Why the Same Surgery, Same Doctor, Same Insurance Can Costs 3x More – Dr. Allison (Alli) Oakes, Trilliant Health-এর সম্পূর্ণ প্রতিলিপি

help me understand a little bit? 00:03:07.862 --> 00:03:12.152 What does the Chief Research Officer do at Triliant Health? 00:03:12.272 --> 00:03:13.293 Yeah, absolutely. 00:03:13.293 --> 00:03:15.032 Well, thanks for having me today, Nathan. 00:03:15.432 --> 00:03:18.522 like you said, I'm the Chief Research Officer at Triliant Health. 00:03:18.573 --> 00:03:21.062 I'm a health services researcher by training. 00:03:21.552 --> 00:03:25.842 and in terms of what our team does at Triliant Health, we use all different types of data. 00:03:26.022 --> 00:03:35.953 Claims data, health plan, price, transparency, data, as well as our provider directory to understand sort of emerging and different trends within the healthcare ecosystem. 00:03:36.372 --> 00:03:41.923 Are there specific problems that you're most focused on or that your research. 00:03:42.358 --> 00:03:46.198 that you enjoy the research most of, out of all of those areas? 00:03:46.258 --> 00:03:46.557 Yeah. 00:03:46.557 --> 00:03:48.957 I think there are a couple themes that we focus on a lot. 00:03:48.957 --> 00:03:51.832 One is sort of this mismatch of supply and demand mm-hmm. 00:03:51.913 --> 00:03:53.758 That we have in our healthcare system. 00:03:53.877 --> 00:03:54.117 Got 00:03:54.117 --> 00:03:57.987 And how we can make sure we're getting the right care to the right patients at the right time. 00:03:58.328 --> 00:04:05.647 but we're also really focused right now on health plan price transparency, which is a relatively new data source, but what it reveals is just. 00:04:05.647 --> 00:04:09.668 This variation across the healthcare system, that doesn't make a lot of sense. 00:04:09.698 --> 00:04:15.907 So within the same market, the same procedure costing very different amounts of money, depending on the provider and the payer. 00:04:16.117 --> 00:04:19.718 So those are some of the major things that we think about and are tackling right now. 00:04:19.987 --> 00:04:23.288 Ah, the idea. 00:04:23.798 --> 00:04:30.708 That, depending on like where in your community you go, you might have a 3, 5, 10 x mm-hmm. 00:04:31.007 --> 00:04:38.677 Difference in the costs that you're, experiencing in your care is like, just mind boggling when so often. 00:04:38.708 --> 00:04:38.947 Right. 00:04:38.947 --> 00:04:42.158 Like when you need healthcare, it's like urgent. 00:04:42.817 --> 00:04:42.908 Yep. 00:04:42.908 --> 00:04:44.648 and it's like personal. 00:04:44.677 --> 00:04:44.767 Yep. 00:04:44.767 --> 00:04:45.937 And it's like high. 00:04:46.538 --> 00:04:47.288 Importance. 00:04:47.288 --> 00:04:51.358 And so knowing that there's that extra layer of like tightrope walk, is yeah. 00:04:51.512 --> 00:04:52.533 Is like really daunting. 00:04:52.802 --> 00:04:52.892 Yeah. 00:04:53.163 --> 00:05:00.533 I'm curious, how did your origin in, this kind of work, how did your origin in health technology, start? 00:05:00.642 --> 00:05:01.812 What got you on this path? 00:05:02.187 --> 00:05:02.637 Sure. 00:05:02.687 --> 00:05:07.338 so sort of thinking back, growing up I was always really interested in health and healthcare. 00:05:07.398 --> 00:05:07.487 Mm-hmm. 00:05:07.728 --> 00:05:11.447 I think for a lot of people that often makes you think maybe you'll be a doctor mm-hmm. 00:05:11.687 --> 00:05:13.307 Or a provider of some kind. 00:05:13.648 --> 00:05:17.608 but after undergrad, I was very lucky to work at the NIH for a couple of years. 00:05:17.923 --> 00:05:24.012 at the Institute for Mental Health and there about 50% of the job was working with patients and 50% was data. 00:05:24.283 --> 00:05:29.112 And it was, while I was there, I realized I was a lot more interested in the data side and sort of system level thinking. 00:05:29.163 --> 00:05:35.403 That goes into healthcare and that was what led me to find the whole world of health services research and health economics. 00:05:35.463 --> 00:05:39.293 So that was what I pursued academically, and got a graduate degree. 00:05:39.483 --> 00:05:42.333 But following that, have tried to work at different stakeholders. 00:05:42.333 --> 00:05:46.713 so I've worked on the provider side at Penn Medicine and the Philadelphia va. 00:05:47.103 --> 00:05:59.843 Have worked on the payer side, sort of at an internal health services research team at Anthem and now at Trilliant where this sort of, Third party player that can leverage data to really try and get at the heart of the issues. 00:05:59.892 --> 00:06:03.612 we don't have a vested interest in telling one anyone any particular answer. 00:06:03.663 --> 00:06:13.112 so our focus is really just leveraging the data to help our clients and partners understand what's going on within their particular market and how they should be thinking strategically about things. 00:06:13.112 --> 00:06:13.442 Yeah. 00:06:14.163 --> 00:06:15.923 Ooh, that's kind of an interesting place. 00:06:16.762 --> 00:06:19.112 To be, if you're agnostic 00:06:19.173 --> 00:06:19.382 Yeah. 00:06:19.442 --> 00:06:23.512 About what the data is, but, excited about delivering it. 00:06:23.517 --> 00:06:23.668 Mm-hmm. 00:06:24.168 --> 00:06:30.077 Sometimes that means you're gonna have customers who have very specific data that they want in a very specific way. 00:06:30.197 --> 00:06:30.288 Mm-hmm. 00:06:30.858 --> 00:06:34.697 Or there might be people who you need to convince that this is its own. 00:06:34.728 --> 00:06:34.817 Mm-hmm. 00:06:35.177 --> 00:06:36.887 Like why it's even important. 00:06:36.918 --> 00:06:37.098 Mm-hmm. 00:06:37.978 --> 00:06:39.687 Most people come to you. 00:06:39.963 --> 00:06:43.052 Like looking for when they want to better understand their data. 00:06:43.202 --> 00:06:43.622 Yeah. 00:06:43.932 --> 00:06:49.723 usually sort of a, a principle or underlying question is just what's going on in my market. 00:06:50.173 --> 00:06:50.413 Okay. 00:06:50.473 --> 00:06:50.923 Yeah. 00:06:50.923 --> 00:06:54.642 But I mean, healthcare data is so fragmented and

siloed. 00:06:54.642 --> 00:06:56.983 So if you think about a particular hospital. 00:06:57.398 --> 00:07:04.117 Or even a particular payer, they generally have really good visibility into what's going on within their own four walls. 00:07:04.117 --> 00:07:06.668 or within their own patient population. 00:07:06.668 --> 00:07:09.458 That's why you wanna be on the company plan, right? 00:07:09.458 --> 00:07:23.288 But if a patient is getting their primary care at one hospital in a market or health system, but they're doing specialty care somewhere else down the road, that hospital isn't actually going to have visibility into that leakage or lost care, if you will. 00:07:23.663 --> 00:07:31.642 So given the amount of data that we aggregate and the way we clean it, it's this ability to really understand a particular patient journey. 00:07:31.673 --> 00:07:31.882 Yeah. 00:07:32.062 --> 00:07:37.312 And every single place that they get care who provides it and what it costs. 00:07:37.343 --> 00:07:39.593 And it's exciting to be able to put those pieces together. 00:07:40.012 --> 00:07:40.023 Mm-hmm. 00:07:40.023 --> 00:07:40.127 It's so good. 00:07:41.317 --> 00:07:43.208 I, I'm honestly a little bit shocked. 00:07:43.208 --> 00:07:46.867 We've been talking about healthcare data and like economics mm-hmm. 00:07:46.869 --> 00:07:47.043 For almost 15 minutes. 00:07:47.043 --> 00:07:47.052 Mm-hmm. 00:07:47.708 --> 00:07:54.038 And I don't know that I've heard you specifically call out like how AI is gonna transform anything. 00:07:54.038 --> 00:07:54.098 Yeah. 00:07:54.307 --> 00:07:55.507 I'm a little bit shocked. 00:07:55.927 --> 00:08:04.687 do you have an opinion about, right, like, we're hearing all about AI is going to solve all of these problems, make workflows faster? 00:08:05.447 --> 00:08:05.507 Yeah. 00:08:06.048 --> 00:08:13.548 Obviously, like one of the things that AI really enables us to do is to understand, translate, and like mm-hmm. 00:08:13.788 --> 00:08:17.668 Get into data, in powerful new ways. 00:08:18.208 --> 00:08:25.197 How, how do you approach, large language models or, you know, the, the boom of AI in the work that you do at Trillium? 00:08:25.228 --> 00:08:31.538 Yeah, so at Trilliant, for a number of years now, AI in the different categories of ai. 00:08:31.567 --> 00:08:36.893 We've been leveraging, you know, large machine learning models for a very long time, but now thinking about. 00:08:37.748 --> 00:08:44.197 The dawn of generative ai, we do really need to think about where is it likely to be effective and where is it likely to be ineffective. 00:08:44.258 --> 00:08:44.378 Mm-hmm. 00:08:44.888 --> 00:08:49.107 So though I'm often a skeptic of things, it does feel a little unavoidable. 00:08:49.158 --> 00:08:52.967 I do think one thing though, about things like generative AI or. 00:08:53.697 --> 00:09:02.697 You know, natural language models, it makes the cleanliness of the data that it sits on top of that much more important. 00:09:02.937 --> 00:09:03.028 Mm-hmm. 00:09:03.298 --> 00:09:12.268 So the idea of garbage in and garbage out is potentially a real multiplier effect when you then just stick some sort of generative ai Yeah. 00:09:12.368 --> 00:09:31.607 So luckily we've really put in the groundwork over the last five to seven years to make sure our claims data and our health plan price transparency data is organized in a way that we think there's the potential for, These sorts of generative AI technologies to plug it and actually leverage it, rather than potentially 00:09:31.613 --> 00:09:31.753 mm-hmm. 00:09:32.212 --> 00:09:37.523 Multiplying inaccuracies and potentially providing people with the wrong answers. 00:09:37.582 --> 00:09:42.712 So the cleanliness of the data has not changed and maybe has actually become that much more important. 00:09:43.263 --> 00:09:56.923 Like for you to, to like maintain sort of the values around the product robustness as well as like the, the authority that the data that you're sharing, provides. 00:09:57.133 --> 00:09:59.413 In order to protect those key principles mm-hmm. 00:09:59.712 --> 00:10:03.582 You actually have to be more ready, more prepared, more organized. 00:10:03.852 --> 00:10:03.942 Yeah. 00:10:03.972 --> 00:10:05.562 which Right. 00:10:05.562 --> 00:10:09.192 Like often we hear about faster, less work, less time. 00:10:09.192 --> 00:10:09.283 Mm-hmm. 00:10:09.523 --> 00:10:11.832 And to, to be more organ like. 00:10:11.832 --> 00:10:14.082 I love that, that paradigm between those two. 00:10:14.712 --> 00:10:17.842 So, Possibly part of your answer to my next question. 00:10:17.873 --> 00:10:17.962 Mm-hmm. 00:10:18.202 --> 00:10:20.332 But, the show is called The Tech Glow Up. 00:10:20.337 --> 00:10:20.498 Mm-hmm. 00:10:20.638 --> 00:10:23.783 And a Glow Up is a notable transformation. 00:10:23.812 --> 00:10:23.903 Mm-hmm. 00:10:24.263 --> 00:10:30.743 What's the Glow Up that you see healthcare needs to have in 2026? 00:10:30.982 --> 00:10:32.182 Needs to have? 00:10:32.212 --> 00:10:32.543 Yeah. 00:10:32.592 --> 00:10:34.663 Good data is one of those things, right? 00:10:34.663 --> 00:10:44.472 It's always been a challenge in our in industry, but as we think about AI and how, and where it plugs in good data is just going to become that much more critical. 00:10:44.562 --> 00:10:44.743 Yeah. 00:10:44.993 --> 00:10:48.773 I think the other place where we need an obvious G Glow Up is value. 00:10:50.232 --> 00:10:52.003 historically, again with sort of how much 00:10:52.003 --> 00:10:52.753 was that bandaid 00:10:53.773 --> 00:11:01.633 with the proprietary prices, it's no wonder that prices within our market are all over the place. 00:11:01.993 --> 00:11:02.052 Yeah. 00:11:02.113 --> 00:11:04.153 and are indefensible in a lot of ways. 00:11:04.153 --> 00:11:10.423 So moving forward, we need to figure out how to get that value equation in place if a particular patient. 00:11:10.783 --> 00:11:19.842

Goes to the same provider and receives the same surgery and they have one health insurance company versus another, that procedure should not cost three x. 00:11:19.962 --> 00:11:20.023 Yeah. 00:11:20.023 --> 00:11:21.493 Just depending on who the payer is. 00:11:21.493 --> 00:11:25.033 So value also needs to be a huge component of all of this. 00:11:25.273 --> 00:11:25.783 Yeah. 00:11:25.842 --> 00:11:27.913 Like almost on all sides, right? 00:11:27.913 --> 00:11:28.332 absolutely. 00:11:28.452 --> 00:11:32.842 So as you think about the work and mission that you have at Trillian mm-hmm. 00:11:33.322 --> 00:11:37.143 what's the Glow Up, you see for the work that you're doing personally? 00:11:38.493 --> 00:11:48.423 A core principle for us, though, we're really lucky at our company to be guided by this noble purpose and that we realize, you know, healthcare is something that impacts every single American in this country. 00:11:48.932 --> 00:11:53.403 It's unaffordable for many and increasingly becoming that much more unaffordable. 00:11:53.898 --> 00:12:04.008 So we really are trying to do our best to leverage data to help stakeholders across the industry make the best sort of evidence-based and data-driven decisions that they can make. 00:12:04.067 --> 00:12:12.408 but again, thinking about value, the amount of money that we spend on our healthcare system and the outcomes that we get, it all just really doesn't add up. 00:12:12.408 --> 00:12:20.878 So I think there's also this question of how do we potentially do more with less, and how do we bring down that spending and those prices? 00:12:21.238 --> 00:12:28.197 And I don't think patients are going to, nor should they be responsible for sort of shopping us out of this problem. 00:12:28.202 --> 00:12:28.523 Mm-hmm. 00:12:28.668 --> 00:12:30.557 I don't think that's how we fix this problem. 00:12:30.557 --> 00:12:33.932 So, another stakeholder, we're very focused on our employers. 00:12:34.857 --> 00:12:40.528 employer sponsored insurance in our country provides coverage for the majority of people. 00:12:40.587 --> 00:12:53.548 How can we be getting data into the hands of employers to make sure that when they are deciding what benefits to choose on the front end for all of their employees, they're actually getting health benefits that will bring value to 00:12:54.003 --> 00:12:54.202 Yeah. 00:12:54.207 --> 00:12:55.677 people care about, right? 00:12:55.677 --> 00:12:59.427 Like another mobile app that'll tell you you're fat maybe isn't. 00:13:00.222 --> 00:13:02.113 Isn't what your employees are looking for. 00:13:02.113 --> 00:13:02.202 Mm-hmm. 00:13:02.832 --> 00:13:03.283 Sorry. 00:13:03.283 --> 00:13:05.202 If you're a mobile app that tells people they're fabulous. 00:13:05.413 --> 00:13:07.182 There's definitely a few of those out there. 00:13:08.217 --> 00:13:13.472 Alli, every innovator's journey, is never one that's taken alone. 00:13:13.613 --> 00:13:14.033 Mm-hmm. 00:13:14.388 --> 00:13:22.038 And especially in like the health tech space, how the role of mentors and guides and coaches. 00:13:22.878 --> 00:13:23.298 And right. 00:13:23.298 --> 00:13:32.607 Like how sometimes even very simple, just like I believe in you keep going, can have such an impressive, important impact on, on people's careers. 00:13:33.087 --> 00:13:41.278 Can you share how a mentor or a coach has been important for your career development and how you've gotten to where you are today? 00:13:41.368 --> 00:13:41.638 Yeah. 00:13:41.758 --> 00:13:44.577 Well, I, I think your point that mentorship is important. 00:13:44.577 --> 00:13:45.658 I couldn't agree more. 00:13:45.658 --> 00:13:51.988 I've been really lucky and fortunate to have had wonderful mentors sort of throughout my career, so. 00:13:52.423 --> 00:14:04.312 I mean, starting in undergrad, the research I was doing there, but then at the NIH, Ellen Lele, working at Penn Medicine, Matth Patel, who really is a healthcare innovator and trailblazer. 00:14:04.543 --> 00:14:10.383 now working at Triliant Health, the CEO, Hal Andrews, all of these people have really one belief in me. 00:14:10.383 --> 00:14:10.472 Mm-hmm. 00:14:10.712 --> 00:14:16.202 But also pushed me in my thinking and challenged some of my assumptions along the way. 00:14:16.322 --> 00:14:18.153 and I would also say that all of them. 00:14:18.932 --> 00:14:24.572 In different ways or another probably gave me a little more responsibility than I was ready for at that exact moment. 00:14:25.243 --> 00:14:30.493 which has led me to grow and figure out new things that I enjoy doing and things I might want to do. 00:14:30.582 --> 00:14:31.842 more or less in the future. 00:14:32.202 --> 00:14:36.852 and I, I literally was remembering the last time I was an asking this question mm-hmm. 00:14:37.423 --> 00:14:40.062 In the context of a healthcare conference. 00:14:40.212 --> 00:14:40.302 Mm-hmm. 00:14:40.543 --> 00:14:41.533 So like in October at Health. 00:14:42.418 --> 00:14:48.238 That particular perspective of like, they gave me more than I thought I was ready for mm-hmm. 00:14:48.488 --> 00:14:49.163 You figure it out. 00:14:49.163 --> 00:14:55.013 And I love that, that's like, that's the human relationship touch, right? 00:14:55.013 --> 00:14:58.332 Like that trust that, is what encourages that. 00:14:58.368 --> 00:14:59.707 Like it's nothing more than like. 00:15:00.533 --> 00:15:02.602 They gave me more than I was hoping for. 00:15:03.143 --> 00:15:05.753 And you rise to the occasion and you learn new things along the way. 00:15:05.932 --> 00:15:07.163 Amazing. 00:15:08.013 --> 00:15:10.842 Alli, we're down to the last question. 00:15:10.873 --> 00:15:11.383 Mm-hmm. 00:15:11.623 --> 00:15:18.043 I love to give people the opportunity to share a spicy soundbite, maybe a hot take on. 00:15:18.868 --> 00:15:24.148 technology, healthcare, ai, data culture really could be anything. 00:15:24.447 --> 00:15:26.607 Do you have a spicy hot

take for me? 00:15:27.597 --> 00:15:32.357 Well, this comes from, my days during my graduate work where I was studying low value care. 00:15:32.357 --> 00:15:36.763 But I do think there's this principle of less can be more actually. 00:15:36.822 --> 00:15:37.243 Mm-hmm. 00:15:37.322 --> 00:15:38.082 Or less is more. 00:15:38.357 --> 00:15:44.837 And I think we're sort of in the phase of more is better in healthcare and have been for a long time. 00:15:45.268 --> 00:15:46.048 but I think. 00:15:46.913 --> 00:16:01.312 Spending 18% of our GDP on healthcare, again, lower life expectancy in our country compared to the rest of the world and some pretty dismal outcomes, we have to take a hard look in the mirror and figure out how and where we can be spending less money on our healthcare system. 00:16:01.312 --> 00:16:01.373 Yeah. 00:16:01.403 --> 00:16:03.802 And get either the same or better. 00:16:06.413 --> 00:16:07.523 Do you have a thought? 00:16:08.513 --> 00:16:12.923 The idea of like, healthcare is so important and so expensive? 00:16:12.952 --> 00:16:13.043 Mm-hmm. 00:16:13.913 --> 00:16:18.202 Like the less, with more, I feel like I have a hard time, 00:16:18.253 --> 00:16:18.523 Yeah. 00:16:18.623 --> 00:16:21.173 So usually that means I just don't have the data. 00:16:21.383 --> 00:16:25.462 So I'm curious, like how, how would you encourage people to think about less is more 00:16:25.493 --> 00:16:25.582 mm-hmm. 00:16:26.013 --> 00:16:27.543 in this context of healthcare. 00:16:27.643 --> 00:16:29.533 I think there's a couple components right now. 00:16:29.533 --> 00:16:31.062 One is certainly price. 00:16:31.153 --> 00:16:31.243 Mm-hmm. 00:16:31.432 --> 00:16:38.062 we know that systematically if we can reign in some of that vari, it's too high and commercial prices that exist, yeah. 00:16:38.273 --> 00:16:43.222 We can systematically reduce our spending by some meaningful percentage. 00:16:43.253 --> 00:16:43.523 Yeah. 00:16:43.663 --> 00:16:48.552 second, just in terms of the amount of technology that's out there, 00:16:48.682 --> 00:16:48.893 Yeah. 00:16:49.613 --> 00:16:51.773 Each of those products should be demonstrating. 00:16:52.628 --> 00:17:03.998 Whether it's ROI or cost effectiveness, there needs to be data to support implementation of those things and when and where possible rapid experimentation, right? 00:17:04.028 --> 00:17:07.208 If some new AI tool gets deployed in a healthcare system, mm-hmm. 00:17:07.478 --> 00:17:12.667 Let's make sure it's being implemented in a way that we can test what its actual effects and outcomes are. 00:17:12.932 --> 00:17:16.508 Are there unintended consequences that we should be concerned about? 00:17:16.758 --> 00:17:16.847 Mm-hmm. 00:17:17.087 --> 00:17:19.847 Let's rip it out and try something else and try again. 00:17:20.028 --> 00:17:24.468 So I think a willingness to de adopt things is also really important and much needed. 00:17:24.827 --> 00:17:27.528 Why did I ask this question at the end of the interview? 00:17:29.127 --> 00:17:35.067 Because, I work on the go to market side, so we're like slightly different sides of the coin here. 00:17:35.442 --> 00:17:40.212 But Right, like on the go-to market side, you might be putting out new ideas once a quarter. 00:17:40.242 --> 00:17:40.363 Mm-hmm. 00:17:40.423 --> 00:17:47.353 On the digital transformation side, you might be like reviewing what you're gonna be doing next year, like every half. 00:17:47.383 --> 00:17:47.563 Mm-hmm. 00:17:47.637 --> 00:17:59.143 Like the timelines for testing some of these, you know, like even just getting a deal inked, you know, I've talked with like large systems, when you're working on an enterprise system, it might take six months of arguing about the terms. 00:17:59.222 --> 00:17:59.232 Mm-hmm. 00:17:59.232 --> 00:18:03.002 So how do you learn how to test something in four, four weeks? 00:18:03.053 --> 00:18:03.563 When it takes. 00:18:04.583 --> 00:18:06.532 You know, 20 to get the contract signed. 00:18:06.623 --> 00:18:07.073 Yeah. 00:18:07.073 --> 00:18:07.462 it is like, 00:18:07.913 --> 00:18:08.813 it's a challenge to 00:18:08.813 --> 00:18:10.103 me, such a magic space to be in. 00:18:10.113 --> 00:18:18.873 I think partnerships go a long way there, but also considering where we could be investing our dollar and things that maybe are a little less sexy. 00:18:18.903 --> 00:18:21.482 So, you know, things like primary care, right? 00:18:21.512 --> 00:18:26.403 Putting, if we think primary care is important and provides value to the system. 00:18:27.123 --> 00:18:33.333 Let's, you know, reconsider how it is that we're prioritizing and paying for it and put our, put our money where our mouth is. 00:18:33.413 --> 00:18:38.363 oh, and again, not expect everything to, when it comes to spending on primary care mm-hmm. 00:18:38.633 --> 00:18:40.073 That it's going to be cost saving. 00:18:40.073 --> 00:18:42.952 Sometimes it's okay to invest real money in that space. 00:18:44.603 --> 00:18:45.932 I love that Alli. 00:18:46.343 --> 00:18:59.202 That was not only a spicy hot take, but like some real specific advice about how to make it meaningful for patients is definitely a takeaway that I am thrilled to have on our talk today. 00:18:59.442 --> 00:19:01.782 Thank you so much for joining me on the Tech Glow Up. 00:19:01.782 --> 00:19:02.053 Absolutely. 00:19:02.053 --> 00:19:02.242 From Vive. 00:19:04.363 --> 00:19:05.383 We've got one last thing. 00:19:05.442 --> 00:19:05.742 Sure. 00:19:05.792 --> 00:19:06.083 Okay. 00:19:06.268 --> 00:19:08.307 1, 2, 3. 00:19:09.538 --> 00:19:10.438 Amazing. 00:19:10.468 --> 00:19:10.887 Awesome. 00:19:10.917 --> 00:19:11.758 Thank you. 00:19:11.788 --> 00:19:12.208 Yeah. 00:19:12.208 --> 00:19:13.258 So much. 00:19:14.038 --> 00:19:15.327 Can I ask you a favor? 00:19:15.778 --> 00:19:26.137 If you really enjoyed this episode, could you share it on your Instagram stories or maybe post the link with what you enjoyed on LinkedIn? 00:19:26.738 --> 00:19:37.637 The sort of sharing and engaging really helps small podcasters like me reach the audience that I know really cares about these kinds of conversations. 00:19:38.028 --> 00:19:41.567 If you've made it this far in the podcast, I really appreciate you. 00:19:42.077 --> 00:19:43.428 Thanks for listening. 00:19:43.548 --> 00:19:50.448 Please make sure to like and subscribe so that you never miss an episode of the Tech Glow Up. 00:19:51.448 --> 00:19:53.728 And hey, can I ask you a favor? 00:19:54.178 --> 00:20:04.538 If you really enjoyed this episode, could you share it on your Instagram stories or maybe post the link with what you enjoyed on LinkedIn? 00:20:05.137 --> 00:20:16.038 The sort of sharing and engaging really helps small podcasters like me reach the audience that I know really cares about these kinds of conversations.