Srini Attili – Executive VP Civilian Business Group, SAIC

Play episode

On the HealthBizTalk podcast, Tony Trenkle interviews Srini, Executive Vice President at SAIC, to explore technology’s pivotal role in modernizing healthcare systems. Srini shares insights on leveraging AI, cloud computing, and cybersecurity to drive innovation and improve patient outcomes.

Transcript Of the Podcast

Welcome to Health Biz Talk, the industry’s leading podcast that brings you today’s top innovators and leading voices in healthcare technology, business, and policy. And here’s your host, Tony Trenkle, former CMS, CIO, and health IT industry leader. Hello, Srini.

It’s great to see you again, and welcome to our podcast, Health Biz Talk. I’m just going to give a short introduction, and then we can get into some of the questions. Srini and I have known each other for quite a number of years since our IBM days.

And today, Srini is the Executive Vice President for the Civilian Business Group at SAIC. And in that role, Srini orchestrates the strategic planning, business development, delivery of solutions and services within the Civilian Business Group. He leads SAIC across the civilian government market, including healthcare, law enforcement, border protection, transportation, and federal financial, as well as state and local.

(1:07 – 4:19)

Srini has been at SAIC since May of 2024. Prior to SAIC, he was a partner at McKinsey and previously held leadership posts at Deloitte and IBM. So, Srini, I hope I got all the important parts.

If I left out anything, just let me know. So, the first question, Srini, is more of a kind of a background. Maybe we can flesh out a little bit more about how you got to where you’re at today.

How did you first get interest in the IT space or even the healthcare space? I know you’ve worked at a lot of different organizations and some very interesting jobs. So, just a short, brief cap of what kind of motivates you and got you to the point where you’re at today. Thank you, Tony.

It’s been a privilege to do everything that I get to do. It’s an honor to serve several mission clients that focus on driving better, efficient government for our citizens, health and well-being for our citizens, and work across different mission areas. So, just some background for you.

I grew up in a small town in Southeast India. I got first exposed to computers back in the 90s as a high schooler and just loved programming. I wanted to be a software engineer.

That’s all I wanted to ever do. I enjoyed coding. I got an engineering degree in computer science back in India and came to the U.S. for a master’s in computer science.

After graduation, I went to Telecom. Bell Atlantic was my first job as a software engineer. Then I got quickly exposed to consulting there.

Just the ability to have an impact on a diverse set of clients that you work with, a diverse set of technologies that you can work with was very appealing to me. So, I went to PricewaterhouseCoopers in the Washington consulting practice. Since then, IBM acquired PwC Consulting.

That’s how I went into IBM. The first 10 years, I worked in the federal space, U.S. Navy, Army, Homeland Security, large-scale systems integration, large-scale mission-critical programs, data analytics, had a lot of patents in that space. Then Affordable Care Act passed.

I intentionally moved into healthcare still with IBM. At that time, I spent 10 years in the broader federal market and then went to serve payers, providers, PBMs. During the Express Scripts MedCo acquisition at that time, we’ve done a lot of systems integration work across consolidating systems across multiple domains, I would say, claims, eligibility, enrollment, verification, so on.

So, really got exposed to the deep tech among payers and providers, too, as well as broadly health tech companies. I went to Deloitte in 2017. So, the commercial healthcare space, again, a lot of payer work, provider work.

And then at McKinsey, did a lot of private equity work in healthcare and health tech as well. So, let me pause there. Anything I can answer or clarify there? No, I think it shows the breadth and depth of your background.

(4:20 – 7:39)

We’re going to drill in a little bit more in the healthcare space and specifically kind of the technology that drives the healthcare space. And I know you’ve looked at this from a number of different perspectives based on your background. But one of the things I know has changed over the past several years has been the move from on-prem software solutions to cloud-based subscription services and other types of arrangements that not only have upended the way buyers buy IT, but also the way sellers sell IT.

I was talking to a colleague of mine I used to work with at CMS. And when I was at CMS, I was in charge of the IT group and was the CIO. And at that time, I had directly under me 50% of the IT procurements at CMS.

Well, my colleague, who just recently left CMS, was a senior executive there in the IT shop. He said, now the central IT shop’s share of the IT budget at CMS is down to 25%. And I think if we went around the government and industry, we’d probably see the same thing because a lot of individual programs and business units are moving more to procure what we would consider IT now, but now with the cloud, it’s a little more ubiquitous.

So what are your thoughts? You’ve seen this evolution happen. And where do you kind of see it going? And then we’ll jump into a little bit more specifics around cyber and a few other things. But just from an overall perspective, what do you think? Absolutely, Tony.

In my opinion, at the end of the day, technology is an enabler to achieve a mission outcome, a business outcome. IT doesn’t matter, right? It is supposed to be friction-free, invisible, unsung heroes in the background, and you’re enabling the mission. You’re enabling a business outcome.

So if you start with what is the outcome we’re all trying to achieve, right, with the IT budgets, IT departments, business users, mission leaders, it is to deliver a mission outcome in the healthcare space, whether it’s patients, payers, providers, caregiver experiences, right? If you start with that, and then obviously all the back office supports that needs to happen. If you start with that, if a business or a mission leader is able to orchestrate, a business analyst, mission analyst, orchestrate the outcome they want to achieve, the functionality they want to achieve without the knowledge of coding, deployment, containerization, you name it, all the IT complexity that is behind the scenes, if I’m able to do that, then why not, right? So I would say the job of technology leaders is to make it as frictionless as possible for the mission business users all the way up to the chain. So now you step back to the conversation you were talking about.

(7:40 – 11:20)

Cloud is not necessarily cheaper. We all know that, right? Just moving from on-prem to cloud. There are certain things you may want to keep, heavy data transactions, things like that you may want to keep because it’s inexpensive, right? Replication, things like that.

My take would be is as a service trend will always, we’ll be always moving towards as a service, because if it’s not your core competency, why build it? If there is a solution, I wouldn’t call software as a service, I would call it solution as a service, because it can be a combination of multiple softwares that are needed to do a job that needs to be done, right? So that trend should continue, going back to kind of how you said, I think the budget should be minimal and minimal, right? Anything the technology office should have, all the back office, the network, the compute, the storage, the cyber, which you can do at scale. So you create the boundaries for your business mission leaders to operate in, and those boundaries should be effectively managed by the IT department, right? Multi-cloud, security, right? Your patterns, right? Your archetypes on sandboxes that you want to use, things like that, that only you need the scale of the enterprise level, that should be done by IT, but everything that goes in there, you should let the business and mission leaders seamlessly, efficiently able to deploy the functionality they need, is how I would say. So if you’re working with a health agency, and as you know, the health agencies have IT folks, but a lot of what they’re focused on is the mission.

So let’s say it’s Veterans Health Administration or CDC, for example. They have a lot of clinicians, scientists, and others who really understand IT from a data perspective, but not from an infrastructure perspective. So how do you help educate the IT leadership at those types of agencies with the fact that you understand the kind of the interrelationship between the two? As I always say, every customer has a customer, so to the extent you can understand how they do that, how do you help support the mission of the IT shop in those agencies? So the top things I would say is, again, make it frictionless, right? It’s about the experience, even the developer experience.

Like you have, let’s say, developers in your organization that are mission-focused, are building mission apps, right? Not worried about the compute, storage, and containerization deployment, like hopefully you automated most of it, and it’s infrastructure as code, and so on, right? So now you have to focus on improving the experience of those developers, mission analysts, IT folks that you have in the agency, so that they can focus on creating better products for their end customers, as you said, every customer. So again, it’s about kind of a heat map, right? Where would you make it as automated as possible, right? Within kind of giving the guardrails, the boundaries, so that people don’t create one-off shadow, not following the cyber protocols, or running up the computer storage bill, unintended consequences, things like that. So you manage the boundaries, and I go back to my previous conversation, the question you had earlier, it is about enabling them to create better mission outcomes as seamlessly as possible.

(11:21 – 20:49)

That should be the focus. I call it the digital dexterity of the organization. Your job as an IT leader is to elevate the digital dexterity of not only your IT group, but groups that rely on IT.

Once you elevate that, give them the right tools, agility comes in. You pull out more products more quickly. You fail fast, and learn from it, and not wait on these long deployment cycles to get something out.

Yeah, one of my challenges that I ran into, and I know it’s still a problem today in a lot of these agencies, is they don’t, and I’m talking more about the health agencies, but there’s a lack of trust of the central IT shop. They feel like in the past, it hasn’t always delivered for them. So it’s nice to talk about frictionless, and things of that sort, but I think some of them have a lot, so some of them tend to try to set up their own internal IT shops to help support that.

Of course, you as a vendor, it creates some challenges, because you’re not only trying to work with the central shop, but also with these kind of mini IT shops within the program areas that are frankly more, obviously more mission focused than the central IT shop is, because they have specific deliverables they have to do. That’s right. Sorry, Tony, was your question on how we avoid the shadow IT or the duplicate IT? Or how do you kind of bring together the shadow IT and the central IT, because the shadow IT often is, I’m not saying it’s at odds with the central IT, but it often is not as complimentary as maybe it could be, although you as someone who may be selling to both may say, how do you kind of see yourself? Are you a facilitator, are you a collaborator, or does it depend on the situation? You hate to answer always that it depends on the situation, but it’s where you start with, and why are we doing certain things? Like if that’s a shadow IT, whether you call it a hub-and-spoke model, whether you call it a federated model, there’s a reason why that function has been stood up.

It costs money, people are not dumb, that somebody’s paying for it, so they are meeting some purpose. So if you step back and say, why does the need exist to have these spokes? Instead of calling shadow IT, I call these spokes, let’s say the hub is the central IT and then the spoke. So maybe the hub-and-spoke model is the right model for the culture of the organization, and as long as you’re not duplicating, as long as you’re not reinventing control, cyber security guidelines, and there is no inefficiency in that, that’s when it makes sense.

Potentially, it makes sense to have a hub-and-spoke model. It makes sense to have shadow IT in this case, as long as the central IT is enabling them, creating the framework guardrails so that the spokes are not duplicating work and instead focused on adding value. There are certain things that you have to do centrally.

It is inefficient to do individually. So get those functions, focus on that, and then have the spokes focus on what they do better and help them be successful rather than fighting it the other way. Yeah, and that kind of turns to another question that you and I have talked about before, which is the role of not only the central IT shops, but the role of the research organizations with cyber security.

As you know, the whole issue around cyber security has evolved quite a bit in the past years. We see a lot more ransom attacks. We see a lot more compromising of data.

There’s a lot of concern as we move into AI that there’s going to be even more challenges happening in the coming years. So what are your thoughts for the roles of organizations like ARPA-H, NIST, and some of the other government agencies that are really trying to set the standards and parameters for how agencies deal with future cyber challenges, for want of a better way of putting it? I think they play an incredible and important role. I am very excited for ARPA-H and their mission, how they are set out.

It’s accelerating. A private company by themselves cannot do it. You have ARPA-H stepping in and helping elevate, potentially mitigate the risk for a small private sector organization that can’t have that scale in terms of access to data or the ecosystem, things like that.

So I know in May, ARPA-H announced a new cyber security program to invest in creating tools for IT teams at the regional hospitals, because honestly, there are so many regional IT hospitals. Not everybody can keep up to the pace of the attacks, keep up to the pace of regulations that are coming in. So it’s good to have a government agency like ARPA-H stepping in to create that common set of framework and tools for hospital IT teams to use.

And this is where the hub-and-spoke model, in this case, if I think about the centralized pieces, the government giving the spokes, right? Everybody has their IT and giving the common tool set, common framing so they can use. I think that that’s an important role they play. And also ARPA-H, I believe earlier this year, partnered with DARPA, the defense side of it, to expand this AI cyber challenge.

Again, this is about designing tools, capabilities. And the best thing the government agencies can do is set the policy, the funding, the incentives to help go do that, and then try the standardization, right? So we have equitable policies, right? They’re fostering innovation and protecting patients, right? And then leading in cyber, because sharing intelligence, it’s not for one hospital or one nonprofit to go do. This is inherently a government function, I would say, is that the scale the government has, the direct intelligence sharing, giving the common standards and tools that everybody can use is huge value, right? Yeah, it strikes me that, as we say sometimes, you’re only as strong as your weakest link.

And I think the challenge that I’ve seen, or certainly one of the challenges, is a lot of these smaller, sometimes rural hospitals, they don’t have an IT staff. And you can provide the tools, but this is where I can see companies like yours and others kind of play in that key role to kind of translate what the government policies and framework are, and probably in some cases with the use of government funding as well, to kind of make sure we do have this hub and spoke model and it works. Because, as you know, broadband’s an issue in some of these small rural communities, and many of them do not have the staff or the knowledge, or frankly, any type of infrastructure that can support that.

We saw that in North Carolina after the recent hurricane, and we’ll see it in other national emergencies. So where do you see the role of industry in helping with some of this? If the government’s providing kind of this framework and tools and policies, where do you and some of the other large vendors fit into this, you think? Yeah, I think you articulated the challenge very well, Tony, right? Even if you’re able to attract the talent, it’s very hard to retain. These are very hard skills, right? Cyber skills, offensive, defensive cyber technologies, maintaining a key… So to your part, in the first part, assuming the government is successful, which I’m confident they will, in laying the frameworks, the tools and capabilities.

The second part I would say is, look, we talk about shared services in the context of an organization, of an entity, right? I would say, whether it’s through public-private partnerships, there should be a concept of shared services that multiple entities can use, and that’s when you get the scale. And that public-private partnership can be with the government to provide those shared services, whether it’s cyber protection, vulnerability detection, threat intelligence. That is where companies like us, who do a lot of work in the data sharing, cyber, border security and defense space, can bring to the health sector and create this shared services concept where you would pay in for the services, but you are part of a large ecosystem of a public-private partnership backed by the government to tap into these services, so you don’t have to go reinvent the wheel every time.

(20:49 – 23:55)

I think a model like that would be ideal and efficient in my mind. Yeah, and you actually bring up a good point, and I’m sure you can see this with SAIC. When I was at CMS, we talked a lot with different agencies, including some of the defense agencies like NSA, because some of the stuff that’s going on in the classified area does have a lot of potential use in the civilian side, particularly when it comes to, like you said, cyber security and identity management and some of the other areas that are done every day in the defense side.

And certainly, because of what DOD has to deal with on a global basis, it’s a scale much larger than most of the civilian agencies have to deal with. So do you see that increasing over the next few years, or how do you see it today? Because I felt like the potential is always there, but it feels to me like there just needs to be more of a push to get that to become more of a – yeah, right. The collaboration between defense – see, it’s interesting.

The places I’ve been in the past, either you have a lot of commercial work that I’ve been in, and you’re isolated, you do civilian work, you’re isolated, or you do defense intel, or you’re in a SCIP, can’t talk to you, right? At SAIC, I feel those barriers are very minimal, right? As I sit on the ELP, I work very closely with my Air Force colleagues, my intelligence colleagues, my Army, Navy colleagues. And I think an opportunity for a defense and intel-driven company that has a significant amount of civilian work, like what we have, can be an interesting play for us to bring in the best practices from cyber, from the intelligence space, intelligence community, into kind of commercial healthcare and civilian health agencies. So that’s a unique role to play, and I hope we can kind of break down some barriers in terms of knowledge sharing and tools and expertise across both, right? Yeah, one of the other challenges I found when I was at CMS, we fostered a partnership with Oak Ridge National Laboratories, which, as you know, runs some of the fastest supercomputers in the world and gets involved in a lot of classified and unclassified work.

The challenge with them was they knew how to solve big data problems, but they didn’t understand the business of healthcare. So a large part of our work with them, before they could become useful, was to really help educate them because they did not understand, recognize, and we deal with large data sets on the health side as well. But if you don’t understand the business problems behind that, which kind of gets back to your whole issue about understanding the mission and things of that sort.

(23:56 – 29:44)

And I think your job is to make it easier for them to understand, right? So, for example, let’s say on the intelligence community, it’s not only within the U.S. intelligence agencies, you’ve got to share the appropriate level and so on, but also our coalition partners, right? The Five Eyes, you don’t get to see everything, right? You get to see only what you need to do, and it’s the same data sets, right? So that problem has been solved. And if you translate that to, hey, I’m in this health system, I got to go to health system Y, and I can’t just share the entire medical record end-to-end. It’s time-based, it’s certain elements of the record in this chronology that I want to see.

So you can draw parallels between, if you understand both sides of the equation and help people realize, hey, the problem is similar, right, that we want to share, right? Yeah, and then kind of flip into that, the other big elephant in the room besides cyber is data, and of course with the growing use of AI. And one of the amazing things we’ve all seen over the last 10 years, five years, is the growth, not only in data, but in various types of data and how data is being brought together. So for example, in healthcare, societal needs are being brought in, so we call them social determinants of health.

So you get beyond the clinical and administrative, but also what are the reasons behind someone’s condition, whether it’s based on the fact that they live in a certain area that’s more prone to health issues or because of a lack of transportation or some other problem that deals with that. So we’re getting more into bringing together different types of data. Of course, data that you get from your wearables and other types brings in even richer longitudinal data.

So then you take all that data and then you add in AI. And as you know, we’ve moved from the AI in the beginnings of machine learning that we saw with Watson at IBM to now we have the generative AI with the chat GPT and other types of AI initiatives that are going on today. So where do you see that as having the biggest impact in healthcare today? I mean, you’ve worked across the sectors and maybe that’s too general of a question, maybe a couple examples of where you see over the coming years, because you remember how we had challenges with Watson in the beginning because of some of the areas we dealt with, they were ripe for using AI, but we weren’t quite there in terms of how to implement it and how to bring in the people who understood the clinical issue.

I think when it works well, it’s magic, right? Right, exactly. But it’s about trust, right? I would say, especially in healthcare, it involves people are most vulnerable, I would say, when they are with their doctor and are facing a health crisis and you trust it, it’s all about trust, right? And that expert on the other end, why would an expert trust an algorithm? You got to be able to have explainable AI, got to be making sure the AI is helping the expert understand how we arrived at that conclusion, right? And we talk about responsible AI, ethical AI, I mean, different variations of it, but at the end of the day, it cannot be a black box. You got to avoid hallucinations, explain if you hallucinate, why did you hallucinate, right? Like, how did you come up with that, right? So, that’s one key piece.

And then we talked about data, explosion of data. We all know that any AI algorithm, any learning a machine does on underlying data sets, the output of that model or the confidence efficacy of that model is very well dependent on the data that’s underneath it, garbage in, garbage out, right? So, governance around data becomes very important, right? Those are like, I would call the non-sexy side of it, right? There’s cosmetic AI and we call it like the gritty tech and operational AI. So, having control on the data that you are feeding into the models for training and learning is going to be extremely critical to make sure you’re getting the right outcomes here.

In terms of where the biggest impact, I mean, we can go off on a laundry list of use cases, but I feel that access to care with the shortage of primary care physicians, we all know this, we kept talking about like for, I think that access to a board certified primary care physician who happens to be a bot is incredible, right? And you can, I mean, that bot doesn’t get tired. The bot can replicate itself a million times and can make a million doctors in an hour as they scale up and scale down, right? I feel that the knowledgeable primary care doctor equivalent knowledge that it’s very hard to get access today, wait times, amount of time you can spend with the primary care doctor. If you can create this personalized primary care doctors for every American out there that they can chat with, they can have a conversation and at the same level of intellectual capacity as a physician can have, that can be game changing.

(29:45 – 36:54)

I think one of the things I’ve seen is as we’ve gone through the different eras of automation is the first thing people say is, okay, we automate things that are kind of the easy, low hanging fruit for use. One of the things with AI that we looked at was, okay, can it help assist say a radiologist or someone who’s a specialist in determining a condition or reviewing data to come up with a more complete analysis? But I think the model you’re talking about would be something that could be possibly implemented gradually as well, because right now, in many cases, we don’t go and see a doctor. We see a practitioner who’s not an MD, but someone who can certainly diagnose if your sore throat is a cold or if it’s COVID or whatever condition it might be related to.

And then as we move up the more difficult cases, and then it becomes more of an assistant. Now, the challenge with that that I read recently is that sometimes that AI by itself, in some cases, comes up with better outcomes and doctors come up with worse outcomes. But then when you put AI and a doctor together, rather than come up with a better outcome, it actually comes up with a worse outcome than AI because in some cases, the doctors override the AI because they feel based on their knowledge, they have a better feel for the situation.

So I mean, I think traditionally, we’ve thought, well, you know, you add AI to the mix, it can just increase the knowledge base. But if the provider has their own biases in there, then you’ve just replaced one bias for another, maybe. I think, again, go back to the explainable AI, right? If the AI, I mean, they’re all smart, accomplished individuals.

If the AI is to explain how it arrived at a decision, I think that builds trust. Right. And you can say the feedback loop, if you override AI, maybe the doctor knows better and explain to the AI, say it goes vice versa too, like why you’re overriding it, why you don’t believe it.

I think that feedback mechanism of the machine learning from the human and the human learning from the machine may be optimal as long as the machine is able to explain to the human why it came up with the recommendation. And the first part I said on a personalized doctor bot for everyone, it works only if you have data from that human constantly feeding the bot. So everything that’s happening in my body, I wake up, I have my RRN.

There’s so much data that we are generating. Just the AI bot, the bot is not touching you, feeling you, diagnosing you. If it has all the critical data coming from sensors and other things, as we advance medical technology devices, that combination can be very, very helpful.

Right. And one of the things I’ve seen in some of the work I’ve been doing is smart toilet seats for elderly people and how it can pick up a lot of information just from that. And one of the challenges I think that clinicians have is they don’t get the access to continual data.

They get snapshots in time. So Srini runs to the doctor’s appointment. He’s just been through a busy day of things.

His blood pressure reads 170 over 110, whereas when he’s sitting in his home, it’s only 118 over 70. And I’ve had it happen with me. And I know.

So then they say, well, we’ve got to put Srini on a blood pressure medication. And now if we have an AI support, then it can say to us, well, look, we’ve been monitoring blood pressure throughout. And then the continuous monitoring.

Exactly. You’re getting the data, not just the blood pressure. You mean all these sensors, toilet seat.

But instead of sending it to a human physician, I have my board certified bot physician constantly analyzing and intervening and providing access to me and answering questions I have. That would be the future. I would say that’s exciting.

Well, how many years away you think that is? A year. As soon as I eat my dessert, it’s like my dog saying, hey, you’re, it’s like continuous monitoring, right? Right, right, right, right, right. All right, Srini.

Well, we’re coming towards the end of the podcast. So turning to the personal side, what keeps you busy when you’re not thinking about government, IT and healthcare and the other things in your day and night job? It’s been busy. It’s been six months at SAIC.

It’s been an incredible ride. I love the job, love the team, amazing platform and the impact we’re having. On the personal side, we have two boys, as you know, growing up on just going through the college apps.

So it’s been stressful a little bit. So it’s more about kind of finding ways to spend, spend more time with them as they’re growing up. The younger one is in middle school.

And then my wife, dad, we didn’t have an intervention here. I have a 40-year-old daughter, and if I’m home, she just comes in with a ball and like bugs me to play. And I picked up on golf, but during COVID, right, so still suck at it.

That’s why I don’t play during the week. I only play during the weekends on a Sunday morning. So yeah, that’s what keeps me busy.

Good, right. And, you know, we’ve hit at a real superficial level, a lot of different topics today. Is there a certain, and we can put this in the show notes, but is there certain websites or podcasts, publications, or anything that you, if someone says, well, I’m intrigued by what Srini talked about, is there places that you go to to get information or anything in particular you would say people should look at? I mean, I have broad general stuff that I do.

I don’t have anything specific on healthcare. Maybe it’s the health tech podcast. They have to come here, Tony, to get more knowledge.

I’ll boost market. Look, from, I love the acquired podcast, if you’ve heard of it. It’s really incredible, the stories that they bring in, the most accomplished executives who started their own businesses and senior leaders.

I love the acquired podcast as much as I can hear. I tune into it. The Morgan Housel podcast, I don’t know if you know that he puts like 10, 15 minute bites of topics around broadly life, finance, philosophy, which I love, easy, consumable bites.

(36:57 – 39:19)

Economist is a go-to. I love the editorials, how they write. I actually learn storytelling from how the economist does it.

The consistency is incredible. Like every piece you read, like the story how it builds up, how they close it. So I love the editorial in Wall Street Journal every day.

So that’s my go-to reads and podcasts. I guess at some point we’ll all have our own concierge AI that can just kind of go out and look at everything and bring us back exactly what we need in a bite-sized format as often as we want. That’s right.

That’s right. Make it easy. Okay.

So now I’m just going to hit you with three quick questions with 30 second answers. And I know these actually can be a lot longer than that, but these are kind of bite-size quotes that we just want to kind of capture at the end here, and then we can bring them back to you later on to take a look at. So one is, what do you think are the biggest technological challenges for military health care? Keeping up the advancements of tech in the austere and non-austere environments they operate in.

Again, I’ll limit to the bite size. I think it’s the cyber threats to hospitals. It’s not the electronic medical records in my mind.

It’s all those point solutions that are interconnected, the bedside devices, the labs, stuff that they have. It’s sometimes outdated, and they are interconnected, and you’re dependent on them so heavily. So protecting that infrastructure would be… Protecting the perimeter, basically, and both the internal and external perimeter.

Yes, yes. And then what about the biggest technology impact on patient care? I think we touched on it earlier, right? This whole concierge primary care doc that knows everything that’s going in your body all the time. Right, right.

That access to care and the continuous monitoring leads to early intervention of diseases, chronic disease management, and hopefully improves the health and well-being of everyone. Right. Well, thank you, Srini.

I appreciate you taking the time. I know you’re very busy and look forward to talking to you again soon. Thank you, Tony.

Really appreciate it.

FTC Disclosure: Some guests pay for advertising on the podcast. Opinions expressed by guests on the podcast are solely their own and do not necessarily reflect the opinions of the host or HealthBiz Talk Show.

Share this
FacebookXRedditEmail
Join the discussion

12,915 comments

More from this show

Menu

Subscribe

Recent posts

Episode 3
Srini Attili – Executive VP Civilian Business Group, SAIC