NASDAQ:CCLD
Michael Kim: Hello, everyone, and thank you for joining us today. My name is Michael Kim, I'm a senior analyst here at Zacks Small Cap Research. And today I'm happy to have with us both Steve Snyder as well as Hadi Chaudhry, Co-Chief Executive Officers of CareCloud, a company that we initiated coverage on earlier this year with a $5 price target. CareCloud is a leading technology company providing a comprehensive suite of proprietary cloud-based solutions to growing healthcare providers across the U.S., and the stock trades on the NASDAQ under the ticker symbol CCLD. With that, welcome Steve and Hadi, great to see you both, and really appreciate your time today.
Steve Snyder: Thanks, Michael.
Hadi Chaudhry: Thank you, Michael.
MK: I really think this is a great time to sit down and chat with you for a number of reasons, but perhaps most importantly, it really seems like CareCloud is at the forefront of increasingly leveraging AI to enhance growth and drive operational efficiencies. With that, it would be great if we could just kind of start big picture. Could you just walk us through CareCloud's vision for artificial intelligence overall, especially the strategic role of the AI Center of Excellence, and how this initiative is positioned to shape CareCloud's future growth in the market?
SS: Sure, we'd be happy to, Michael. And thanks for taking the time to talk with us. And if we step back for a minute and we just consider the big picture, we see AI, truly speaking, as not just a technological trend, but it's truly something that is potentially transformational from the perspective of how integral it'll be to our industry and it's the same with regard to our long-term strategy, really, really key to our long-term strategy. So our AI vision is really centered on figuring out how to drive smarter, faster, more efficient healthcare delivery. Primarily, our focus is on the healthcare providers, but also by extension with regard to the patients and the payers, trying to accomplish those same objectives. And this really means using AI to reduce administrative burdens that are on our healthcare providers with the goal of enhancing clinical outcomes, ultimately, and also potentially unlocking new business models and product options as we continue to leverage AI. And really, what we've tried to do is, in order to take that vision and operationalize it.
SS: What we've announced recently is the creation of what we're calling an AI Center of Excellence, and the AI Center of Excellence is really, essentially, a strategic hub. And what it does is it brings together on kind of a cross-functional expertise basis, data scientists, engineering team members, product-focused team members, client success team members, and the like, and it pulls them all together with essentially kind of a key mission or maybe a two-fold mission. That's really, first of all, to figure out how to continue to accelerate the integration of AI across our entire company, across our entire product ecosystem. That's one of the fundamental objectives. The second would be to really deliver measurable value to our clients with a real focus on kind of specific, practical, outcome-focused innovations. So, if we think about this maybe a little bit more specifically in the context, for instance, of AI-driven RCM automation tools, the idea is to develop tools that will increasingly take the lead in terms of reducing claims denials and ultimately improving days in AR and really managing a key part of the overall denial management, the claims processing, and the like with AI. And also over time, our belief is that these AI tools, things like we've announced CareCloud Cirrus Notes and other AI solutions, will really enable our clients to increase their efficiency and to deliver ultimately a better quality of care.
SS: So, kind of longer term, our strategy really positions us, we believe, to enhance operating leverage by automating these sorts of mundane, repetitive workflows, labor-intensive workflows. That's a key part of it. The second key part of it would be to increase overall client stickiness and retention by really taking the AI tools and embedding them into the clinical and the administrative workflows of the practice. And then long term, to potentially open up new revenue opportunities or to further empower our existing solutions with AI in a way that makes them increasingly sellable. So this is kind of the overall strategy in a nutshell, as it were. And we believe that the overall AI Center of Excellence will really be an engine that will help us really scale up and to take advantage across the entire business of CareCloud with regard to the AI opportunities that exist.
MK: Got it. That's super helpful. Appreciate that. So, why is this the right moment for CareCloud to prioritize AI? And what internal or external factors are driving that decision?
SS: Yeah, great question. And your question kind of alludes to this. We really do think that this is the right time for us to really kind of seize on the AI capabilities that exist presently within our own company, but also the opportunities that we can unlock as we move forward. And it really is for two primary reasons. It's really this kind of external readiness, the time is right, and then kind of internal capabilities or internal maturity. On the external side, I think healthcare, we think healthcare, like many different industries, but healthcare is truly speaking really at a tipping point for at least for our providers. The pressures from a reimbursement perspective, the labor shortages that continue to exist and put strain, especially on the smaller physicians' offices, but also in larger practices as well. And kind of this wave of transformation is really pushing providers increasingly even further. It's pushing them in the direction that they've been pushed for years and years and years. And that's really towards figuring out how to do more with less. So at the same time, from AI capabilities, especially if you think about these kind of larger language models and kind of advanced automation, they've matured to the point where we believe we can more effectively deploy them in the healthcare setting.
SS: So we really do see, kind of in summary, a bit of an increased openness from healthcare providers, and even on the payer side, an increased openness. And an understanding and appreciation for the fact that the AI really has to be leveraged in order to solve lots of these problems that continue to predominate in the industry. So then, if we think about this internally, and Hadi can talk about this a little bit more later, but I think this moment is actually also right for CareCloud. So we've been building for the last 20 years a really deep kind of cloud-based data infrastructure across revenue cycle and clinical and patient engagement domains. And we have the data that we believe will enable us to kind of to build as a foundation or use as a foundation to train and to deploy, to scale, to iterate these AI models on a real-world basis. So unlike maybe some players in this space, they have phenomenal ideas and talented R&D team members, but lack the actual real-world experience and the data to be able to test and iterate, and change these models in real time. We really have that. So you put these two pieces together, industry demand for efficiency and intelligence, and then our CareCloud's readiness and capability to use the resources we've been developing for the last two decades to deliver on that need. And we just really think it's the right time. It's the clear inflection point for us really to also go big in AI.
MK: Understood. And Steve, you kind of alluded to this earlier, but just curious, in what ways do you expect AI to contribute to CareCloud's growth, whether it's through the creation of new revenue streams, operational efficiencies that improve margins, or just the enhancement of the company's existing capabilities?
SS: No, Good question, Michael. If you think about from a growth perspective, we've grown historically largely through acquisitions. So acquisitive growth has really been a key part of our overall growth strategy, and then supplemented or augmented by organic growth. And that's really been because in this space we really believe that our infrastructure, our model, our technology enable us to be able to onboard customers from other players in our space in a way that is highly cost efficient or customer acquisition costs are in relative terms lower in the traditional acquisitive growth as opposed to organic growth. So, I guess speaking first just for a minute from the perspective of the acquisitive growth, increasingly the billing companies and other vendors in our space that we've acquired over the years, they really recognize that AI will really fundamentally transform the industry. And it's putting pressure that they see and they recognize on their existing business models. And most of these companies really lack the internal capability to respond to what's happening in the larger industry. So, really, as we engage in these sorts of acquisition discussions, companies are looking towards someone like CareCloud, someone like us to really be a partner to help them modernize and help them be able to kind of meet these challenges really head on.
SS: So that's part of the reason why. Even though we've been kind of building up this capability for some time from an AI perspective, part of the reason that we've formalized this and we are pulling all the team members together and then scaling up further from an AI, the AI center of Excellence, is really to reinforce the point to the broader market and to other vendors that we really have, we believe, increasingly the capability to really help them kind of meet these challenges really head on. And for these acquired companies, we can really provide them with a very clear roadmap that they can embed in, and they can use our technology, it'll be embedded in their operations and in our solutions. And to the extent that their solutions provider potentially integrates into their solutions in a way that enables us to kind of post-deal integration to really add value. That's at least one way. Second, we really believe that AI will also help us further expand margins. So we're increasingly, as I mentioned before, really using AI to further automate traditional manual labor-intensive processes. And especially, that's especially true in the RCM space and in the back office operations.
SS: So, for example, AI models today are increasingly helping us provide assistance with regard to appeals or claim editing, and denial management, and even document management, the utilization and triaging of documentation. So, that sort of efficiency, while not necessarily growing the top line, directly translates into, will directly translate into gross margin improvement over time. So, really continuation of the trend that we've seen during 2024, with expanding margins and increasing free cash flow. And then the kind of, maybe a third point, kind of final point would be also on the revenue side that incorporating this AI technology into our existing solutions makes them stronger solutions as we're going to market and as our solutions are being compared against the other competitors in our space. But then, also we have the improved ability to really further monetize some of these newer offerings and new solutions. So, kind of together, these efforts are really making AI a true foundational kind of growth driver for us. It's helping us both in terms of the top line and accelerating our top line growth, we believe over time, but also helping us to long-term improve operating leverage and the like. So if we think about this year, this year we've really spoken about relatively modest growth, but we really see, as we're getting to the point where we're able to embed AI increasingly throughout our operations, long term, longer term expansion, further expansion of gross margins, and then longer term to helping us to really be positioned, whether it be for acquisitive growth or organic growth.
MK: Got it. And then just from an investment perspective, curious how CareCloud is planning to fund the development and expansion of the AI Center of Excellence?
SS: Sure. That's a pretty easy one. What we're really doing is we're really funding that with our internally generated cash flow.
MK: Got it. Okay. And then what areas of the business are being prioritized for AI development and how are those priorities selected?
SS: Excellent, that's a great question. And maybe Hadi, do you mind jumping in on this one?
HC: Sure. Thank you, Steve. So as Steve has said, if you think about it, the summary of the crux of the discussion is that healthcare, as similar to many other industries, is going through a complete paradigm shift at the moment. Our goal or our broader vision is to deliver an end-to-end AI-enabled experience for the healthcare providers. And also how to adapt the AI for the back office operation where we can optimize the cost and deliver the results in a more effective and efficient way. So whether it's the patient engagement, whether it's the EHR or the practice management, whether it's analytics or RCM, we are hitting on each one of these areas at the same time. And that's why the whole idea or the vision of starting this AI Center of Excellence so we can start developing at a scale. It's not that we pick up patient engagement and forget about the revenue cycle management piece. So right now the way the team is structured, or the teams are being structured, that how we can at scale, hit on all of these areas. And once you start talking about that or trying to go into the next level, for example, the one thing could be, yes, let's continue to leverage the external LLMs which have matured over time, as Steve mentioned.
HC: And in addition to that, our own 20 years worth of the representative data, and as we all know, healthcare is a very highly regulated industry. So, sometimes these large language models may not be able to provide exactly what you're looking for. So with the help of our own data, we believe we can generate many small and large LLMs. One could be for, as an example, for agentic AI, we are trying to focus on the RCM with the help of agentic and the conventional and the generative AI. So all of these sections, because of the data, we will be able to train our own LLMs. So to answer to your question, our goal is to simultaneously address and hit on all of these areas, patient engagement, clinical documentation, revenue cycle management, practice management, and analytics.
MK: Got it. Very, very helpful. And then finally, just looking ahead over the next 12 to 36 months, just curious, what does success look like for this initiative? So, what specific metrics or milestones or business outcomes will you use to measure progress?
HC: Sure. And maybe I can answer it and then maybe Steve can give a little more color to it. There are various aspects of the various key metrics we have identified and have assigned and either already have assigned some internal numbers or are continuously working to assign the numbers. As an example, the three larger areas, the broad categories would be, one is our talent matrix, and the second one would be the technological output matrix, and the third one could be categorized as the client impact matrix. So if we talk about, first of all, the talent matrix, as if our goal is to get to 500 AI professionals by fourth quarter, by the end of the year, so we have internal matrix defined that how effectively and quickly we can hire those resources. We have to go out to the different universities at times, we have to go to different job fairs at some times, to hire the right talent. So that's one. And as we speak, we already have hired over 100 resources, a combination of AI professionals, and we are on track to achieve that 500 goal, we believe.
HC: The second one, the subcategory could be the skill depth. Once we have those people, we have the matrix defined where how quickly we can onboard them and train them and assign them at the different areas, whether it's AI engineers, whether it's prompt engineers, whether it's machine learning experts, the product engineers, product launch specialists, and the like. So that's our second internal matrix. The third one is the training related in the certifications. If any of the certain specific certifications are needed, our goal is to, over the next two quarters, get those employees skilled and trained so they can start producing the compliant products in an effective way.
HC: If we talk about the technology output matrix, of course, there were three major goals in that area. One is at the rate we can start launching the products. Our goal is to launch a certain number of new products or modules each quarter. And as we mentioned during our most recent earnings call, we will share these details on every earning call each quarter to keep everyone up to date on how we are performing in those products. And to your earlier question, those products would relate to the EHR, clinical documentation, revenue cycle management, patient engagement, and the like. The second one is the cycle time, how much we can reduce the cycle time, how quickly we can prototype and from prototype, go to the production level by the end of this year with the help of these 500 people. The third one you can categorize as the innovation index... Let's take an example of our clinical and financial data models, how quickly we can train those specific models, which can position us in a unique way in this healthcare industry. The fourth we are focusing on is the true, which is the client impact matrix. One is the growth, how much we are able to grow with the help of these new AI products that the center will be delivering, whether it will improve the efficiency of a healthcare provider, or the way he or she is delivering the care. The second one is the new revenue we can generate with the help of the new AI-based products. The second one is the adoption rate. There are many products that we are working on which may not be able to generate a direct dollar value because it's an improvement in the existing PHR, as an example.
HC: So one important matrix for us is our adoption rate, how quickly we can help our clients adapt to those new technologies with the help of an AI. And then with all this, we have one major matrix of cost optimization, the reduction in the expenses. Because once the offshore expenses and even the front-end processes start to improve, the cost structure should further be improved and optimized. And then it will also help us in the net revenue retention with the help of these new products embedded into the platform. Our products are more competitive, more sellable. This ultimately should result into increasing the retention rate of the clients.
MK: Excellent, Steve and Hadi. This has been great. I think it's clear that CareCloud is at the forefront of leveraging AI in ways that support the company as well as the healthcare providers more broadly. So again, really appreciate your time today. Look forward to continuing to follow the company's progress and learning more about the opportunities as they relate to AI more broadly. So if anyone has any follow-up questions, please feel free to contact me at mkim@zacks.com, or you can reach the company at ir@carecloud.com. So thanks again, Steve and Hadi. Thanks, everyone, for joining in and have a great day.
SS: Thanks, Michael.
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