Sameer Sethi, SVP and Chief Data Analytics Officer at Hackensack Meridian Health, discusses pioneering efforts in healthcare data modernization, cloud migration, and AI implementation. Learn how these innovations enhance patient care, drive operational efficiencies, and shape healthcare delivery's future.
Sameer Sethi, SVP and Chief Data Analytics Officer, Hackensack Meridian Health
Megan Antonelli, Chief Executive Officer, HealthIMPACT Live
Welcome 0:00 Welcome to Digital Health talks. Each week, we meet with the healthcare leaders making an immeasurable difference in equity, access and quality. Hear about what tech is worth investing in and what isn't. As we focus on the innovations that deliver. Join Megan Antonelli, Janae sharp and Shahid Shah for a weekly no BS deep dive on what's really making an impact in healthcare.
Megan Antonelli 0:30 Hi and welcome to digital health talks. This is Megan Antonelli, and I'm here today with Sameer CT Samir is a seasoned healthcare technology executive with over two decades of experience in data analytics, digital transformation and strategic leadership. Currently, he is the Senior Vice President and Chief Data and Analytics officer at Hackensack Meridian health. He's at the forefront of leveraging data driven insights to improve patient outcomes in healthcare delivery, with a career spanning roles at prestigious organizations like McKinsey, Mount Sinai, Bon Secours, mercy, health, Samir has consistently demonstrated his ability to drive innovation in healthcare analytics. We are thrilled to have him here today to share the work that he's been doing at Hackensack. Hi Samir, thanks so much for joining us.
Sameer Sethi 1:19 Hey, Megan, thanks for having me. This is great. Really excited and excited about this opportunity.
Megan Antonelli 1:24 Yeah, well, you know, we've known each other for a bit. I know we've met, and we've certainly had a lot of folks from Hackensack join us at health impact. So I'm so excited to have this conversation. And as I was reviewing your bio, I hadn't realized, you know, what an amazing number of hospitals and organizations you've been at. So tell us a little bit about, you know, your your career journey and how you got, how you found yourself at Hackensack?
Sameer Sethi 1:51 Yeah, absolutely. And, you know, it's been, it is, in fact, been a really good journey. You know, I, I tend to describe my role or my journey, and I tried to draw a line when I left financial services and joined healthcare. My journey in healthcare primarily started off at Mount Sinai Health Systems in New York. Prior to that, I worked for Deloitte, doing financial services at the same time, I also marry an occupational therapist who's who's very passionate about being, you know, being a part of healthcare and making people better. And I think, I think spending time with her got me to a bit of realization around why, that I can do things, I can obviously make a living and at the same time do something that would somehow impact, you know, the life or the quality of care that a patient receives. So that, I think, is what, what led me into healthcare. I started off in doing EMR implementations, and then eventually realized, and, or actually, should I say, caught myself always looking behind the curtain. So obviously digitization and patient or clinical workflow was, was, was really important, but I caught myself always looking behind the scenes and seeing what what data is being generated, how it sits, how it's being used. And that's when I felt that I should start my journey with working with data and developing developing insights from this data. And that's what got me to to Mount San i i started off in the data and analytics team there and then, and that was my first, also journey of starting to move data and analytics on Cloud. Prior to this, it was always most of it was on prem. Even there was most of it was on prem, but we started to venture with putting data and analytics and insights and dashboards and these assets on the cloud. So I was there for for, I believe, almost two and a half years, and then eventually saw myself, or caught myself, should I say, admiring my own ivory tower. You know, we were given the opportunity to build some assets that were making a difference. And then after a while, I felt that I needed an opportunity to to look outside of Hackensack of Mount Sinai and start to see what other health systems are doing. In addition to that, I felt that I was always on the execution side of the house and didn't have the opportunity to develop or sell strategy. So I saw myself looking at management consulting, sticking with healthcare, sticking with providers, but that's when I reached out to or found an opportunity at McKinsey. At McKinsey, I spend a majority of my time focusing on building data analytics strategy for providers. I did a little little bit of insurance slash pair work, but most of my work revolved around working with provider systems, around digital enablement and and data and analytics enablement. I did that for on around three years or so. But then again, the cycle came around where, you know, I felt that I had learned enough about strategy and it was time to move back into the business. My heart was, was always in working at hub. Hospitals and being as as close as possible to to patient care. Again, taking this back to at least, you know what I learned from from my now wife, which is, which is that you know what's important is the patient. So that's when I, I left McKinsey and joined bond scores, Mercy Health, as as as as their chief data officer did similar work there, picked up a few more things AI was really starting to become stronger and prevalent. So start to learn, learn about that. And the other larger piece was automation. That's where, you know, that was a piece of my, at least knowledge and career that I hadn't done enough in but always been around it. So I felt that I wanted automation had to play a big part in at least reducing cost of care or reducing burnout. So I picked that portfolio, in addition to AI data analytics and bit of digital as well. So so that journey was around three years or so, again and then, and then finally, I joined Hackensack Meridian health in 2002 into 2022 as a data analytics officer. So I've been here for a little over two years. My my role here is is broken into four parts. One is descriptive analytics, which is where we use data and data to provide business or even patients, at times, definitely clinicians with with what has happened and sometimes why that's happened. The second piece is, is, is predictive analytics, and this is where AI comes in, which is where we build models that starts to early, detect or predict things, whether it be for the purposes of running a hospital or treating a patient. The third piece is, is automation. This is where we are looking at tasks that are performed by by folks that are mundane in nature and have an opportunity to automate. So we we emulate human function. We also use automation to to provide patients with more information. So as a part of the delivery of, for example, getting a patient ready for a procedure, generally it was, it's some human would provide information, literature, reading knowledge. And now we have rpa, or robotics Process Automation provide that. So that's the third piece of my portfolio, and the fourth is software development. This is where we, we do develop some full stack software. But those are, you know, those aren't really that isn't, isn't why we exist as a software development team, but primarily things that bring it all together. Where? So we use software development to bring either send AI messages across different systems or bring various capabilities together and put it into package software. So that's what I do at Hackensack Andrea and health. So it's been interesting journey.
Megan Antonelli 7:53 Yeah, that is an amazing journey. And it, you know, I mean, I really appreciate you breaking down the, you know, the types of data. You know, we talk about it a lot, you know, the data. And, of course, the healthcare is so data heavy, and there's so much, right? And, you know, I don't know, I've written a couple 100 session descriptions where I've said, you know, data is the new oil and all this, you know, we talk about it all the time, but to really think about, you know, where it is, where it's coming from, and then what you're doing with it, and the way that you segmented that out is, really, is interesting. And then, so you joined Hackensack in 2022, Hackensack meridian. And so that was post merger, right? So it's been, you know, I'm sure there was a lot of transition tell us a little bit about, you know, kind of coming in at that time, kind of, I mean, is sort of mid pandemic, and what that that journey has looked like in terms of the digital transformation and the current vision?
Sameer Sethi 8:50 Yeah, absolutely. So I think the digital or digital enabling vision is obviously providing patients and clinicians with the right kind of tools and asset to improve outcomes, right, and also to reduce cost of care, but so, so I think that's that, that's the higher vision. In my opinion. I think what has changed, at least since I have joined, is I do commend my team for this, is that we have become bolder with technology, right, in a safe way, obviously, with the right level of governance. But you know, as if you look at the, you know, in the news at at Hackensack, you've seen that. You know, we have, you know, with our leadership, you know, we have ventured down the path of moving things onto the cloud, right? The reason we have done that is because the business hack and sack the people we care for the communities that we function and demand faster action, right again, within reason with the confines of governance and, you know, security and privacy, but it requires us to have the right setup in place and set up means, you know, people process technology. Right to action faster and deliver assets faster. So I think what's happened, at least, the change that I have seen is business, our stakeholders, our leaders, are more open to change. Are more open to technology enablement. At least, my impression, you know, when I came in, people were still a bit shy about moving in that direction. There were definitely ingredients that obviously led to where we are today, much prior to me joining but I think the cloud enablement and making sure that we have the right setups in place to go faster and deploy faster is what is? What has drastically changed?
Megan Antonelli 10:44 Yeah, yeah. Well, tell us a little bit about that cloud strategy in terms of, you know, it's funny. I was just, I just remembered, I think so I met Dr Shafiq Rob, who was the CIO at Hackensack, many years ago at a conference. We did a conference on big data, and it was, and it was, it was early, I want to say I was back like, right when Moneyball came out, you know, and it was, and I had, it wasn't my conference. I had taken it over for someone, and the chairperson had canceled or something, and and took over, and it was like the best, and he was so good, so that, and it is, in fact, part of my journey into getting back into healthcare and provided provider data and all of that. But back then, you know, it was visionary, but it was definitely still hypothetical, and it was pre cloud. Tell me a little bit about how that cloud, you know, how that's evolved, and where that look, what that looks like right now?
Sameer Sethi 11:41 Yeah, absolutely. And Dr, Rob is, you know, he's been, obviously, did great work at Hackensack, you know, I see his is prints all over. The great work that was, that was done today, right? And a lot has happened since then, where, you know, the way I and again, just taking us back to 2022, you know, there were some attempts made prior to me joining to start to move our data and analytics infrastructure onto GCP, which is Google Cloud Platform. What we were able to do starting April 2022 was, is to, is to actually make that real. What that what that means is, is we actually deployed the first use case on GCP in June or July of 2022 we gave ourselves 90 days, you know. And infrastructure was obviously there, right? But we gave ourselves 90 days of and I said 90 days of me joining to put our first use case in production. And since then, we have close to 430 use cases in production. So we've been working really hard in moving our assets, our data and analytics assets, into into the cloud. Why? Because we can do it faster. We no longer have to wait for servers to be purchased and commissioned. Instead it, I, at least I, I and I generalize things, there are a few more other steps to take. But you know, doing things on the cloud is as at times, as simple as swiping your credit card right so, and obviously, a lot of a lot of foundational work has to be done prior to that which Hackensack was in the process of doing. But now that we have in place, when we need to deploy something, as long as we confine ourselves to a certain framework which we have put in place, that is, that has privacy and security considerations, we can deploy really fast. So as you can imagine, four and 32 use cases within two years is quite a bit of work. It would have been hard to do that on prem and cloud is a lot easier. So having done all this and learned so much from moving data analysis infrastructure on Cloud, we are now vehncing down putting applications on cloud. So we signed a considerable, sizeable deal with Google last year where we decided to move mostly all our applications on cloud, and we have many we have started to move move epic on cloud, and that's starting to come together. But in addition to Epic, you know, which is, are obviously the largest and most impactful and important application for for us here at Hackensack, you know, we are looking at moving almost everything, all the ancillary applications that support our system, that support our clinicians, that support our patients onto the cloud. And I believe doing that will allow us to obviously go to market faster, react better to the needs of the business and so so that's been our journey. That's been our vision. That is our vision now is, is to get out of the business of of of managing on prem data centers. And I say that loosely because I don't think we'll ever be out of the data center business. We will have certain things that will always exist in on prem data centers. But the intention and the vision is to move mostly. Everything, uh, reasonably, onto, on, onto the onto the cloud. So it's been a journey. You have learned a lot, and I think there's a lot more to do.
Megan Antonelli 15:07 So, yeah, I mean, that's amazing. So so many applications, and it sounds like speed and agility is part of, you know, is, is the real kind of driver. But are there, you know, particular examples of use cases or, or, you know, sort of the, you know, elements that really make this like, of course, we're going to take the rest, you know, like we because that clearly was a decision. How did that, you know, what were the positive outcomes from the the original applications that were moved, or the original data that was moved, that drove you to the sort of saying, yes, you know, we're gonna, we're putting everything,
Sameer Sethi 15:45 yeah, before I answer that, Megan, I wanna talk a little about speed and agility, if that's okay. So, so you know when, when, when you obviously start to put business cases around, around cloud migration for, for good reason. You know, healthcare and all organizations primarily, are very health conscious, right? Cost is always a consideration. It's something that's always considered. If you look at the cost of computing on Cloud, if done correctly, it is cheaper, right? While, while I say that, it's also important. Every time I have done this, we have ended up spending more, but that's not because it's not an apples to apples comparison, if you were to look at the same exact workloads on on prem versus on Cloud. Yes, Cloud is cheaper, but what happens is, as soon as you start to make yourself available to the business and prove that you can put more things in production a lot faster, you start to see larger workloads and more workloads. So that's the reason. What I mean to say is an organization should real organization should realize that, yes, you will save money moving on to the cloud when it comes down to just an apples to apples, workload to workload. But what's going to also happen is that you will get a larger and bigger demand of of technology enablement, so you'll end up spending more, right? So it's a, it's, it's, it's a good place to be, but it's, you know, when I'm asked about whether it will save us money, I say, No, it will not save you any money. It will, you will actually spend more money, but, but the value that you will drive from this is going to be lot more as being on prem so I think that's that's important to consider, right? Going back to your question of the value, you know, obviously, you know, at least for data analytics specifically, you know, the value is, again, you know, apples to apples, what we were maintaining on on prem versus what we're doing on Cloud, is a lot easier. We no longer have to worry about upgrades as an upgrading our hardware anymore. It's someone else's problem. In this case, Google's problem. To do it, our team focuses on the business and being, being in the business of not not managing servers, but instead being be in the business of managing workloads that benefit the patient, right right, and benefit our clinicians. So, so first off, that's the highest value there. Second, we talked about speed. The third is sort of related to speed. Is, is the kind of use cases that we can do, right? An example that I will use is, is, is continuity of care. For example, in the healthcare continue is very difficult. This is where we are not able to have insight into into where our referrals are going right at times, while, while clinicians make the right choices for patients of where they should go for from, from from one episode to the other at times or options aren't, aren't always looked at correctly, right and for the right reasons, and that's because, because burnout, because of the speed at which they have to go, that requires a lot of data and analysis. So what we did here is, is very quickly, and when I say quickly, within weeks, we were able to build an asset, you know, on, you know on GCP, that allowed us to consume a very large amount of data, process that data and provide insights back to physicians, saying, Hey, you referred to this location, but if you refer to y location, it would have been better for the patient. From a continuity of care perspective, we would have, you would have kept the patient within the system, that we would have gotten better care, more information, the information would have been contained. And sometimes for the benefit of the patient, that's not, that's that's not the right thing to do, and they are referred out. But such use cases are take time to put together. When you think about on prem, right and through cloud enablement, and not just servers, but even the tools that are available to us on Cloud, we were able to spin this up a lot faster. There are various other use cases, especially when it comes down to AI, where we have built AI models and deployed it very quickly and digitized the clinician experience that has allowed us to is to is to save lives or. Reduce the burden of being in and out of physicians in or in and out of hospitals, readmissions and things as such, by just doing that.
Megan Antonelli 20:09 Yeah, that's amazing. I mean, I mean, I think, you know, when you think about the referrals and, and how hard that is to kind of track and, and, you know, and I think it goes back to your the point you made about, you know, it's not cheaper, but it's better, right? I mean, because you're giving these insights, and then, to some degree, I imagine there are, there's financial benefits to figuring out where those patients should go versus not go, in addition to the benefits for the patients. But you talked about how your wife sort of inspired you and said, You know, it's the patients that matter. That's what you brought, what brought you back to healthcare. So tell us a little bit about some of these other applications where, you know, these, these types of strategies, have led to improve patient care and patient outcomes.
Sameer Sethi 20:56 Yeah, let's, let's use, and let's use an AI use case, since, I mean, since people, there's a lot of conversation around that. So, you know, we have, very recently, you know, deployed a product or a capability which which allows us to to to predict mortality, right in patients. The reason we do that is because we are trying to engage our clinicians and their families on the thought of, if required, as required end of life care. I went through an episode recently, not I mean, not too long, with a family member where, where it was identified that that that she would she, should have gone through at least the interview with the palliative care team a lot sooner, right? And but unfortunately, that wasn't identified. Clinicians, at times can get very passionate about, you know, fixing the patient, and they don't want to give up for good reason. And at times they they struggle with the signal of, you know, it's time to talk about end of life care. So we built a capability on Google Cloud. We call it we call it the SI CC, which is serious in serious illness continuum Connect, which predicts mortality and and identifies the or recommends a need for to a clinician, not to a patient, to a clinician that they should start considering palliative care, or, at times, hospice for a patient, for critically, critically ill patient, that is much needed, right? As you can imagine. It's primarily a nudge. And the way we did this is, this is Ai plus digital at its best, not only do we build an AI model that that predicts mortality, but we provide that that signal in a way by which a clinician can clinician can appreciate it. What that means is, is this isn't an AI application that is or a signal that's provided outside of their workflow. Instead, this is a signal, or what we call is a BPA in epic So, and the scenario is, or the or the workflow is, hypothetically, there's a there's a pro, there's clinician that is, that is putting in an order for pain medication for a patient, right as a part of that, and I you know, as they put that order in, they'll get a pop up that says, you know, we have identified this critically ill patient, you know. And as part of the model is signaling an X percentage of mortality you should therefore consider, you know, palliative care or hospice, and there's a meter for that. And then the clinician takes that decision, and as soon as he or she says, Yes, I agree, it creates another order, right? And which would, which would then engage a palliative care team to come in and have conversation. So that's where we have, we have taken AI from a modeling perspective and predicting capability, and then digitization or, or, or or making the building a proper digital workflow for and have brought all these things together. What that allows us to do is, is, is, is increase the need, or, should I say, I'm saying, increase the flow of patients going to these consultant these consulting situations where they're meeting with palliative care team or, or a hospice care team to see if that, if that is needed and it's time for that kind of care, the result of that is reduction in readmissions. You know, we actually did a comparison where we looked at a patient without this capability and a patient with this capability. And it's amazing. It's a startling difference that you see, you know, this patient that didn't have this capability, you know, died at the hospital within, I believe this was just a 3030, or 40 days of, you know, a certain episode. Whereas, with this capability, capability, the patient, you know, and then, by the way, as a part of that death, you know, the patient went in and would have gone in and out. Or the hospitals as part of reindeer admissions. Instead, this capability would have allowed this patient to to live longer and then die at home. Right? Which? Which, which would have been preferred. So, so those are the things that digitization, plus AI brings in, right, which is the ability to provide better care, more customized care at times, and then more timely care as well,
Megan Antonelli 25:26 yeah, and that's so important. And you know, we talked a little bit about this, I mean in terms of the quantification of value. And you know that quality of life, certainly at that point of your life, you know, and how you can improve that it can be sometimes hard to measure, you know, and as a as a numbers guy, you want it to be numbers, but you know, there's nothing you know. There's no question that you know, sort of that being at home and being with your family in those times is so important and easily not something that if the capability and the technology is difficult to get there, you know, if someone said, Come, you know, buy this system for x, y, and we can fix this problem. It might not be prioritized, but being able to have that agility and speed to do it within the organization because of the foundation you've laid allows you to build those things in house, which is great. You talked a little bit about, you know, obviously, having some on prem, some in the cloud. You know, we've seen a lot of incidents over the particularly this summer and year, you know, where there's been some security breaches, there's been some downtime, redundancy is important. Talk a little bit about, you know, what you guys have done in terms of ensuring, you know, sort of security and, you know, ongoing readiness within the organization,
Sameer Sethi 26:51 yeah, absolutely, security is key. Privacy is really important. You know, we as you know, I, in some form, I'm a patient myself. My families are patients as well, right? And managing that data appropriately is very important, so that, I think, comes before anything becomes, before agility, you know, before it comes before speed to market. And I think, I think that the journey there starts with, with putting the rules in place, right? You know, you're, you know, my team, for example, experiments quite a bit, but the experiments within confined spaces, right? And their rules around which which, what we can or cannot do. So so I think having a team and a leadership that not only provides provides the funding, but the aspiration to to keep data as secure as possible is very important. But there's a, there's a balancing act there between, you know, innovation and being the ability to to experiment with things, and and and being careful, and that's, in my opinion, not, not hard to do, right? It all starts in building the foundation. Right? It's in, our team has done a tremendous job in putting that together. Hackensack has invested considerably in setting up security, whether it be on prem or on the cloud, to make sure that is as safe as possible. Redundancy is an interesting topic. Obviously, us as a health system have to have redundancy, but again, you know within reason, right? It is. So what we have done is we have built the ability for us to come back online as fast as possible, and then and then, and then, figure things out from there. But there are certain situations where redundancy is not possible, right? It's not practical. So we have what we what we do Megan is we find where redundancy is possible and practical and where redundancy is not possible, in which case we how do we come back online? How do we make sure that becomes available as as you know, as quickly as possible. How can we come back into in we will have to go offline, obviously, if something bad were to happen. But how do we come back? Come back quickly and come back safer and stronger? Those are the things that we focus on. So it's a mixture of redundancy and being prepared for the worse, right?
Megan Antonelli 29:15 Yeah, no, of course. And you talked a little bit, I mean, just in terms of as we've only got a few minutes left, but you know, you said that, you know the digital transformation process and and the sort of attitude has been bold or bolder, which is great, but again, that's also a balance around that. But as you look to the next 510, years, you know, certainly with the role of AI. And you know how, how much easier it's going to make to do amazing things with data. Tell us what you think, you know, what we should expect from organizations, you know, on the on the leading edge, like hack and tech Meridian and others.
Sameer Sethi 29:56 Yeah. So first of all, AI is here to stay. Right? It is not going anywhere. I think there's been a rapid acceleration of of of the need and the ability of AI, you know, for in last year, year and a half, which is good and bad, you know, what's happened in the last two years is, is, I mean, if we've been doing AI for quite some time, the whole world healthcare has been doing AI for quite some time. I tend to divide this into two kinds, which is machine learning AI, which is what we've been doing all this time, and then now generative AI. So AI is not old. I'm sorry, AI is not new. Generative AI is new. But what we'll see is that we will see more and more of both generative AI and machine learning AI. Why? Because I think the world has woken up to what it can do right and generative AI is responsible for that. So there are huge pressures on on our organizations to to build AI capabilities for the betterment of of our clinicians, of our staff and of our patients, you know. And by the way, that it hasn't just come, come from an aspect of, you know, a few companies did good things. These companies and and and their competitors also delivered ways by which we can, we can develop AI faster, right? So, a use case, and it isn't just cloud. Cloud did speed things up. But when generative AI came in, what also came behind it is these, these things that's called large language models, right? And these large language models are, is, are things that today, that we can, you know, very easily, get access to, right, securely. We didn't have that level of access before. We don't have this. So when we built, when my team, built models prior to what happened, what happened now, just two years ago, we built our own models. We didn't build large language models. We built small machine learning models, but we never had access to large language models, right? So, and now we do so as a result of that, we have more AI capabilities. So what's going to happen here, Megan, is that I think the needs of AI will will increase in the next few years. We as an organization have to be, have to be ready for it, right? What that means is you have to, we have to come up with, with making the data available. So these AI skill because AI functions on large amounts of data, right? So you have to make sure we are readier with data. We have to make sure that we have the right infrastructure in place to deliver this. Not all AI is safe AI. So we need to have, we need to make sure that when we deploy AI within the health system that it is confined to to the to the security and privacy parameters, right? It's important that we have the ability to govern and monitor the usage of how that data is being used. All that is important. So I think, I think, in summary, what I'll say is that, is that the need for AI is going to continue. We have to build up our muscle and our capabilities to make that data available. And it's not as simple as just just buying AI. It's about building the infrastructure, sending powers, putting policies in place, having the right governance that looks at AI and says, What is good AI, what is not good? Ai, all that has to happen in the next few years. You know, we have seen tremendous growth, and I can see where organizations, if they are not prepared to manage this influx, are going to suffer,
Megan Antonelli 33:30 yeah, no, it's so it's so interesting. And I think when you think about that, sort of the parallel journey that this has all been on, right with the, you know, sort of the shift of focus to the importance of data, the capabilities that the cloud has given us, you know, and the scalability. And now, of course, you layer on AI generative and machine learning to, you know, to sort of look towards what's possible. And it's, well, it's just a pleasure to hear what you guys are working on and and how, you know, how quickly it's been able to, you know, and we talk about how slow things move in healthcare, but if you really think about it, to be able to go from, you know, April of 22 to now, where you've got that many applications working towards, you know, improving patient outcomes and quality of life, that's, that's pretty amazing,
Sameer Sethi 34:19 yeah. And definitely, I think, for for Hackensack, and I'm sure lot of organizations out there, technology and ability is no longer slow in healthcare, right? We are moving fast. We are moving safely. We are moving within the confines of privacy and security. But I think gone are the days where, where people can say that, you know, technology, decision, in healthcare take time. They don't right. We have, we don't have any excuse. We have the enablement from partners, you know, as such, Google and others, right, to go at this faster and, you know, so I think, I think, I think technology's AI is here to stay. Technology's gonna move more, really fast, and we all have to be ready for it,
Megan Antonelli 34:59 right? Yeah, well, that's great. It's a great way to wrap up. Thank you so much for joining us. Samira on digital health talks, it's been a pleasure to talk to you. And you know, you know it's, it's, it's an amazing journey. And yes, we do, we all have to be ready,
Sameer Sethi 35:15 yeah, for this opportunity. This has been great, really nice talking to you again.
Megan Antonelli 35:19 Absolutely so fun.
Thank you: 35:21 Thank you for joining us for this week's health impacts digital health talk. Don't miss another podcast. Subscribe@digitalhealthtalks.com and to join us at our next face to face event, visit healthimpactlive.com you