Speaker 0: This is Scott Becker with the Beckers Healthcare Podcast. I'm thrilled today to be joined by one of my favorite leaders, a brilliant, brilliant leader. We're joined today by Seema Verma. Seema is the executive vice president and general manager of Oracle Health and Life Sciences. She's had a brilliant career.
We're gonna ask Seema to talk about what Oracle's focused on now, where they're going, and a lot more. Seema, can you take a moment for anybody that's new to either yourself or to Oracle Health? Tell us a little bit about yourself and about Oracle Healthcare.
Speaker 1: Sure. Well, gosh. Talking about yourself is always a a little bit, awkward. But, so my, you know, my career journey has been around spending a lot of time in in health policy. And, before coming to Oracle, I was the administrator for CMS.
And so most, like I said, that that was sort of the the pinnacle of a lot of time on policy issues, working with state governments, working with, the federal government, and also, you know, being on the front lines working at a at a public hospital. And so all of that I think all of that experience was about trying to solve the problems in health care and then try to do that at the highest level, at CMS on a policy level. So, you know, I'm taking this turn into the IT world and tech world, and I'm very hopeful that this is the place where some of these long standing problems that we've had in health care can can get solved in a way that isn't quite possible, with government, with policy regulation, and even with funding. There are just some things that technology, I think, is better suited to solve.
Speaker 0: Thank you. And talk a little bit about last year, we had a chance to talk a lot about Oracle's vision for next generation EHR. Since then, Oracle Health has announced the new EHR is live for ambulatory surgical centers, ambulatory organizations, of all sorts. Why was getting highly focused into ambulatory important? And tell us a little bit about why the interoperable foundation will help health systems make progress with AI and modernization.
So the movement towards ambulatory, then how will this impact health systems?
Speaker 1: Sure. So, you know, I think we started with ambulatory. I mean, if we think about what's gonna be in the EHR, there's a lot of different workflows. And with ambulatory, there's a smaller number of workflows than we have with the acute. So a little bit less complex.
And, essentially, this is all gonna build up a lot of the work that we're doing for the ambulatory will also feed into the acute EHR. But, you know, I think what's different here, the approach that we're taking is very different. What we're seeing in the market today is older technology and bolt on AI solutions. And the reason why I think that's problematic is that when we think about AI and how AI works, AI works as well as the data that it has available. So how the model was trained, but it also needs access to that real time data.
And I think we at this point, most of us have used AI tools that we understand. Right? It's only as good as what you're telling it to do and the information that it comes that that that has available. And so if we think about health care, in health care, a lot of the decision making is multifaceted. Right?
We're not making decisions off of one area. So if we think complete health record? You may or may not. So, again, the AI is only as good as what information is right in front of it. But with health care, the decision making goes beyond that.
Right? It's it's the patient's preferences. It's it could be genomic information. It could be what the insurance company is going to pay for. So you need a lot of information.
You need access to that information, in a real time way for the AI to be more than just a a research tool. Right? I mean, for it to be really effective and deliver and address some of these long standing problems in health care, you need to have access to real time data. Older technology that is built on older databases, they're not built to retrieve real time information. They are systems of record.
They're not systems of retrieval. And that's really the the difference here. What Oracle's building is something that is built for AI. Right? We're not bolting on solutions.
We're not retrofitting something that's older. We're saying this is the future of AI, and we're building our databases and the underlying infrastructure, to be able to retrieve that real time information. We are able to pull information from different systems. So it could be that, you know, supply chain information or HR. You know, you need to have a lot of information, the ability to retrieve it in real time and to be able to access it from different sources.
And so that's the foundation of the EHR that we're building. It is live on ambulatory, and we continue to take input from our customers. And, you know, it's, it's exciting. And I think our customers are really excited about what they're seeing because they can see the potential of a different way and to make their jobs easier, to make, the ability to deliver high quality care, a lot easier than it has been in the past. So, a lot of excitement, and, we are also going to be talking about different other modules.
Right? So this is ambulatory. We'll be talking a lot about oncology.
Speaker 0: But let's talk about that now, oncology. Because on oncology is an area where data, research, and care delivery have to come together quickly. In fact, we see so many places in in the situation currently where oncologists almost seem like they're overwhelmed because there's only so many oncologists versus 2,000,000 new diagnosis a year. How can Oracle or or technology or oncology providers how can better connected data and intelligence help that work and support more informed timely oncology care?
Speaker 1: Well, that's the foundation of it. And if we think about the data that we're trying to bring together in oncology, it is a lot more complex. There's a lot more pieces to it. So we can start with genomic data. We're looking at tumors.
There's a lot of that understanding the genetic makeup of those tumors, and that really does inform the treatment. So there's genomic data. There's imaging data. There is laboratory data. There's surgical data.
There's all of these different pieces. And, you know, what we see a lot of times is even if some of the information is available, even some of that genomic data, is often in in the form of a PDF. Right? So it's not as easy to analyze. And then I think with oncology, the other big piece of it is clinical trials, understanding what clinical trials are available, where a patient can be enrolled, and understanding that all at once.
And so there's really no other area or no other area of medicine that combines all of those different pieces, and it's very much multispecialty. Right? There's gonna be an oncologist. There's a surgeon. There's, a lot of different providers that are that are involved in one's care when it comes to oncology.
So that's why we're starting there. You know, even things like just facilitating tumor board, right, where there's a lot of different providers weighing in on a patient's care. Even those things are difficult because of the information sharing. So part of it is information sharing, but a lot of it is also just the complexity, of the disease itself.
Speaker 0: Thank you very, very much. You've also, Seema, aside from oncology, ambulatory, we've got a couple more subjects I'd love to discuss. Wanna talk a little bit about life sciences, a little bit about revenue cycle, and also about trustworthy AI in health care. So so you've recently wrote an article about having trustworthy AI in health care. As AI gets deeper and deeper into systems, clinical administrative workflows both, how is Oracle Health approaching human oversight, human in the loop, transparency, privacy, and accountability?
And what should health systems think about when they think about the protection of everything and just the the concerns about AI moving on its own? What should health systems expect from technology providers?
Speaker 1: Sure. Well, this is a really important area because especially when it comes to you know, we're talking about patient care. So it's gotta you know, safety has to be at the first and foremost. And we wanted to put out this information because we wanted under our customers to understand how we are developing it and all the steps that we're taking. And moreover, I hope that it also serves as a standard and that other tech companies out there also have an opportunity to understand how we're doing it and that we're we're setting a bar for the entire tech community.
But, you know, it starts with human centered design. Right? So that means that humans are always in the loop, that the AI is not acting alone. It's making suggestions, but the the decision making is always, something that the human has to have oversight. The other areas around transparency and explainability.
It's not just saying, you know, here's the conclusion. Right? This patient has x or y or z. It's actually telling you why and where that information came from, you know, why and where that information came from. You know?
We're saying don't use this drug because the patient has an an allergy. Okay. Well, I didn't know my patient had that allergy, and I don't think that's accurate. We literally footnote everything so you can click on it and go back to the source. Or if it's an external data source, so a certain piece of literature, it's gonna tell you exactly where that came from.
So it's explaining why it's coming to that conclusion as well. So I think of it as, remember in math class where it was, show your work? You don't just put the answer on the paper and, you know, you get you get some points taken off if you don't show every step. It's kinda like that with, our approach in AI. So it really tells you that.
The other thing I think is that we can't assume that you put it out there. And just like all a lot of technology that's out there, that has to be continuously evaluated and validated to make sure that it's accurate and reliable. So it doesn't stop with, you know, you you kinda put it out there. You're constantly monitoring that. So it's pre deployment validation and then ongoing monitoring.
The other big huge piece is cybersecurity and security and privacy. You know, with AI, you're moving data, and we've gotta make sure that at every step that the security and privacy is there. So we have to really think about the privacy by design principles and built in those safeguards into the AI systems at the very, very beginning. Whereas if you're bolting on, it's not gonna be as secure. I don't think safety is a one and done.
We continue to monitor and continue to find ways to improve it.
Speaker 0: Thank you very, very much. And and, generally, people wanna work with companies that have deep resources that could address these issues that aren't just trying to get in the door, but also have the support to back up the privacy and
Speaker 1: the
Speaker 0: trustworthy issues and and security issues that are so important today. Sima, let me ask you about revenue cycle. Health systems are obviously constantly trying to work on revenue cycle. I think there's the latest said I saw was 2 to 300,000,000,000 a year spent on revenue cycle. What where is the biggest opportunity near term and longer term that health system providers can use to improve things for revenue cycle?
Speaker 1: What what we're seeing in the market is a lot of point solutions, which I think are helpful for addressing single tasks. But when we think about revenue cycle and how it works, there's the front office, the middle office, the back office. Right? And all of those pieces have to come together. And we spend a lot of money because there's a lot of coordination that has to happen between the front, middle, and back office.
With AI, there's an opportunity to bring all of that data together. I do worry that we're gonna be spending a lot that that there's a, potential for providers to spend a lot of money on point solutions. But, ultimately, it all has to start with common data and to be able so let's think about a denied claim. Well, maybe it was denied because the prior authorization wasn't there. Well, in order to do the prior authorization, you need to be able to get into the clinical record and have the right information at the right time.
So it it starts with being able to bring together a foundation of all of that data and all of those systems together so that the AI can work across it and it isn't just, you know, focusing on one particular solution. So think about coding. So maybe you've got an AI tool that helps you with coding, but that's only part of the revenue cycle. Right? You gotta code it.
You gotta submit the claim. If there's a denial, you need to be able to respond to it, you need to be able to go back to the clinical record and pull the relevant information, to justify the claim. So, again, it starts with that common data foundation and the AI being able to work across the system and not just in the EHR. There's some clinical data, but there's also financial data. And then I would say the last piece of it is ERP.
Right? Ultimately, this is all financial information that goes into an ERP, and you wanna be able to make sure that that whole system can work together.
Speaker 0: Thank you. Talk also, Oracle Health and AI is is bigger and bigger in life sciences overall, in research and bringing new therapies to patients. Where do you see the greatest opportunities in sort of the life sciences areas?
Speaker 1: Sure. And that's a, you know, that's a great point. And the way Oracle is coming at health care is not just with one point solution on the EHR. Right? It's a it's a much more it's a much more larger play, and it's focused on targeting and transforming the entire industry.
Because I think there's a realization, you can't transform one part of it and think that you're gonna do all of it. It's not gonna work that way. And that's why, you know, a point solution, an EHR, it it's not going to get at it. And the the clinical trials world and life sciences is is very, important to health care. We think about all the new treatments that are out there.
We gotta make sure that providers on the frontline know about those treatments and therapies. And also, in order to accelerate innovation, we need to make sure that people are aware of the clinical trials that are out there. And that's that's a, you know, that's an area that we're very weak on. One to two percent of the population actually participate in clinical trials. And usually, those are the ones that are coming out of the larger academic health centers.
And so there's an opportunity to really accelerate research, cures, and treatments if we have more real time information. And it's bidirectional. I think for for pharmaceutical companies and med tech companies, they need to be able to, enroll patients into trials. They also need to be able to have access to data to understand the impact of the therapeutic intervention. So whether it's a drug or a device, what happened five years later?
I think there's a lot of information out there that we are not able to, to understand, and there could be a lot of lessons learned just from the data that we're not using today. And what I mean by directional, let's say there are new treatments and therapies. The reality is, a lot of doctors and nurses, there's so much going on that they're not always aware or have access to the latest treatments and therapies. And so that clinical decision support becomes really important and to be able to make sure that providers have access to real time data and information.
Speaker 0: Thank you. Just a couple more questions, Seema, and thank you so much for your time today. Oracle has its health and life sciences summit coming up this year. What should health system leaders prioritize first in in general as they tend to modernize operations and adopt AI? And then in terms of prioritizing, then what do you hope they take away from the Oracle Health and Life Sciences Summit?
So what should else assist us prioritize first? And then what do you hope they take away from the Life Sciences Summit?
Speaker 1: You know, for our summit, first of all, it's a global conference, so we have people coming from all over the world to participate. And we wanted the summit to be where obviously, we're gonna talk about what Oracle is doing and and profile some of our new products, but it's also supposed to be an opportunity for sharing and around, try to all work together around transformation. So bringing some of the greatest minds together. We've got a lot of different speakers that are not talking necessarily about Oracle, but they're talking about what's going on in the market. And we hope that it's a learning opportunity for the participants to learn from others, to understand not only technology, but regulation and policy that's going on because that's gonna impact the work that they do, and it impacts their their decisions around technology as well.
So it's really intended to be a learning experience for the participants. The other thing I would say is when it comes to what what do we want health care leaders to take away? And for me, the takeaway is to understand an approach to AI. It's not just about one solution, a bolt on solution, but it really is an enterprise approach, which is how do I use and utilize AI? And so it's not just a buying decision, but it's also, okay, what about my what about the impact of AI on my processes?
Right? I may need to do things differently because the AI is able to do this part of the job. We don't need to do that anymore, but what do we need to do? So there's technology. There's the the process transformation.
And then the third piece of it is also from that buying decision, understanding that you can't have an AI strategy unless you have a data strategy. It has to start with how do I bring my data together. And so building that understanding of how AI works, we hope that it'll help them when they're making decisions about AI, whether it's purchasing decisions, whether it's governance, which is very important. And then the third area is around transformation of the way that they do their work.
Speaker 0: Seema, incredible to visit with you again today. I followed your career, it seems like, for a long time. Just a brilliant person, brilliant career, and amazing the work you're doing at Oracle Healthcare. Thank you so much for joining us today on the Becker's Healthcare podcast. What a great pleasure.
Thank you so much.
Speaker 1: Thank you, Scott. Thanks for having me. Hope to see you soon.
We're gonna ask Seema to talk about what Oracle's focused on now, where they're going, and a lot more. Seema, can you take a moment for anybody that's new to either yourself or to Oracle Health? Tell us a little bit about yourself and about Oracle Healthcare.
Speaker 1: Sure. Well, gosh. Talking about yourself is always a a little bit, awkward. But, so my, you know, my career journey has been around spending a lot of time in in health policy. And, before coming to Oracle, I was the administrator for CMS.
And so most, like I said, that that was sort of the the pinnacle of a lot of time on policy issues, working with state governments, working with, the federal government, and also, you know, being on the front lines working at a at a public hospital. And so all of that I think all of that experience was about trying to solve the problems in health care and then try to do that at the highest level, at CMS on a policy level. So, you know, I'm taking this turn into the IT world and tech world, and I'm very hopeful that this is the place where some of these long standing problems that we've had in health care can can get solved in a way that isn't quite possible, with government, with policy regulation, and even with funding. There are just some things that technology, I think, is better suited to solve.
Speaker 0: Thank you. And talk a little bit about last year, we had a chance to talk a lot about Oracle's vision for next generation EHR. Since then, Oracle Health has announced the new EHR is live for ambulatory surgical centers, ambulatory organizations, of all sorts. Why was getting highly focused into ambulatory important? And tell us a little bit about why the interoperable foundation will help health systems make progress with AI and modernization.
So the movement towards ambulatory, then how will this impact health systems?
Speaker 1: Sure. So, you know, I think we started with ambulatory. I mean, if we think about what's gonna be in the EHR, there's a lot of different workflows. And with ambulatory, there's a smaller number of workflows than we have with the acute. So a little bit less complex.
And, essentially, this is all gonna build up a lot of the work that we're doing for the ambulatory will also feed into the acute EHR. But, you know, I think what's different here, the approach that we're taking is very different. What we're seeing in the market today is older technology and bolt on AI solutions. And the reason why I think that's problematic is that when we think about AI and how AI works, AI works as well as the data that it has available. So how the model was trained, but it also needs access to that real time data.
And I think we at this point, most of us have used AI tools that we understand. Right? It's only as good as what you're telling it to do and the information that it comes that that that has available. And so if we think about health care, in health care, a lot of the decision making is multifaceted. Right?
We're not making decisions off of one area. So if we think complete health record? You may or may not. So, again, the AI is only as good as what information is right in front of it. But with health care, the decision making goes beyond that.
Right? It's it's the patient's preferences. It's it could be genomic information. It could be what the insurance company is going to pay for. So you need a lot of information.
You need access to that information, in a real time way for the AI to be more than just a a research tool. Right? I mean, for it to be really effective and deliver and address some of these long standing problems in health care, you need to have access to real time data. Older technology that is built on older databases, they're not built to retrieve real time information. They are systems of record.
They're not systems of retrieval. And that's really the the difference here. What Oracle's building is something that is built for AI. Right? We're not bolting on solutions.
We're not retrofitting something that's older. We're saying this is the future of AI, and we're building our databases and the underlying infrastructure, to be able to retrieve that real time information. We are able to pull information from different systems. So it could be that, you know, supply chain information or HR. You know, you need to have a lot of information, the ability to retrieve it in real time and to be able to access it from different sources.
And so that's the foundation of the EHR that we're building. It is live on ambulatory, and we continue to take input from our customers. And, you know, it's, it's exciting. And I think our customers are really excited about what they're seeing because they can see the potential of a different way and to make their jobs easier, to make, the ability to deliver high quality care, a lot easier than it has been in the past. So, a lot of excitement, and, we are also going to be talking about different other modules.
Right? So this is ambulatory. We'll be talking a lot about oncology.
Speaker 0: But let's talk about that now, oncology. Because on oncology is an area where data, research, and care delivery have to come together quickly. In fact, we see so many places in in the situation currently where oncologists almost seem like they're overwhelmed because there's only so many oncologists versus 2,000,000 new diagnosis a year. How can Oracle or or technology or oncology providers how can better connected data and intelligence help that work and support more informed timely oncology care?
Speaker 1: Well, that's the foundation of it. And if we think about the data that we're trying to bring together in oncology, it is a lot more complex. There's a lot more pieces to it. So we can start with genomic data. We're looking at tumors.
There's a lot of that understanding the genetic makeup of those tumors, and that really does inform the treatment. So there's genomic data. There's imaging data. There is laboratory data. There's surgical data.
There's all of these different pieces. And, you know, what we see a lot of times is even if some of the information is available, even some of that genomic data, is often in in the form of a PDF. Right? So it's not as easy to analyze. And then I think with oncology, the other big piece of it is clinical trials, understanding what clinical trials are available, where a patient can be enrolled, and understanding that all at once.
And so there's really no other area or no other area of medicine that combines all of those different pieces, and it's very much multispecialty. Right? There's gonna be an oncologist. There's a surgeon. There's, a lot of different providers that are that are involved in one's care when it comes to oncology.
So that's why we're starting there. You know, even things like just facilitating tumor board, right, where there's a lot of different providers weighing in on a patient's care. Even those things are difficult because of the information sharing. So part of it is information sharing, but a lot of it is also just the complexity, of the disease itself.
Speaker 0: Thank you very, very much. You've also, Seema, aside from oncology, ambulatory, we've got a couple more subjects I'd love to discuss. Wanna talk a little bit about life sciences, a little bit about revenue cycle, and also about trustworthy AI in health care. So so you've recently wrote an article about having trustworthy AI in health care. As AI gets deeper and deeper into systems, clinical administrative workflows both, how is Oracle Health approaching human oversight, human in the loop, transparency, privacy, and accountability?
And what should health systems think about when they think about the protection of everything and just the the concerns about AI moving on its own? What should health systems expect from technology providers?
Speaker 1: Sure. Well, this is a really important area because especially when it comes to you know, we're talking about patient care. So it's gotta you know, safety has to be at the first and foremost. And we wanted to put out this information because we wanted under our customers to understand how we are developing it and all the steps that we're taking. And moreover, I hope that it also serves as a standard and that other tech companies out there also have an opportunity to understand how we're doing it and that we're we're setting a bar for the entire tech community.
But, you know, it starts with human centered design. Right? So that means that humans are always in the loop, that the AI is not acting alone. It's making suggestions, but the the decision making is always, something that the human has to have oversight. The other areas around transparency and explainability.
It's not just saying, you know, here's the conclusion. Right? This patient has x or y or z. It's actually telling you why and where that information came from, you know, why and where that information came from. You know?
We're saying don't use this drug because the patient has an an allergy. Okay. Well, I didn't know my patient had that allergy, and I don't think that's accurate. We literally footnote everything so you can click on it and go back to the source. Or if it's an external data source, so a certain piece of literature, it's gonna tell you exactly where that came from.
So it's explaining why it's coming to that conclusion as well. So I think of it as, remember in math class where it was, show your work? You don't just put the answer on the paper and, you know, you get you get some points taken off if you don't show every step. It's kinda like that with, our approach in AI. So it really tells you that.
The other thing I think is that we can't assume that you put it out there. And just like all a lot of technology that's out there, that has to be continuously evaluated and validated to make sure that it's accurate and reliable. So it doesn't stop with, you know, you you kinda put it out there. You're constantly monitoring that. So it's pre deployment validation and then ongoing monitoring.
The other big huge piece is cybersecurity and security and privacy. You know, with AI, you're moving data, and we've gotta make sure that at every step that the security and privacy is there. So we have to really think about the privacy by design principles and built in those safeguards into the AI systems at the very, very beginning. Whereas if you're bolting on, it's not gonna be as secure. I don't think safety is a one and done.
We continue to monitor and continue to find ways to improve it.
Speaker 0: Thank you very, very much. And and, generally, people wanna work with companies that have deep resources that could address these issues that aren't just trying to get in the door, but also have the support to back up the privacy and
Speaker 1: the
Speaker 0: trustworthy issues and and security issues that are so important today. Sima, let me ask you about revenue cycle. Health systems are obviously constantly trying to work on revenue cycle. I think there's the latest said I saw was 2 to 300,000,000,000 a year spent on revenue cycle. What where is the biggest opportunity near term and longer term that health system providers can use to improve things for revenue cycle?
Speaker 1: What what we're seeing in the market is a lot of point solutions, which I think are helpful for addressing single tasks. But when we think about revenue cycle and how it works, there's the front office, the middle office, the back office. Right? And all of those pieces have to come together. And we spend a lot of money because there's a lot of coordination that has to happen between the front, middle, and back office.
With AI, there's an opportunity to bring all of that data together. I do worry that we're gonna be spending a lot that that there's a, potential for providers to spend a lot of money on point solutions. But, ultimately, it all has to start with common data and to be able so let's think about a denied claim. Well, maybe it was denied because the prior authorization wasn't there. Well, in order to do the prior authorization, you need to be able to get into the clinical record and have the right information at the right time.
So it it starts with being able to bring together a foundation of all of that data and all of those systems together so that the AI can work across it and it isn't just, you know, focusing on one particular solution. So think about coding. So maybe you've got an AI tool that helps you with coding, but that's only part of the revenue cycle. Right? You gotta code it.
You gotta submit the claim. If there's a denial, you need to be able to respond to it, you need to be able to go back to the clinical record and pull the relevant information, to justify the claim. So, again, it starts with that common data foundation and the AI being able to work across the system and not just in the EHR. There's some clinical data, but there's also financial data. And then I would say the last piece of it is ERP.
Right? Ultimately, this is all financial information that goes into an ERP, and you wanna be able to make sure that that whole system can work together.
Speaker 0: Thank you. Talk also, Oracle Health and AI is is bigger and bigger in life sciences overall, in research and bringing new therapies to patients. Where do you see the greatest opportunities in sort of the life sciences areas?
Speaker 1: Sure. And that's a, you know, that's a great point. And the way Oracle is coming at health care is not just with one point solution on the EHR. Right? It's a it's a much more it's a much more larger play, and it's focused on targeting and transforming the entire industry.
Because I think there's a realization, you can't transform one part of it and think that you're gonna do all of it. It's not gonna work that way. And that's why, you know, a point solution, an EHR, it it's not going to get at it. And the the clinical trials world and life sciences is is very, important to health care. We think about all the new treatments that are out there.
We gotta make sure that providers on the frontline know about those treatments and therapies. And also, in order to accelerate innovation, we need to make sure that people are aware of the clinical trials that are out there. And that's that's a, you know, that's an area that we're very weak on. One to two percent of the population actually participate in clinical trials. And usually, those are the ones that are coming out of the larger academic health centers.
And so there's an opportunity to really accelerate research, cures, and treatments if we have more real time information. And it's bidirectional. I think for for pharmaceutical companies and med tech companies, they need to be able to, enroll patients into trials. They also need to be able to have access to data to understand the impact of the therapeutic intervention. So whether it's a drug or a device, what happened five years later?
I think there's a lot of information out there that we are not able to, to understand, and there could be a lot of lessons learned just from the data that we're not using today. And what I mean by directional, let's say there are new treatments and therapies. The reality is, a lot of doctors and nurses, there's so much going on that they're not always aware or have access to the latest treatments and therapies. And so that clinical decision support becomes really important and to be able to make sure that providers have access to real time data and information.
Speaker 0: Thank you. Just a couple more questions, Seema, and thank you so much for your time today. Oracle has its health and life sciences summit coming up this year. What should health system leaders prioritize first in in general as they tend to modernize operations and adopt AI? And then in terms of prioritizing, then what do you hope they take away from the Oracle Health and Life Sciences Summit?
So what should else assist us prioritize first? And then what do you hope they take away from the Life Sciences Summit?
Speaker 1: You know, for our summit, first of all, it's a global conference, so we have people coming from all over the world to participate. And we wanted the summit to be where obviously, we're gonna talk about what Oracle is doing and and profile some of our new products, but it's also supposed to be an opportunity for sharing and around, try to all work together around transformation. So bringing some of the greatest minds together. We've got a lot of different speakers that are not talking necessarily about Oracle, but they're talking about what's going on in the market. And we hope that it's a learning opportunity for the participants to learn from others, to understand not only technology, but regulation and policy that's going on because that's gonna impact the work that they do, and it impacts their their decisions around technology as well.
So it's really intended to be a learning experience for the participants. The other thing I would say is when it comes to what what do we want health care leaders to take away? And for me, the takeaway is to understand an approach to AI. It's not just about one solution, a bolt on solution, but it really is an enterprise approach, which is how do I use and utilize AI? And so it's not just a buying decision, but it's also, okay, what about my what about the impact of AI on my processes?
Right? I may need to do things differently because the AI is able to do this part of the job. We don't need to do that anymore, but what do we need to do? So there's technology. There's the the process transformation.
And then the third piece of it is also from that buying decision, understanding that you can't have an AI strategy unless you have a data strategy. It has to start with how do I bring my data together. And so building that understanding of how AI works, we hope that it'll help them when they're making decisions about AI, whether it's purchasing decisions, whether it's governance, which is very important. And then the third area is around transformation of the way that they do their work.
Speaker 0: Seema, incredible to visit with you again today. I followed your career, it seems like, for a long time. Just a brilliant person, brilliant career, and amazing the work you're doing at Oracle Healthcare. Thank you so much for joining us today on the Becker's Healthcare podcast. What a great pleasure.
Thank you so much.
Speaker 1: Thank you, Scott. Thanks for having me. Hope to see you soon.
