—they’re numeric but reflect the experience.

If a provider has low ratings, we drill down. Often they say, “I just don’t have enough time with my patients.” And it’s not just visit length—it’s quality.
Those were some of our pre-implementation metrics.

Another key part of our approach: bringing our performance engineers to the practices. We call them performance excellence engineers. They’re incredibly skilled at turning data into actionable information.

But in recent years, they’ve been working remotely or from centralized offices—not their fault, it’s how we structured things.
We’ve found that when you put them at the point of care—whether it’s the OR, ED, primary care, or endoscopy suite—they really begin to understand the work. That’s when they can help translate practice data into something actionable.

This is classic business school 101—Toyota production system. Bring the people doing the work into the improvement process. We had drifted from that. Now we’re returning to it and seeing the benefits

Ritu:  That’s awesome. Dr. Angelo, I was at a conference recently where Abridge—one of the main players in the ambient space—shared that 81% of notes didn’t need to be edited by doctors. That led to a huge jump in satisfaction. Are you seeing similar numbers?

Angelo: Ritu, this is a very interesting question, and it’s interesting for a number of reasons. First of all, full disclosure — we are using Abridge, so that is our partner in this space. One thing that’s been wonderful about that relationship is they’ve craved our feedback about the product.

They want to iterate, and that’s why they were such a natural partner for us — we share that DNA around innovation. They want the product to keep getting better and better as we use it.

To your comment about 80% of the notes not requiring editing — this is a really interesting data point that we are looking at. We’re actually tracking the number of times notes do or do not get edited, and there’s a signal in both directions that we need to be careful about.

We need to be careful about the people who never edit their notes — because that’s one kind of story — and also ask about the people who are always editing their notes.

I can tell you that in my practice — and I have more than 20 years of clinical experience — and as someone who likes his notes done a very specific way, I’ve actually been quite happy with the quality of the content.

And when I have edited, it’s been primarily not to correct factual errors, but to change the language to better match my personal style. So it’s really been more about the aesthetic of the note rather than the content itself.

I’ve found the content to be quite good. And when it hasn’t been good — this is the other nice thing about it, Ritu, you mentioned earlier that it’s embedded in our EHR — we can provide feedback immediately.

So if I see a note that didn’t come out the way I expected, we have a chat box — we can go right into it and talk directly to the development team and say, “This is why I don’t like this note.”

We can also see the stems — the original stems of our conversation — and how the natural language processing model created the note from those stems. And again, having that direct pipeline to the developer has been critical.

Just a few weeks ago, the Abridge team came to us. They did an extensive two-day site visit — and again, they wanted to hear directly from us, from our doctors, from our clinical providers — how they were engaging with the technology, what was working, what wasn’t.

With very rare exception, most of our people have said — and I hesitate to use the term “game changer” because everyone uses it and it’s almost lost meaning — but this has been disruptive in the best possible way.

This has enhanced our practice. I’m able to close my notes. I’m able to leave the office at the end of the day not having to go home and work after dinner, or work in my pajamas, or while I’m trying to do something else — wanting to spend time with my kids, or being taken away from my hobbies and interests.

This is allowing me to close the books at the end of the day, feeling good about the quality of the content.

And again, so much of the upside is that ability to spend time — and we’re really interested to see how we improve on that one key metric: Did your provider spend enough time with you? I’m going to be really interested to see how that improves with the implementation of this technology.

Ritu: Yeah, so in the same conversation they also mentioned that some of their physicians are seeing Russia traffic for the first time, which can be, you know, good or bad depending on how you look at it.
So to your point about being able to close the notes and go home — that’s really good to know.

Angelo: I think it is. One thing we really like about this technology is the modularity. You can use it for certain elements of your note and not others. You could use it for the entire note if you like, and that flexibility has helped adoption.

If you’re really good at documentation and only want to use it for your history of present illness, you can do that. We’ve encouraged everyone to try it, and almost universally, people have at least given it a shot.
Most who’ve tried it have stuck with it — and we’re tracking that with metrics. We know what percentage of users only tried it once, how often it’s being used across different note types, etc. We’re seeing very strong onboarding of the technology. We’re not at the plateau yet — we’re still ramping up — but it’s exciting to see adoption growing.

Rohit: Yeah, shifting gears — I know we haven’t touched on this yet, but Angelo, what are your thoughts on value-based care? Is that something your practice is actively thinking about?

Angelo:  It’s very important—and it’s part of what we’re trying to do. I’ll tell you, in this part of the country, the Southeast United States, I think the move toward value-based models—bundled care and similar approaches—has been slower.

When we look at our colleagues in the Northeast or on the West Coast, we see folks who’ve been very innovative and progressive in this space. We’re certainly trying, and I think there are a number of reasons why adoption has been slower here—probably more than we can cover in this conversation.

Nevertheless, we’re very excited about the opportunities to improve care efficiency and reduce costs. We’re looking at how to bundle services in a way that clearly shows value—taking that classic formula of quality divided by cost. We believe our quality—the numerator—is very, very high. Now it’s about shrinking the denominator and reducing costs.

There are ways to do this that may involve technology, but also some analog solutions too.

We work very closely with payers and are really interested in what they have to say. Over the past few years, we’ve built strong, bilateral relationships with payers to understand what’s on their radar in terms of quality and performance metrics. At the same time, we want to hear from our own clinicians—what’s important to them?

Sometimes that aligns with what payers are looking for, and sometimes it doesn’t. But when it doesn’t, we have strong enough relationships that we can go to payers and say, “Hey, this matters to us.”

For example, our pediatricians have raised concerns about adolescent immunization rates. A payer might be more focused on vaccines in the first year of life or among older adults—like meningococcal, pneumococcal, or RSV vaccines for seniors. But we’re hearing from our team that teens aren’t coming in for pre-college immunizations.

In most cases, payers are at least willing to have that conversation.

We’re also thinking about how to clearly demonstrate our value proposition—especially with so many large employers moving into Central North Carolina. With the research triangle and three major universities here, this region is growing fast.

We want to make sure that when employers include us in their health plans, we can go to them and show the value—whether it’s for hip replacements, organ transplants, or more common services like primary or preventive care.

Historically, large academic medical centers haven’t always been great at cost containment. But we’re learning. We’re identifying opportunities, especially around clinical operations. There’s a lot of potential for efficiency there—and when you focus on that, you can truly begin to lower the overall cost of care.

Ritu:  That’s awesome. I would like to ask you about AI education and the trustworthiness of AI. Did you face any barriers from your physicians on those regards? Sometimes we see that physicians can be reluctant to try new solutions. There was a New York Times article that showed when ChatGPT was diagnosing on its own, it was much better. But when the physician came in, they brought in their own biases, and it actually did worse because they didn’t trust the AI and took it in a different direction. How have you made sure everyone is educated and had the right context within your organization for such a wide rollout?

Angelo:  Again, fascinating question. We could spend a whole podcast on just this. We have certainly needed to address this issue, but I’ve been pleasantly surprised that most of our clinical providers—physicians, advanced practice providers, physician assistants—have asked questions in a very thoughtful way. They’re not necessarily showing hesitancy, but they are trying to keep pace with developments in this area. They want to ensure we’re good stewards of the technology.

They’ve asked questions like, “What are we doing with these recordings? Are we using patient data to improve the system? Are we training the language models with our information?” They’ve also wanted to know about the process for obtaining informed consent. And yes, we are certainly asking for permission every time we use this technology, in every instance.

One thing I’ll mention is that we have a very robust structure here for digital innovation. We have several teams, and one that focuses specifically on AI. They’ve done great work ensuring our faculty are educated on basic terminology around AI, natural language models, and generative AI. Most people feel they have amazing colleagues providing these educational materials.

Another key step was the pilot program. Before rolling out anything, we did a well-thought-out pilot. We tested a few technologies and opened it up to a large number of clinicians across the entire system. This was incredibly beneficial because during the pilot phase, which lasted several weeks, clinicians provided us with feedback. We conducted pre-, intra-, and post-pilot questionnaires to evaluate all the technologies and assess what worked and what didn’t.

During the pilot, we also did a lot of education. We had review modules, town halls, and went to all 18 of our clinical departments to answer questions. Our data and IT teams, including many doctors, were also part of those conversations. That was a huge benefit because when you have IT leaders who are also physicians, it builds a lot of credibility with the clinical audience. I think that was key to winning people over.

Rohit:  Yeah, I’ll chime in with another one, Angelo. We’ve all been hearing about agentic AI, and how agents are the next big wave in generative AI. We’re currently working with several clients to define use cases for implementing agents in the workplace, as they’re talking about creating a new digital workforce that includes both humans and agents. Any thoughts on that?

Angelo:  It’s an area that’s going to grow exponentially. Right now, we’re testing the ability to use AI to screen inbound messages. As I mentioned earlier, one of the outcomes of opening up digital access to our patients is that we’re receiving thousands of messages per day. That’s a good thing—we want our patients to communicate with us—but it’s a lot of work, real work.

We have an AI innovation research team working on models that can review these messages and sort them into categories. For example, distinguishing between a message that just says “thank you for the great care” and one that says “I need a prescription refill” or “I need an urgent appointment with my orthopedic surgeon.”

The next stage is not only sorting these messages but starting to build at least the skeleton of a response. This is a really complicated problem, even for humans to do. But what’s exciting is that we have people who recognize that this is another way to alleviate some of the clerical burden on our clinical teams. If we can reduce the number of messages that require human attention, we can focus on the urgent ones. The others are still important, but they’re not as urgent. If we can triage this, it will be incredibly helpful.

And to your point, Rohit, I think the future will involve a synergy between human and technological capital. If we can create that synergy, perhaps people won’t lose their jobs but will take on new kinds of work. That’s the future I see.

I’m old enough to remember the early days of the internet, and the doom-and-gloom stories that came with it. What we’ve learned is that we need to approach these tools with an open mind and recognize that we can use them responsibly if we choose to. It’s about figuring out the most responsible and applicable use cases, and being good stewards of the technology.

I’m encouraged because many people in the space, especially those applying these solutions to healthcare, really understand the practice of healthcare. That’s why partnerships with institutions like Bridge, or technologies like Copilot, are so important. When you have a strong clinical-technological partnership, amazing things can happen.

Rohit: Angelo, would you like to share some closing remarks?

Ritu:  I think we need another podcast for more questions! But Angelo, thank you for being such a great guest and providing really insightful answers. We’d love to hear your closing thoughts.

Angelo: This was a fascinating conversation, and again, I love what you guys are doing with this show. I hope we can convene again at some point. I’d love to come back.

In closing, one thing I’d leave the audience with is that these feel like incredibly challenging times, but we can do incredibly challenging things in response. Sometimes, the most amazing road to success is when you’re pressed and facing obstacles. The tools we have today have amazing potential. We just need to be good stewards of that potential—thoughtful stewards of it.

We shouldn’t be afraid. Instead, we should continue to ask the fundamental questions and think of the future as a synergistic one, where we combine the best of our human capital with technological tools and innovation, figuring out the next step forward.

I believe in the idea of abundance. I think we have a future full of great potential, but it’s a matter of deciding how we engage with that abundance. How do we take the tremendous potential of natural language models, generative AI, and other technologies, and apply them thoughtfully, rationally, and in a way that speaks to the needs of those moving these things forward?

In our case, we listened to what our people were telling us about their work and realized that the only way to really move forward is to change the work itself. We can provide support, offer a shoulder to cry on, or an arm to hold in times of distress. But when we change the fundamental nature of the work, that’s when we begin to truly change people’s engagement with it and make them feel supported, with a clear way forward.

Doctors are resilient. We don’t lack resilience. What we need are tools to help us engage with the work by changing the terms of the engagement.

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. Hi Dwight, welcome to The Big Unlock podcast. My name is Ritu Uberoy, and I’m one of the co-hosts. We’re very happy — this is season six, and we’re glad to have you back. Delighted to hear new perspectives, especially on AI and your initiatives at your new organization. Let’s get started with your introduction and then dive into a few topics.

Dwight: Great. Really great to meet you and be here, Ritu. I’m Dwight Raum, Chief Digital Information Officer for Rochester Regional Health. We are a health system with about nine hospitals in Western and Northern New York. We span the spectrum from large, urban acute care hospitals to small, rural regional hospitals.

We also have a large ambulatory practice throughout the region, and we really take care of patients from birth to death — and every aspect of healthcare in between. I’ve been with the organization just over a year.

Before joining Rochester Regional Health, I was in the startup world for a short time. Prior to that, I served at Johns Hopkins Medicine as the Chief Technology Officer and Interim CIO. I’ve been in healthcare for quite a long time. I really came up as a technologist, which is increasingly unusual in senior IT leadership roles. But I’ve truly enjoyed my time in healthcare — the mission really calls to me.

 Q. That’s a very interesting introduction, Dwight. It seems like you’ve seen all three aspects of technology and implementation — being at Johns Hopkins, which is a world-renowned teaching hospital, then moving to a startup, and now at Rochester. What was your role at the startup, and what did the startup do, before we get into your current role?

Dwight: Sure. It was technically a joint venture between Independence Blue Cross and Comcast. The startup was called Quil Health at the time. We were trying to build an in-home monitoring system that would allow seniors to age in place.

That consisted of various IoT sensors placed throughout the seniors’ homes. From those sensors, we ran AI models to anticipate and predict certain behaviors and patterns. Insights were drawn from the data and shared with caregivers.

It was a really interesting idea — just struggled to get it across the line, if you know what I mean. But I think it had tremendous potential. It actually speaks to the opportunities we have with signals and AI, and how that can really transform the way we provide care.

Q. Yeah, that’s very interesting. We had a webinar yesterday where we were presenting some case studies. One of the case studies we were going to present — but didn’t — was for Cherish Health. They do something very similar, radar-based. They have a device used in senior homes that can detect falls, go through walls, and anticipate events using AI.

Dwight: Yeah, I mean, the technology has evolved certainly over the last several years, and radar fall detection has really improved. But I still think there are a lot of challenges — in terms of privacy and getting over that technical hurdle for seniors to adopt and use the technology with trust.

Those are still barriers that aren’t easily overcome, even with the technology in place.

Q. Absolutely. Okay, great. So tell us a little more about your role at Rochester Regional Health — what the digital landscape is like there, your experience with generative AI, and some of the initiatives happening at your organization.

Dwight: Absolutely. As I mentioned, we’re a nine-hospital system. We’re mostly on one EMR — we’re an Epic shop, single instance. We do have some smaller EMRs, but we’re slowly but surely moving everything into a single instance.

In terms of initiatives, I’d group them into a couple of categories. The first is optimizing the system and the engine of care delivery we already have. That means ensuring throughput, doing so in a safe and high-quality manner, and providing access to our patients.

The second part is really transforming how we deliver care — shifting from a traditional fee-for-service model to a much more risk-based, value-based model. We’re focusing on treating patients for their outcomes instead of on a transactional basis.

That shift brings a lot of changes — not just in how technology works, but also in how our operators and clinicians provide care. It’s a transformational change for the system.

A lot of credit goes to our CEO, Dr. Chip Davis, who’s really leading that charge and planning for the future. When 2030 comes around and CMS starts to pay more earnestly based on value, I think we’re going to be very ready here in Northern New York to take on that challenge.

Q. In terms of the Clinical Access Center — is it a unified center? We recently worked with Sentara on a huge multi-year project where they brought everything into one place. Now they’ve implemented AI on top of that to handle call routing.

We did a webinar on their case study, and they shared something interesting — because of the AI they implemented in the call center, they can now predict when call volumes will be lower. That allows nurses to take flexible time off, which has been a big plus for them.

Dwight: I think that’s a great comparison for us. We do have a unified call center, and we are absolutely using AI for call routing and predicting high call volumes, etc. We’ve really been able to drive efficiency using AI in the call center.

Our call center is located right next to our command center, so we’re constantly looking at overall system operations — understanding volume, where to shift resources, where to move patients, and so on. Combining that with the call center is incredibly powerful.

Our call center isn’t just receiving calls — we’re also driving outbound traffic. And the third leg of the stool is that we’ve just rolled out a digital front door on our website — a robust digital experience for patients. It offers capabilities for patients to serve themselves, schedule appointments, and find the closest care options. We’re really trying to cover the full experience and serve patients as consumers as best we can.

Q. For the digital front door, do you have AI as well? Like virtual chatbots? How does that work?

Dwight: We’re not deploying chatbots in a very robust way at this point. Honestly, I’m not sure I see them as a strong tool for care delivery. There’s probably some limited front-door use for chatbots, but I don’t think they’re a great solution overall.

That said, our digital front door does provide access to direct virtual care. Patients can usually get an appointment within an hour or less — they’re able to see a provider almost right away. So it’s really about combining access with technology to deliver care where patients are, in almost real time.

Q. So. Sentara mentioned they use something called Edia for the AI part of their call center. Do you know which AI implementation you’re using?

Dwight: Yeah, we’re a customer, and there are embedded AI tools within Genesys — that’s what we’re using.

Q. So, how big is the technology component at Rochester Regional? And do you see a role for a Chief AI Officer? That’s a hot topic these days — ethics, governance committees, Chief AI Officer roles. Would love to hear your thoughts.

Dwight: That’s a broad question — let me take a shot at it. In total, we’re about 400 people in our IT organization. I might have had a bit of a Freudian slip earlier — I’m very much trying to reorient the organization to think about AI, but more broadly, innovation.

So, how do we bring technology and new ways of thinking to transform the tools and experiences we deliver?

To your specific question — do I anticipate us having a Chief AI Officer? The simple answer is no. I don’t think that makes a lot of sense, quite frankly.

What we have done is create a Center of Excellence for AI. It’s a group of the right people who need to be at the table to do two things. First, there’s the “pull” — a lot of demand from our operators who see new tools and ask, “Hey, can I get this?” We need to be thoughtful about privacy, contracts, ethics — all the right teams are part of the COE to address those questions and respond responsibly to that pull.

But there’s also the “push.” AI has been evolving at an incredible pace — even in just the last 6 to 18 months. It’s impossible for any one person, or even our providers, to keep up with it all. So the second part of our AI COE’s responsibility is to articulate what’s possible — the art of the possible.

We’re maintaining an initiative list of opportunities we think are ripe for AI — places where we can deploy capabilities, whether it’s LLMs or machine learning, to have an immediate or significant impact.

Q. And for all your AI implementations, are you planning to stay within Epic, since they’re building their own AI tools? Or are you looking to integrate outside solutions too?

Dwight: I think we’ll always look at what we already own first. Epic is a chassis in many ways — we can plug in different Epic modules and activate them. So yes, we’ll definitely start there.

But that doesn’t mean we’ll only look there. As I mentioned earlier, Genesys offers incredible AI capabilities — that’s an example of something outside of Epic but still integrated into our environment.

So no, this isn’t going to be an Epic-only play. But there are definitely some low-hanging opportunities within Epic that we’ll evaluate, ensure they provide value, and then activate them for our providers.

Q. Okay. I’m just mulling over your answer because other CIOs and CTOs we’ve been talking to feel the need for a Chief AI Officer, for the same reasons you mentioned—trust, governance, and the pace of change. It’s difficult for one person to keep up. Some believe you need a dedicated person whose job is to look at that landscape, understand what’s happening, and decide what’s best for the company. Just playing devil’s advocate here, trying to look at both sides.

Dwight: My reaction is, if we were in the business of developing AI ourselves, I would agree. But we’re not. We’re a system focused on serving our patients. Our priority is patient care, and we’ll use AI to support that. I do think it requires a collection of experts. From legal, privacy, and algorithmic safety standpoints—maybe a Chief AI Officer could be the lightning rod that brings that all together.

But honestly, we’ve come up with a methodology that works well for us in the COE. It allows us to bring together those experts and, more importantly, align their interests. Even if you have a Chief AI Officer, they still have to orchestrate all the soft power and influence that drive real change. In my view, the AI COE approach forces that to happen from the start.

Q. Yeah, that’s a very astute observation. That’s exactly what we’ve learned in our webinars—it can’t be mandated; everyone has to buy into it. It’s a behavioral change, especially with these new tools coming in.

The other point of view I have, which came up in a webinar yesterday, is that the EU has passed a law mandating AI training for any company using AI. What do you think about the training landscape? Are all your employees getting AI training?

Dwight: No, we haven’t mandated broad-based AI training yet. That’s likely to happen depending on the tools we’re using. We talk about it a lot as leadership. As we approach deployment of smart ML models for diagnosis or therapies, there will be very targeted and intensive training around each one.

Q. So as of now, people just learn on their own? Both—like LLMs or ML tools.

Dwight: So, as I said, I’ve personally hosted several webinars across the organization to provide baseline information about how tools work. But we’re not yet offering formalized training. The challenge is that healthcare is very busy and, in many ways, understaffed. Capacity is a real concern. We have to be cautious and ensure training is impactful.

My sense is that as opportunities become more acute, and we identify specific use cases, we’ll do more. We’ve also deployed an AI policy that specifies what is considered safe use for generative AI.

Q. In your hospital environment, do you have people using ChatGPT and possibly uploading data without realizing it could become public or used to train the model?

Dwight: That’s been part of our internal communications campaign—clarifying what’s appropriate. It’s hard to police, and I can’t say for certain it hasn’t happened. But we’ve provided guidance and education. A lot of people don’t realize that unless you opt out, anything you type can be used to train the model.

Q. Exactly. Okay, great. So, Dwight, talking to multiple CIOs and CTOs, the main generative AI success stories we’re hearing fall into two categories: scribing and ambient. What’s your experience with either, and do you have a success story?

Dwight: Honestly, we’re a little late to the game. We’ve just started our pilot for Ambient Digital Scribe. We’ve done early testing, and it’s clearly a win for providers. It helps reduce pajama time and after-hours documentation. But these tools are also incredibly expensive, so we’re evaluating ROI—how to unburden providers while sustaining the investment financially.

Q. Yeah, very interesting. At HIMSS last year, Nuance shared that ambient tools save about six minutes per patient. That doesn’t sound like a lot, but across multiple physicians and patients, it adds up quickly.

Dwight: Absolutely. Over the last 15 years, digitizing healthcare hasn’t been seen as a net plus by most providers. It’s negatively impacted their quality of work. COVID contributed to burnout, but so has technology fatigue. Any opportunity to unburden providers using tech—we should seize it.

Q. Good to know you’re in the early stages and testing. Any other digital programs you’d like to highlight?

Dwight: Yeah, I think, you know, there are several that I would highlight, and I would kind of back it up to a higher level for us. As I mentioned earlier, Chip, our CEO, has really been very much driving transformation. But I think a supporting function there is really innovation writ large, and AI fits into that innovation model as well.

So we’ve really begun to create an entire innovation program for all of RH, where we have two parts to that program. The first is focused on internal performance improvement—transformation and innovation. And then we’re coupling that with an accelerator program to actually take some of those innovations to market, and also to partner with other startups to bring them back into RH.

We’re doing this in a very prescriptive and deliberate way, ensuring it’s aligned with our strategy. So, where we have opportunities or challenges, we’re trying to line up those companies that may actually fit and help us solve some of those niche problems. I’ll give you a really good example of this.

Healthcare is currently experiencing a drop-off in employees, and there’s certainly a challenge in maintaining staffing. Nurses, I think, are the most acute area where we see that. But we’re also in this perfect storm where demand is increasing at a remarkable rate. So that gap between supply and demand—the only reasonable way to think about closing that gap is through technologies.

As you mentioned, AI—and we talked about Abridge as a digital scribe—as one way of doing that. But there are certainly other opportunities in that space. Just scheduling nurses, for instance, is incredibly burdensome right now and leads to tremendous inefficiency. Every little point of improvement we get there pays multiples in dividends.

So as we look at opportunities, a good example is nurse scheduling—can we apply AI to actually improve the speed and efficiency of that process? And the answer is yes. That’s a great example of a strategically aligned innovation opportunity that we’re actively pursuing at RH.

And I can stack up numerous examples like that. We talked about the digital front door and trying to improve access by casting a broad digital net, but also empowering our patients with tools to self-serve and gain access to care as quickly as possible.

It’s those types of activities that we see as the through line from the innovation continuum I was talking about earlier. So while an innovation may start by solving a very acute problem within RH, we believe we can develop those ideas into something that scales across the entire organization—and potentially even goes to market more broadly.

Q. That’s really interesting. The accelerator program—does it include in-house participation too?

Dwight: It does. A great example is a physician in emergency medicine who developed an app called COIST. It’s a heads-up display on an iPad that guides cardiac resuscitation based on ACLS guidelines and documents the process in real-time. We helped develop it, and it’s a great example of internal innovation with life-saving potential. It also eases the provider’s burden in emergency care—and it’s not a problem unique to RH. We believe it has wider potential.

Q. Yeah. So they’ve seen really good success with longitudinal care plans and generating care plans—still with the human in the loop to make the final call.

Dwight: I think especially with LLMs, that’s critical. I don’t see that changing anytime soon. One of my personal hobbies is coding, and I use LLMs all the time. It’s remarkable how good they are, but also remarkable how they hallucinate—just make things up. So I think there’s a definite need for caution and keeping a human in the loop.

That’s why I think RAG implementations have seen greater success—because you can bound them, put guardrails in place, and ground them in truth.

Q. Any challenges you’d like to talk about? What do you feel is your biggest challenge with technology?

Dwight: Yeah, I mean, I think, you know, the financial pressures in healthcare are certainly still very acute. And I think, in a lot of ways, that’s the oxygen you need to really drive wholesale innovation and transformation. So that’s certainly a challenge.

I think cybersecurity is also something that continues to be a top-of-mind concern for any CIO these days. And, for that matter, the accompanying investments you have to make to really tighten things up, so to speak. Mm-hmm.

Those are the challenges I see. And there are challenges around workforce, too—maintaining a team that can really drive and advance the ball is very critical as well.

By and large, I’m an optimist by nature—and a technologist by nature. I actually think there is real opportunity. If you take a step back and look at that gap I was talking about earlier—around supply and demand—and, for that matter, how much we spend as a country on healthcare, I do think there’s good reason to be optimistic about the future and how technology can play a really important role in helping us, as a society, meet the demand of healthcare and actually improve people’s lives.

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. Thank you for joining the podcast, Sophy. It’s great to have you here.​ Likewise. I’m Rohit Mahajan, managing partner and CEO of BigRio and Damo Consulting, and the host of the Big Unlock podcast. As you know, it was started by Paddy a long time ago. We’re honored to continue this podcast. We’re now in the 150s episodes, so it has come a long way. Sophie, could you please introduce yourself?

Sophy:  Thank you. My name is Sophy Lu. I am currently the Senior Vice President of Digital Integration and Business Relations at Northwell Health. Previously, I’ve held multiple executive roles at Northwell, including System CIO, and I’ve been with Northwell for approximately 15 years. I grew up in the application space. Prior to that, I was trained as a chemical engineer. I enjoy continuous learning, integration, connecting people, and solving problems by bringing common to chaos. I live in Brooklyn, happily married with two grown kids. I have four Labradors and two Norwegian cats.

Q. That’s awesome. So, how does a chemical engineer like you, Sophy, wind up in one of the largest healthcare systems in the country? Please tell us a bit about your journey and about Northwell as well.

Sophy: Absolutely. I often say it’s a mix of luck, timing, and a lot of hard work, along with the networks you’re exposed to. Initially, I wanted to be a kindergarten teacher, but my love for science and math led me to chemical engineering. I worked in that field for about seven years after graduation, focusing on process and industrial design for chemical plants. This role took me across Europe and Indonesia.​

During that time, I was drawn to sustainability, working on clean energy projects. About 20 years ago, I was involved in expanding liquefied natural gas and combined natural gas. While these concepts are well-established today, back then, scaling them in the transportation and energy industries was quite ambitious.​

As the engineering sector shifted more towards the central United States, I wanted to stay close to my family here. I explored other opportunities, sending out numerous resumes at job fairs. A consulting company noticed my background and brought me on board to work on technology integration and implementation, leveraging my industrial gas experience.​

This consulting role involved extensive project management, business integration, and participation in mergers and acquisitions. Notably, I worked with St. Vincent’s Hospital, which expanded from a single institution to about seven or eight. Unfortunately, it eventually faced bankruptcy. This experience provided deep insights into management during financial crises and organizational turnarounds.​

This was my first exposure to healthcare—a sector dedicated to care and wellness but often challenged by reimbursement issues and technological limitations. After St. Vincent’s closure, I was recruited by Northwell Health to work in their application space. I joined during the inception of their revenue cycle implementation, marking the beginning of their patient journey and EHR initiatives. Since then, I’ve been with Northwell and have never looked back.​

Q. Oh yeah, yeah. So you spent many years at Northwell and that’s a fabulous, you know, segue from your industrial engineering and engineering background into healthcare. You know, and you got a glimpse up close of a, of an institution that, that went through that phase as well of M& A and, you know, turn around.

So Sophy, tell us about your new role. I understand that you have a new role here at Northwell and that you are looking at, uh, some very different initiatives. So how’s that coming along and, and. What can you share with the audience? 

Sophy: Yes. So as we started the digital transformation journey at Northwell, laying out the foundation of simplifying the ecosystem and selecting strategic functions that we wanted to implement relative to sort of the cloud foundation, the data platform and evolving our EHR for the entire organization, there was a need that we looked into that we wanted to focus on for digital transformation integration and business relations. And what does that mean? If you look at the heart of where technology and a seat at the table, along with operations and clinical care and patient experience has married, the new discipline of digital relationship management really is trying to make sure that we are bridging operations to technology to patient at the center.

Right. And some of those objectives, no matter whether you’re looking at an adoption of technology that you just are implementing, like an EHR or your new emerges and acquisitions of how are you going to integrate the business  looking at building out a new facility? And what are you thinking about relative to innovative workflows and engagement and experience for, for the patient and the families, right?

And so you look to bridge in that relationship, strategic alignment, you want to make sure that the stakeholders are engaged in the decision making and the journey. And you want to facilitate effective communications because everybody doesn’t always speak the same language at the same time. It is what’s important to me and what’s relative to my role at that time, right?

You want to get synergy across that and you promote the continuous improvement and selection of what works best to meet the mission and the vision of whatever that initiative that you’re working on. Always focusing on process improvement.  Efficiencies and helping out the users, whether the users persona is the clinician or the operations or, of course, the patient and the family as they’re going through the continuum and last, at least you want to ensure you’re translating all that into something that is executable, um, have a translation, understanding that the implementation is not technology only right that are addressing the problem you’re trying to solve, you’re addressing the vision that you have sort of enabled across the institution, and then you want to measure and look at how you’re doing in that implementation real  often and repeat. Basically, uh, there is constant tweaking and recalibrating because that changes life changes and roles change.

Q. Yeah, of course. And you mentioned Sophy previously, before we started the podcast, about change management and cultural alignment, right? Would you illustrate that with an example from your new role? What works, how does it work, and what works best? You also mentioned something interesting about measuring it—any such measures you actually use in the business enterprise?

Sophy: Yeah, I’ll use a couple of examples. Right now, our main focus is preparing for our EHR implementation. As part of this readiness, while we ensure best practices and enterprise standards in our processes, we also prioritize operational engagement. We work closely with operations to understand their needs, ensuring workflows complement and meet their requirements.

We focus on operational input regarding readiness and translating that understanding across departments. Another key aspect of cultural success is our partnership with perioperative teams. Since our organization has grown through mergers and acquisitions, we don’t have a standardized system across our 21 institutions. This presents challenges, especially in OR throughput.

Think about OR throughput—not just scheduling surgeries, but also coordinating surgeons, care teams, room logistics, pre-op, and post-op processes. We collaborated with operations to understand these challenges and explored technology solutions to optimize data and improve real-time decision-making. This turned out to be a great success. We now have significant measurable outcomes in optimizing OR scheduling and real-time adjustments.

Planning on paper is easy, but managing the complexity of multiple ORs, surgeons, and a triple-digit workforce in real-time is challenging. We successfully balanced workloads, improving clinician and patient experiences while optimizing throughput. While efficiency was a key outcome, we also increased volume and revenue. Most importantly, we brought joy back to clinicians and improved patient scheduling.

Q. That’s a great example, Sophy. Thank you for sharing that. You mentioned that Northwell has grown significantly through mergers and acquisitions, now comprising 21 organizations. Given your involvement in this process, could you tell us more about Northwell’s approach to M&As?

Sophy: Absolutely. Northwell Health is the largest healthcare provider in New York State and one of the largest employers, with nearly 90,000 employees. We have 21 hospitals—11 of which are Magnet-designated—and almost 1,000 outpatient facilities. Our workforce includes about 12,000 physicians and over 19,000 nurses. We continue to grow while investing in community education, research, and outreach.

One of our CEO’s key visions is intentional growth. We don’t expand randomly; we grow in a way that ensures integrated, continuous care for the communities we serve. This approach also applies to our M&A strategy. We prioritize synergies, ensuring that mergers align with our mission and values. We focus on contiguous growth, enhancing referral networks, and leveraging our size and capabilities to improve healthcare access and quality.

Most recently, we’ve been working on a major merger with a health system about a third of our size. While it’s not yet approved, it would be one of our largest acquisitions.

Q. That’s awesome. So, Sophy, you mentioned earlier in our conversation that there is no standard playbook for M&As and that success comes from finding complementary technology strategies. Could you share your thoughts on how mergers and acquisitions are approached at Northwell and what makes for some success stories?