AI to Predict & Personalize Long-Term Care Planning with Lily Vittayarukskul

🚀 Welcome back to the Steadfast Care Planning podcast, the show that helps you plan for care to live well. In this compelling episode, we’re joined by Lily Vittayarukskul, founder and CEO of Waterlily Planning and an ex-NASA data scientist. Lily shares her pioneering approach to utilizing AI and massive data insights to personalize extended care planning, inspired by her personal experience navigating her aunt's long-term care journey.

👨‍👩‍👧‍👦 In our conversation, we explore how Lily’s tool aids financial planners and individuals by predicting long-term care needs using over half a billion data points, examining costs, care trajectories, and much more. We discuss the challenges posed by financial planning for care, including insurance policies, the rate of return, and the emotional impact of preparing for aging and loss of independence.

🔍 Lily also outlines the technical and human element considerations her tool incorporates, such as disability, family caregiving involvement, and the importance of genetic and lifestyle factors. Moreover, she discusses how her models strive for accuracy, the implications of healthcare inflation, and the crucial role of family history and personal health in planning.

💡 Whether you are a financial advisor, an individual planning for the future, or a tech enthusiast curious about AI applications in healthcare, this episode offers valuable insights into the evolving landscape of long-term care planning using AI.

In this episode they covered:

🔹 How Waterlily integrates with existing financial planning resources to enhance long-term care planning

🔹 Explaining the concept of unbiased data sources as mentioned by Lily, and why is it crucial in building accurate predictive models for extended care

🔹 The fluctuating nature of caregiving commitment and how it impacted his personal well-being

🔹 Some of the technical limitations Waterlily, and how they impact its effectiveness in predicting long-term care needs

🔹 The human element involved in long-term care planning and how to address variations such as family involvement and personal health history

🔹 An accuracy rate of 70-80% in predictive insights

🔹 The importance of lifestyle and genetic factors in long-term care planning

🔹 Future enhancements or features for Waterlily to further improve the planning and management of long-term care for individuals and families

For more information about Lily Vittayarukskul and Waterlily, please visit:

https://www.joinwaterlily.com/

Or, LinkedIn:

https://www.linkedin.com/in/lily-vittayarukskul/ ______________________________________________________________________________

➡️ Watch this podcast:  https://youtu.be/7EjrNWfqjtE 

#LongTermCare #LilyVittayarukskul #SteadfastCarePlanning #JoinWaterlily #AIandHealthcare

For additional information about Kelly, check her out on Linkedin or www.SteadfastAgents.com.

To explore your options for long-term care insurance, click here.

Steadfast Care Planning podcast is made possible by AMADA Senior Care and Steadfast Insurance LLC.

Come back next time for more helpful guidance! 

Transcript

Kelly Augspurger [00:00:02]:
Hey everyone, welcome to Steadfast Care Planning, where we plan for care to live well. I'm your guide, Kelly Augspurger. With me today is Lily Vittayarukskul, founder and CEO of Waterlily Planning and ex-NASA data scientist. Lily. Thank you so much for being here.

Lily Vittayarukskul [00:00:18]:
Amazing. Thank you for having me.

Kelly Augspurger [00:00:20]:
Today we are going to be talking about artificial intelligence and software that's being used to predict and personalize extended care planning. And Lily is really doing some phenomenal work in this area, so I'm really excited to have her on the show. Lily, can we jump right in?

Lily Vittayarukskul [00:00:37]:
Absolutely.

Kelly Augspurger [00:00:37]:
So I know that you've created some powerful AI that can help predict and really personalize extended care planning, which is called Waterlily. Right? But before we get into the details of how this works, Lily, what's your backstory? Why did you create Waterlily?

Lily Vittayarukskul [00:00:54]:
So, for the same reason as a lot of people in the long-term care space, I stepped in due to a personal experience on the topic. So when I was 16, my aunt was diagnosed with terminal stage colon cancer, and what was initially a very intense medical event quickly devolved into a two and a half year ordeal, navigating her long-term care needs. And so that experience, that made me realize how incredibly devastating it is to navigate care for one of our loved ones from, I'm sure, you know, from a physical, financial, and relational standpoint. And so that's what caused me to become passionate about how can we actually help so many other families that are going to navigate this topic of, you know, that aging loved one or at the end of life, essentially mitigate those outcomes, mitigate those really poor outcomes. And our core focus is, well, I think if we can try to get them to plan for this ahead of time, or have that conversation ahead of time, that would make a world of a difference. It's just a question of how do you get that conversation done earlier, or in advance?

Kelly Augspurger [00:01:58]:
Okay, and you are not alone in that personal experience. Right? You're exactly right. Where so many individuals, myself included, come into the industry because of that personal experience. And it really motivates us to help other people and families plan ahead so that they don't have to experience, really, the painstaking, you know, experiences that we've had to endure. So I'm sad that you had to go through that, but obviously, there's motivation there, and there's a purpose in that pain. And so I'm so glad that you have turned it into a positive and are really doing something incredible. And so I can't wait to talk about this, Lilly. Tell us, what is Waterlily, and how does it work?

Lily Vittayarukskul [00:02:35]:
What Waterlily does is we built AI to personalize long-term care planning. And so the way that works is we essentially pull from over a data set of over half a billion data points of families that already went through that long-term care event, or they did not go through that long-term care event. And what we did was we built predictive models on top of that, so that we can learn a little bit more about who you are. We have an intake form where we ask basic demographic, health, and wealth questions, and every single one of those inputs goes into our models to predict what will your long-term care story look like? What does that cost and care trajectory, including how the family may step in for your care by default.

Kelly Augspurger [00:03:16]:
A half a billion data points. Did you guys hear that? I can't even fathom that amount of data that you're actually using. Where do you gather these data points, Lily?

Kelly Augspurger [00:03:25]:
How do you...Is this from, you know, home care agencies, facilities, the government? Like, where do these data points come from?

Lily Vittayarukskul [00:03:31]:
So we have multiple data sources. That's actually the most important thing is you actually gather from, like, multiple diverse data points so that you don't have bias in your population, if possible. And so we have both public and private access to government institutional data, as well as academic institutional data as well. So a lot of the renowned sort of, like, studies or private data sets, we've essentially done a ton of research and gathered those. In addition, of course, we're considering data partnerships as well to further strengthen that data set with existing, you know, players in the industry.

Kelly Augspurger [00:04:06]:
So you're using data points of people who have been through extended care and people who have not. And you're looking at it to see, okay, what are the differences, and how do we analyze that and be able to use that to predict future extended care for other people? Correct?

Lily Vittayarukskul [00:04:21]:
That is exactly correct. Where we try to understand who you are so that we can map you to families that look like you or individuals that look like you, and what was their trajectory? And so we show you that trajectory. It really is off of, like, historical data to get as precise as possible about what your future may look like.

Kelly Augspurger [00:04:39]:
So what kinds of questions are you asking, in addition to the data points that you already have from, you know, just on a national level, what kind of information are you gathering from an individual in order to predict extended care?

Lily Vittayarukskul [00:04:52]:
We ask a little over two dozen questions in the intake form, as I mentioned, around, like, financial, health, and demographic information, and every single one of those answers, essentially are direct inputs into our model that we figured in. And so we didn't want to, like, ask you, you know, hundreds of questions. If we could, I would. I would love to do that, but we don't have that time. And so we went and we essentially tried to find the variables that had the highest predictive value for your trajectory. And so for the demographic, we ask, are you partnered? Are you widowed, or divorced, or married? That has a huge impact. Your coupled status has a huge impact on your trajectory.

Lily Vittayarukskul [00:05:31]:
We also ask how many people live in a household with you? You know, what's your current age? Do you have children, siblings that we need to know about? Where do you live? What's your current zip code? For wealth we do ask about income, or your net worth. I think we already know there's a lot of research that's done into the social, economic factors that play into the quality of life that you have. So wealth, in that aspect, does play a role. And health questions. So as a high level overview, you know, of course, we ask about any sort of standard diagnoses that you've had, any orthopedic surgeries, injuries, or chronic conditions, because knowing where your physical health state is at, we have a sense of how that will progress over time. And, of course, we ask about mom and dad, as well, in their trajectory. So that gives us a bit of your genetic factors, and I would say social factors about how you grew up, the habits that you've developed that you may have gotten from your parents. So that's like, a little light overview on some of the exact questions that we ask that goes into our model, but hopefully that makes sense. When someone goes through it, you know, we get the response back,

Lily Vittayarukskul [00:06:32]:
"Wow, you really got to know me a little bit without asking overly sensitive questions."

Kelly Augspurger [00:06:36]:
Right, and I've gotten to go through a case with Lily and see it live. It really is...It's fascinating, the questions that you do ask and then the results that come from those questions. And you have a short and a long version, right, Lily? tell us about those differences.

Lily Vittayarukskul [00:06:52]:
The key difference between taking the short version versus the long one is that there's a 15% overall model accuracy difference. So if you want to spend another minute or two, you know, answering a few more questions, for us, that actually boosts your predictive accuracy by, on average, 15%, which is actually a pretty big jump. But we also recognize that sometimes people just want to get their initial results pretty immediately. But what I've seen in the real world is the vast majority of real clients try to do the long version just because of how expensive this event is, and they want to get that piece right.

Kelly Augspurger [00:07:25]:
Got it. So the short version, what is the accuracy with that? We know that it's 15% more with the long. What's the short version accuracy?

Lily Vittayarukskul [00:07:32]:
So we have essentially all types of accuracy, but we definitely hit north of at least like 70% or 80% across all of our models. But every single kind of insight that we give you around, "What's your likelihood?" We know what age it'll happen at, or how family members step in, those are all independent models, and so they have independent accuracy metrics associated with them.

Kelly Augspurger [00:07:53]:
The Steadfast Care Planning podcast is sponsored by the Certification for Long-Term Care, CLTC, an in-depth training program that gives financial advisors the education and tools they need to discuss extended care planning with their clients. Look for the CLTC designation when choosing an advisor. If you're looking to become a CLTC, enroll in their masterclass and enter "Kelly" in the coupon code field for $200 off. We know that there are lots of statistics on the risk of needing care, right? Especially in our industry, a lot of people hear, 7 out of 10 people over the age of 65 need some form of extended care. That's a very broad number. A lot of things that go into that. That's not a statistic that I use personally.

Kelly Augspurger [00:08:39]:
But do you see that in the usage of your AI it tends to be along those lines, Lily? As far as likelihood of people needing care, like, you know, how many years, what are kind of the averages and the types of care that your model is predicting for people? And we know that people are different, right?

Kelly Augspurger [00:08:58]:
It's very customized, and so everyone's going to be different. But what kind of trends do you see?

Lily Vittayarukskul [00:09:03]:
I'll be completely honest. And this is actually, it's fascinating to me. So one of the important things about building these types of models is that you actually get your data sources right, and you want to make sure that they're unbiased in some way. And the best way to do that is you actually visualize the data and you visualize the predictions and see how well does that map to the average statistics that we actually see out there in the real world that's being thrown out. There's been independent studies that're done. And so what's been fascinating is, you know, for that average likelihood some people say 7 out of 10 but that's not the more stricter one of just, two plus sort of self-care activities. And so we also know that HIS essentially has a source that is, it's 52% of Americans that have two plus ADL needs.

Lily Vittayarukskul [00:09:47]:
And when we modeled out every single person in our data set, and we mapped out what was their trajectory around their likelihood or their age, that middle point, or that average in our population matched, actually, what was these statistics in terms of majority of people average have around 50% to 54% likelihood of needing long-term care. And then, of course, we have your tail ends of two standard deviations. So a lot of our graphs actually look like normal curves in terms of the average. It's around that 52%. Or for females or males, you know, the age is around 80 to 84. Depends on male or female. It's really closely matching, actually, those averages.

Lily Vittayarukskul [00:10:30]:
And so what's really fascinating is it actually helps validate that there is minimized bias as much as possible in our data set. And also, too, is that it was so fascinating to see that there's so many people that fall outside of that statistic, as well. And we've seen that in our real world cases with real clients is that when they share, "I already have back problems and I'm 40 years old," or "I have a certain diagnoses," or "I'm divorced," they look different than the average.

Kelly Augspurger [00:10:57]:
So it could be less, could be more. We definitely have a range of what that looks like. What about even as far as time period? I mean, you're giving estimates on, you know, you might need care for x amount of years, right, in your data that you predict?

Lily Vittayarukskul [00:11:11]:
Yes. So how long you'll need care for, on average, most people we see around two and a half, maybe a little over three years is the average kind of statistic that we see, which, again, matches what we see in the national averages. But again, it's a spectrum. We've definitely seen people fall above that four years, five years, even six years, especially if they share, they have cognitive impairment or their parents had cognitive impairment in some way. We noticed people with memory issues actually have an extended period of having long-term care needs. So that was actually really fascinating for us to see that as well.

Kelly Augspurger [00:11:47]:
Thanks for providing those insights, Lily. I think it's really important when we're planning for extended care, knowing history, knowing family history. Do we have cognitive issues in our family? Do we have a history of physical impairments? If so, is it because of lifestyle choices? Right. Like, if my family members, if my parents end up having physical impairments, is some of it, you know, because they don't take care of themselves, they're not eating right, they're not exercising, they're not doing the right things? Or is this more cognitive? Do we have a history of cognitive in my family? Dementia, Alzheimer's, Parkinson's. Right? Those types of things are really important when planning for the future to see, okay, who's going to provide care, where do I want to receive and can receive care, and how am I going to pay for that care? And so you can help clients through financial advisors, and we'll talk about that in just a few minutes, right? But you can help them really pinpoint into a more accurate timeline of,

Kelly Augspurger [00:12:41]:
"We predict you need to plan in this way because of your family history, because of your lifestyle choices and your health and your finances and all these different data points in order to provide better planning and then better solutions for that planning." Right?

Lily Vittayarukskul [00:12:57]:
I think you were very spot on with that. I'll just share a small commentary on mom and dad, we ask maybe 6 questions of the overall, you know, 30 or so questions in the longer form that, you know, we could ask 30 or 35. So, as you can tell, like, they don't play the largest role in terms of what the rest of your trajectory looks like. Exactly for what you just said, which is they might have some major lifestyle differences than you. And we could actually see that when we notice mom and dad had these problems, and you told us you had this health status, you know, in some way, or here's how your education may have differed or something else every single one of those pieces actually, about your individual lives, sort of like, what I do differently, we totally take into account.

Kelly Augspurger [00:13:39]:
Yeah, so genetics matter, but lifestyle, and lifestyle choices matter a lot, too. So I really appreciate how you take a comprehensive approach and you're considering all of these different facets to the whole person, because we are a whole person, and so we need to look at this in a really holistic lens to see how do we really best plan for this. Okay, Lily, tell us who can access Waterlily, and how can they access Waterlily?

Lily Vittayarukskul [00:14:05]:
So right now, we're partnering with carriers, insurance distributors, as well as wealth advisors, and offering our software to them in order for them to use this for their population of interest, like for advisors and agents it's for their clients, and for carriers we're tapping into the policyholder population and assisting them. So that's currently how our product is accessible today. However, we're quickly building out relationships and partnerships all over the place of just who are all the businesses or programs that are essentially on the hook for long-term care costs. They're probably going to be interested in this topic. So we're lightly starting to talk to credit unions as well as states and some health plans. And so things are going to be really interesting in terms of how do we essentially scale what we've built in order to offer access to anyone that may need it in a related relationship that they may have with someone else. Of course, we are actually internally quietly beta testing as well, consumer access as well.

Lily Vittayarukskul [00:15:10]:
So if a consumer is interested in using our product, we are trial testing right now to connect them with an advisor to essentially at least get access to your insights without having to have a conversation. So if you're interested in that, you could just go to our website and sign up for our waitlist and you might be one of our early access users in that case.

Kelly Augspurger [00:15:29]:
Very cool, because I envision that this will be desirable amongst lots of different channels. Right? Like the brokers, the distributors, the states and consumers individually. And so I think making it accessible to lots of different people in a variety of channels is a really smart idea. I think, you know, the thing to consider when just speaking to consumers specifically is there's powerful data behind what Lily is doing here. And it does make a lot of sense when you're doing this alongside a financial planner.

Lily Vittayarukskul [00:16:01]:
Right.

Kelly Augspurger [00:16:01]:
Because there are definitely financial components that need to be considered. And we haven't talked in depth about this, Lily, but financials is one of those pieces. And so I know just based on the case study that we did together is you talk about how much would you want to self-fund, how much would you want to set aside? Would you want to consider a long-term care insurance policy? What would that look like in combination of the self-funding and a policy together? Or how much would you need to set aside today and earn a rate of return of x, right? In order to produce a big pool of money later on. So there are a lot of variables, and if a consumer isn't financially savvy, it might be a little overwhelming to them. I don't know.

Lily Vittayarukskul [00:16:42]:
Right.

Kelly Augspurger [00:16:43]:
So working alongside a planner, I think is really helpful and makes a lot of sense, which is why you're targeting and you're working alongside financial planners, right?

Lily Vittayarukskul [00:16:52]:
You are exactly correct in that sense for just the financial expertise that you have in navigating. Just like what is the difference between self-funding, or buying insurance and what may potentially be a better ROI? You're able to have a really good and clean conversation about that, and we just help out with that. We help facilitate that conversation to the best of our abilities in that we map out this client's unique care trajectory, and then we map on top of that any sort of policies you're considering on that trajectory. And we mapped it to the strictest rule on that policy. So things that are really hard to catch for a human, we essentially use software to try to help the advisor understand. Now, here are the limitations of the policy. So even though the max benefit says it's this amount, we expect a projected cost coverage of x dollars, instead, which then you could better communicate that to the consumer. In this case, I think on the other side, the other reason why I cared a lot about working with wealth advisors and financial advisors at first was because of how close they sit in their clients life, in general, and how they end up being a mediator on a lot of different difficult conversations.

Lily Vittayarukskul [00:18:00]:
So it's not just about that financial piece. It's about how do I talk to mom or dad about this, or how do I talk to my kids about this? And oftentimes, these advisors are a wealth of information and wisdom because they've seen it probably multiple times, dozens of times over with other clients. And they know how to navigate this better than trying to do this alone.

Kelly Augspurger [00:18:17]:
Right, and this is not just a financial conversation. You know that, I know that, right?

Kelly Augspurger [00:18:21]:
This is very much a conversation about your family and about your preferences. And it's emotional. It really is. And so having a tool to be able to use to really walk through that process, right? By answering individual customized questions and addressing each of these individually, knowing that we need this information in order to offer the best recommendations. That's the comprehensive approach that advisors really need to take in order to properly plan for extended care. There are different ways to plan for extended care, but if we're not doing it comprehensively and really addressing all of these things, then we are not doing, in my opinion, what's in the best interest of our client. Right?

Kelly Augspurger [00:19:01]:
This is not just about money. This is about what is the plan, who's going to provide care and then what level. And I know your data even takes that into consideration, right Lily?

Kelly Augspurger [00:19:10]:
As far as, preferences, do we have family that might want to be involved? How much do we want to depend on professionals, right?

Kelly Augspurger [00:19:17]:
And family. And I just really appreciate you have considered the angles and what really needs to be considered here. The Steadfast Care Planning podcast is sponsored by AMADA Senior Care. AMADA provides complimentary consultation with a senior care advisor to find the right care, from in-home caregiving to community care, as well as long-term care insurance, claim advocacy, and unique support partnerships for financial advisors to address family transitions and generational retention. To learn more, visit www.SteadfastWithAmada.com. What limitations, Lilly, would you say that Waterlily has that is not considered? And I know you can't ask thousands of questions to the person because there's just not enough time in a day, but is there anything that stands out to you? Where, okay, yeah, these data points have not been considered, and this is the reason why.

Lily Vittayarukskul [00:20:14]:
That's a really great question. First and foremost, we have technical limitations of, you know, how far in advance we could actually project given the data set that we have. And so we recommend, and it's in our data disclosures and disclaimers page on our website, that this software is really only intended for individuals that are 40 years old and above. So anyone that is below 40, we just don't have enough data on you to really cleanly map out what the next few years look like, the few years after that, etcetera. The other main limitation that we have, and I love that you pointed out is usability of software. So you can't ask so many questions and get this value because people are just trying to get a primer on this topic and just start to understand it. So you want to reduce the time to value. One of the things that we don't take into account that I think is incredibly crucial, is learning more about the relationships in the family.

Lily Vittayarukskul [00:21:10]:
So right now, we ask, "Do you have a spouse? Do you have children, or siblings?" This gives us a sense of, are there other family members and children and spouse that could take on that care? Now, hypothetically, but I think that our hours of care that we predict could very much be largely influenced by what's that relationship between the spouse. What's that relationship between your children, specifically your eldest daughter, or your youngest daughter? How far do they live? Things like that, that you already have these conversations. You know, advisors already have these conversations. And for other family members, of course, it's not just the youngest sibling that steps in and tries to take care of you. That's common. What's also incredibly common is nieces and nephews, or grandchildren are also playing a really large role as a caregiver in our society. So those are questions that I wish I could ask, but there's always that tension between how can we help a consumer or a client quickly understand the value of planning on this topic and further accuracy of the planning itself.

Kelly Augspurger [00:22:13]:
Yeah. So, thinking about a couple, and I can think of even people I know personally where we've got partners, or they're married, where if they were to go through the software with you, it would say, "Yes, okay, we've got a couple here," but one of the couples, one of the spouses is disabled, so they can't count on their spouse to provide that care. Right? Yes, it's a positive that we're married and we've got that potential caregiver, but we know in reality that that spouse is not going to be able to provide care. So I see what you mean as far as, yeah, that definitely could be a limitation. So that's where the human element comes into play, right? Probably from the financial advisor, or whoever you're working with to say, "Okay, yes, we're married, but we already know that we're not going to depend on so and so to help in that caregiving process because they're not capable," or maybe my kids live another state away, or in another city and they don't want to provide the care. Maybe they'll help to coordinate care, but they don't want to help provide care, which is pretty common, too, as far as kids go, adult kids.

Kelly Augspurger [00:23:10]:
So, yeah, that's interesting. Thanks for providing honest feedback with that. I appreciate it. Lily, tell us, how do you even predict the cost of care and what goes into that as you're planning within your AI?

Lily Vittayarukskul [00:23:22]:
So we distill down cost in three ways, three kind of simple ways in a not so simple kind of calculation, which is we take into account care setting, family involvement, and inflation rate are the key things that go into cost. And so when it comes to care setting, like knowing whether or not you're going to be living at home, or you're going to be going to a community. And at what time in your extended care trajectory is that going to happen, plays a really large role in terms of what is that monthly cost going to look like or the hourly cost of care coming into the house and how long you're going to have that care for. So again, like that plays a role, then family involvement. Oftentimes, if families step in, and we currently predict for physical caregiving hours, if family members are going to step in, we currently assume that it's subsidized, that you're not actually paying family members any amount of money. And so they could be the difference of, you know, care costing $350,000 or $200,000. Like that's how massive the help could actually be instead of having professionals come to the house instead.

Lily Vittayarukskul [00:24:26]:
And then the third piece that is probably the trickiest cognitive piece to teach is the healthcare inflation rate. A lot of people assume that, oh, you know, long-term care or extended care, in this case, you know, 70% likelihood. Let's assume it's three and a half years and let's look into a nursing home in your area. It's about 10k. So do you have about 350k to self fund, or not? And people are like, "Oh, well, I'll have 350k, I guess in of course in 20-30 years when I'll actually need that care. And they're not taking into account how those are today's cost. And actually those costs are going to grow at what we use is 5.4%, which is off of CMS's actuarial tables for the projected growth of healthcare costs over the next ten years.

Lily Vittayarukskul [00:25:08]:
So in other words, that cost will double every twelve years. And that's a hard thing to cognitively wrap your head around. And so it can easily freak someone out about, "Oh, costs are going to grow by that fast!" And I'm only hoping that like my money performs in the money market at 6%. But also this is where insurance comes in, or creating a self-funding plan today comes in, because if you were to wait until you need care, of course, that future dollars doubling every twelve years, yes, that's going to happen. But if you strategically think about how you want your money to grow, or how you want to invest your money today, that actually plays a pretty massive role in like heavily subsidizing what that cost looks like. So something that might be... it's crazy to think about like maybe in 30 years your costs might go from 375k to two million. But if you plan today, you could probably cover that cost with around 200k.

Lily Vittayarukskul [00:26:03]:
That goes to 2 million. You're like, how does that work? And that's a whole point of talking to advisor that's the whole point of going through a software that does the math for you to help you realize it's an incredibly special thing to plan today, and it's heavily advantageous to do that today.

Kelly Augspurger [00:26:16]:
Right. Care costs today are very different from what they will be in the future. And so we really want to come up with what is the best, most efficient way to be able to plan and pay for this care. And it sounds like that's what your software really pinpoints and identifies. This is what we need to consider. Okay, now, what is the most efficient way to do this and to pay for this? Lily, can you share any final advice on how people can best plan for care to live well?

Lily Vittayarukskul [00:26:41]:
First and foremost, just even thinking about it is a huge step forward in actually getting to build a really good plan because I think this is a really complex topic. There's a lot of emotional aspects, the actual physical complexities, or logistical complexities associated with "As I grow older and I can't do the same things that I used to be able to do to stay independent, what am I supposed to do different now?" I think that one of the things, and this might be a bit of a left field sort of approach to this, but something I thought a lot about on this topic is philosophically just, why don't people talk about it? What makes it so hard? And, you know, very few of us have had 24/7 bedridden care for several months to really get a gauge of what is that going to feel like. What is that state I'm going to be in? And how can I get ahead of the mental challenges that actually even come with that. That usually kind of come out in our relationships and hurt our relationships, as well. And when we were taking care of my aunt, that was one of the struggles. We didn't know how to talk about this and she didn't know how to talk about this. There was no psychological preparation for this topic, and I think that would be massive. Is just separate from software, separate from resources that you read online.

Lily Vittayarukskul [00:27:56]:
It's that recognition that this is a really difficult topic because what I saw was the sense of losing who we were. In a way, when we lose our independence, that becomes scary. That's why we don't want to talk about it, because a lot of who we define ourselves as is like, "I am an artist," or, "I am an engineer." I really love taking walks, or hikes, or having a dog or doing things that are around being able bodied. And something that I think would be so crucial is, you know, how can we think about who we are and how valuable we are still when we're not as able bodied, or we're not as capable? Because it's going to happen, but it doesn't necessarily mean that you're less worthy and you should feel guilty about receiving care. I think that's one of the major barriers that we have. It's something that I'm trying to also navigate and try to understand. But how can we build mental resiliency around aging and get ahead of the guilt, or the negative emotions associated with losing a former version of ourselves and potentially trying to refind a new version where we come from more stable and healthy mental place that we could probably have better conversations with our family members and then we could also have better cognitive decisions around, "Yeah, I'm going to need this care."

Lily Vittayarukskul [00:29:06]:
So how can we tactically figure out what's the most effective and efficient way to go about this?

Kelly Augspurger [00:29:11]:
I love that answer. So we're really redefining the way that we think about and plan for extended care. I think you're spot on with, I would say 99.9% of people don't want to talk about it because it is uncomfortable. And the lack of control, I think that's huge. You know, losing control, losing independence, losing who we once were and what we were able to do is scary. And so if that happens, "Oh, I don't want to talk about that."

Kelly Augspurger [00:29:37]:
"That's never going to happen to me." Right?

Kelly Augspurger [00:29:38]:
Like so many people put themselves in that spot. But when you've had a personal experience like you've had, like I've had, like so many Americans have had, it's very real and we know it can happen. And so how do we best protect our family, ourselves, and our finances in the process? Well, you've got to plan for it, which means you have to talk about it. Right? You have to have these conversations ahead of time before you're in it and you're in crisis mode because at that point, you don't have all the options on the table and nobody wants that because then you lose even more control and even more independence. So, yeah, planning ahead, talking about it, running the numbers, you know, working through scenarios and removing some of the stigma by just having this conversation. I think we're doing that, Lilly. So I appreciate that.

Kelly Augspurger [00:30:25]:
I appreciate your answer. Lily, where can people find more information about you and Waterlily and how you help people?

Lily Vittayarukskul [00:30:32]:
I will make myself easily reachable because I actually really enjoy having conversations like this with you, Kelly, or with consumers, or all sorts of individuals that are interested in the aging space. So you can reach me directly at: Lily@JoinWaterlily.com. And also the other way, in case you may forget that you're not really an emailing sort of person. Our website is also a wealth of information where you don't have to go and buy something, really. If you go to our website, you're welcome to potentially get, you know, free access to our software, or subsidized access, as we're navigating, "How do we increase consumers that are able to access the software?" So you'd actually join on our waitlist, or contact us through JoinWaterlily.com. And if you're looking for resources on this space, we have an unbiased blog where we wrote a ton of stuff on things that we didn't see on the Internet around Medicare, and private pay care, or Medicaid, which people don't know the differences of, or what is that checklist of how do we talk to our parents about their finances or that care get ahead of this? Or, what are the different caregiving roles and what are some legal considerations that we need to take into account as a caregiver? Things like that. That's on our blog at Blog.JoinWaterlily.com. And so definitely read those and then contact us if you have any feedback about what additional topics we should write on, as well.

Kelly Augspurger [00:31:52]:
Fantastic. Whoever's listening, I implore you to visit Lily's website, send her an email, contact, find out more information. Because, Lily, I just see you are on this trajectory of you are quickly making headway in the LTC space, and I'm really excited to see where this goes. But I know that you're going to make some big needed changes in our industry, and I'm just really appreciative of your time today. Thank you.

Lily Vittayarukskul [00:32:14]:
Thank you so much. Kelly, it's been a pleasure. You asked wonderful questions.

Kelly Augspurger [00:32:18]:
Have a great day. Thanks, Lily. Bye.