The Future of Artificial Intelligence in the Revenue Cycle

Introduction to AI in Revenue Cycle Management
Welcome to the session, "The Future of Artificial Intelligence in the Revenue Cycle."

In October 2024, a survey was conducted by the HFMA on the impact of AI and automation on provider organizations.

During this session, Jeffrey Becker, vice president of portfolio marketing at FinThrive, will unpack the survey and draw implications for the future of AI on the healthcare industry's revenue cycle management.

Before we get started, we'd like to thank the sponsor, FinThrive.

To our audience, enjoy the session.

Thank you so much, and thank you everybody for joining me today. I'm really excited about the, content that we have, created for you this morning.

My name is Jeff Becker. I'm from FinThrive. I run our market research department.

And over the last six months, we've been doing an extensive amount of research on, trends and adoption, patterns within artificial intelligence and revenue cycle management.

In general, I think that we can all agree that artificial intelligence is really currently and going to continue transforming the way that we operate as an industry in healthcare and, across all industries. And, but we have a specific invested interest in the role that artificial intelligence is, currently playing and will be playing within revenue cycle management. We have an immense amount of opportunity to streamline processes and accelerate, payments for hospitals and improve experiences, financial experiences for patients. And so with this much opportunity in front of us and the, relatively limited overall adoption of artificial intelligence in revenue cycle today, we wanted to dig into this topic and really start peeling back the layers to understand where the opportunity space is, where the early successes are are bubbling to the surface, and then what's, what the barriers to further adoption look like for our space.

Research Methodology and Agenda Overview
So without further ado, I will jump in and, we can go ahead and start taking a look at, how we approach this research, our underlying methodology and premise was, and then we'll start looking at some of the results.

So for an agenda today, I'm gonna take us through, an overall level setting on some of the terminology we'll be using throughout the presentation, what we mean by machine learning, what we mean by AI, what is a large language model, what is natural language processing.

So with that out of the way, we will move into, current state of adoption, of AI and revenue cycle. I'll walk you through some of the methodology behind the research that we conducted, and then we will look at how, healthcare organizations are leveraging AI within rev cycle today.

From there, we're gonna look at sort of a future state. Where are the priorities? Where are the investment priorities and the perceptions of value, bubbling up to the surface within revenue cycle. And so we'll we'll dig into that and spend some time sort of forecasting where we think that AI is going to be within revenue cycle in the coming years.

And then we'll close with a discussion on barriers to adoption.

With the volume of innovation underway in AI and healthcare more broadly, there are some common themes and trends that you'll see, that inhibit our enterprise organizations from being able to take this and deploy this seamlessly into end user workflow, be it clinical or financial. And so we'll we'll look at that, and and we'll sort of study what's inhibiting us and what what problems do we need to address as an industry before we can unlock, accelerated adoption of AI across our workflows.

And from there, we will, quickly conclude, and that will be it for our session today.

Understanding AI Terminology and Concepts
So to start, let's just talk about AI and healthcare more broadly, and and some basic overview of terminology.

So when we talk about AI, I think that what you'll find is that, names and types and definitions get conflated pretty regularly.

For some, you'll see new AI, new AI types, new AI models, new approaches are are being released and published. It's seemingly annually. There'll be groundbreaking research that leads to new innovation in AI, And it can be hard just to keep up with the different kinds and types of AI, what they mean, what they do, and where they really plug in as a tool within, healthcare and revenue cycle and sort of, how they're used in workflow. And so we're gonna spend a little bit of time looking at, the different types of AI, and what they do. And I think that when you think about AI, rather than thinking of it as, complex computer programming, it's easier to really just think of this in terms of what do they do.

When we boil when we boil it down, AI really focuses on doing two things, extracting insights from datasets and driving action with those insights. So we have tools like natural language processing and optical character recognition and speech recognition and computer vision. And these are all tools that were designed to help computer systems extract information from data.

Computer vision, extracting information from pictures, speech recognition, extracting information from audio files, and then natural language processing and optical character recognition, really helping to further extract information from those and and create discrete data.

Driving action where we see, AI leveraging, tools to actually, make decisions and take next steps. Large language models is really, the underpinning of generative AI and is, then the the the biggest advent in AI in in many years. And so, you're seeing that in in all sorts of different industries as we're adopting large language models. But fundamentally, what that is is, creating text, creating, a deeper understanding of, questions that are being asked and then delivering, and creating, narrative in in a response to that. Chatbots, very similar, a more engaging, approach to engaging with humans and creating bidirectional conversations that can drive actions. And then robotic process automation, which in and of itself is not AI, but it is an enabling force that, we can use in a in a framework called intelligent automation to take insights derived from AI and move those forward.

So, rather than talk about all types of AI and the ways that AI are developed, I think that the best way to frame our conversation together is to really focus on, the right types of AI, the the vernacular that we use in healthcare, and then really what that means within revenue cycle.

So more broadly, artificial intelligence is, just the the capability to leverage computer programming and and, computer models to mimic human intelligence, through experience and through learning, and machine learning in particular, are computer systems that are able to learn without explicit instructions. So they they learn from patterns, they learn from, observation and and correction.

With these tools as a backdrop and these understandings as a backdrop, there's a number of different kinds of actual implementable technologies that drive, workflow improvements for us. And some have been around for many years such as predictive analytics.

Predictive analytics is a statistical technique. It looks at historical data, and looks at, large, troves of historical data, and then analyzes that for patterns that can be used to predict a future outcome.

Predictive analytics is used in healthcare in many ways and revenue cycle.

You'll see predictive analytics used to determine whether a prior authorization is needed.

You'll see predictive analytics used to, forecast whether a claim is going to be denied.

There's a number of uses for predictive analytics. And we would see predictive analytics as one of the more mature use cases that, AI ML use case, technologies that we have in revenue cycle.

Computer vision is another. Computer vision allows us to take an image, and, extract meaning from that image. So there's there's a number of use cases within revenue cycle that will resonate.

Taking a picture of someone's, driver's license and extracting the the details from that or their insurance card is is an emerging area within revenue cycle where we're seeing computer vision having a meaningful impact. Outside of revenue cycle, there's been a groundswell of innovation in computer vision helping radiologists and pathologists, interpret medical images. And and so there's a, there's a very robust corpus of research in computer vision within healthcare more broadly.

Speech recognition and natural language processing.

I think that the one speech recognition really is one of the earliest, implementations of AI in revenue cycle. I think we all remember as, clinical documentation was moving from transcription to, point and click electronic health record documentation.

We speech recognition was being implemented in tandem in that time to, alleviate clinical documentation, associated burnout, allowing clinicians to pick up a microphone and dictate their notes and having speech recognition and natural language processing, really trained with a clinical, ontology and ensuring that the natural language processing engine understood what the doctors were saying as they were transcribing their notes and capturing that effectively and accurately. That was one of the early use cases for, speech recognition and NLP in revenue cycle. And, of course, that has grown to, be be one of the largest areas of, AI utilization in revenue cycle today.

Virtual agents, you'll you'll see the use of virtual agents within, patient engagement, patient intake capabilities, answering questions, facilitating very simple workflows through appointment reminders to appointment cancellations and rescheduling and, basic simple, workflow based, exchanges with with patients and others is facilitated through, virtual agents.

Intelligent automation is really, as we talked about briefly, the combination of some type of AI and then robotic process automation. So, a couple of good examples of, what that looks like is claim status checking. I think that's a a common one is, using predictive models to determine the probability that there is a status on a claim and then running RPA to log in to payer portals and actually, check the status of that. Another one is, insurance discover rebilling. So using predictive models to, sort sort work lists on the probability that a found coverage will be reimbursed and then using automation to actually work through that list and rebuild those, those, elements of found coverage.

And then the last is large language models, CHAC GPT, and, Google's Gemini and all the, emerging work that we're seeing in large language models is really starting to find homes across revenue cycle as well. So when we think about large language models, we think about generative AI, and that really compelling human like narration, and and text generation. So we're seeing that, currently move beyond just text generation and, be implemented to support sort of, virtual agents and and, bidirectional spoken, communication. And so there's a lot of interesting and emerging things that are coming about through the, research that's happening in large language models.

And in healthcare and in revenue cycle in particular, you're gonna see, coding, documentation, clinical documentation, even, pre drafting insurance appeals letters on behalf of clinicians. These are all areas where large language models are able to pull, compelling research, primary data from the patient's record, pull that together, and, create, out an output that is text based that is, of a higher quality than we would have been able to do with natural language processing alone.

Current State of AI Adoption in Revenue Cycle
So with a sort of underlying, basis in place, let's dive into taking a look at the current state of adoption of AI within revenue cycle. So we surveyed, the underlying hypothesis here was that, our industry lacks primary market research focused on monitoring AI and automation use cases within revenue cycle. We've been, running this research for a couple of years now, and we think that it is critically important to, creating a more equitable, and frictionless healthcare economy that someone is monitoring the, the, maturity of different AI use cases within revenue cycle and highlighting areas that are working, highlighting areas that are emerging, and monitoring and and really even identifying areas that are sort of, falling flat and and not delivering value.

And by publishing this out to the industry, we can contribute to the modernization of revenue cycle, sort of across the industry. And that is fundamentally our goal. So, we we run this survey annually. We publish the results annually.

And the intent here really is just to make sure that, someone is gathering and and presenting out on what's happening within, revenue cycle management, AI.

So we surveyed a hundred and one unique organizations, director, VP, SVP, or CFO, all within revenue cycle roles.

We feel like we have a good distribution of, net, net patient revenue, bed size, electronic health record on, in place as well as what department our survey respondents were reporting in from. In total, we have, over a little over ten thousand unique data points that we exited our survey from.

So the first question that we asked is broadly, without moving into specific workflows and use cases, where, where AI and automation was being used within their organizations today.

Documentation and coding, surfaced to the top followed by claims processing.

Overall, adoption of AI and automation and revenue cycle remains low. What you'll see here is none of these areas were reported by even fifty percent of responding organizations as having, as being benefited by AI or automation. So while it's encouraging to see that documentation encoding is, is approaching that fifty percent threshold, there are no workflows within revenue cycle that are predominantly automated or enabled by AI. The majority of our workflows are all still, manual, and and not, benefiting from these technologies.

Medical coding and automated clinical documentation are the leading use case of TrueAI in revenue cycle today.

We talked briefly about this was also the earliest implementation of AI with natural language processing supporting the doctors, documenting and transcribing their notes. And the innovation in that space has continued to evolve.

And and so it's it's, not surprising to see that sort of, the centralized point of, the most adoption. It's been in place the longest and it's seen the most overall impact.

Pure RPA based automation use cases are are actually driving the highest level of overall satisfaction across all reported satisfaction across all use case areas are in claims processing.

These are simple RPA based claims status checking tools, and, they save an inordinate amount of time. They're very reliable. They're high they drive high levels of satisfaction. And so in these cases, it's sometimes the simpler solutions that drive, more reliable and, higher levels of satisfaction.

We still see, overall, highest levels of satisfaction are in claims processing, followed by patient payment tools, enabling self-service patient payments, bringing capabilities to the patient payment process to, provide, personalized payment plans, consolidated statements. There's a lot to like for patients, within the AI capabilities that are coming to the patient payment space.

And it also drives real value and measurable, ROI for health systems. So there's a real win win there.

From there, denials and underpayment recovery is showing some outsized opportunities, Documentation and coding, prior authorization, and patient intake are all areas where we're seeing, increasing adoption, but really not quite seeing that breakthrough into high levels of satisfaction. So I anticipate and will continue talking about this that you're gonna see continued, a continued innovation in those spaces and, implementation of AI and RPA breakdown into the detailed workflows until, those areas, continue to drive, satisfaction.

So when we step back, when we survey the, market on overall adoption of AI and automation. We actually dig right down into the individual use cases within each of these areas to understand, and and others beyond this to understand really, where's the current state adoption, where's the current state satisfaction, and where's the perceptions of value. And those are the three things that we really wanna understand as a body of research and and publish to the market. And so what we're looking at here, and I'll I'll take us through this in a little bit more detail as we go, is the current, level of adoption of different, AI powered solutions across all of these spaces from patient intake to prior authorizations to patient payments, clinical documentation, claims and denials.

Emerging Technologies in Patient Intake
So let's go ahead and start with patient intake.

There's a number of different steps in the patient intake process. We're seeing even, really a surge in in this, as a result of COVID nineteen, but we're seeing an increase in self-service tools to allow patients to, self schedule, run they pre run through a previsit task task list with a chatbot based, agent, confirm eligibility, take a picture of their insurance card, take a picture of their driver's license, and interact with a fully autonomous agent from the health system to manage those pre arrival tasks, as well as self check-in. So, letting the health system know when they've arrived and then, getting instructions on how to navigate to the, to the office that they're visiting.

So a number of emerging technologies moving into that patient intake space, providing, alleviate workforce alleviation to that front office staff and ensuring that, we're enabling patients to financially clear themselves before their visit. We're providing them with the tools that they need to check-in and reducing our overall waiting room, patient volume, and and just providing an overall more, enjoyable experience for patients.

Challenges in Prior Authorization Processes
The next area that we will look is prior authorization. So, there's a number of, AI deployments within the prior authorization space. Year after year, we see prior authorization being the the top problem to solve with technology in, revenue cycle, followed back and forth by by denials management. But prior authorization continues to be, the big problem in the room.

Now there are, as we can see by overall level of adoption, status monitoring, is really the top, use case that is currently being, deployed in provider organizations.

But there's a number of different steps that, drive improvements to prior authorizations that we're seeing an uptick in adoption.

Automated authorization determinations, so understanding whether this patient needs an authorization when that referral first comes in or when that order is initially placed. That is can be a very complicated problem to solve, but there's an immense amount of value in solving that. And so the approach that organizations that are building to these, to address these problems are using today is a combination of predictive modeling and large language models, wherein historical data can be used to, predict whether a patient with this type of insurance in this plan, seeing this provider for this type of service is going to need prior authorization, priority care.

And predictive models are powerful tools, but they only tell you what has, happened historically. They're they're not as good as following along with changes.

It takes some time for changes to have to show up in a predictive model.

Large language models are then able to monitor payer policy letters and look for those changes daily to to identify when a payer's policy has changed, and a prior authorization maybe is required where it wasn't in in earlier, weeks or months. And so this combination of leveraging large language models to monitor payer policy letters and leveraging predictive modeling to predict the probability that a prior auth is needed is allowing technology, developers to create engines that are, highly accurate and, tunable over time to, ensuring that we are able to flag end users as those orders are being placed or those referrals are coming in that a prior authorization is needed.

Advancements in EHR Data Extraction
Additional work is happening in the, form completion space. This is the, automated EHR data extraction, and prior authorization form completion is the highest rated overall, AI use case in in all of revenue cycle. Organizations need help completing these prior authorizations.

So there's a lot of work, focused on doing just that, pulling data out of the EHRs and, helping with form completion on these prior authorization requirements.

And then the the last piece that I think is interesting is, appeals letters. So when prior authorization is denied, you'll see this also in the denial space, helping, accelerate the appeal process by pre drafting appeals letters on behalf of the clinicians.

In the patient payment space, you can, see here as well the the overall level of adoption of these different technologies with, price estimation, being the highest level of overall adoption.

Another area that I thought was worth, digging into a little bit more is, omnichannel based collections efforts. And this is, a an approach to persona modeling that allows health systems to leverage, machine learning to understand the right time, the right channel, and and the right approach to take with patients, to compel them to take action on outstanding balances, whether it be a text message with a link to a digital payment platform or whether that be an email with a proposal for a multi, year or multi month payment plan.

Omnichannel based collections efforts have shown to have a dramatic impact on our ability to actually collect outstanding balance from patients. And when as we're seeing a a continued rise in high deductible health plans, it's important that we meet patients where they are.

We give them a modern digital experience and allow them to, engage with us the way that they would engage with any other, industry in in so terms of payments and that, we are building a more seamless financial experience for patients as their, probability of actually paying, patient, out of pocket, balances decreases as the overall balance increases. And so it's important that we build the right kind of experience for those patients. And there's a number of different, places within that that we're seeing AI and ML be deployed.

We just talked about omnichannel collections, personalized payment plans, also seeing interesting work happening in propensity to pay modeling, wherein we are providing more predictive modeling to identify the probability that a patient is going to be able to pay for the care that they need.

Innovations in Clinical Documentation
Next area that I think is, interesting and and worth digging into a little bit more is clinical documentation and coding and in particular, ambient clinical documentation. So we talked earlier about, speech recognition and natural language processing in in the earlier, era of revenue cycle management innovation where where those tools played and had an impact. But today, in part because of large language models, but even predating that, what you'll see is actual, audio equipment in the exam room with the doctor and the patient transcribing those notes on behalf of the clinician, and then fully generating an encounter note on behalf of that clinician, almost entirely alleviating the administrative work that, would have been required just five years ago to capture that encounter note.

So, a significant amount of innovation has gone into, reducing provider burnout and the administrative burden associated with clinical documentation, and that is moving rapidly in the right direction. And so today, audio, feed from the exam room can fully translate to a, appropriately formatted clinical note that the doctor can review, sign, and populate the EHR.

Predictive Denials Warnings and Their Impact
We're seeing a lot of momentum, or a lot of interest in, predictive denials warnings. So tell me before I send this claim to my payer that it has a high probability of being denied. The overall goal here is to continue implementing technologies upstream that are going to reduce denials as a ultimate outcome.

And so predictive denials is a great example of that.

Training those models on very similar, taking very similar approaches to the prior authorization determinations, looking both at how we're using historical data to identify probability of a claim being denied, but then also leveraging large language models to monitor policy letters and identify when, policies have changed that'll impact the probability of a denial and bringing those two technologies together to increase the accuracy of those, predictive models.

Managing Denials and Underpayments
And the last area that we can dig into a little bit is denials and underpayment management.

There is a, there is an emerging shift in how we are enabling, healthcare organizations to manage denials and manage underpayments moving from one off workflows to appeal these to, bulk workflows where, clustering models will review all denials, all underpayments, and use machine learning to identify similar reasons for denial and and bucket those in in a in a common bucket that can then be a bulk appeal. So workflows are being developed around clustering models that will group similar denials, group similar underpayments, and then workflows are being established to enable organizations to accelerate and automate bulk appeal workflows.

And and that work is all underway, and and we're seeing that starting to show up in the market research that we're doing.

So gives you a good sense of across revenue cycle, where the current state workflows lie, what some of the technical details and the values, of each of those use cases really looks like.

Evaluating AI Value Realization
But I think what's more important or more interesting is to look at the same information through the lens of what value is it delivering for, these organizations. Are the most adopted technologies also the highest value, delivering the most value? Or, if we were gonna look at that same layout in through the lens of a heat map of a value, where would the pockets of value really emerge, and then where are the investment priorities moving forward? And do those two areas align?

So if we step back and we look at the very same view, we'll see all of the different areas where AI is being deployed within revenue cycle and each of these lists really being sorted by current state adoption. How well adopted are these different, AI uses across these, departmental areas?

The color coding that you'll see represents overall perception of value realized.

So this is a value realization heat map wherein we can see, some pockets immediately jump out, clinical documentation, both high value perceptions of high value and high adoption. So nice alignment between what's being adopted and what's delivering value.

Prior authorizations, an area where, the the the most high value capabilities are are climbing to the tops of, the the adoption ladder in that space.

Denials, we see that there's still high value capabilities relatively low in overall adoption. So we think that there's a lot of potential for further improvement in the denials and under management space as these high value capabilities climb that overall adoption ladder.

And more broadly, you'll see that prior authorization, clinical documentation, and denials, these are the real areas where, this heat map is red. This is where we're seeing the most reported success and value realization from AI deployments in healthcare today, with lesser overall, value realization in patient intake and patient payments as well as in claims processing. So, we'll continue as every year, we'll continue to monitor and, publish out on the AI and automation use case heat map for revenue cycle.

But for this year, I think the big takeaways are, there's a there's a significant improvement in overall, perception of value around prior authorization.

Clinical documentation has seen a significant increase in overall adoption, and we're starting to see, some interesting, lower adopted use cases and denials starting to climb climb that adoption ladder. So I would anticipate that this is gonna look very different next year.

So what does that mean from a, planned investments standpoint? So, the number one area of planned investments for the next twelve months is denials and underpayment recovery, and I think that that makes perfect sense. When we look at current levels of adoption and current levels of, value realization, there is there is, opportunity in the denials and underpayment recovery space for, additional value for for our health systems. And and so we want to see those in particular, those areas see increased adoption.

We have high value capabilities that are seeing overall low adoption. So, where in documentation and coding, the highest value capabilities are also the highest adopted, we don't see that in denials and underpayment. And so it's good that that is, highest level of overall focus. Prior authorization is as, same thing.

We have growing level of adoption of those, high value capabilities, but we wanna see that, continue to increase.

And then we come to documentation and coding, which is already very well adopted, but it's not ubiquitously deployed across healthcare. So, these are these are the right three areas where our our healthcare organizations should be focusing within revenue cycle on experimenting with AI deployments and and, pushing AI into workflow. And so this is, this is encouraging to see that, planned investments really nicely align with, perceived value or a realized value and, current levels of overall adoption.

Barriers to AI Adoption in Revenue Cycle
So the last area that I wanted us to cover is barriers to AI adoption. We've looked at, the current state of AI deployment.

We've looked at the perceptions of value or value realization and and kind of unpacked where where AI is thriving and where AI is not. And the last thing that I think that is important to consider as we start talking about a path forward is, what's inhibiting broad based deployment of AI in revenue cycle? And there are some common, sorta barriers that our CIOs and CFOs are collectively looking at and and work having to overcome in order to realize these gains.

And the first is just legacy infrastructure.

I think that what's interesting is that, on the clinical side of healthcare, we saw a good deal of, consolidation or platform, pivots to sort of platform adoption where most of your workflows were within one, system, centralized data model underpinning it, and underlying integration points that though they present their own challenges, they are e an easier environment for many to orchestrate workflows than revenue cycle where it's frequently point solution by point solution by point solution, all with very different integration capabilities, all with very different, data extraction capabilities. And so building and deploying automations and, AI models in that, revenue cycle environment is is much more difficult today.

And that's, the leading really, the leading barrier is infrastructure and integration challenges.

From their budget, there is sig a significant white space within revenue cycle tech stacks today, that is not, AI related. And so for many organizations, it's about addressing, the white space in their in their technology modernization plans, before moving on to, what they would perceive as lesser proven, more riskier, implementations of technology.

And then the last piece is, that health system leaders see issues on both sides of what we would call a build or buy decision. If you are looking to the market to support you in bringing AI into your revenue cycle, there's been there are reported vendor reliability concerns. We've had vendors, in recent past have issues, reputational issues in this space. And so, those lessons, those those memories don't fade quickly.

And so that's something that we need robust, proof points as and research like this as we start to identify areas that we're seeing concurrence within the market that there's there's real value here, that this is something that is reliably providing value. And I think that as we continue to, research revenue cycle and and pinpoint those areas of, concurrence from revenue cycle leaders that there's there's value in these pockets, we'll see some of that, concern mitigated. And then lack of internal expertise. So if you're looking at build, read, buy, data scientists are are hard to recruit for, maintain.

They're they're not they it can be difficult to take an approach of training your own predictive models and deploying those into workflow.

And certainly, beyond just internal expertise, you need the, the tooling for for modern data science work as well, and both of those can be problematic.

Conclusion and Future of AI in Healthcare
So in conclusion, we've looked more broadly about AI in healthcare, the kinds of AI that are that are making their way to, revenue cycle workflows, the the way that we talk about them and some examples of what that looks like in healthcare. We've talked about current state adoption, what what kinds of AI are being, deployed. And then we've really looked at what's the relationship between current level of adoption versus, actual value realization. And there are some disparities between where we're seeing AI being deployed versus where revenue cycle leaders are telling us they're realizing value.

And some of those high value opportunities, high value use cases, are relatively low in the overall, adoption, heat map. And so dug a little bit into looking forward, where are we seeing plans for further, investment and adoption, and it's, assuring to see that there's tight alignment between value and planned investments. So where where AI is delivering value aligns nicely with where health system leaders are planning to invest moving forward. And so I think it's gonna be a good year for revenue cycle AI and automation.

I think that health system leaders are making the smart choices on where to invest. And as that comes to fruition, I think that this heat map is gonna be much more consolidated high value activities at the top of that heat map.

And so I'm excited to be able to report that back out to everybody, next year.

Introduction to FinThrive
A little about FinThrive. FinThrive is a the industry's first revenue cycle management platform.

We are built on best in class point solutions from patient access through revenue integrity, patient financial services.

But, really, what differentiates FinThrive, in addition to having best in class point solutions is that we are underpinned by the industry's first data fabric built purpose built for, building and deploying AI into revenue cycle workflows. So, we are a fire enabled data fabric. At our core, we are built on modern architecture. It is our hypothesis and belief that revenue cycle needs to evolve for, patient financial experiences to evolve and for payer provider friction, to be effectively managed.

And and for that, you need a fundamentally different approach to revenue cycle management. So, for anybody interested in learning more about who we are as a company, the research that we do, the technology that we develop, I would invite you all to learn more. There's a QR code here. And other than that, I just wanna thank everybody for spending some time with me today, learning a little bit more about, the research that we're doing here at FinThrive.

This is, this is all exciting stuff to us. So if this is if this is the kind of thing that excites you as well, I would encourage you to reach out. We do a lot of research on, revenue cycle modernization.

And so, I invite you to follow along, and learn more as we all move through, transforming revenue cycle together.