Good afternoon, everyone. Welcome to the J.P. Morgan Healthcare Conference. My name is Anne Samuel, and I'm the Healthcare Technology and Distribution Analyst here at J.P. Morgan. We're thrilled to have Health Catalyst with us, to present today. With us are CEO Dan Burton and CFO Bryan Hunt. They're gonna do a brief presentation, and then we can open it up for Q&A. So with that, let me turn it over to Dan.
Thank you, Anne, and good afternoon, everyone. It's great to be back here at J.P. Morgan once again. We're excited to share a company overview and an update about Health Catalyst, and we appreciate your interest. I'll begin with an overview and some commentary about our company, and then we'll turn it to Bryan, and then as Anne mentioned, we'll open it up for questions. At an overview level, Health Catalyst is a leading provider of data and analytics technology and services to healthcare organizations. We offer a solution with three components.
The first is a data platform that enables our health system clients to integrate data in a flexible, open, and scalable way, to then analyze the data that is located in that platform, in that single source of truth, to understand and enable insights about performance, whether it's clinical, operational, or financial performance, where the performance is strong and where there are opportunities for improvement. The third component of our solution is our services expertise, where when we identify with our clients an opportunity for clinical, financial, or operational improvement, we then work side by side with our clients to enable the changes that are necessary to realize that measurable improvement. Our clients, who are primarily healthcare providers, use our solution to manage their data, derive those analytical insights, and operate their organizations in a way that they can continuously improve.
The mission of the company, which has been the case since our founding over 15 years ago, is to be the catalyst for massive, measurable, data-informed healthcare improvement. The way in which we accomplish that mission, the how, with each client each year, is represented with this strategic framework that we refer to as our company flywheel. If you begin at the top of the flywheel, when we begin a relationship with a client, we realize that client is really taking a leap of faith. That by working with Health Catalyst, that the three components of our solution, our data platform, our analytics applications, and our services expertise, when combined with their best efforts, will yield measurable improvement.
And as that occurs, as we work closely together on delivering measurable clinical, financial, and operational improvement, when we realize those improvements, the trust between us and our clients builds and deepens, and our clients choose to renew their relationship and often expand that relationship, while also referring us to their colleagues in other organizations. And the next year, we work on more improvement activities, and that improvement flywheel starts to spin faster and faster over time. We'll share some data about what we've observed as our tenure with clients lengthens, that we do in fact see that flywheel spinning faster and faster. At the center of our strategic flywheel is our team members and their engagement.
We recognize that our team members are the individuals responsible for building the technology of the data platform and the analytics applications layer, and for providing the services expertise that enable our clients to realize those massive, measurable improvements. And so we as a leadership team have a primary focus on enabling great team member engagement. We use third parties to measure that, and I'll share some of that data with you in a few minutes. At a high level, from an investment highlight perspective, I would, I would share five components that we think about and, and try to optimize for.
First of all, we're grateful to be recognized as an industry leader in an important component of healthcare, solving a really large problem, the problem of $1 trillion a year of waste, or about $0.30 out of every $1 that's spent in the U.S., is inefficient, doesn't lead to better outcomes. The problem is, to know what to fix, which of the $0.30 should be avoided, you need deep data and analytics expertise. That has provided us with a meaningful TAM of about $8 billion, and we're grateful to have been recognized as a leader in healthcare data and analytics for quite some time. Second, the solution that we provide isn't just technology, and it's not just services.
It's a comprehensive solution, and we've developed, over the last 15.5 years, depth of expertise in each of the three components of our solution. At the data platform level, we now have experience with over 300 different data sources and large volumes of data from a data storage and management perspective, often up to 100 TB of data per client. At the apps layer, we now have 12 full application suites and a library of dozens of lighter touch analytics visualizations that we offer to our clients. Finally, at the services expertise level, we now have over 1,000 analytics and other domain experts that are deep domain experts in the healthcare industry, in their field, that can help our clients to realize those measurable improvements.
That comprehensive solution has led to the third highlight that we would share, which is that we have produced meaningful, measurable, clinical, financial, and operational improvements over the past 15 years at significant size and scale in partnership with our clients. We have measured client-approved improvements worth over $1.5 billion. We have over 345 case studies published and thousands more that we have realized in partnership with our clients. As I mentioned a few minutes ago, we also measure the number of improvements that our clients realize over time, and as clients spend more and more time working with Health Catalyst, we see that improvement flywheel spin faster and faster, with more and more improvements per year as time goes on. This is enabled by world-class team member engagement.
We use the Gallup Organization to measure and compare our team member engagement to a broad database of millions and millions of survey respondents. And over the last decade, we have consistently scored between the 94th and 99th percentile in Gallup's database, measuring team member engagement. We've also been recognized 85x as a best place to work. And all of this contributes to an attractive operating model for the company, where we have a large percentage of our overall revenue that's recurring in nature, over 90%. We have a large market opportunity with a targeted long-term 20+% revenue growth rate. We have a loyal client base that chooses to expand with us, and in 2023, we were profitable for the first year in over a decade.
That profitability, as we shared on Monday, and as I'll discuss in a few minutes, is projected to more than double in 2024. Now, in terms of the problems that we help our health system clients overcome and solve, there are some very foundational, important problems that they're facing, where understanding and having a partner that helps you deal with and manage data complexity has become centrally important, and that has been an important reason why we've become such an important strategic partner to our clients. First, as I mentioned before, there's $1 trillion a year of waste. $0.30 out of every $1 is wasteful, and because of the significant financial pressure that our health system clients are under, they need to understand where that waste exists and how to fix it.
That also has led to changing economic models, with value-based care adding important opportunities, but also significant complexity. Once again, to manage that complexity, you need data excellence and data capabilities and analytics capabilities. All of that complexity is what we help manage with our clients so that they can make better decisions and continuously improve from a measurable improvement perspective. We are fortunate to work across the healthcare delivery ecosystem with many of the largest, most innovative health systems as clients, as well as scaling down the healthcare delivery ecosystem to smaller physician practices and rural health systems. We work in the academic medical center environment, the specialty environment, and we have found that our solution scales up and down that ecosystem and produces measurable improvements across the board.
We're fortunate to have been recognized externally by several organizations for our product leadership, our services leadership, and our client evangelism. We focus in a detailed way on the three components of our solutions, starting always with the bottom of this slide, the data platform. It's the foundation of everything that we do. We continue to invest, and I'll speak in a few minutes about our next-generation data platform and our excitement about the innovative advances that it enables, so that we can continue to accomplish our goal to provide the most robust healthcare-specific, open, flexible, scalable, and self-service data platform for analytics.
In the middle of this diagram, we have also focused on important use cases at the apps level that are critical for health systems to manage effectively, whether they're clinical, quality, population health, financial, or operational, so that they can optimize their performance. Then finally, at the top of this slide, we have focused on building out the right services expertise that over time, we have realized enables us to massively improve and speed up the level of improvement that our clients can realize, speed up that, that improvement flywheel. Now, let me speak for the next few minutes about our next-generation data platform that we're so excited about rolling out. I want to recognize in the audience Yohan Vetteth, who is the Chief Analytics Officer at Stanford Health Care.
He has been an incredible innovation partner for us for over 12 years. As I speak about the history and the evolution of our data platform architecture, I just want to call out our friends and colleagues at Stanford. They've been an innovation partner with us for the entirety of that 12.5 years, and that includes being an innovation partner, one of the first of our clients to implement this next-generation data platform. On this slide, to the right of this spectrum that the slide represents, is a more customizable, open, and flexible platform architecture. That's really where we have been in the past, and that's where we grew out of. There's significant advantages to that kind of an architecture, and it's very customizable, it's very flexible.
And each client has benefited from that flexibility in some really meaningful ways. But every strategy has upside and downside, and that includes technology architecture strategy. On the other end of the spectrum, there are many point solution companies that are built specifically for one use case or two use cases. They tend to be towards the left side of the spectrum, and they tend to standardize more and restrict more, be a lot less open and flexible in the way that the data architecture is oriented. And while there are downsides to that strategy on the left, there are also meaningful upsides.
Part of what we've realized with our clients, and as the industry has matured, we and our clients have realized some of the important benefits, the important use cases surrounding more standardization of data definitions, of data relationships, and important use cases that can only be enabled, including AI, machine learning, benchmarking, and regulatory use cases that require more standardization. So we have moved, over the past few years, and significantly, with this next-g eneration data platform, towards the middle, and even skewing slightly towards the left-hand side of this spectrum, which we believe is a winning position where we begin from an architectural perspective with more standardization, and then we allow and enable, at another level, customization and flexibility that our clients still need, where there are use case-specific examples and client-specific examples so that that can continue to be enabled.
As we designed this next-g eneration data platform, we focused on five primary objectives, and I'll touch on these only at a high level and would encourage those who are able to come to our Annual Healthcare Analytics Summit, which will be next month, in Salt Lake City. And if you're a skier, there's an added incentive to come and enjoy the slopes as well. We'll be diving deep with our technology leaders and experts in describing this next-g eneration data platform. We'll have a few of our clients that have already migrated to the next-g en data platform that can share their experiences as well. Yohan will be there as one of our presenters as well. But the five high-level objectives that we have used in designing the next g en data platform include that the platform should be integrated in important ways.
It's much more modular. It's much more flexible. It enables our clients to leverage investments that they already have made or would like to make in cross-industry technologies. It's intelligent. We wanted to make sure that we have had the benefit of 15+ years of deep focus on healthcare data. Let's embed the learnings from that experience into and codify the intelligence that we've gained and that our clients gained in the process. Let's make sure it's modern, so that all of the, all of the current and future innovations that are coming out of Silicon Valley from a tech perspective can easily be plugged in and leveraged in this data platform. Let's make it extensible.
This has to do with the need for a foundation of standardization that then can be extended and accessible, which is the fifth goal, to a broad array of our clients in several different roles. I'm pleased with the ways in which those five objectives are being realized in the next-g en data platform. Now, don't worry, we're not going to go through every line on this slide. This is a busy slide, but it's a helpful slide, and I'll just touch on some of the detail in the data lake architecture that we are leveraging in this next-g en data platform. Let me just highlight a few things about this architecture.
First of all, as I mentioned in the last slide, we wanted to make sure that this next-g en data platform enabled the use of some really important capabilities from a core technology perspective from three primary technology partners, but you can see many more are also leverageable in this data architecture. Our three primary technology partners are the Microsoft Azure Data Lake Environment, Snowflake, and Databricks. And those three capabilities are leveraged at a really significant level in this new Lakehouse architecture. The other important foundational change that we've made in this updated next-g en data platform architecture is towards the bottom, you see those three columns, which is a medallion approach to data transformation and data curation, where we, we take the curation and transformation of the data in three stages: the bronze, the silver, and the gold level.
At the bronze level, which is more of a raw data ingest into the data lake, in the Lakehouse architecture, there is very minimal transformation that occurs in the data. That's very similar to our legacy architecture, and there's many advantages to that. But more and more use case-specific advantages come through the next two layers in the Medallion, the silver and the gold layer, where we standardize and normalize the data, and then we focus on specific use cases that we know are high value from a data perspective that then leads to the far right of this diagram of high-value analytics.
That's where we want to make sure across the board, including with the use of AI, that all of our, our analysts, all of our clients' analysts, are enabled to make better analytics decisions, through the right infrastructure and architecture on a daily basis. Now, let me describe some specific examples of ways in which we're enabling, through our next-gen data platform, those better decisions. So virtually every presentation I think that I've heard here at J.P. Morgan has a couple slides on AI, so no different here. We're excited as well. Now, we have been excited about AI for a number of years, not just over the past six months or the, the past year.
We've been investing in our primary AI property, which is Healthcare.AI, for a number of years, and it's actually deployed across over 95% of our DaaS subscription clients today. I'm gonna walk through a specific use case in a moment, but at a high level, one of the things that we focus on as an analytics company is: how can we leverage AI specifically to help those who are analyzing data do so more effectively, more quickly, and make better decisions? So let me walk through an example. At the top of the top right of this slide, you see a simple line chart. This is charting sepsis mortality at one of our clients over the course of several years. It's a simple chart. We have tested the concepts of the value of AI across multiple clients.
We started with ourselves, multiple levels in the organization. Didn't matter the function, whether they were data scientists, analytics engineers, or in other functions at Health Catalyst or at our clients. It didn't matter the level in the organization that the individuals participating were at, including at the CEO level, at the board level. We found that about 45% of the time, when individuals look at a graph like the one on the top right, they misinterpret the data, about 45% of the time, answering basic questions like: What is the average sepsis mortality rate? Where are there true outliers, on the high side or the low side? Has there been a change in performance? If so, is the change positive or negative? And is there a new trajectory? If so, where are we headed over the next six months?
About half the time, all those individuals, without the aid of artificial intelligence, would answer those questions erroneously, incorrectly. At the bottom, you see the same chart, but with the overlay of Healthcare.AI included. Now it's much more clear what the average sepsis mortality rate is. Now you can see outliers more clearly. You can see when performance has changed and whether it's improved or degraded, and you can also see a projection over the next six months of where we might be headed. And those same individuals, when they are benefited from Healthcare.Ai in answering those basic questions, answer at a 95% accuracy level versus that 55% accuracy level without Healthcare.AI. Importantly, 95% of our clients today are utilizing Healthcare.AI. We've enabled it as part of the data platform to be pervasive across thousands and thousands of visualizations.
You can see there are about 7,800 calls per day to Healthcare.AI. This is one of the ways that AI is making healthcare decisions more accurate, more data-informed, and it's making healthcare better. Now, let me conclude with just a couple of updates. Earlier this week, we shared a few updates about the end of our fiscal year and calendar year, and some thoughts about moving forward. I'll share a few thoughts, and then I'll turn it to Bryan for a few financial highlights. We were pleased that our Q4 and full year 2023 results across the board from a revenue and an EBITDA perspective preliminarily will be coming at or above our guidance midpoint. We're grateful to see that consistency of performance.
Also, we were pleased to see strong bookings performance throughout the year in 2023. From a new client perspective, our performance actuals came in as we expected them. We also anticipate that our 2023 Dollar-Based Retention will be solid at around approximately 100%. We did experience a few delays in some large Tech-Enabled Managed Services offers and expansion opportunities that we still believe we will close in the near future. And as we've shared many times, it is difficult to precisely predict exactly when those deals will close, but they tend to be very large, long-term, and sticky deals. As we have shared earlier this week from a forward-looking commentary perspective, we'll provide more details at next month's earnings call.
But we continue to see positive signs from a bookings perspective, and anticipate that as we provide guidance for new client bookings and existing client expansion, that they will be meaningfully higher in 2024 than what we've achieved in 2023. And we're grateful to be a little ahead of schedule as it relates to our profitability, and shared earlier this week, that we anticipate, our EBITDA performance to be meaningfully higher than the consensus estimates for 2024, and that we believe we'll stay ahead of schedule in 2025 as well. With that, I'll turn it to Bryan.
Thanks, Dan. I'll be brief. I think we end with some financial highlights, and then we can get to questions. I know there are several. But I'll just end with a few of the things that are important, we believe, from an investor and operating model standpoint. As Dan mentioned, we benefit from a highly visible revenue stream, so recurring revenue for technology, as well as the vast majority of our services offerings. We have a long-term revenue growth target of 20% plus that is driven by two factors. So the first is a Dollar-Based Retention rate, which is a measure of the renewal and expansion profile of our existing DaaS subscription clients, which over time have proved to be very sticky and very fruitful in terms of the expansion opportunity.
That historically has been in that 100%-112% range, and as Dan mentioned, we expect that will continue to improve moving forward. The second driver of growth are new client additions. So we operate in a market that is large and still early in terms of the greenfield opportunity to land new platform clients. And as Dan mentioned, that metric was a strong performance in 2023 and came in in line with expectations. Moving down the P&L, we've benefited over time from improvements in gross margin and profitability, driven by positive mix shift, as well as by improvements in both our technology and services gross margin segments. And then lastly, that growth and profitability has translated down to the bottom line with improved performance in EBITDA.
Dan mentioned 2023 was an exciting year for us to generate positive EBITDA margins, to be a little ahead of schedule and kind of outperform our expectations, as well as to give an initial stake in the ground around EBITDA for 2024, that will be approximately $25 million, continuing that progress, and then updating our target for 2025 to be 10% EBITDA margins or greater as we continue to drive toward that long-term 20%+ EBITDA margin target. So we're excited about that profitability and growth progression as well. I'll pause there, and I know, Anne, there are probably several questions. Thank you.
Great. So I'll kick it off with the first couple, and then, we can open it up to the audience. You guys have a really good bird's-eye view of, you know, what's going on in the hospital ecosystem just based on where you sit. Can you discuss what some of the biggest challenges are that they face today, and how has that changed maybe over the last five years since you went public?
Yeah, it's a great question, Anne, and we have seen a lot of change since we went public just before the pandemic, so it's been a breathtaking 4.5 years. And in particular, I would share that I think from the summer of 2022 through the summer of 2023, there was incredible financial pressure that our clients were facing across the ecosystem. And as you mentioned, we do have a, a useful bird's-eye view in working with over 500 healthcare organizations, and across the board, they were experiencing unprecedented financial pressure. What that meant for us was we needed to shift our focus from a technology and a services perspective to those parts of our portfolio that delivered a near-term, guaranteed hard dollar cost savings or revenue enhancement. And so that was really a significant focus shift for us.
We also stayed focused mostly on existing clients and making sure that they were able to survive and thrive through that difficult environment. Starting in the second half of 2023, particularly in Q4, and we see that coming into 2024, we've seen some gradual improvement in the end market financial environment, and more of an openness with existing clients and with new clients to exploring some more of those technology offerings that may not offer that hard dollar near term ROI, but are very useful in measurably improving clinically, for example or improving patient engagement. So we're seeing more of those opportunities, more of those green shoots with existing clients, and we're seeing more openness with prospective clients to have those kind of conversations.
And maybe just, you know, going back to the IPO, you know, you always really hammered home that you drive clinical, financial, and operational improvements. So what are they looking for the most? Is it financial right now just because of where they sit?
Yeah, it was dominated by financial focus from summer of 2022 to summer of 2023. As you know, Anne, we came from, you know, a clinical background, and so we love doing clinical improvement work. That really slowed down during that 12-month period. It's started to pick back up now. Operational improvements I would put in that same category as financial improvements. Often, they have a specific hard dollar ROI associated with those, so those continue to be a really important focus. But it's great to see, particularly over the last six, seven months, more of that balanced focus coming back into the fore with clinical improvement being another important priority that's coming back.
You know, you've been talking a little bit about these green shoots that we're starting to see, things getting a little bit better. You know, is that because the labor pressures aren't as bad? Is that because, you know, the budgets are loosening a little bit? You know, how is that translating into, you know, willingness to move forward with spending?
Yeah, it's a great question. So the financial pressure, I think, from summer of 2022 through summer of 2023, really had at it, at its core, this mismatch between, you know, revenue reimbursement rate increases being more like in the 2%-3% range, and inflation related cost increases on the labor and supply side being more like in the 8%-10% range. And for health systems, the majority of which operate with a very thin operating margin, even in good times, you know, 3% is a good year for most of our not-for-profit health system clients. That put them underwater. And I think as inflation has come down, those labor costs and supplies cost increases on a year-over-year basis are coming down much more into alignment.
And the majority of our clients now are either at that break-even 0% operating margin or slightly positive. There's still a spectrum of performance, but that's a meaningful improvement from where we were in the summer of 2022. And I think what that enables is a little bit more of a balanced mindset about investments. When you're underwater, you can't think about next, you know, three years from now, two years from now. You can't think about longer-term investments in patient engagement and clinical improvement. You're just trying to survive this year. And so that's what we've seen, an openness to, I think, more balanced approach to investment, which I think is welcome.
That's great. And, you know, you've, you've navigated the current environment extraordinarily well, considering, you know, what it's looked like, in the backdrop for you. So can you talk a little bit about, you know, why your Tech-Enabled Managed Services product is so important, and maybe how a contract for that might look a little bit different than your traditional contracts, and why that might be a little bit lumpier?
Yeah. Yeah, this is a good example. Tech-Enabled Managed Service is a great example of most of the best ideas at Health Catalyst. They didn't come from us; they came from one of our clients. Yohan has produced some amazing tech innovations and products from our relationship with Stanford. And another set of our clients helped us understand that Tech-Enabled Managed Services in a few areas could be a really differentiated value proposition for us to offer to our clients. And the fundamental idea was, you've already built the technology around certain processes. That's maybe 60% or 70% of the way that the process gets better. If you took the other 30% of the people component and you managed it end to end, you could probably do it better, faster, and cheaper than we could do it.
I had a CEO tell me that nine years ago, and she was specifically focused on chart abstraction. As I came back to the leadership team, and we thought about it, we thought, "I think maybe she's right." And it turns out, she was right. And so in certain places, like chart abstraction, we already have the technology we've built. We've acquired technology that automates what is otherwise a very manual process with highly trained and expensive clinical resources going in, looking at the charts, extracting the necessary information to submit registries of patients to certain regulatory bodies. Technology can do most of that in an automated way, and it actually is more accurate, and it's a lot cheaper, and it's a lot faster. But you still need that 30%, and so Tech-Enabled Managed Services enables us to rebadge and employ those individuals.
But then, as we realize those efficiencies, we're also able to redeploy a number of those individuals upskill, where they now are able to work on clinical improvement work, which is a much better use of all that great training that they have than it is, you know, just reading a chart. And so it's a win for us. It's a win for our clients 'cause they realize guaranteed savings of typically 10%-15%. But what we found is where the client might spend $10 on a process like chart abstraction, when we apply our technology and our process improvement and scale, we can often do it for $7. We share in that savings with our clients, 50/50, and the team members also get a huge benefit in that they continue to be employed.
This isn't primarily about laying off team members. It's redeploying team members up license. So it's a win for the client, it's a win for Health Catalyst, it's a win for the team members. And those are large contracts. We typically ask for a long-term commitment from those clients as well, given that they're receiving cost savings. We're making a long-term commitment to them, and so these are usually 5- or even 10-year contracts. And we also ask our clients to sign that long-term contract relationship with the tech as well as the services 'cause we need to use that tech to produce those cost savings. But that's worked really well.
I'd just add to the end of that around questioning around, like, timing of those types of contracts. The average DaaS subscription client with us spends about $2 million a year of tech and services, whereas the 10% or so of our DaaS subscription clients who have a TEMS contract with us, the average contract size is around $8 million-$9 million a year, and our largest contract is around that $15 million of ARR. So they're very meaningful, as Dan mentioned, contracts.
Mm-hmm.
And they involve long-term agreements, they involve, team member transfers, and so they can be, a little, a little more tricky to precisely forecast the timing of. We had some of that this, as we mentioned, on Monday this past year, with that Dollar-Based Retention rate commentary, which is kind of a forward-looking metric to 2024 revenue. So there is, there is an impact there, but the extent of that impact is, is really dependent on how quickly those, those TEMS contracts sign, into 2024. So that can be a little bit of a, a, a challenge. But long term and midterm, we like the concept of doing what's right for the client relationship. If it takes more time, we still benefit from that five-year locked-in contract. That's very meaningful growth over a long period of time.
That's great. I have so many more questions, but we don't have so much more time. So I really wanna highlight your profitability and your focus there. Can you just help us understand, you know, you're focusing a lot more on your existing clients, some of these Tech-Enabled Managed Services contracts. So how should we think about how that will impact your margins and profitability?
Yeah, I think, one of the things we love about our solution set is we can grow and expand really dramatically with clients. As Bryan mentioned, our, our largest client relationship is north of $15 million a year. But from an operating leverage perspective, what we found is, you know, that relationship started at $1-$2 million a year, and has expanded meaningfully over time, and now includes a Tech-Enabled Managed Services component in a couple of departments. But throughout that experience, we've had one account executive that manages that one relationship. So there's incredible sales and marketing leverage. There's also really meaningful R&D leverage that we're seeing, where, as we focus on a few areas where we can provide true differentiation, the data platform layer, and then at specific use case layers, we found that our clients like us being focused in that way.
And with that focus, that enables us to be more focused in our R&D spend. And there's almost no incremental R&D required for those Tech-Enabled Managed Services, Solutions and Expansions. And, we've largely completed the next-generation data platform investments, so there's really meaningful leverage that we're seeing in R&D moving forward on the tech side as well. And even as we see those green shoots with new clients, we continue to see a lot of operating leverage in those OpEx categories, even as we grow, and I think that's one of the reasons why we were confident increasing our EBITDA guidance for this year, and increasing the target for next year. We have a clear line of sight that gives us that confidence. Anything you'd add, Bryan?
No, that's great.
Great. Well, thank you so much. It's wonderful, as always, to hear how you're doing. Thank you to the Health Catalyst team for joining us today, and thank you for all of you in the audience, for being with us today.
Thank you all.
Thank you.