Transcript
Speaker: Welcome to This Week in Surgery Centers. If you're in the ASC industry, then you're in the right place. Every week, we'll start the episode off by sharing an interesting conversation we had with our featured guest.
Speaker: And then we'll close the episode by recapping the latest news impacting surgery centers. We're excited to share with you what we have. So let's get started and see what the industry's been up to.
Speaker: Hi everyone, here's what you can expect on today's episode. Will Evans, HST Senior Director of Data Science and Insights, is fresh off from presenting advanced financial strategies at the ASCA conference last month.
Speaker: I wanted to spend a few minutes with Will recapping the second half of his presentation for those who may have missed it, which includes four key metrics for assessing financial health, how to identify the most important metrics for your unique ASC, and ways to incorporate benchmarking.
Speaker: If you're looking to strengthen your ASC's financial performance, this is the perfect place to start, and you'll also hear a success story or two along the way. After my conversation with Will, we'll switch to our data and insights segment.
Speaker: You're likely familiar with our full State of the ASC industry report by now, but we've recently released 12 new benchmarking reports that are shorter, solely focused on the data, and take a deeper dive into one single specialty at a time.
Speaker: Today, want to spotlight the average monthly revenue for two longstanding tried and true specialties, gastro and ophthalmology, versus two more up and coming specialties, cardiology and total joints.
Speaker: When I was looking through these reports, I just thought that there were some really interesting takeaways in terms of specialties that have high volume or low volume, but also might have high revenue or low revenue per case.
Speaker: So I'll break down the average monthly case volume and average revenue per case and the trends that we saw there. I hope everyone enjoys the episode, and here's what's going on this week in surgery centers.
Speaker: Hi, Will. Welcome to the podcast. Hi, Erica. Thanks for having me. Can you share a little bit about yourself, please? Sure. I'm Will Evans. I'm the Senior Director of Data Science and Insights for HST Pathways.
Speaker: And part of what I do here is help lead HST with some of the product strategy around data products. But I also get to work with Erica on creating the State of the Industry Report and a lot of the benchmarking metrics that we publish for broader industry consumption.
Speaker: I know we were talking, we usually talk every week, but since ASCA, we've had a few weeks off, but now we're back at it. So thank you. I wanted to have you on because you gave an excellent presentation at ASCA a month or so ago on advanced financial strategies.
Speaker: And I wanted to recap what you covered for those who might have missed it. But first, tell me a little bit about your experience at ASCA this year. Any highs or lows? The highlight was being asked to come back and present again. to be honest, last year we saw a lot of enthusiasm, I think, around the presentation we gave on kind of the basics of building out a data pipeline and starting build a data-driven culture at an ASC.
Speaker: And then this year coming back and even giving like a more advanced presentation that focused on what's the art of the possible for analytics and data-driven decision-making. I was really, to be honest,
Speaker: pretty surprised at the appetite for just that level of advanced thinking and data analysis from people in the ASC industry. And one of the things that I've always liked about those sorts of presentations is a lot of times someone's like analytical journey isn't really prescriptive. It really is more helpful just to see where people have gone and what sort of things they've done before so that you can start pulling inspiration from seeing that journey.
Speaker: And that was one of the things I was really happy about this year, especially was having like almost 145 people, I think in our session that we're just getting to see that broad range of going from start to really advanced Monte Carlo simulation levels of analysis that you can create to help drive decisions at your ASC.
Speaker: And so that was, it was just really like, fulfilling for the nerd side of me of saying like, yeah, there's people in this industry that like this kind of stuff. And even if they're not going to go all the way to the hundredth percentile, there's a lot of really valuable pieces of information that they can pull along the way to help them move down that path.
Speaker: Yeah, I agree. I think, I know we were nervous going into it. I'm like, okay, Friday afternoon session on advanced financial metrics, how is this going to go? But it was ah packed room. Questions were great. We got the feedback afterwards. And I think it was exactly what we had expected. A lot of people were into it.
Speaker: Some people thought some of it was a little over their heads, but we knew that too going into it. and But also to show people, to your point, what is possible. It's like, wow, we are really at...
Speaker: the kind of bare bones right now, but look at what we can do if we commit. So. Yeah. Yeah. So during that presentation, you did a great job of kind of laying the foundation of why data is important, how to collect it how to analyze it.
Speaker: But for our discussion today, I'd like to cover the second half of your presentation, which got more into suggested metrics to track, how to identify metrics that are important to your ASC, which I think is such an interesting angle, and then how to incorporate benchmarking.
Speaker: So let's start with the four metrics you recommended for assessing your ASC's financial health. What are those and why are they important? Sure.
Speaker: So the four metrics that we recommended starting off with were I would characterize them not as the kind of and the initial building blocks to data-driven decision-making at your ASC. Like anyone can recommend, you need to measure case volume, things like that.
Speaker: But this is taking that and starting to extend that forward into more forward-thinking proactive operations for your ASC. And so the four that we recommended were net revenue per case, liquidation rate, the claim denial rate, and OR utilization.
Speaker: And I'll start with net revenue per case. So this one, it's pretty straightforward. The ideal thing that you're trying to measure here is what is your expected, your average expected reimbursement per case at your ASC.
Speaker: Now, this can be broken down in a bunch of different ways. You can break it down by specialty. You can break it down by physician. But really the goal is you're trying to assess or create a proxy for how much you should ideally be paid on each case that you're performing.
Speaker: And so the calculation that we recommended was basically what is the sum of your expected reimbursements for a case for cases during a given month. And you just divide that by the count of cases that you did during them.
Speaker: the second one that we The second one that we recommended is the liquidation rate. And this is, if you're starting with your expected net revenue per case, the liquidation rate is essentially what percentage of that expected net revenue are you actually receiving.
Speaker: And so the recommended calculation here was you're summing up your payments and dividing it by the expected reimbursement on a given set of cases. And this is the kind of the percentage that you can take where now if you understand your expected net revenue, you can multiply that liquidation rate times your expected net revenue.
Speaker: And that will give you essentially an estimate of how much cash you're going to pull you're going to get in the door from each case after you've reached that terminal point of collections. We know in an ideal world, everyone's getting 100% of what their what their contract fees are saying that they should.
Speaker: That doesn't always play out in in a reality though.
Speaker: The third one that we're recommending is your claim denial rate. And that's just a pretty straightforward calculation in terms of it's the number of claims that have a denial divided by the count of all claims or cases that you're submitting for reimbursement.
Speaker: And this is really there to just help you identify trends and denials so that then you can take those pieces of information and combine that with denial reasons or denial codes to understand what are some of the operational drivers, what are some of the operational drivers of your denials,
Speaker: And from here, if you can start to trace that back upstream in your RCM processes so that one of the frequent things that we see in our data at HST is that lack of a preauthorization is one of the leading causes for claim denial.
Speaker: And if you can trace that back upstream to your front office RCM processes like insurance verification and preauthorization, then you can start to tease out some of those process changes that you can make yeah well that one will have downstream positive impacts on your RCM performance.
Speaker: The fourth metric that we proposed, it's a little bit more advanced and there's different variations on it, but it's your OR utilization. And this is there to help provide an assessment of how efficiently you're using your OR time.
Speaker: And then once you have that calculation, you can start to combine that with other sources of data like payments or revenue so that you can calculate your OR profitability down to the minute.
Speaker: And One of the variations that we recommend on this one is not just calculating your OR utilization, but bringing in block time utilization, as well as the percentage of your OR time that is blocked, so that you have that full breakdown of everything that's happening within your within each of your ORs each day.
Speaker: And you can start to assess like what's the art of the possible in terms of increasing OR utilization, increasing the amount of your OR time that is scheduled, or filling in that unscheduled blocked or that unscheduled OR time with either drop-in cases or things like that so that you can be a little bit more nimble as you're planning out your schedule at your ah at your ASC throughout the month.
Speaker: Got it. Okay. So the four metrics that could be useful for every ASC are the net revenue per case, liquidation rate, claim denial rate, and ah OR utilization. How can...
Speaker: and ASC identify the most important metrics for their own unique facility?
Speaker: Sure. The reason I find this exercise helpful, and I think it fits really well with how we think about things at HST is we want to enable our customers to to perform proactive operations.
Speaker: And You can talk to a lot of people in the industry and they'll be able to make you recommendations and say, these are the most important metrics. This is the ballpark of where you should be. You can use benchmarking to assess, like, are you doing well? Do you have room for improvement?
Speaker: And that's something that will get you to, that will, if you take that advice, it'll likely get you day yeah to the average or likely even slightr slightly better than average. But if you're really focused on trying to get to that tail end of the distribution where you're really maximizing your and you're optimizing the performance of your ASC, that's when it helps to get into a little more of how can I figure out specifically for my circumstances, what are the levers I need to move to really drive increased performance?
Speaker: And so to do that, there's kind of two major steps that we that I recommend. The first one is building a functional model that is, it's basically a functional financial model that emulates reality at your surgery center.
Speaker: So at a Just a basic conceptual framework is you do something like you multiply your average monthly cases by your expected net revenue per case, and you multip and that will give you an assessment of essentially what is under ideal circumstances, how much you get paid.
Speaker: Then you can take that multiply times your liquidation rate. And that's if you use that formula, that's basically going to approximate what you're likely to see in payments.
Speaker: and expected net revenue at your facility on a monthly basis.
Speaker: Now, if you take that basic simplified model to really start figuring out which one of these drivers are the best for me, or which one of these drivers are going to be the most impactful for me, what you can do is then you can run simulations with those functional models.
Speaker: And there's a number of different ways that you can run those simulations. But the one that is a classic is Monte Carlo simulation. And there's software out there that can help you do that. You can also build Monte Carlo simulation using macros in Excel if you are inclined to do so.
Speaker: But you take that functional model and you run those simulations based off of your historical data. And that starts to paint the art of the possible in terms of based on your historical performance, what is likely to happen and what are the main drivers of your payments or your expected net revenue.
Speaker: Now you can build those models out to be significantly more robust. You can include claim denials, you can include changes in payer mix or specialty mix, and that will start to illustrate Essentially, what are the ways that you can change some of those variables and some of this kind of strategic decisions that you can make at a facility level to start moving the needle for, to start driving your metrics up or down, depending on what your goals are.
Speaker: Got it. Okay. So we are, to repeat that back to you, building functional models and then running simulations. We're just going to oversimplify it. Yeah, no, that's a good summary.
Speaker: Perfect. And I wonder too, because me, person until I sat down to do this presentation with you, did not know what a Monte Carlo simulation was. But I'm curious if you think for those listening who are like, they're in, but they just don't know where to start.
Speaker: ChatGPT, hey, how do I run a Monte Carlo simulation in Excel? Is that our friend? I haven't asked chat GPT that question, but I get, I'd be willing to bet it would get you dangerously close to having a pretty good start.
Speaker: All right. So let's say an ASC kind of identifies their own metrics. They're often running. How can they start incorporating benchmarks?
Speaker: Sure. So this is one of the things that I kind of love about this use case is incorporating benchmarks is a really simple step. But once you've done some of that foundational work of building a functional model and running Monte Carlo simulations, or just honestly, even building a functional model, incorporating benchmarks really shows you what, how realistic your plan is.
Speaker: And what I like to do is incorporate when you incorporate benchmarks, don't just measure your current performance current performance against a relevant benchmark, but also measure where your plan is against that relevant benchmark to understand that Are we trying to do something that is that's never been done before? Like we're trying to perform at the 100th percentile.
Speaker: this This is barely even on the distribution for the benchmark. Or are we doing something where we're just trying to move from slightly below average to slightly above average? Because I've worked in places before where I've had an executive that's come to me and says, hey, we need to take this metric and figure out how to get it to 100%. And you go look at benchmarks and you say that's not really realistic, but...
Speaker: Unfortunately, my hands being forced, I have to go figure out how can we get as close as possible. Whereas if we can build a functional model and incorporate benchmarks and say, yeah, that lever looks like is really important, but it turns out this other one that we can ignore and we're pretty bad at, if we just move that up to being average, that's really going to have a huge impact for us.
Speaker: So incorporating benchmarks looks like a couple of different things, but really the two key points are Identify relevant benchmarks that are kind of apples to how you measure your data.
Speaker: And then compare that against your current performance as well as your plan.
Speaker: Perfect. Do you have a favorite success story of a surgery center that has started looking at data and seen success? Yeah. one of my favorite success stories is it's one of our customers that...
Speaker: They were focused on, well, they realized that they basically had a problem with upfront patient collections. And they just from historical experience knew that their collection rate was pretty low when they looked at it in aggregate.
Speaker: Like on individual cases, sure, sometimes they did pretty well, but when they pulled back and looked it monthly or a quarterly basis, they saw that they were basically unable to collect on right around 90% of their cases, any tangible amount of upfront payments.
Speaker: And so they basically started collecting a lot of the data and assessing where are we having these shortfalls and where are we identifying these issues. And they realized that it was a lot of manual processes, primarily around things like verifying eligibility and calculating benefits that were causing them to basically not collect payment on the vast majority of these of the cases that they were seeing.
Speaker: And so as they went through and they did that analysis, they decided, hey, we need to get a patient estimate tool that's going to help us remove some of those manual burdens so that then essentially, once you smooth out that piece of the process, it removes a lot of the friction for the employees to start being able to perform those eligible eligibility checks and calculate patient estimates so they can send it out.
Speaker: And just that process of automating those portions of the basically scheduling and patient communication process, they were able to see about right around 40% increase in their upfront collections.
Speaker: So it's a good example of, they didn't try to get to a hundred percent, like we're going to collect a hundred percent of every single patient deposit that we need, but we know that we can do better than only collecting on 10%.
Speaker: And so just trying to get to that middle part of the curve where they wanted to get to just, we'll just be average. Everyone can be average, right? So going through that process of identifying, we don't need to be world-class, but we're going to get to pretty good.
Speaker: That's one of my favorite stories because it's just someone had realistic expectations. They went out, they did the work, they collected data and they identified where they can improve.
Speaker: Perfect. Inspiration for all. Any final words of wisdom before we wrap up?
Speaker: Yeah. One of the things that as an analyst, I always noticed would get in my way was it was a little bit of the paralysis by analysis. And I remember I had a boss that once upon a time said, don't let good be the enemy of great.
Speaker: And just giving myself that permission of saying, you know what? I don't need to get to the hundredth percentile or have every single thing figured out before I can start making improvements.
Speaker: And giving myself that permission to say, I don't know everything. I don't have all the answers, but I have some of them and they're pointing me in this direction. That's enough to go off of a lot of the time when you're trying to make data-driven decisions.
Speaker: You don't need to get to a full-blown like Monte Carlo simulation model before you can start making improvements.
Speaker: Love it. We do this every week with our guests. Will, what is one thing our listeners can do this week to improve their surgery centers?
Speaker: So sticking with the data analysis and communicating data results to your board, my recommendation would be where you can, where it's possible.
Speaker: Anytime you're displaying data, try to show a trend if you have historical data for it and try to compare that against your plan. A lot of the times when you're trying to communicate with data, giving someone the so what behind a data point can be tough. And it's tough to always cover that in voiceover.
Speaker: And so if you can show them a data point versus a plan or a data point versus a trend, it helps to build in a little bit of that so what so that literally you're looking at just a chart and your audience will start to figure out like, oh, I don't know all the details behind this data point, but I know that trend's going down.
Speaker: And that's either good or bad, depending on the context. And especially if you include your plan against that trend, that's going to do, you're going to do yourself a lot of a lot of favors by just helping your communications be very concise.
Speaker: Perfect. Thank you so much for coming on today, Will. We really appreciate it. For sure. Thanks for having me, Erika.
Speaker: HSC Pathways recently released 12 benchmarking reports with each report taking a deep dive into one single specialty at a time, comparing data from 2023 to 2024.
Speaker: Using our own unique data set from our clients, we were able to extract data points so that anyone in the industry could compare themselves to their peers. Two quick disclaimers, we only pulled data from clients who gave us permission and we omitted any extreme outliers.
Speaker: So today I wanted to take a look at the average monthly revenue for four different specialties. The first two specialties are longstanding, tried and true, gastroenterology and ophthalmology.
Speaker: And then the second two are more up and coming specialties. So cardiology and total joints. To identify the average monthly revenue, I simply multiplied the average monthly case volume by the average net revenue per case.
Speaker: For more context, part of the reason I wanted to look at this data from this angle is because revenue potential is obviously a key factor to consider when evaluating which specialties to prioritize in your ASC.
Speaker: So you'll see that some specialties bring steady margins through consistent volume, while others offer higher per case returns but require more investment to scale. So whether you're planning your next service line expansion or just fine tuning and tweaking your case mix, understanding these trends can help guide smart business decisions.
Speaker: There's a lot of kind of shiny objects out there right now, more so in the new specialty ah realm. And it doesn't mean that there isn't a ton of potential there. There's just just some gotchas and some things to keep in mind along the way.
Speaker: So, okay, here's what we saw. Let's look at the two newer specialties first. So Total Joints has the highest revenue per case by far, averaging around $16,000 per case.
Speaker: But it has the second lowest monthly case volume at 78 cases per month. That still adds up to a very strong monthly revenue of just over $1.25 million, dollars which is the highest of the four specialties we're looking at.
Speaker: Cardiovascular procedures show the lowest average case volume of 20 cases per month and have an average revenue per case of around $4,700. This results in a total monthly revenue of about $94,000, which is the lowest monthly revenue of the four specialties that we looked at.
Speaker: And then switching to the longstanding ones, ophthalmology brings in much higher volume, averaging 378 cases per month with an average revenue per case of around $1,900. So when you do the math, that equals about monthly revenue.
Speaker: And finally, Gastro tops the listed case volume with 416 cases per month. While the average revenue per case is the lowest in this set at around $1,400 per case, the high volume still delivers a solid monthly revenue of about $590,000. what's the takeaway? Okay.
Speaker: so what's the takeaway This really highlights the different financial dynamics across ASC specialties. You can see a clear contrast between high volume, lower reimbursement specialties like gastro and i and lower volume, higher reimbursement specialties like total joints and cardiovascular.
Speaker: So for example, total joints may only perform about 78 cases per month. But because of the high average revenue per case, the specialty generates more than $1.25 million dollars in monthly revenue, which far exceeds other specialties on the list.
Speaker: And then on the other hand, specialties like GI and ophthalmology rely on much higher case volumes to drive substantial revenue, despite much lower reimbursement per case.
Speaker: So understanding these dynamics is critical when evaluating growth strategies, resource allocation, and even payer negotiations for your ASC. Different specialties require different operating models to achieve strong financial performance, and this type of data can help you just make more informed decisions if you are interested in going the direction of one of these newer specialties, or maybe you just hold true with the ones that you have now.
Speaker: If you're a visual learner and want to see the table itself, head to the link in the podcast episode notes to see the data and the written explanation as well. And that officially wraps up this week's podcast.
Speaker: Thank you, as always, for spending a few minutes of your week with us. Make sure to subscribe or leave a review on whichever platform you're listening from. ah hope you have a great day and we will see you again next week.
