The Future of Healthcare and Data: Insights from Global Perspectives

In today's rapidly evolving healthcare landscape, technology plays an increasingly crucial role in shaping the way we approach patient care, data management, and innovation. A recent discussion with Joslyn and Travis, two experts in the field, shed light on the complexities and opportunities that lie ahead.

The Global Healthcare Landscape: A Complex Web of Systems

I think uh like within Europe for example um the the structure of the the payment is is very different from uh the US uh so how uh globally applicable is Sol this I had the opportunity to cover Global healthcare for Oracle which I'm sure many of folks know um as a big technology provider um so you know Global healthc care operates um differently in terms of how you know payers uh the payment system works a lot of times it's a it's more of a a social approach to it based on taxation it's government driven with some some private option typically available at times but all the same problems still exist in many ways um you know quality weight times uh high cost readmission um issues and otherwise so I think a number of companies are working on that um for us um you know we're primarily focused right now on on the US and the work we're doing here um I do think over time as we mature as a company as we grow and as we develop that a lot of the work that we do particularly direct to employer um or through other channels to employer where we use our solutions for reference-based pricing we're looking at claims we looking at Cost we're looking at prediction I think those are very relevant for employers around the globe

The Challenges of Expanding Healthcare Solutions Globally

there's just last thing I'll say about that Richie is that that is a there's a massive sometimes barrier to entry for companies to do work in other parts of the world every almost every region has their own set of quality standards cyber security standards um data rights and usage restriction um it it becomes highly C it's high cost and sometimes it's a big barrier to entry actually for Innovation um so I think it's something interesting to think about that policy really could help move forward the industries across the world if we had ways to better you know to to reduce the cost of entry for technology companies and businesses different topic probably for a different day but um something think about too yeah I can certainly imagine how the data cleaning would become um dramatically harder if You' got different um like diagnosis codes for different countries and things like that and different data formats as well uh

The Role of Technology in Healthcare: A Promising Future

I'm super super excited about these um I think GPT like artificial intelligence uh Technologies and what it can really lead us just like what Travis mentioned earlier about the doctors and providers these days are using these ambient Technologies to really com rather than type hand type those uh EMR information when they talk with patients now is using technology to to translate those um you know conversations into a digitalized standardized uh you know like data sets where you could really uh leverage that uh normalize the data rather than you know messy uh unstructured data um to uh further predict and better um better prevent certain condition from worsening or or happening so I think um that's what I'm super uh excited about in this industry yeah I think um we talked about a little bit um this idea that you can start to focus um a little more on Wellness than just sickness um I think is important um the really giving uh individuals a the ability to have some autonomy over their their own health and and wellness and choice is important um so I think there's an emotional element to it as well over time where you know we're we're predictive models can help with Wellness but we're also bringing cost control uh we're bringing other transparency solutions to the table that just make it E just just makes it freaking easier it's hard man it's hard figuring out like this discussion today you know I mean we live our lives doing this every day I can only imagine how the average consumer feels sometimes so um kind of demystifying it making it simpler making it easier making it less cognitive burden to have to figure out where to go what to do um I'm excited that I think the the next decade in healthcare is going to be the decade of of true and meaningful Innovation and it's going to really come to the market and it'll be through technology um so that's that's pretty cool thing to be a part of all right yeah just empowering people to make decisions about their own healthare that sounds uh like a wonderful goal

Empowering Individuals with Access and Autonomy in Healthcare

The future of healthcare lies at the intersection of innovation, technology, and patient empowerment. As we move forward, it's essential to recognize the importance of giving individuals autonomy over their own health and wellness. By leveraging data-driven solutions, predictive models, and transparency, we can create a more personalized and effective approach to healthcare.

Conclusion

The world of healthcare and data is rapidly evolving, with technology playing an increasingly critical role in shaping the future of patient care. As we navigate this complex landscape, it's essential to consider the challenges and opportunities that lie ahead. By working together to address these challenges and harness the power of innovation, we can create a more equitable and effective healthcare system for all.

"WEBVTTKind: captionsLanguage: enyou know I say data is like the new oil it's like you got to find it refine it and distribute it and so being able to do that in a reasonable way is going to be really important and I think platform products that have scale and that offer scale are going to be one of the biggest ways that potentially to control cost in healthare over time hi Travis and Justin welcome to the show hey Richie thanks for having us so I'm actually going to start this with a confession so I've been living in the US for eight years but Healthcare is still very mysterious me uh so just for the benefit of myself and for our audience um can you tell me what happens when someone gets a treatment so who ends up paying who yeah um well you're not alone I've been living here my whole life and it's mysterious to me as well so um but you know one of the things we're trying to do at multiplan is kind of simplify and and uh educate and demystify to some degree how the process works um but really there's you know there's essentially two kinds of health insurance in in the US so it's government funded um Medicare and Medicaid and then there are Commercial Insurance products um typically those are born by payers that bear the risk Andor employers that are self-funded that bear that risk um when someone has a an issue or needs to see um you know a provider or um someone that's delivering care they have the choice to go in network and those Network Agreements are typically negotiated between providers and um payers of healthcare and employers and sponsors um and then there's a pre-negotiated discount or rate and that's a that's a pretty easy path I suppose most people are used to that um that have some method of of insurance or they can um there's some out of network options that exist and there are some cases where an employer isn't inside of the network or out of the network where care is sought and that's a very different path with a very different cost structure around it so I can see where it's confusing uh no doubt about it um but as I said you know what we're trying to do is um you know demystify the extent we can and also control and contain cost to the extent that we can as well okay so um primarily Insurance based and then you got this difference between in network where it's like specific providers and then out of network where it's like oh you want treatment from someone else and maybe have to pay a bit more all right so it sounds like a fairly elaborate system what are the problems with this um Justin do you want to explain the the the downside of the system sure like Travis mentioned um it's fairly complicated uh a few I would say U main problems with the US system uh for the average consumers um number one I would say the price is not transparent at the current level like it's not very easy to know how much uh you're facing out of your pocket prior to your getting the care uh in a lot of the situations and also secondly the price can be drastically different from provider to provider even for the same exact procedure that you're seeking um just simple example like getting an x-ray getting an x-ray in the hospital comparing to getting a x-ray in the Imaging Center the that price can Difference by 10 times or even more uh and then third um like patient with the insurance they typically only care about their portion of the out of-pocket amount um and have um no idea of like that main risk barrier whether it's the payer which is the insurance company or their employers who is actually the risk barrier are actually paying for the service so a lot of the times it's hard to educate the the consumers or the patients to shop for the service that are shoppable um and then lastly you know the the patient decision making on the provider selections often time is also less about quality or cost and more about how soon I could get that next appointment and how easy for me to get to that doctor so that I think that's the area with the recent government requirement of price TR transparency we are trying to help filling that Gap is to um help bring that transparency helping to bring that cost information quality information to the consumers um to the payers and providers so they can provide that information to the people um for them to make a wiser decision um be prior or before there um by consuming the care okay so yeah uh with that lack of transparency is very difficult to know how much you're being charged and where you're going to get the best price I guess there's no like price comparison service like you get for shopping sites uh no equivalent for healthcare in general okay all right uh Travis did you have anything to add to that are there any other uh problems you see the system yeah I mean it's interesting I mean there's you know the price for the same service can be different so if you're going through a Medicare channel it's a price if it's in network nego it's a price if it's out of network it can be a price so part of what we're trying to do collectively with all the transparency data and with machine learning and other capabilities is to essentially you know bring transparency to the table but also evaluate those prices against some fair and reasonable basis and determine what an appropriate cost for something might be and bring that to the industry in a way that helps contain costs over time and so um there's a lot of challenges with that um I also served the provider and patient Community for 22 years before I joined the CEO of multiplan um and you know there's a lot of um disperate data out there um you know taking that data normalizing it making it useful um is something that's really important um as it relates to healthcare and particularly in terms of bringing value but also on the you know the patient provider side um as well we can certainly have a little discussion about that um for my past life too if it's useful to your listen okay uh I love that you mentioned data and I I really want to get into details of what the data is and how it's used maybe just to begin with at a high level um how can data be used to help solve this uh problem with transparency around um costs there's there's several ways so we offer a number of services so one of the things that we do is we do offer a Network discount so we have 1.4 million providers that have a discount with us and we offer that Network as a primary error WAP Network Beyond that we also offer services where we look at claims and we evaluate those claims against some rational basis like a Medicare as a basis and then we essentially repic those claims so when you look at an outof Network claim sometimes it can be drastically higher than you might see for the same service in network so we use data we use information we use machine learning we evaluate those claims we look at them we Peg them to some basis and then we have a market clearing or what we think would be a fair process by which we work between payer and provider to essentially resolve that claim and keep cost down for employers employees and patients um furthermore um the team is working on transparency products that take a massive amount of mrf files and data that has been essentially there's been some policy around that making that mandatory that that cost dat to be provided um in justest that normalize it use it in a way that's um you can bring that forward so you're now getting um hopefully cost and quality uh to the table in a in a reasonable meaningful way all right that's interesting uh in that case um Justin do you want to go into a bit more detail about like um what are all these different data sets that you have and where do they come from typically those uh claims data come directly from the insurance company and then we have a series of you know ingestion data processing um Pro steps to normalize and standardize those data that coming from the insurance company um often time you know the the way the data is being laid out or sent uh are different uh by by different companies so and then after we normalize them we will go ahead and enrich those data further so for example the data will come in as procedure code or diagnosis code and we enrich them with the description give them the categorization to help them further analyze their data better um and then furthermore uh like Travis mentioned with the price transparency data that's being public available although it's very messy and then uh you know it's massive that um that we use a lot of machine learning techniques teques to sort those out and then clean those St out so then we can um leverage the actual utilization data of the clients coming from the insurance company and then uh reference that with the public available data um from the transparency files to find that balancing point and to compare you know like whether services are being um you know paid at a reasonable um price point or if there is a better Network the client should be picking based on how um how their employees are located or how they consume their the cares okay it sounds like uh you've got an awful lot of um data claims do then that might be kind of the crook of this so um can you just tell me like what types of data is it is this like a lot of just sort of Text data that where some doctors scroll some notes somewhere and you're trying to work out um what that actually is or what what what sort of um data cleaning do you do um mostly the data come when it comes from the insurance company those are post adjudicated claims data so they are uh standardized and um mostly like um they have been processed by the uh by the insurance company um so they have the diagnosis information the procedure information so you know what has been done to that patient and uh which provider um and then what is the the cost what is the billing amount what is the discount rates and then um you know and what is the out-of pocket amount for the patients so those are typically the information we're getting from the insurance company and then uh with our and then one thing that I didn't mention earlier was um after we ingest those data we also apply prediction to it um that like our with the machine uh learning techniques we can predict someone based on their past 12 month claim experience what is the likelihood of this person of getting a certain disease onset uh probability will be like or what is this person's future 12 month spent uh is likely going to be in a certain range so with those type of information we can better uh you know pinpoint the type of patient that we should focus on or if they have a point solution vendors and often time we can direct them to the right targeted population for them to uh do early intervention okay um that sounds really cool um that the fact that you can make predictions about like how much people are going to have to spend over the next year things like that to help people budget are you able to share any details on what those machine learning techniques are that you use yeah so um um before we were purchased by uh multi plan uh the company was founded out of a MIT spin out project uh led by Dr burmith um that's a pronounced uh professor in the optimization techniques so we mainly uses the uh decision tree methodologies um to kind of op finding that optimized uh decision for the individual based on their past um care Pathways that we could see in the claims and then finding those uh detectable signals in their claims and then determine okay based on all these signals combination of the signals how much uh spend this person is likely going to spend or uh what type of uh disease this person's uh likely to have um and then it's not widely any disease we can predict and often time is those ones that have a c certain patterns that can be traceable or trackable U those are the ones that um can be detected better that's fascinating and as uh the use of decision trees means that it's quite easy to explain what's going on which goes back to your idea of um having transparency around like how everything works correct and I'm glad you mentioned that interpretable because uh I think that's one main differentiator in our methodologies comparing to other companies that are applying AI or machine readable me uh methodology to their uh data often time it's a black box like you can't uh understand or uh what's going on before the results being spin out but with the decision tree like you can actually interpret the the results or prediction and have a better understanding and then um like guide the model if needed um to better fit the the prediction or results that you um you know like you're seeking for just going back to like the main use case of this which seems like it's trying to save money for people so they're not spending so much on Healthcare uh so um I guess uh if you are uh paying for insurance like what's the sort of Step One into like making use of all this um technology and uh and T analysis in order to save money on uh on your healthare um Travis do you want to go with this yeah yeah happy to um actually want to make a little comment on what Jocelyn said too if that's okay um it you know as a as an industry you know my view is that um you know we we the BST acquisition was made because we actually believe that's the future of healthcare is data you know I say data is like the new oil it's like you got to find it refine it and distribute it and so being able to do that in a reasonable way is going to be really important and I think platform products that have scale um and that offer scale are going to be one of the biggest ways that potentially to control costs and healthare over time and so you know quality and cost are two of the biggest issues that are faced in healthcare and I think the use of technology is going to be the thing that ultimately um helps quality go up access go up and costs go down and I think that will be scale Technologies and so you'll see that from major major technology players that are interested in the space because of that and also you know as moved to the cloud and other other capabilities I think it's really important for care um you know there's you know lots and lots of Point Solutions out there there's lots of things to do I mean as an individual you have to just be proactive in your own search in your own care if you you're part of an employer plan um you know it's likely that someone like us or or another uh technology provider provides capabilities where you can actually search you can do some search where you can look at some price quality and availability data or it it can serve up someone that's in your network that is at a particular price and quality that's emerging and getting better and better over time as the data and the data science improves and as the transparency data which we wildly support as as a as a policy initiative um becomes available we can use that and we can start to map um as I mentioned cost quality to availability and that makes the process more simple so you can start to have choices um but it still feels a lot like hunting and pecking um you know or or luck or refer Network or I just know somebody um so I I you I think you have to Advocate on behalf and and don't underestimate the tools that you may have through your employer or through government websites that are actually starting to develop and do better here um of which you know we offer we offer some of them that others do as well um so that's I think that's kind of the state somewhat the state we're in uh I suppose the other part of this is uh things like um the provid side of things so like the the doctors and the hospitals just just having better data affect um their costs and um and yeah is it going to affect uh the the monetary situation for these providers as well yeah I mean I put on my my healthc care hat and yeah in total um yeah I think um a couple you the thing that's going to be really fascinating I think with um the provider and the patient side not to take us too far off track here but there's a couple really interesting developments one is um you know the ability to use ambient Technologies and listening and other techniques to where you're now collecting data um in a way that's very different than the past which is essentially you know the keyboard so we we always used to say you know in Prior life that the most dangerous thing in healthcare is the pen because you were writing script now it's oh the most dangerous thing in healthcare is you know the keyboard now it's a voice too conversation where you can actually collect um information and data in an ambient way I don't know if people like that word or not but I've heard an actionable way it's what I would prefer to call it um that is done real time and also you can run rules and you can use other Technologies behind that to where you're taking a lot of um what would be administrative work off and burden off of providers um and caregivers and now they're able to focus on the patient and the actual conversation that's happening and then you can run prediction behind that so there's fascinating things going on in healthcare I encourage you know your listeners to really look at it as a cool space to go work I mean prediction models using data around um you know I've seen really interesting models around um Suicide Prevention looking at Social determinance combined with medical data to get early detection or early prevention Technologies to people or capabilities to people opiate other abuse situations where you can look at State data and you can look at other data and you can determine a full view of someone's health care and what's happening so you can intervene in a way versus wait for someone to present there's some really fascinating things that are happened um with that and also around Precision medicine where you're looking at the genotype and you're figuring out the best way to actually prescribe in a precise way certain therapies or procedures or protocols that yield the best result I mean that the next 20 years is going to be incredible I wish I had 30 years left in my career I probably don't Jocelyn probably does but I mean it's going to be like the best 30 years because it's going to be the most exciting time because this is all available now um because a lot of it's been digitized um in the US and across the globe actually so sorry I got a little excited there Richie no no um I love the excitement and that's kind of interesting that a lot of the stuff you mentioned about um well okay if you want to improve cost in healthcare got to intervene early and detect things before they become a big problem um I can certainly see how that's the the case with opes you want to stop you want to uh identify the problem before people have uh messed up their bodies um so uh are there any other areas where you think um there is uh room for like some some big improvements in in health care there so um like are there any particular disease types or any particular conditions where you think there's room for a lot of um use of data to improve the situation yeah I think that there are um quite some areas where um especially I think the shoppable services where people can have the time to evaluate multiple options that's where uh we have the better chance to inter uh you know uh to provide using information to Pro provide more actionable insights before or someone making that decision so I would say things like uh uh Orthopedic surgeries um or um trying to um you know find the the best provider to treat kind of a long-term disease conditions um that's when uh I think using data and how having that well-rounded information um both cost and quality um to to help make that uh optimal decision for the patients um I don't know Travis if you have um some more to add I would answer I'll answer the question a little a little differently than a specific uh conditioner area what I would say is that um for us a really important thing is um figuring out what to work on the the world of things to work on is enormous um but the resources to do it are finite and so you know having product life cycle capabilities that allow us to understand the market to understand the needs of clients and then ultimately to make things that matter is really important or you end up doing quote unquote science projects that don't have scale benefits and so companies you know I think have to think deeply about how they actually create product life cycle um so they can make organic capabilities that matter most to the markets they serve um and and then you you start to surface the things that can have the biggest impact some of which have been mentioned here today um and so there there's there's just many many many things you could look at from a morbidity and co-morbidities perspective and insights but um picking them and and having uh impact and getting in the market is also important as you run the business since Justin you mentioned uh the the of like long-term um diseases and Travis you talk about which areas have the biggest impact um I'm curious is to whether um is it the um I know phrases so uh there if feels like with um Healthcare there are some things which are kind of cheap but happen a lot so something like flu um happens to millions and millions of people but also it doesn't have a big impact and then you have some things which are rare and kind of devastating so cancer is less common but has a tremendous impact um so are there is there like one area or the other where um you think data is most important data as a um usable commodity where I said earlier is you know the new oil is going to be um interesting in the sense that it's not all uh in healthcare usable because of lots of rules lots of regulations lots of Phi um lots of other things like that so um if you could truly figure out the best way to look at Social determinants um to bring in search information to combine that with the medical record um you really could start to predict with much greater accuracy certain things like obesity in certain uh areas um looking at um you know diabetes instances of diabetes um certain things like that could have predictive capabilities or qualities and that way you could start to Target programs around wellness and other things in targeted geographies and areas that or you could spend social dollars in a way that's meaningful in a community to help prevent the spread of some of these things over time through education and otherwise and so I think that um most things are predictable in some way if you can get the right amount of data but there's a large debate in healthcare around what's acceptable what's usable um is it Phi meaning um uh health information that should be protected should it be usable should it be ingested is it cyber secured um all of those things are just a massive part of the data infrastructure um that have to continue to be answered so that we're able to use predictive models in a way that makes sense and most meaningful probably didn't answer your question but I thought I said no uh that was really interesting um yeah so uh the idea that um there are some sort of limits on what you can do ethically is very interesting because I suppose you can sort of try and ingest everything about everyone's lives um sort of Facebook style and then uh get more information for better predictions so can you talk me through like what are the limits on uh what data you can get uh in order to make predictions I think uh when we kind of analyze um those insurance claims data uh another uh some other um data that we can combine to help making some more um accurate pre uh predictions are things like soci social vulnerability index just as an example um so you can use a public available data in combination with the the client or the the patients um specific data together um to bring that bridge uh to kind of bring bring the gaps um so uh you are not exactly using some of the specific Phi information but using uh some general information knowing this person lives in this ZIP code and then by knowing by public information what is the social uh index look like in that area you can make certain prediction around okay for people live in a certain geographic area what do they like like what their uh average Ed educations are uh what their uh you know dominant ethics uh in that uh in that geographic regions are so uh things like that when you combine the specific data with uh more General data together uh you can still make some uh pretty useful predictions um around Healthcare and I suppose related to this um there's an awful lot of um data privacy regulations around Healthcare data you got like in the US this the hipper regulations so how do you go about ensuring compliance with all these regulations yeah I can I can take a step I mean um we have very high standard on secur ing the data that we uh collect from the um the various uh sources uh sources so um not only we're Hippa um compliant we're also you know High trust certified um just as a example like certain techniques we use to protect those information is even when the data comes in uh to hit our server that the very first step we do is to separate the Phi information and the claim information of that individual into two separate repositories and using a common person ID to link the two information together so the uh the Phi is stored in a completely separate uh storage place compared to the the claims information that way as another layer we're trying to you know like further protect the information um become idential identi uh in the UN likely events of things U you know like certain data being breached one thing that um you've both mentioned a couple of times is the idea of uh quality and cost being separate things so um I think a lot of the focus is on this has been let's try and reduce the health care costs for um people but can you also use data in order to improve the quality of outcomes as well yeah I mean absolutely I think that's uh you know one of the major major themes of the industry as a whole right now is uh figuring out how to best use data in a way that's meaningful to prove outcomes um you know our our Focus as a company really is has been I would say primarily around um you know providing Cost Containment um waste and abuse Solutions um Network capabilities and and now we've added the decision sign science piece um but we're also you know very much want to facilitate um you know anything that we can do to help the providers and clinicians and otherwise um figure out ways to use data in a meaningful way I mean you know I came from the MR background so I don't you know I don't want to talk too much about where I was but um you know there's incredible amount of data that's generated and there's an insights and Technologies and rules and capabilities um that are there that ultimately allow for much better care and capability and early you know detection of things that never existed in the past and the thing that's most interesting is it brings protocols forward in a way that in the past you know someone would have to cognitively process all this but now they actually can think about things in a very different way because it's BR brought forward than having to go find it so it's it's kind of changing the way work is done over time um and you know I I'll just say this Richie we you know our we want to serve the whole ecosystem of healthcare so we actually are working with we we want to work with Rural America to look at the idea of maybe helping them figure out ways to create better access to care in rural parts of the country to help keep cost down and quality up um we want to bring those transparency products to to you know the full Continuum of Care actually um as we go forward to to kind of help with some of these problems we're just start discussing I like the idea of just helping out rural Americans um have you got any other success stories where some projects have worked and it's improved people's Healthcare as a data platform where we have all these uh prediction Powers um one example is um you know using the claims data and then we predict those uh High likelihood of people of uh getting msk surgery in the next 12 month that way when um the client have a msk point solution mendor that supports the uh like specifically support the members and help them guide and navigate them to uh through the whole journey of their um you know surgery events Now by knowing who those uh you know like high high probability members are getting those surgeries are extremely helpful and then improve that outcome tremendously because rather than them uh trying to you know like wait for people to contact them they actually know who those people are so they can proactively as a point solution vendor contact those members um that are having the conditions and then uh like understand which stage of the disease they're in and then help them um find the Optimal Care pathway for their msk conditions um some may not even need a surgery some may need a surgery so you need help finding the right provider the combin cost and quality information so uh having the data and predict in and then trying to early inter intervent and using the data further to find the the right combination and the cost and then the quality providers I think that's the the type of problems using data we're trying to solve together I think something J we talked a little bit about it I think she hit it really well I mean that's a great example and I think what you're what I believe is with the future of healthcare and data will really be moving from sick care which today it's like I'm sick I better find someone that can help me or maybe I should show up somewhere if I'm really not feeling well to more of a wellness point of view where you can you know look at data look at information you can have early detection or prediction of the conditions that may um may present themselves and then you can Target certain groups of individuals or C or or or certain conditions and then you can do things around that to to to create wellness and you know you see that with Registries which get you know putting together Registries Based on data um that you can actually reach out to people versus waiting for them to show up when they're at their worst and that's a big that's a big change that I think can happen over time um in healthcare that really would make a big difference for the world's population in terms of health and wellness and benefit that is really interesting because yeah now I think about it you don't even go the doct when there's a real problem happening uh and if you can intervene earlier then um it's probably going to be cheaper as well as a better experience for whoever's sick okay uh now this all sounds very exciting and I'm sure there are a lot of people who are interested in working in this field so um if you want to get involved in like uh data science or data analysis uh in the healthcare field like what sort of technical skills do you need well I'll starting you can say what really is needed Jin so so I mean you're talking to someone who learned to code cobal okay so that that's uh that's my expertise is cobal cing um but no nonetheless I mean so I look you know I think it's you know again F fascinating area um you know as opposed to some other interesting areas of Technology you know I I I personally believe you can put a mission to it which is cool um you know serving health and wellness and and and otherwise around the world is an important thing to do so you can get behind that um I think informatic the healthcare informatics will be a just a huge need for that across the you know the particularly in the United States but also um NHS and other parts of the world um having worked in those those Health Systems myself across across the world data science analytics machine learning um AI the the attributes of you know intellect and problem solving and you know I would say um curiosity will be important so I think employers myself you know look for attributes as much as skills and how you show up in a in a way that you know is is indicates that you'll you'll you'll grind out when things aren't going well um but you'll also seek new capabilities and skills over time are really important and Jocelyn could probably speak much more eloquently to the technical capabilities needed so yeah so uh that that was really uh interesting that um it's a lot about attributes as much as skills um but JN do you have anything to add on uh on technical skills that you need for working in this field I think on top of that just maybe a little bit of un basic understanding of the US healthc care system um like a little bit understanding of you know some basic uh you know coding or whether it's you know like the the diagnosis coding system the procedure coding system just so you know like how things are being categorized are being coded are being interpreted to to better apply the right Technologies onto the right data um I think uh that that two combination together will really power up um the uh the way that we leverage data and then make it uh you know super actionable and predictable okay yeah so just understanding like what these disease codes actually mean and uh how they relate to like a real condition okay um now we've talked a lot about the US um healthc care system I am curious as to whether any of these sort of um approaches you're taking um are applicable to other Medical Systems around the world I I think uh like within Europe for example um the the structure of the the payment is is very different from uh the US uh so how uh globally applicable is Sol this I had the opportunity to cover Global healthcare for Oracle which I'm sure many of folks know um as a big technology provider um so you know Global healthc care operates um differently in terms of how you know payers uh the payment system works a lot of times it's a it's more of a a social approach to it based on taxation it's government driven with some some private option typically available at times but all the same problems still exist in many ways um you know quality weight times uh high cost readmission um issues and otherwise so I think a number of companies are working on that um for us um you know we're primarily focused right now on on the US and the work we're doing here um I do think over time as we mature as a company as we grow and as we develop that a lot of the work that we do particularly direct to employer um or through other channels to employer where we use our solutions for reference-based pricing we're looking at claims we looking at Cost we're looking at prediction I think those are very relevant for employers around the globe um there's just last thing I'll say about that Richie is that that is a there's a massive sometimes barrier to entry for companies to do work in other parts of the world every almost every region has their own set of quality standards cyber security standards um data rights and usage restriction um it it becomes highly C it's high cost and sometimes it's a big barrier to entry actually for Innovation um so I think it's something interesting to think about that policy really could help move forward the industries across the world if we had ways to better you know to to reduce the cost of entry for technology companies and businesses different topic probably for a different day but um something think about too yeah I can certainly imagine how the data cleaning would become um dramatically harder if You' got different um like diagnosis codes for different countries and things like that and different data formats as well uh okay all right so uh maybe uh a plan for a a different company in in a different country then yeah you you probably won't invite us back but if you do we'll talk about that too love super um so uh just to wrap up what are you most excited about in the world of healthcare and data at the moment I think I'm super super excited about these um I think GPT like artificial intelligence uh Technologies and what it can really lead us just like what Travis mentioned earlier about the doctors and providers these days are using these ambient Technologies to really com rather than type hand type those uh EMR information when they talk with patients now is using technology to to translate those um you know conversations into a digitalized standardized uh you know like data sets where you could really uh leverage that uh normalize the data rather than you know messy uh unstructured data um to uh further predict and better um better prevent certain condition from worsening or or happening so I think um that's what I'm super uh excited about in this industry yeah I think um we talked about a little bit um this idea that you can start to focus um a little more on Wellness than just sickness um I think is important um the really giving uh individuals a the ability to have some autonomy over their their own health and and wellness and choice is important um so I think you know that access autonomy understanding your choices through transparency is frankly can be a comforting thing when you're in a in a moment where in a confusing process where someone you know or love isn't feeling their best um is is disconcerting and so I think there's an emotional element to it as well over time where you know you're we're predictive models can help with Wellness but we're also bringing cost control uh we're bringing other transparency solutions to the table that just make it E just just makes it freaking easier it's hard man it's hard figuring out like this discussion today you know I mean we live our lives doing this every day I can only imagine how the average consumer feels sometimes so um kind of demystifying it making it simpler making it easier making it less cognitive burden to have to figure out where to go what to do um I'm excited that I think the the next decade in healthcare is going to be the decade of of true and meaningful Innovation and it's going to really come to the market and it'll be through technology um so that's that's pretty cool thing to be a part of all right yeah just empowering people to make decisions about their own healthare that sounds uh like a wonderful goal excellent all right uh thank you so much for your time Joslyn and Travis like uh yeah lots of insights there s was great thank you yeah thank you very muchyou know I say data is like the new oil it's like you got to find it refine it and distribute it and so being able to do that in a reasonable way is going to be really important and I think platform products that have scale and that offer scale are going to be one of the biggest ways that potentially to control cost in healthare over time hi Travis and Justin welcome to the show hey Richie thanks for having us so I'm actually going to start this with a confession so I've been living in the US for eight years but Healthcare is still very mysterious me uh so just for the benefit of myself and for our audience um can you tell me what happens when someone gets a treatment so who ends up paying who yeah um well you're not alone I've been living here my whole life and it's mysterious to me as well so um but you know one of the things we're trying to do at multiplan is kind of simplify and and uh educate and demystify to some degree how the process works um but really there's you know there's essentially two kinds of health insurance in in the US so it's government funded um Medicare and Medicaid and then there are Commercial Insurance products um typically those are born by payers that bear the risk Andor employers that are self-funded that bear that risk um when someone has a an issue or needs to see um you know a provider or um someone that's delivering care they have the choice to go in network and those Network Agreements are typically negotiated between providers and um payers of healthcare and employers and sponsors um and then there's a pre-negotiated discount or rate and that's a that's a pretty easy path I suppose most people are used to that um that have some method of of insurance or they can um there's some out of network options that exist and there are some cases where an employer isn't inside of the network or out of the network where care is sought and that's a very different path with a very different cost structure around it so I can see where it's confusing uh no doubt about it um but as I said you know what we're trying to do is um you know demystify the extent we can and also control and contain cost to the extent that we can as well okay so um primarily Insurance based and then you got this difference between in network where it's like specific providers and then out of network where it's like oh you want treatment from someone else and maybe have to pay a bit more all right so it sounds like a fairly elaborate system what are the problems with this um Justin do you want to explain the the the downside of the system sure like Travis mentioned um it's fairly complicated uh a few I would say U main problems with the US system uh for the average consumers um number one I would say the price is not transparent at the current level like it's not very easy to know how much uh you're facing out of your pocket prior to your getting the care uh in a lot of the situations and also secondly the price can be drastically different from provider to provider even for the same exact procedure that you're seeking um just simple example like getting an x-ray getting an x-ray in the hospital comparing to getting a x-ray in the Imaging Center the that price can Difference by 10 times or even more uh and then third um like patient with the insurance they typically only care about their portion of the out of-pocket amount um and have um no idea of like that main risk barrier whether it's the payer which is the insurance company or their employers who is actually the risk barrier are actually paying for the service so a lot of the times it's hard to educate the the consumers or the patients to shop for the service that are shoppable um and then lastly you know the the patient decision making on the provider selections often time is also less about quality or cost and more about how soon I could get that next appointment and how easy for me to get to that doctor so that I think that's the area with the recent government requirement of price TR transparency we are trying to help filling that Gap is to um help bring that transparency helping to bring that cost information quality information to the consumers um to the payers and providers so they can provide that information to the people um for them to make a wiser decision um be prior or before there um by consuming the care okay so yeah uh with that lack of transparency is very difficult to know how much you're being charged and where you're going to get the best price I guess there's no like price comparison service like you get for shopping sites uh no equivalent for healthcare in general okay all right uh Travis did you have anything to add to that are there any other uh problems you see the system yeah I mean it's interesting I mean there's you know the price for the same service can be different so if you're going through a Medicare channel it's a price if it's in network nego it's a price if it's out of network it can be a price so part of what we're trying to do collectively with all the transparency data and with machine learning and other capabilities is to essentially you know bring transparency to the table but also evaluate those prices against some fair and reasonable basis and determine what an appropriate cost for something might be and bring that to the industry in a way that helps contain costs over time and so um there's a lot of challenges with that um I also served the provider and patient Community for 22 years before I joined the CEO of multiplan um and you know there's a lot of um disperate data out there um you know taking that data normalizing it making it useful um is something that's really important um as it relates to healthcare and particularly in terms of bringing value but also on the you know the patient provider side um as well we can certainly have a little discussion about that um for my past life too if it's useful to your listen okay uh I love that you mentioned data and I I really want to get into details of what the data is and how it's used maybe just to begin with at a high level um how can data be used to help solve this uh problem with transparency around um costs there's there's several ways so we offer a number of services so one of the things that we do is we do offer a Network discount so we have 1.4 million providers that have a discount with us and we offer that Network as a primary error WAP Network Beyond that we also offer services where we look at claims and we evaluate those claims against some rational basis like a Medicare as a basis and then we essentially repic those claims so when you look at an outof Network claim sometimes it can be drastically higher than you might see for the same service in network so we use data we use information we use machine learning we evaluate those claims we look at them we Peg them to some basis and then we have a market clearing or what we think would be a fair process by which we work between payer and provider to essentially resolve that claim and keep cost down for employers employees and patients um furthermore um the team is working on transparency products that take a massive amount of mrf files and data that has been essentially there's been some policy around that making that mandatory that that cost dat to be provided um in justest that normalize it use it in a way that's um you can bring that forward so you're now getting um hopefully cost and quality uh to the table in a in a reasonable meaningful way all right that's interesting uh in that case um Justin do you want to go into a bit more detail about like um what are all these different data sets that you have and where do they come from typically those uh claims data come directly from the insurance company and then we have a series of you know ingestion data processing um Pro steps to normalize and standardize those data that coming from the insurance company um often time you know the the way the data is being laid out or sent uh are different uh by by different companies so and then after we normalize them we will go ahead and enrich those data further so for example the data will come in as procedure code or diagnosis code and we enrich them with the description give them the categorization to help them further analyze their data better um and then furthermore uh like Travis mentioned with the price transparency data that's being public available although it's very messy and then uh you know it's massive that um that we use a lot of machine learning techniques teques to sort those out and then clean those St out so then we can um leverage the actual utilization data of the clients coming from the insurance company and then uh reference that with the public available data um from the transparency files to find that balancing point and to compare you know like whether services are being um you know paid at a reasonable um price point or if there is a better Network the client should be picking based on how um how their employees are located or how they consume their the cares okay it sounds like uh you've got an awful lot of um data claims do then that might be kind of the crook of this so um can you just tell me like what types of data is it is this like a lot of just sort of Text data that where some doctors scroll some notes somewhere and you're trying to work out um what that actually is or what what what sort of um data cleaning do you do um mostly the data come when it comes from the insurance company those are post adjudicated claims data so they are uh standardized and um mostly like um they have been processed by the uh by the insurance company um so they have the diagnosis information the procedure information so you know what has been done to that patient and uh which provider um and then what is the the cost what is the billing amount what is the discount rates and then um you know and what is the out-of pocket amount for the patients so those are typically the information we're getting from the insurance company and then uh with our and then one thing that I didn't mention earlier was um after we ingest those data we also apply prediction to it um that like our with the machine uh learning techniques we can predict someone based on their past 12 month claim experience what is the likelihood of this person of getting a certain disease onset uh probability will be like or what is this person's future 12 month spent uh is likely going to be in a certain range so with those type of information we can better uh you know pinpoint the type of patient that we should focus on or if they have a point solution vendors and often time we can direct them to the right targeted population for them to uh do early intervention okay um that sounds really cool um that the fact that you can make predictions about like how much people are going to have to spend over the next year things like that to help people budget are you able to share any details on what those machine learning techniques are that you use yeah so um um before we were purchased by uh multi plan uh the company was founded out of a MIT spin out project uh led by Dr burmith um that's a pronounced uh professor in the optimization techniques so we mainly uses the uh decision tree methodologies um to kind of op finding that optimized uh decision for the individual based on their past um care Pathways that we could see in the claims and then finding those uh detectable signals in their claims and then determine okay based on all these signals combination of the signals how much uh spend this person is likely going to spend or uh what type of uh disease this person's uh likely to have um and then it's not widely any disease we can predict and often time is those ones that have a c certain patterns that can be traceable or trackable U those are the ones that um can be detected better that's fascinating and as uh the use of decision trees means that it's quite easy to explain what's going on which goes back to your idea of um having transparency around like how everything works correct and I'm glad you mentioned that interpretable because uh I think that's one main differentiator in our methodologies comparing to other companies that are applying AI or machine readable me uh methodology to their uh data often time it's a black box like you can't uh understand or uh what's going on before the results being spin out but with the decision tree like you can actually interpret the the results or prediction and have a better understanding and then um like guide the model if needed um to better fit the the prediction or results that you um you know like you're seeking for just going back to like the main use case of this which seems like it's trying to save money for people so they're not spending so much on Healthcare uh so um I guess uh if you are uh paying for insurance like what's the sort of Step One into like making use of all this um technology and uh and T analysis in order to save money on uh on your healthare um Travis do you want to go with this yeah yeah happy to um actually want to make a little comment on what Jocelyn said too if that's okay um it you know as a as an industry you know my view is that um you know we we the BST acquisition was made because we actually believe that's the future of healthcare is data you know I say data is like the new oil it's like you got to find it refine it and distribute it and so being able to do that in a reasonable way is going to be really important and I think platform products that have scale um and that offer scale are going to be one of the biggest ways that potentially to control costs and healthare over time and so you know quality and cost are two of the biggest issues that are faced in healthcare and I think the use of technology is going to be the thing that ultimately um helps quality go up access go up and costs go down and I think that will be scale Technologies and so you'll see that from major major technology players that are interested in the space because of that and also you know as moved to the cloud and other other capabilities I think it's really important for care um you know there's you know lots and lots of Point Solutions out there there's lots of things to do I mean as an individual you have to just be proactive in your own search in your own care if you you're part of an employer plan um you know it's likely that someone like us or or another uh technology provider provides capabilities where you can actually search you can do some search where you can look at some price quality and availability data or it it can serve up someone that's in your network that is at a particular price and quality that's emerging and getting better and better over time as the data and the data science improves and as the transparency data which we wildly support as as a as a policy initiative um becomes available we can use that and we can start to map um as I mentioned cost quality to availability and that makes the process more simple so you can start to have choices um but it still feels a lot like hunting and pecking um you know or or luck or refer Network or I just know somebody um so I I you I think you have to Advocate on behalf and and don't underestimate the tools that you may have through your employer or through government websites that are actually starting to develop and do better here um of which you know we offer we offer some of them that others do as well um so that's I think that's kind of the state somewhat the state we're in uh I suppose the other part of this is uh things like um the provid side of things so like the the doctors and the hospitals just just having better data affect um their costs and um and yeah is it going to affect uh the the monetary situation for these providers as well yeah I mean I put on my my healthc care hat and yeah in total um yeah I think um a couple you the thing that's going to be really fascinating I think with um the provider and the patient side not to take us too far off track here but there's a couple really interesting developments one is um you know the ability to use ambient Technologies and listening and other techniques to where you're now collecting data um in a way that's very different than the past which is essentially you know the keyboard so we we always used to say you know in Prior life that the most dangerous thing in healthcare is the pen because you were writing script now it's oh the most dangerous thing in healthcare is you know the keyboard now it's a voice too conversation where you can actually collect um information and data in an ambient way I don't know if people like that word or not but I've heard an actionable way it's what I would prefer to call it um that is done real time and also you can run rules and you can use other Technologies behind that to where you're taking a lot of um what would be administrative work off and burden off of providers um and caregivers and now they're able to focus on the patient and the actual conversation that's happening and then you can run prediction behind that so there's fascinating things going on in healthcare I encourage you know your listeners to really look at it as a cool space to go work I mean prediction models using data around um you know I've seen really interesting models around um Suicide Prevention looking at Social determinance combined with medical data to get early detection or early prevention Technologies to people or capabilities to people opiate other abuse situations where you can look at State data and you can look at other data and you can determine a full view of someone's health care and what's happening so you can intervene in a way versus wait for someone to present there's some really fascinating things that are happened um with that and also around Precision medicine where you're looking at the genotype and you're figuring out the best way to actually prescribe in a precise way certain therapies or procedures or protocols that yield the best result I mean that the next 20 years is going to be incredible I wish I had 30 years left in my career I probably don't Jocelyn probably does but I mean it's going to be like the best 30 years because it's going to be the most exciting time because this is all available now um because a lot of it's been digitized um in the US and across the globe actually so sorry I got a little excited there Richie no no um I love the excitement and that's kind of interesting that a lot of the stuff you mentioned about um well okay if you want to improve cost in healthcare got to intervene early and detect things before they become a big problem um I can certainly see how that's the the case with opes you want to stop you want to uh identify the problem before people have uh messed up their bodies um so uh are there any other areas where you think um there is uh room for like some some big improvements in in health care there so um like are there any particular disease types or any particular conditions where you think there's room for a lot of um use of data to improve the situation yeah I think that there are um quite some areas where um especially I think the shoppable services where people can have the time to evaluate multiple options that's where uh we have the better chance to inter uh you know uh to provide using information to Pro provide more actionable insights before or someone making that decision so I would say things like uh uh Orthopedic surgeries um or um trying to um you know find the the best provider to treat kind of a long-term disease conditions um that's when uh I think using data and how having that well-rounded information um both cost and quality um to to help make that uh optimal decision for the patients um I don't know Travis if you have um some more to add I would answer I'll answer the question a little a little differently than a specific uh conditioner area what I would say is that um for us a really important thing is um figuring out what to work on the the world of things to work on is enormous um but the resources to do it are finite and so you know having product life cycle capabilities that allow us to understand the market to understand the needs of clients and then ultimately to make things that matter is really important or you end up doing quote unquote science projects that don't have scale benefits and so companies you know I think have to think deeply about how they actually create product life cycle um so they can make organic capabilities that matter most to the markets they serve um and and then you you start to surface the things that can have the biggest impact some of which have been mentioned here today um and so there there's there's just many many many things you could look at from a morbidity and co-morbidities perspective and insights but um picking them and and having uh impact and getting in the market is also important as you run the business since Justin you mentioned uh the the of like long-term um diseases and Travis you talk about which areas have the biggest impact um I'm curious is to whether um is it the um I know phrases so uh there if feels like with um Healthcare there are some things which are kind of cheap but happen a lot so something like flu um happens to millions and millions of people but also it doesn't have a big impact and then you have some things which are rare and kind of devastating so cancer is less common but has a tremendous impact um so are there is there like one area or the other where um you think data is most important data as a um usable commodity where I said earlier is you know the new oil is going to be um interesting in the sense that it's not all uh in healthcare usable because of lots of rules lots of regulations lots of Phi um lots of other things like that so um if you could truly figure out the best way to look at Social determinants um to bring in search information to combine that with the medical record um you really could start to predict with much greater accuracy certain things like obesity in certain uh areas um looking at um you know diabetes instances of diabetes um certain things like that could have predictive capabilities or qualities and that way you could start to Target programs around wellness and other things in targeted geographies and areas that or you could spend social dollars in a way that's meaningful in a community to help prevent the spread of some of these things over time through education and otherwise and so I think that um most things are predictable in some way if you can get the right amount of data but there's a large debate in healthcare around what's acceptable what's usable um is it Phi meaning um uh health information that should be protected should it be usable should it be ingested is it cyber secured um all of those things are just a massive part of the data infrastructure um that have to continue to be answered so that we're able to use predictive models in a way that makes sense and most meaningful probably didn't answer your question but I thought I said no uh that was really interesting um yeah so uh the idea that um there are some sort of limits on what you can do ethically is very interesting because I suppose you can sort of try and ingest everything about everyone's lives um sort of Facebook style and then uh get more information for better predictions so can you talk me through like what are the limits on uh what data you can get uh in order to make predictions I think uh when we kind of analyze um those insurance claims data uh another uh some other um data that we can combine to help making some more um accurate pre uh predictions are things like soci social vulnerability index just as an example um so you can use a public available data in combination with the the client or the the patients um specific data together um to bring that bridge uh to kind of bring bring the gaps um so uh you are not exactly using some of the specific Phi information but using uh some general information knowing this person lives in this ZIP code and then by knowing by public information what is the social uh index look like in that area you can make certain prediction around okay for people live in a certain geographic area what do they like like what their uh average Ed educations are uh what their uh you know dominant ethics uh in that uh in that geographic regions are so uh things like that when you combine the specific data with uh more General data together uh you can still make some uh pretty useful predictions um around Healthcare and I suppose related to this um there's an awful lot of um data privacy regulations around Healthcare data you got like in the US this the hipper regulations so how do you go about ensuring compliance with all these regulations yeah I can I can take a step I mean um we have very high standard on secur ing the data that we uh collect from the um the various uh sources uh sources so um not only we're Hippa um compliant we're also you know High trust certified um just as a example like certain techniques we use to protect those information is even when the data comes in uh to hit our server that the very first step we do is to separate the Phi information and the claim information of that individual into two separate repositories and using a common person ID to link the two information together so the uh the Phi is stored in a completely separate uh storage place compared to the the claims information that way as another layer we're trying to you know like further protect the information um become idential identi uh in the UN likely events of things U you know like certain data being breached one thing that um you've both mentioned a couple of times is the idea of uh quality and cost being separate things so um I think a lot of the focus is on this has been let's try and reduce the health care costs for um people but can you also use data in order to improve the quality of outcomes as well yeah I mean absolutely I think that's uh you know one of the major major themes of the industry as a whole right now is uh figuring out how to best use data in a way that's meaningful to prove outcomes um you know our our Focus as a company really is has been I would say primarily around um you know providing Cost Containment um waste and abuse Solutions um Network capabilities and and now we've added the decision sign science piece um but we're also you know very much want to facilitate um you know anything that we can do to help the providers and clinicians and otherwise um figure out ways to use data in a meaningful way I mean you know I came from the MR background so I don't you know I don't want to talk too much about where I was but um you know there's incredible amount of data that's generated and there's an insights and Technologies and rules and capabilities um that are there that ultimately allow for much better care and capability and early you know detection of things that never existed in the past and the thing that's most interesting is it brings protocols forward in a way that in the past you know someone would have to cognitively process all this but now they actually can think about things in a very different way because it's BR brought forward than having to go find it so it's it's kind of changing the way work is done over time um and you know I I'll just say this Richie we you know our we want to serve the whole ecosystem of healthcare so we actually are working with we we want to work with Rural America to look at the idea of maybe helping them figure out ways to create better access to care in rural parts of the country to help keep cost down and quality up um we want to bring those transparency products to to you know the full Continuum of Care actually um as we go forward to to kind of help with some of these problems we're just start discussing I like the idea of just helping out rural Americans um have you got any other success stories where some projects have worked and it's improved people's Healthcare as a data platform where we have all these uh prediction Powers um one example is um you know using the claims data and then we predict those uh High likelihood of people of uh getting msk surgery in the next 12 month that way when um the client have a msk point solution mendor that supports the uh like specifically support the members and help them guide and navigate them to uh through the whole journey of their um you know surgery events Now by knowing who those uh you know like high high probability members are getting those surgeries are extremely helpful and then improve that outcome tremendously because rather than them uh trying to you know like wait for people to contact them they actually know who those people are so they can proactively as a point solution vendor contact those members um that are having the conditions and then uh like understand which stage of the disease they're in and then help them um find the Optimal Care pathway for their msk conditions um some may not even need a surgery some may need a surgery so you need help finding the right provider the combin cost and quality information so uh having the data and predict in and then trying to early inter intervent and using the data further to find the the right combination and the cost and then the quality providers I think that's the the type of problems using data we're trying to solve together I think something J we talked a little bit about it I think she hit it really well I mean that's a great example and I think what you're what I believe is with the future of healthcare and data will really be moving from sick care which today it's like I'm sick I better find someone that can help me or maybe I should show up somewhere if I'm really not feeling well to more of a wellness point of view where you can you know look at data look at information you can have early detection or prediction of the conditions that may um may present themselves and then you can Target certain groups of individuals or C or or or certain conditions and then you can do things around that to to to create wellness and you know you see that with Registries which get you know putting together Registries Based on data um that you can actually reach out to people versus waiting for them to show up when they're at their worst and that's a big that's a big change that I think can happen over time um in healthcare that really would make a big difference for the world's population in terms of health and wellness and benefit that is really interesting because yeah now I think about it you don't even go the doct when there's a real problem happening uh and if you can intervene earlier then um it's probably going to be cheaper as well as a better experience for whoever's sick okay uh now this all sounds very exciting and I'm sure there are a lot of people who are interested in working in this field so um if you want to get involved in like uh data science or data analysis uh in the healthcare field like what sort of technical skills do you need well I'll starting you can say what really is needed Jin so so I mean you're talking to someone who learned to code cobal okay so that that's uh that's my expertise is cobal cing um but no nonetheless I mean so I look you know I think it's you know again F fascinating area um you know as opposed to some other interesting areas of Technology you know I I I personally believe you can put a mission to it which is cool um you know serving health and wellness and and and otherwise around the world is an important thing to do so you can get behind that um I think informatic the healthcare informatics will be a just a huge need for that across the you know the particularly in the United States but also um NHS and other parts of the world um having worked in those those Health Systems myself across across the world data science analytics machine learning um AI the the attributes of you know intellect and problem solving and you know I would say um curiosity will be important so I think employers myself you know look for attributes as much as skills and how you show up in a in a way that you know is is indicates that you'll you'll you'll grind out when things aren't going well um but you'll also seek new capabilities and skills over time are really important and Jocelyn could probably speak much more eloquently to the technical capabilities needed so yeah so uh that that was really uh interesting that um it's a lot about attributes as much as skills um but JN do you have anything to add on uh on technical skills that you need for working in this field I think on top of that just maybe a little bit of un basic understanding of the US healthc care system um like a little bit understanding of you know some basic uh you know coding or whether it's you know like the the diagnosis coding system the procedure coding system just so you know like how things are being categorized are being coded are being interpreted to to better apply the right Technologies onto the right data um I think uh that that two combination together will really power up um the uh the way that we leverage data and then make it uh you know super actionable and predictable okay yeah so just understanding like what these disease codes actually mean and uh how they relate to like a real condition okay um now we've talked a lot about the US um healthc care system I am curious as to whether any of these sort of um approaches you're taking um are applicable to other Medical Systems around the world I I think uh like within Europe for example um the the structure of the the payment is is very different from uh the US uh so how uh globally applicable is Sol this I had the opportunity to cover Global healthcare for Oracle which I'm sure many of folks know um as a big technology provider um so you know Global healthc care operates um differently in terms of how you know payers uh the payment system works a lot of times it's a it's more of a a social approach to it based on taxation it's government driven with some some private option typically available at times but all the same problems still exist in many ways um you know quality weight times uh high cost readmission um issues and otherwise so I think a number of companies are working on that um for us um you know we're primarily focused right now on on the US and the work we're doing here um I do think over time as we mature as a company as we grow and as we develop that a lot of the work that we do particularly direct to employer um or through other channels to employer where we use our solutions for reference-based pricing we're looking at claims we looking at Cost we're looking at prediction I think those are very relevant for employers around the globe um there's just last thing I'll say about that Richie is that that is a there's a massive sometimes barrier to entry for companies to do work in other parts of the world every almost every region has their own set of quality standards cyber security standards um data rights and usage restriction um it it becomes highly C it's high cost and sometimes it's a big barrier to entry actually for Innovation um so I think it's something interesting to think about that policy really could help move forward the industries across the world if we had ways to better you know to to reduce the cost of entry for technology companies and businesses different topic probably for a different day but um something think about too yeah I can certainly imagine how the data cleaning would become um dramatically harder if You' got different um like diagnosis codes for different countries and things like that and different data formats as well uh okay all right so uh maybe uh a plan for a a different company in in a different country then yeah you you probably won't invite us back but if you do we'll talk about that too love super um so uh just to wrap up what are you most excited about in the world of healthcare and data at the moment I think I'm super super excited about these um I think GPT like artificial intelligence uh Technologies and what it can really lead us just like what Travis mentioned earlier about the doctors and providers these days are using these ambient Technologies to really com rather than type hand type those uh EMR information when they talk with patients now is using technology to to translate those um you know conversations into a digitalized standardized uh you know like data sets where you could really uh leverage that uh normalize the data rather than you know messy uh unstructured data um to uh further predict and better um better prevent certain condition from worsening or or happening so I think um that's what I'm super uh excited about in this industry yeah I think um we talked about a little bit um this idea that you can start to focus um a little more on Wellness than just sickness um I think is important um the really giving uh individuals a the ability to have some autonomy over their their own health and and wellness and choice is important um so I think you know that access autonomy understanding your choices through transparency is frankly can be a comforting thing when you're in a in a moment where in a confusing process where someone you know or love isn't feeling their best um is is disconcerting and so I think there's an emotional element to it as well over time where you know you're we're predictive models can help with Wellness but we're also bringing cost control uh we're bringing other transparency solutions to the table that just make it E just just makes it freaking easier it's hard man it's hard figuring out like this discussion today you know I mean we live our lives doing this every day I can only imagine how the average consumer feels sometimes so um kind of demystifying it making it simpler making it easier making it less cognitive burden to have to figure out where to go what to do um I'm excited that I think the the next decade in healthcare is going to be the decade of of true and meaningful Innovation and it's going to really come to the market and it'll be through technology um so that's that's pretty cool thing to be a part of all right yeah just empowering people to make decisions about their own healthare that sounds uh like a wonderful goal excellent all right uh thank you so much for your time Joslyn and Travis like uh yeah lots of insights there s was great thank you yeah thank you very much\n"