up next we have Aron AER the principal executive architect and field CTO and Jonathan Albom the federal CTO at service now all right thank you everybody um again my name is Jonathan Albom and I'm the federal CTO for service now and before joining service now uh I had the opportunity to serve as a Chief Information officer in the federal government for many years and I worked at a number of agencies but along my um journey in government I worked at the food and nutrition service which is part of USDA and at the food and nutrition service I worked on the food stamp program and the wick program and um really all of the different nutrition programs the USDA runs uh across the country so I had a little um introduction into this broader healthc care topic and in those jobs and the recognition that Healthcare is a big topic it's complicated it has um there a lot of different players and stakeholders through this process and you know I I bring that perspective into my role at service now especially as we deal with complicated topics at Cross government agencies Andross levels of government like healthcare there aren't Simple Solutions and you know in our our environment today there's a lot of uncertainty around around these topics and to help untangle some of this I'm lucky to be joined by my colleague Arun a who gets a chance to work with service now colleague service now customers across uh a number number of agencies but focuses a lot on Healthcare so we're really lucky to have them today thank you thank you Jonathan so again Arun Ayer is my name the full name is there if you look me up on LinkedIn so you get to the right person um but I've been in service now in the service now ecosystem for around 11 years uh various different uh business models and customers but I also spent a um time in a a health care company on the back end of things in the provider experience and the member experiences so that combined with what I do now currently with federal civil and federal health care uh is where I see some of the challenges that we have what are the kinds of things that you're hearing from different service now customers that are using Healthcare Solutions right so I I do want to go back before I get to that I just a curiosity question um I know we talked about a lot of EHR here but I do go to one physician uh she's actually a ey doctor she still has paper files do you anybody does anybody have that experience with their Physicians where they still have the paper folders and show off hands that's way too many that's way too many no the reason I say that is because you know we were talking about the challenges that we uh you know find in U various different um uh organizations or you know in the healthc care space um one of the main things that I'm I'm hearing about in in my um architect Community is what we see the challenges of digitization that has happened but it is very desperate in levels of maturity and it it it really presents a challenge saying oh we do have automation or we do have digitization but things don't talk to each other and where does this impact it really impacts in the access to care the quality of care as well as the cost of care and not to talk about even the outcomes that may come out of that so that's the challenges that everybody that I talk to is saying okay it seems like we have digitization we have automation but it doesn't talk to each other so what are the what are the things that you found effective in trying to bridge those gaps and orchest healthare more effectively so um so I I want to kind of present The Challenge and how it shows up um in fact this morning when we were driving into this session we heard about the number of nurses who are retiring prematurely uh because of the time that they're spending not in empathizing with the patients they're caring for but the time that they're spending in in actually being the human um middleware between different systems that they're using so there's a lot of burnout there's um nurses leaving and there's underst Staffing of that entire area and oh by the way we talked a lot about AI you know up to now but that's one job which is not going away because that is something that is a human- to human interaction that we still want but supported by AI so that's one of the things that we're looking at but so what what are the things that you have to do to bring those Health Care workflows together there's lots of assets out there we know there's lots of healthcare devices too many devices in some in some cases people lose devices they don't have control of devices it just reinforces the idea of of silos and and and lots of data sets that if brought together could make Healthcare delivery much more effective again what what are the best practices you've seen for for yeah so I think you kind of reminded me of uh the whole device challenge that we have I mean when you look at a hospital bed or if you look at what's happening in a clinical setting there are so many different desperate devices that are providing data and information for the caregivers to act on but they are you all have their own user interfaces they have their own data models they have their own Technologies but there's no way to kind of bring all all of that to be actionable and I think that's where you're going with the fact that if you look at it um holistically all these individual systems uh need to be brought together in the context of the patient and their experience and somebody said how are we treating everybody the same way you know with the same kind of medication not knowing the specifics whether it's a test results whether it's the vitals that when you come into a clinic or whether it's an infusion pump all of these device that are around the um individual patient and different caregivers addressing that need of the patient that's where I think you orchestrate and you to get away from those being desperate and bringing all of them into a single platform so it sounds easy to do I mean conceptually it's easy right we understand the mechanics of it but what um what makes it so hard you know why why have so many organizations struggled and had multiple um multiple projects try and do the same thing so um I I think the one of the main challenges is that we are not looking at it holistically I think we take a particular um problem and we solve it and solve it optimally in just for that point solution but we need to kind of look at it um holistically and I think that's the point I was making you know I think we keep using that word the humans are the middle bear and they're the ones who understand what this device says and how that reading uh needs to be interpret interpreted and used versus what the uh the clinical observation is versus what the other uh device around that same patient needs to be understood so the workflow that happens is not captured because now we depending on those individuals who have lot of institutional knowledge who have a lot of training uh but when they leave and that's the challenge I presented earlier now you got to start all over again because you not captured that into some way to imp it can be a lot of manual processes you're describing it could be phone calls or it could be emails or text messages or an a uh a very highly uh customized approach to get information from one system into another uh we we just heard you know before Indian Health Service there's not enough uh people at a lab they might be inputting U lab results into a system it's a very um stepwise process and you know that creates opportunity for human error um it creates something of a chaotic environment at times especially in a large complicated healthc care setting so there are there are definitely um you know best practices and ways to make this less complex and you know one of the things you know I think an important takeaway from this kind of conversation is always to understand how how does the data and the work that's a representation of that data how does it flow through the organization you look at you know the sort of digital chaos up there and there's got to be um a some set of normal processes that we do when we get this information to get it to the right people to provide the right kind of care often times those things might not be Doc doed or if they are documented uh the version documented isn't what is currently um you know how it works the process has changed but I think there's always this opportunity to step back and think about what are those data flows what's the data how does it flow how should it flow and be prepared to address the opportunities that are available through tools like service now through automation through emerging Technologies like like AI which you know that sort of segue into artificial intelligence we've all been talking about it through through this morning but what what are the sort of AI concepts are room that you think are really important for for healthcare yeah so I think um I'm going to slightly get on my soap box here um so I think we all kind of try to think of it you know and it sounds like it's a you know shiny new object that we are all pursuing towards and a lot of the funding kind of goes towards that but there are some foundational things that the organization needs to have to even make AI possible right uh and what do we do in in some of these places you talked about data that the quality of the data is really going to make it useful for us to apply AI or generative AI um and um I think there was some conversation earlier about are we doing enough of other things before we get to AI uh there are all these disparate systems are we connecting them somebody talked about robotic process automation yeah that's one way of integrating reducing the swivel chair uh needs of the individual maybe like the nurse caregiver who's looking more at screens than at the patient because of the challenge spending 45 to 60 minutes in a day in you know part of caregiving is this huge administrative uh and civil chair uh overload so are we doing enough to get the data uh in a place that it can be used for training and for further use so there are specific areas that we can use Ai and generative AI before we start getting into a place where we are submitting to what AI can do for us uh let me just elaborate that on a little bit so we talk in in terms of AI we talk about human in the loop we talk about human on the loop and you talk about human out of the loop in the healthc care space we still are not in a maturity level where we saying we can go to the human out of the loop so then coming down one level we say can we have human on the loop we're not even there I think we should be looking at opportunities where it is human in the loop which means everybody talk is talking about is AI going to take your job I think there is that is only part of the statement it says AI is going to take your job and make it better and that's where we want to think about how we want to use Ai and generative AI does that make sense yeah I I I think so I think that there is this conversation we're having about can you trust the results of what uh a large language model spits out and we know that there's some questions that we all have about how do we apply these capabilities inside uh organization let alone uh you know a healthcare setting and and trust that the results are accurate trust that the results are going to tell us the right thing to do and you know I'm not sure if we're there yet and you know to a Run's point we don't necessarily know how these models are trained sometimes we don't have complete information about the U you know the the effectiveness or the accuracy of the output so I think it really comes back to this idea of risk management and understanding what you're trying to do with the models how those models become the assistant that you're describing U making your job easier what are the kinds of use cases that we're willing to to apply and you know within the service now context we're very focused on establishing large language models based on service now data based on um customer data through an opin process based on industry data and having these very domain specific models with real Providence around the data and real understanding about how the models are working so we have a high level of trust about the output and that output is you know baked into the workflows that service now offers and you know there's a couple of um kinds of workflows that were really been focused on recently applying them not just in healthcare settings but in other Industries and again tailored around industry specific data that we have control over so you know what I'd like to get your perspective r on a couple of these things and how we manage the risk around you know things like case summarization virtual assistance um something we call now assist to help you move forward through through a process a lot faster yeah I think you brought a very important point it also triggers the thought around there's that executive order 3960 where um it talks about trustworthy AI so just don't say AI just don't say generative a but trustworthy as you pre-qualified saying that if it is not trustworthy let's not go there right and in doing that what Jonathan just described in the very focused Lang large language models which are using your data in a secure way it's not being shared with anybody else so there is you know there's always this cyber attacks on the data itself which is one way of poisoning what AI can do so we try to protect all of that and now we take it to specific ific items that can benefit your organization um and we talked about the time that the nurses spend in uh you know looking at various systems charting rounding various different things which is not really next to the patient not really providing that personto person care what can we do in that that the case summarization is a very good example so if you have nurses switch over write reports U initially and then update it and then there's a history and if they have to look that up there's a lot of time spent in just going through that to summarize what's the next course of action or what is uh the current situation of that patient and case summarization does that for you again it's human in the loop it's not saying that this is the summary and that's it it gives you the opportunity to read that edit it and kind of um either validate or kind of decline what the Gen AI gave you or actually edit it and say okay now I am comfortable with it right yeah and I think something you know uh similar nature having a a virtual agent that has a real conversational uh capability it's not a pre-programmed input response but something that can understand context and then synthesize look through lots of information these generative AMS are very good at reading and understanding large volumes of information and synthesizing that into an output that can help someone in a patient care setting or help someone who's providing U health care move more rapidly through a process be directed to the right information or have um some administrative tasks taking over taking care for them you know those are things that speed the process along and get people refocused on providing care as opposed to the administrative task so I think things like this like these Concepts have uh tremendous potential in healthcare and other Industries to again as Arun said to make us a lot a lot more effective and I think that's part of the future but you know beyond AI there's other Technologies there's other capabilities that you know are emerging in the space and you know you talked to a lot of our our customers you're having deep conversations with them where where do you think the future is going based on what you're seeing and what you're hearing yeah so I think multiple um speakers and others you know as we were talking have brought up this point that you know we are going to meet our patients or you know where they are which means are they in a hospital yes are they in the home yes tell Health are are we going to go to the workplace and do that yes so more places more locations where Healthcare is going to be provided that's one more devices we're all wearing you know wearables a ring you have an a ring and have a watch and we it's always capturing all this data something is going to be done with that data right because that's why we are wearing it to track manage monitor health and others you know observations fall detection various things all this data is coming back to your original point about the data so more data more devices more locations all of this is going to happen and we have to have you know those guidelines you know to avoid the risk exposure uh because again this is monetizable uh and people are using that for you know Bad actors are using that so protecting that data although there is a spread that's kind of the future that we're going to be prepared for U as we go forward is so being able to use the data effectively and understand what it means capture it as part of a a workflow a process and using Automation and and AI create an environment where conceptually Health Care can become a lot cheaper and it can be delivered at the spot that it's needed to your point we can move from uh Health Care to Wellcare to be focused on Wellness to be focused on giving me anticipatory Health Care based on maybe what the data is telling me keeping me out of the hospital and I think all those things create a create a really bright future at service down we're very excited to be a part of this working across a number of agencies in the healthc care space and really across the Federal level and at the state and local level and worldwide and across commercial Healthcare so we're we're um we're excited to be part of this journey um thank you for your your time today we appreciate the conversation and look forward to continuing it um during the rest of the day thank you