← Becker’s Healthcare Podcast
Becker’s Healthcare Podcast
Sunil Dadlani, EVP and Chief Information and Digital Transformation Officer at Atlantic Health System
00:24:00
Loading summary
Transcript24 lines
- [00:00]
Molly Gamble
Molly welcome back to the Becker's Healthcare Podcast. I am Molly Gamble and today I am joined by guest Sunil Dadlani, Chief Information and Digital Transformation Officer at Atlantic Health System. Sunil, thanks so much for being my guest today and joining me.
- [00:13]
Sunil Dadlani
Thank you so much for having me, Sunil.
- [00:15]
Molly Gamble
We are setting out to dig deep into a big topic, leadership in AI and there's a lot there. But before we dive in, let me just turn to you and have you share a bit more about your role and your organization with our audience.
- [00:28]
Sunil Dadlani
Sure. You know, my name is Sunil again, Sunil Dadlani and I'm EVP of Chief Information and Digital Transformation Officer as well as Chief Cyber Security Officer. And recently my responsibilities have been more widened and I'm now responsible for venture studio and innovation. So that's a pretty wide responsibility for Atlantic Health System. And just a quick brief overview of Atlantic Health System. Atlantic Health System is one of the largest and the most reputable nationally recognized integrated healthcare system. With nationally recognized eight hospitals, more than 500 plus ambulatory sites. We are about 20,000 plus team members and we serve the community for more than 5 million-plus community members in northeastern region, including metropolitan areas of New York, New Jersey and Pennsylvania.
- [01:25]
Molly Gamble
And Sunil, I mean your scope of your work, it's so broad. Like you said, you touch a lot of different parts of the health system innovation, AI, cybersecurity. Today we're going to probably be focusing on AI the most, but I imagine some of what we'll be discussing pertains and spills over to other disciplines too. Even outside of technology. Maybe. So this concept of AI leadership, AI, it's taking up an increasing amount of attention from leaders across all different industries. But in a health system, a health system as integrated as Atlantic Health System is, as you just helped us better appreciate. What does AI look like to you? What are some signs of effective AI leadership?
- [02:09]
Sunil Dadlani
Sure, that's a great question and it's a loaded question. And let me start by saying that, you know, there is lots going on in the world. There is so much disruption, there is so much unprecedented change, unknowns. And that's the world we are navigating. This is going to be the new normal. And among many of the disrupting factors, I think artificial intelligence technology advancement is one of the most prominent and prominent disrupting factor. And while the potential of AI is absolutely indisputable, but again, it comes with its own risk and it comes with its own concerns. And each industry is impacted by AI advancements and healthcare being no exception. When we talk about AI leadership. Every organization is pursuing its own journey and Atlantic Health based on my role and especially from the lens I'm looking. First of all, the AI leadership is not just pertained or confined to one particular title or one particular department. It's an enterprise undertaking. You know, it belongs to the entire enterprise and it requires collaboration with all the cross function leadership and it requires internal and external partner ecosystem. So it's a very comprehensive undertaking. And AI leadership does not just limit itself to merely just deploying some algorithms or launching some pilot projects. It goes much beyond that. It is about how do you embed technology advancements and non human intelligence into the very fabric of how care is perceived, delivered and in every fabric of healthcare, whether it's operations, financials or most importantly, patient outcome and patient safety.
- [04:11]
Molly Gamble
Not one particular role or department. And also like you said, sometimes the press releases and the pilots can draw a lot of energy and attention. But leadership here is really more about integrating it daily into every aspect of operations and the organization. Like you said. I want to talk to you about culture. Sunil, I imagine effective AI leadership also builds a certain type of culture in an organization. How do you measure that? How do you think about that? There's a lot of change management involved in this work. But what's the culture that you're trying to work toward related to AI at Atlantic Health System?
- [04:48]
Sunil Dadlani
That is such a great question. And it really starts, you know, there are many, many different lenses that you can see through it. You know, one is about creating AI vision, creating AI strategy, how it gets aligned to, aligned to the business strategy and business goals. But where really rubber meets the road is all about culture because it is a disruptive change and by default or by nature, humans are resistant to change. It's not a change that is a problem, it's always a resistance to change that is a problem. And how do you build a culture of an organization where you create some norms and environments? First of all, the environment should be so conducive that you are enabling smart risk taking. When you do AI leadership and you embed that AI leadership, you have to accept that it comes with its own risk and risk means there will be failures. So how do you build a culture of creativity and continuous learning where you know that you are going to fail, but you have to fail fast and you have to feel smart and you have to learn smart from, from, from failures. So secondly, how do you adopt AI leadership and AI culture that really transforms and it challenges how you have been looking at the business from Whichever business function, you are in a very different lens because the human mind really follows a path of least resistance. Something has been working for last 25 years and it is working fine. And you don't want to change. The AI leadership and AI disruption essentially involves, you know, challenging the status quo and looking at the things and completely revamping it to, to, to create, to unlock the productivity and to create some breakthrough, breakthrough advancements. So this is fundamentally changing how you look. Because if you ask me how AI leadership and AI innovation, what does the definition mean? It really means looking at things from a very different angle. You know, you are, you are embracing curiosity over comfort, right? You are willing to take risk, you are willing to empower decision making at every level. And when you're trying to solve some big challenges or if you're trying to pursue some opportunities, you are taking a multidimensional approach. When I say multi dimensional approach means top down, bottom up, inside out, and outside in. And you have to embrace and understand that you cannot do it alone. You need a very strong partner ecosystem that is an extension of your organization. In having this AI journey, you hit on something.
- [07:32]
Molly Gamble
To me, I just had a talk with another leader and we were talking about this leader, I think relates to what you had shared where probably the biggest concern they have is complacency versus outright failure. You rather have outright failure where you try and you fail fast. You feel smart, like you said, and it's inarguably that that did not go well, that failed. But complacency, I think to your point about not challenging the status quo or taking the path of least resistance, it can be gradual, it can be harder to detect, it can really, really hurt an organization and a culture. So I think that's such a great portrait you painted of what the culture around AI looks like.
- [08:11]
Sunil Dadlani
Absolutely. And again, another dimension is always people are very scared. Will AI take their job? And the response is always that AI is not going to take your job. Somebody who knows AI will definitely take your job. So, you know, you have to. Before you do the transformation or reimagine or reshape industry or your organization, it is imperative that you have to reimagine and redefine yourself. That means you're signing up for continuous learning. You know, gone are the days where you are, you can expect that, you know, with one particular degree. You can have 40, 50 years of your career in just one organization. If you see, if I have to define really how things have changed, you know, there is a reason why Fortune 500 companies for from 2000 year 2000, more than 55 to 60% of those companies don't exist anymore. And there is a reason. Not that they were not a great company or they didn't have a product or services, but they fail to realize how quickly they have to reimagine and revamp themselves. Second part is, you know, it's also very important that I consistently emphasize on, is that innovation is also about changing your behaviors, changing your action, and how do you embrace unknowns or unpredictability when you're working in an environment that is constantly changing and there are a lot of unknowns, how do you make yourself comfortable while being uncomfortable? I think that's the mindset you need to have when you embrace AI leadership.
- [09:44]
Molly Gamble
Right. Can you. One more question, Stan, then we'll move on. But I'm really interested in this because this is so often the kind of conversation around AI. We're not talking about the tools themselves, right? We're talking about how this transforms the way people relate to each other and their work with those tools in place. And I wanted to talk about AI maturity. This is a concept. I'm curious to get a sense of how you spot this, what it looks like to you. It's probably different than AI familiarity or AI experience. I mean, people can be using the tools, but you're not necessarily maturing as an organization. With AI. What does AI maturity? How do you define that?
- [10:22]
Sunil Dadlani
You know, that's again a great question. And before you come to maturity, you have to understand what your organization's AI roadmap looks like. You know, there are different roadmaps and different stages in the roadmap. You know, are you in the foundational phase where you are just building and testing and building governance infrastructure? Are you in the execution phase where you're deploying pilots? Are you in into the institutionalization phase where you're moving beyond the pilots? Or are you in the differentiation phase where you have built that model, you have the infrastructure, you have the governance, you have the AI literacy and maturity within the organization, you have the partner ecosystem where you are moving beyond the pilots and you are scaling at the enterprise level and you are solving some real world problems. And next final phase is are you at a national leadership where you are publishing, partnering or setting standards? I think that's one quick way to understand where your maturity level of the organization is. And are you building solutions and AI which are ethical and secure by design, by default and by demand? I think these are some of the areas that you can really measure where your AI maturity is. And secondly also you can look at how well defined and curated use cases you have. Is your organization matured enough to pick up use cases with the very defined outcomes and roi and you are selecting right kind of AI technologies and tools to solve those problems. Is it integrated in the enterprise workflows? You have the maturity model of continuous monitoring and iterations and is it impact driven, not just pilot phase. So these are the ways you can really quickly test that how mature is your organization?
- [12:08]
Molly Gamble
Very helpful to keep in mind. Let's talk now about what you're up to at Atlantic Health System. What are some of your most immediate priorities in AI adoption right now? You painted a really rich picture of the vision, the culture maturity that you're working toward. But what's in front of you today, Sunil, that you can get us up to speed on?
- [12:28]
Sunil Dadlani
Yeah, that's, that's a great question again. And you know, I would say that most important part is to you start with prioritization because there is so much noise first of all in the AI world that every product, every service and every organization is AI organization. There is a lot of noise. Second part is do you have clear defined goals of what you want to pursue? The hardest part in the AI leadership is prioritization and saying no and no to even good things. Right? You have to be laser focused on. You really have to go through with certain parameters like what's the impact, what's the value it's going to bring, what are the biggest problems that you're trying to solve for the organization or for the industry or what are the opportunities that you're trying to pursue. You cannot be solving every small problem with the AI because that's where the failure occurs. And most of the time AI development happens in the very siloed manner. And most of the time 85 to 90% of the AI initiatives, they never see a light in a day after pilots. So most important part is the prioritization. Having said that, there are three or four key areas that we are really focused on where we want to innovate with leveraging AI. One is purely the clinical decision making clinical area and second I will define is the non clinical area. Third is of course patient experience and engagement and even a provider clinician experience and engagement. It's a total experience and engagement. So we are very focused on experience and engagement because that's a key differentiator and healthcare system typically has lagged in experience and engagement. You know, more it is becoming more and more like a hospitality industry. Where people expect experience like Uber or Netflix or Airbnb or they want a product or services like Amazon or Apple products. Right. They expect that healthcare should be also like this, commoditized, like that. Second part I talked about on the operational side, how do you improve operational efficiencies? Whether that can be in the revenue cycle management, denial management, referral management, management. There are so many things where we are focused on and we are taking some major initiatives. Third is of course a clinical decision, clinical area which is, you know, for the right reasons. It's, it's a tough area because, you know, it involves directly with patient outcome and patient safety. So, you know, we have several initiatives that are, that we have scaled beyond pilots. You know, that one can be, you know, clinician efficiency and burnout using ambient technology. Ambient technologies are in basket messaging, tuning. Right. Second is using generative AI in that area. Third area is clinical decision making. Another area which is ripe in terms of AI, where AI has made a significant advancements in terms of clinical areas is imaging. That's one key area where the technology has really, really moved fast and far much more advanced. So these are some of the areas that we are looking at. Another area is of course hospital efficiencies. How do we do patient throughput? How do we improve our or efficiencies? How do we improve our ER efficiencies? How do we reduce length of stay, how do we reduce readmissions, how do we reduce adverse events? So these are some of the different areas that we, as in health systems, are laser focused.
- [16:10]
Molly Gamble
Mm. I wanna go back, Sunil, because something that you said stood out to me, how difficult it can be to say no. So you just described some extremely serious endeavors and some ways that AI is being integrated into workflows that are pretty high stakes. And so you've got a lot that you're managing and then you've also got new opportunities. And the hard part, as you said, is saying no, even to good things. What's been one of the toughest nos. If you can keep it general and just recount what was the tool at hand or the project or the need? Like did you have to either pause or say no to and why was it so difficult for you?
- [16:47]
Sunil Dadlani
You know, sometimes you have to. I will, I will not get into the really the vendor or the technology where we say no. But I will tell you the reason. Sometimes it becomes difficult. One, one, one difficult area is when you have to say no is when there is, you know, clearly no defined measure of success or ROI that's one area where you you see that you know you cannot move forward. And second area you say no in the beginning. Also before you define, before you move forward with the pilot is when you're trying to solve the problem for a small facility or an individual provider, you have to look at the bigger picture. That is the problem that we are trying to define. Is it big enough at the system level that will move the needle? Right. Third example would be are there any technical challenges, are there any regulatory challenges, are there any reimbursement challenges, whether it's a right use case, but there is no financial viability of the project itself, whether it will pay for itself or not. Right. And again understanding that many times when you take this, when you take AI initiatives, especially in the healthcare, not everything will be measured in a tangible financial ROI based also, but it should have a clear ROI that how it is going to improve your quality scores, how it is going to improve your patient safety or patient outcomes. That has to be really clearly understood because there is a huge difference between measure of success at the pilot level versus operationalizing it. When you scale that at an enterprise level, maybe you don't have the resources you have to look at from a broad picture that what you are trying to solve and do you have that capability.
- [18:32]
Molly Gamble
So you have some more other rules of the road of how you weigh yeses and nos. And I've heard that too. It's almost like another CIO was saying, an innovation officer that she was really curious and connected to other leaders about how they think about no. And I think that the trade offs are real. You can't do everything. You need to prioritize like you said and be selective about what you're pursuing at any given time. Let's go back to the people you know. You talked about how AI is helping clinician and clinical decision making. The clinicians experience with things like burn out. It's so important that AI not add to clinicians burden. Can you talk about how you are supporting your people as you roll out these more advanced tools? How do you stay in touch with their experience of these technologies, feedback from them and really stay close to their core values and concerns?
- [19:25]
Sunil Dadlani
Again, I will start with what not to do. This is not something that you want to pick up a solution, technology solution and just enforce it to the clinical leaders or to the operational leaders without understanding. And sometimes you try to solve operational problems but it creates a downstream clinical problem. Or sometimes you are solving for the clinical problems then you are creating a downstream problem for the operation side so it is so much dependent on cross functional teams coming together right from the problem definition, understanding the baseline, understanding the measure of success, how do we measure and then involving them in also decision making of what technologies and what technology partners we will use so that there is a continuous collaboration in terms of selecting and asserting our partners technologies tied up to the problem. Last thing you want to do is select a technology that is in search of what problems it can solve. You don't want to do that, right? Second part is also making sure that they are embedded, what we call human in loop so that these clinicians, they know their workflows best than anybody else. So you need to know that where in the workflow, what's the value chain and where these technologies are going to be embedded in what workflows and how it is going to improve their cognitive burden or their administrative burden, how it is going to solve for that. You have to really involve physicians and care team members. Then also you have to make sure that these leaders are also involved in consistently providing continuous feedback, continuous refinement of the solution. And even when they are moving beyond the pilot and when it is implemented, they are the ones who are taking a lead in terms of measuring the performance of AI solutions. Because AI solutions over a period of time their performance degrades, right? So we want to make sure that these are the people who are taking a complete ownership when we are solving some key problems for them. And last thing any clinician want want is, you know, more number of clicks or more number of applications or technologies where they want to, they want to spend their time more on administrative administration. They don't want this. They want their time to be more focused on patient experience, patient engagement and really understanding and giving them a good face time so that they can really do the right diagnostics and create a right treatment plan. I think that's where you say that they are performing at the top of their license.
- [22:03]
Molly Gamble
Yes, very much so. I think sometimes I find the approach of what not to do to be just as if not more effective than best practices. So I think Sunil is fantastic guidance for leaders like you and many other leaders too. Some of this it doesn't need to be in the context of AI. These are just really strong leadership principles. Is there anything I did not ask you Sunil? You want to make sure our listeners hear from you today?
- [22:27]
Sunil Dadlani
No, I just want to have a concluding comment that you know is not one time undertaking. It is not one project. It's a continuous journey. You have to continuously evolve and the two Things that I want to leave you with is you don't have to do everything what is what you see in the market. Because if you get too ahead of the technology. Technology is. Technology has two traits. It doesn't forget and it doesn't forgive. So if you get too head ahead of that even then you are going to be short ended and you are going to be. You are. You are going to be irrelevant. There is a cost. Second part is if you lag too much behind the chasm between where the advancements has happened and where your organization is, if it becomes too wide beyond a third threshold, it's almost impossible to catch up with your competitors and where the technology advancements are. And then just like a law of gravity, you know, the organizations become irrelevant.
- [23:24]
Molly Gamble
Right. And it's finding that sweet spot between those two. You, you can't do everything you see in the market and you can't lag too much behind. And you need to find the tempo and the pace and the sweet spot somewhere in between those two extremes.
- [23:37]
Sunil Dadlani
Well said.
- [23:38]
Molly Gamble
Yeah. Sunil Dadlani again, listeners, Chief Information and Digital Transformation and Cybersecurity Officer of Atlantic Health System. Always a pleasure to catch up with you. I really appreciate your expansive view on this topic and so much more.
- [23:54]
Sunil Dadlani
Thank you so much for having me.