Ep. 132: Gavin Fabian – From Case Costing to Profit Forecasting
Here’s what to expect on this week’s episode. 🎙️
What does “mature” case costing actually look like in a real ASC — and why should it be pretty boring when done right?
On the latest episode of This Week in Surgery Centers, HST Pathways’ Chief Innovation Officer (and Casetabs founder) Gavin Fabian joins the show to talk about:
- Why most case costing efforts fall apart at the cost-data level
- How to clean up your item master and documentation workflows
- Moving from retrospective spreadsheets to automated case profitability forecasting
- Using AI and better data to align surgeons while preserving culture
In our data segment, we also introduce a new custom metric from HST’s recent demographic benchmarking report: OR Minute–Case Gap by Sex, and how it can help stress-test your schedules, staffing, and block times.
Watch or listen to the full episode for more practical strategies and recommendations.
Episode Transcript
[00:00:00] Alex Larralde: Hi everyone, and welcome back to this week in Surgery Centers. My guest on the show today is Gavin Fabian, HST Pathways’ Chief Innovation Officer, and the founder of a company called Casetabs, which many of you are likely familiar with and is now the Surgery center care coordination and scheduling product within HST’s platform.
But my conversation with Gavin today focuses on what mature case costing looks like out there in the real world. And why in Gavin’s words, it should look “pretty boring.” Good case costing starts with clear, consistent documentation of supplies and implants and weak cost data is usually the reason this effort falls apart for ASCs.
From there, we talk about the next stage of this work — case profitability forecasting. Gavin talks through how centers can move from a manual case-by-case process to automated forecasts that can predict revenue and cost before the case even hits your schedule.
We’ll also talk about why the timing of these forecasts is critical if you want the chance to change the outcome.
It’s a really practical and grounded conversation about taking data and turning it into insights and decisions that protect patient care, but also the long-term financial health of your center.
Then in our data segment, we drill into a metric from our latest data report, Demographic Trends and Benchmarks for ASCs, that can tell you about how patient biological sex impacts procedure duration and intensity within the same specialty. And that metric is called the OR Minute Case Gap by Sex. And if that sounds like a mouthful, don’t worry, I’ll explain how to calculate it, what it’s for, and what it can tell you about scheduling at your center.
I hope you enjoy today’s episode and here’s what’s going on this week in surgery centers.
[00:02:06] Alex Larralde: Hello Gavin. It’s so awesome to have you on the podcast today. Thanks for taking the time to join us. Before we kick things off, talking about case costing, I’d love it if you could just give our listeners a quick introduction.
[00:02:18] Gavin Fabian: Sure. Uh, thanks for having me. My name is Gavin Fabian. I lead innovation at HST.
I got here because I started a care coordination platform for surgery centers called Casetabs. That’s now the scheduling system at HST. And then prior I spent time as a product manager for, spine implant companies, where I got really familiar with how. Expensive supplies and implants can be for surgeries and how variable they can be as well.
But yeah, that’s my background.
[00:02:53] Alex Larralde: Fantastic. And like I mentioned today, we’re going to talk about case costing, not just the fundamentals for those starting out, but we’ll also get into kind of that next level. How you can take this work forward into predicting case profitability.
So, to kick things off, tell me a little bit about what mature case costing looks like at an ASC, and then how would profit forecasting extend that process from hindsight to foresight?
[00:03:22] Gavin Fabian: I think mature case costing is pretty boring.
I think it comes down to every day, taking the time to document procedures accurately, which, when you’re doing three, 400 cases a month, you can kind of lose track of the process. And you may have employees that are difficult to manage, to continue documenting cases.
There may be supplies that are low cost that don’t feel like they’re worth the time to document, but I really think in, in general, case costing and profit forecasting kind of falls apart at the cost data level. It doesn’t usually break down at the revenue level. Most centers have accurate contracts, and certainly they have good data historically on what they’re getting paid for procedures.
So, you can usually have a, a good set of data to forecast on the revenue side. Data hygiene on the cost side is really everything if you want to have accurate, feasibility screening, at the center. So
[00:04:23] Alex Larralde: that makes sense. So having data trust and data hygiene is critical to having the right inputs.
So yeah. In order to get there, what do you recommend teams do?
[00:04:33] Gavin Fabian: A lot of times just having the goal of being able to forecast profitability of upcoming cases is the impetus or the motivation to start being more diligent and taking the time and seeing the value in documenting individual cases?
I’ve developed a tool, uh, as you know, that forecast profitability of upcoming cases and oftentimes, at least half the time when we start to implement the tool, we find that cost forecasts are off because the cost data is no good. And so, you know, what ends up happening is that inaccuracy, uh, because of poor data, is what motivates the center to take three months and start getting it right.
Three months is usually all it takes to see the difference. If you really start going from no documentation or very little documentation to quality documentation, you can get in a pretty good spot. I just think it’s like you need a motivating force.
Because no one likes taking extra time to do documentation unless there’s a real payoff at the end that people can see.
[00:05:39] Alex Larralde: Absolutely. Where do you see those cracks forming in the data or those gaps? Really, where do those typically exist for most ASCs?
[00:05:47] Gavin Fabian: I think there’s two categories where centers miss on cost documentation. So, the one is just the actual humans entering the supplies and implant costs aren’t adding everything. And oftentimes the breakdown is more so on the supply side because. They’re viewed as the lower cost items that have less of an impact on overall profitability.
And so oftentimes it’s supply costs that don’t get added. The second category of miss on the cost side is the way your inventory or practice management system. Categorizes supplies and implants. There are times where supplies get documented as implants and vice versa. And it’s like, why does that matter, right?
If the total cost is right, but because implants can be reimbursed by certain payers, it’s effectively their cost. It really matters in the way you forecast profitability if the implant cost is actually offset by a reimbursement. And so those are the two areas. It’s like not documenting supplies because it’s perceived as kind of inconsequential but it’s not really.
And then just the way the costs are categorized in the system. And that is often the easiest one to change because it doesn’t require kind of workflow change or change management. It’s simply going into your item master. And making sure that your items are categorized correctly, which oftentimes can be done in an hour using like a mass edit utility of some sort.
[00:07:20] Alex Larralde: Right. Totally. That makes sense. So there needs to be accurate data and it needs to live in one central place so that it’s accessible and can be used by different systems so where should that single system of record live and who should really own the governance of that data and ensuring that all those cost inputs are correct and accurate?
[00:07:40] Gavin Fabian: I mean — I’ve really only seen it live within a practice management system. I think there are systems that can support the procurement process or pricing negotiations, but really, it’s typically the practice management system that is the source of record on the cost data and the revenue side as well.
And from an ownership level, I think it has to come from top down and, and not just at the administrator level, but the physicians and board members should be tracking their case costing. Because if they’re not looking at it, why are the teams underneath going to document it? And if there’s accountability top down, it creates like an incentive or a, a really kind of a daily urgency to making sure these things get done if you know it’s reported on at the highest level.
[00:08:32] Alex Larralde: And so, as we think more about the actual process of forecasting profitability, there are likely other data inputs, right, that you need to have in order to do that.
Can you tell me about those? Maybe it’s like reimbursement rules or, or like contract rules that need to be encoded. What other data do you need in order to start to predict profit?
[00:08:53] Gavin Fabian: Yeah. So, if you have the data to forecast effectively in a traditional way, you typically have the data needed to be able to automate your forecasting. So traditionally what’s happening when centers kind of screen cases is they’ll usually have an Excel model of some sort, and sometimes this is done in even more manual ways, but effectively, a scheduler puts a case on the books and then starts the process of getting the materials team to estimate supplies and implant costs for the case, and then the business office to estimate revenue on the case. And on the revenue side, typically the center is referencing contracts that are relevant to that case or really one contract, but then.
They’re looking at the scheduled CPTs and saying, okay, like what, what would we get paid if these scheduled CPTs are what actually happened in the case? And then on the cost side, they’re typically looking at past cases that were similar and what are the costs for those cases. Just describing it and imagining doing that 400 times a month is scary and is really the reason why most centers actually don’t do that process for all 400 cases. Oftentimes, the centers will have certain payers that they know can be problematic, and then they run the process for a subset of their cases that they think might be a problem and then the rest kind of get a pass, which isn’t always good because sometimes those cases have issues as well.
To move towards a model where you’re doing the forecasting in an automated way, you just point your tool or your software program at that same exact data. But a software program can basically run through that logic that is in your manual process in a totally automated and instant way. So, the materials manager doesn’t have to reference past cases.
The system just sees this, the case on the schedule and goes and digs through your data to find those past cases that are similar. On the revenue side, the system doesn’t need to dig up CPT codes from past cases. It can just look at your actual contracts and see what’s scheduled. But it can even go beyond that.
For example, oftentimes centers will get a case from a doctor’s office with five CPT codes but really like only two or three of them end up getting billed. And so traditional way of forecasting revenue on the case would involve the business office taking those CPTs at face value and overshooting what they think the center’s going to get paid.
An automated model can provide you with multiple, frameworks for looking at the revenue forecast for example, if typically, only two of the CPT codes are billed, an automated model could just show you that, hey, this is the revenue forecast based on what’s scheduled but based on what’s actually happened in the past.
For cases like this, you’re actually going to get 40% less. And that’s where automation could be helpful because no set of humans at the center have the bandwidth to not only forecast all 400 cases a month but then provide you instantly like three different models and then show you when the models conflict.
Like in the example I just shared you just can’t do that. Uh, with, you know, unless you want to hire like a staff of five extra data analysts, which no one has bandwidth to do.
[00:12:26] Alex Larralde: So, somebody gets these models, they have three potential scenarios, right? How should they be interpreting that data and then making decisions based on it, or should they be at all?
[00:12:36] Gavin Fabian: Yeah. At least when we talk to centers and implement the tools that we’ve developed we compare this to like a hurricane forecast, so everyone’s familiar when there’s a, let’s say a hurricane and the Caribbean and everyone wants to know like, how’s it going to impact the US?
There’s those spaghetti models, right? And there’s all those lines showing like. Where it’s going to go, because every model has a different take based on the inputs it’s getting and how they value those different inputs. In our forecasting tool, we have kind of three models for looking at revenue, the kind of scheduled CPT codes where you just take what’s scheduled at face value.
Then kind of historically what’s happening when you have this kind of case, and then what are you actually getting paid? Not the contract rate, but like historically, what have you been paid? If all three models kind of point to the same outcome, which is like. You’re going to get $9,100 to $9,300 in revenue.
You can be pretty confident that it’s going to be around there if the models completely diverge, where one model’s saying your reimbursement is going to be, $5,100 and then there’s another at $13,000 something’s going on. I think at that point, when you have that divergence in the models, then have the staff review that because oftentimes there’s a reason that no forecasting models ever going to understand, your materials manager may know after talking to the doctor and the Stryker rep.
That this patient that’s coming in is osteoporotic and is going to need some special X, Y, and Z. And so, it’s going to drive up the implant cost. Like no model’s going to know that because it doesn’t happen enough for you to have the data to predict it. But you’ll be able to get the answer. Is it closer to 13,000 or is it really 5,000 from the conversation?
But that’s really where the power of these forecasting models exists. Because if you’re trying to manually forecast 400 cases, you’re never going to have the time and bandwidth to zoom in on that one scenario where the models diverge and be really thoughtful and have a discussion about it. You’re going to be just caught up in the noise of your 400 cases that you’re scrambling to figure out how to run a manual process on and often don’t ever get to it.
[00:14:53] Alex Larralde: And maybe that one that you needed to zero in on was going to make or break your month, and you really needed to catch that, right?
[00:15:00] Gavin Fabian: Yep. It often does, right? Like in that example, like if you’re seven thousand dollars off on a case like that can really be material.
[00:15:07] Alex Larralde: Absolutely.
So that kind of leads me into my next question in thinking about this kind of data and forecasting where can these signals actually appear in the care journey so they can help you make better decisions in real time? What does that look like today?
[00:15:21] Gavin Fabian: Yeah. Forecasting models are helpful if they are applied and visible early in the journey of getting a case scheduled and performed. There’s been retrospective reports forever and they’re useful, but they don’t tell you what’s going to happen on this upcoming case, and so they really connect the dots and make the data actionable.
Is really the point of a forecasting tool. I’ve seen it be applied successfully in kind of two parts of the process. So, one is when a case gets proposed, meaning the practice is sent over, but it’s not formally on the schedule yet. It gets automatically forecasted and only when the center approves the case does it actually hit the schedule.
The second place I’ve seen it be applied successfully is after an initial screening. It goes on the formal schedule, but then if once it hits the formal schedule and gets automatically forecasted. It shows as flagged or, or you know, a cautionary case. Then it goes to the administrator for review, to have a conversation with the doctor potentially about, hey, we want to keep this case, but it’s been flagged.
What can we do on this case? Can you talk to your rep? Can you talk like what? What can happen? So definitely it needs to happen in the scheduling process. The question is just. Do you forecast it before it ever goes on the schedule or right after it goes on the schedule. But if you’re doing it for example, the day before, like there’s not much of a point.
Because there’s not much you can do, no matter what you find.
[00:17:03] Alex Larralde: How does one take this information and then translate that into insight that can help change surgeon behavior. How should this data be communicated to them from your point of view?
[00:17:14] Gavin Fabian: Yeah. The first priority for surgeons and nurses and, even the administrators is providing good patient care and making sure patients are safe at the center.
But the staff also know and the surgeons that if the center is financially underwater, that it can no longer perform its mission. So, there is a delicate balance and what I found is that. When physicians have the data and they can see how their cases are impacting the center, that they are best suited to make those set of trade-offs if trade-offs are necessary on how to make sure that they provide excellent care while also having a viable center that can continue doing so long term.
And, that part is missing at most centers, most doctors are not really aware of how their lineups are impacting the center. Certainly, they have conversations with staff or administrators who will say like, why are you still using Arthrex? It’s five times more expensive than the other physicians.
But that’s not a holistic picture in the doctor’s mind that will shape behavior. The doctor may believe that it gets better outcomes or that’s one procedure. But my other procedures, like I am, I’m more cost efficient. So, if you put the data in the hands of the, the doctors, and ideally, share how other physicians at the center are performing those same procedures.
It can really drive more efficient decision making, but you gotta give the doctors the data and you can’t give the doctor like a 2000 row Excel spreadsheet and expect them to like, go and run the filters and report on it. Like you gotta have a dashboard that’s easy to engage with that just puts it right in front of them.
[00:19:04] Alex Larralde: Something simple and straightforward. Of course, when you start to make those metrics transparent and they start to see, okay, maybe somebody else is onto something over here, they’re doing the same thing I’m doing, but in a much more cost-efficient way,
[00:19:16] Gavin Fabian: yeah. But you want to be thoughtful and think through all the ways those conversations can go sideways before you have them. I’ve seen it play out in all sorts of ways, uh, that are often unexpected.
One example is it really fired up a group of physicians in one specialty who felt they were underappreciated by the hot shots in a different specialty. But when they got the data, they saw that actually the hotshot specialty was problematic for the center, and they were the heroes.
And so, it created this like kind of, hey, like, yeah. We’re the heroes at the center. So, I think oftentimes when you share the data, it can be helpful if you’re going to compare physicians to, to create subgroups. There’s, I mean, obviously you’re going to have physicians at the board who see everything, but if you’re going to compare hip replacements, show the doctors that do hip replacements. You probably don’t want to compare a GI physician with an orthopedic physician. It’s probably not going to create helpful action from that. And, and another thing too is you want to compare multiple metrics to get the full story.
So, for example, like, if you just share profit per minute or profitability per procedure you may find one doctor whose knee replacements are, less profitable than another doctor. But if you look at their costs may be actually similar and which leaves you wondering, well, how, how is one doctor more profitable than another?
But their payer mix may just be totally different because they’re serving a different patient population. So, you want to be thoughtful in not just looking at a singular metric, but try to compare a few so that you really understand the whole picture. Like in that case, you probably don’t want to give the doctor with a different payer, mix a lecture on cost, because when you actually dive into it, you’re going to look a little silly because that’s not what’s driving the efficiency,
[00:21:23] Alex Larralde: That’s a great point. That there’s more to the story, for each of these than just the number that you’re looking at. It’s very different if you have a largely say Medicare population ages. 65 to 80 getting orthopedic procedures versus, you know, maybe pediatric dental procedures. Right?
[00:21:43] Gavin Fabian: Yeah.
[00:21:43] Alex Larralde: You’re definitely looking at, at a very different type of payer mix, for sure.
[00:21:47] Gavin Fabian: Exactly. I think this is probably the hardest to accomplish at a multispecialty center, right? Like when you have a spine dedicated center with three ORs and three doctors who kind of all went through the same training. Like it’s often easier to just arrive at apples to apples than a center that is doing, you know, 800 cases a month across like six different specialties. It can be tough.
[00:22:15] Alex Larralde: So, let me ask you this. What are the biggest ways that you see these efforts? Stall out or fail, is it a culture issue?
Is it about the complexity of contracts? Where do you think the biggest points of failure might be for an ASC working to kind of move from that hindsight to foresight model?
[00:22:32] Gavin Fabian: I think that on the surface everyone wants to have good data, have real time forecasting right when cases hit the schedule.
And so, on the surface, whether a new process is proposed to do that, via the manual kind of template, Excel sheet process, or buying a software tool to do this for you. It seems like the right thing to do on the surface. The ROI is easy to justify. Like you should have an idea of are you going to lose money or make money on a surgery?
So, it’s easy to say yes oftentimes, but it’s also easy to just think that the tool’s going to solve the problem and not actually design a workflow on how is this going to be used every day because. The center has a way of doing things already that has inertia, and unless the entire team is bought into, we are going to, put the case here, then this person’s going to review it, then add, an, uh, administrator to approve it if it meets these thresholds.
Like there needs to be workflow design when a new tool is implemented. And I, I think that is a step that’s often missed. And so, you have high expectations of implementing a new tool, and the workflow is actually not designed, and so the staff never really buys into it or knows how to use it, and you just don’t get the results.
So, I think just being deliberate with the staff and saying, hey, we’re, you know, using this tool for these reasons and let’s spend, three sessions going into how this should work. Who’s going to do what and then let’s do regular check-ins to like modify that workflow if it’s not working or we need to make course corrections.
But I think workflow design in some iteration in the first couple months is really important. Or things often fall apart.
[00:24:32] Alex Larralde: That’s a great point. Technology for the sake of technology is usually not a winning strategy.
[00:24:37] Gavin Fabian: I would add, like, before you get started, you want to be clear-eyed about the quality of your data. And if the data is 70% of the way there, there are strategies to implement some, rules, for example, to cover up for the 30% of data, that’s not great.
And make an effort to get there. So have like a, a data hygiene plan, even if you’re not great yet. Like just have an understanding of where you’re at and where your gaps are. Because otherwise you may get a forecast and you’re like, that’s not right. But it’s really that you have been categorizing your implants improperly and you just need to go and correct that.
So, I think it starts with have a plan for how to understand and improve data hygiene, and then a workflow that you’ve all agreed upon and be ready to iterate over the first couple months.
[00:25:26] Alex Larralde: That makes sense. So that early data coming out of the system, regard it with some skepticism as you identify and work out those kinks in your process.
[00:25:35] Gavin Fabian: Right.
[00:25:36] Alex Larralde: Okay. Another question for you, what is the potential of AI here and how has AI already helped with this process to date?
[00:25:45] Gavin Fabian: Yeah. So, I think AI can be used to plug gaps in the data. So, let’s say there’s a center that has poor categorization of implants versus supplies. An AI model plugged on top of the solution could very quickly figure out okay I know that this anchor is not a supply.
I’ve seen enough data to know that this needs to be categorized within implants. So, I think that what AI models are really good at is getting a worldview of how things should work based on a massive amount of data and then noticing when the world is not working in that way. And then being able to make corrections if you allow the model to make course corrections.
We have some of the beginnings of that in our forecasting tool where if there’s a gap in supply and implant cost data, you can actually use what we call our smart cost feature to fill in gaps. But I think that that the models will get increasingly powerful and more approachable so we can fill gaps in where data’s not great.
Another area, I think that we can use AI tools to help centers get the most out of the capabilities of their software platforms. So, for example, we’ve introduced in, in our tool a chat bot named Kaia, and Kaia’s trained on the entire code base of the tool, as well as all the support marketing materials.
So, if you ask for example, how do I adjust my settings as it relates to payers, it’ll tell you how to do that and provide you the links to go do it. Or I’m an ortho center, how do I get the most out of this tool? Or are there certain strategies for making sure I’m flagging the right cases? You can actually have a conversation with effectively like an expert trainer.
On the product, like right within the tool. Yeah, those are a couple ways I think kind of more futuristic than that is reducing the need for actually documenting. Supply and implant costs in the OR with computer vision, that’s just basically watching what’s going on and what’s being implanted.
There’s all sorts of tools I that, that are going to reduce the need for documentation just across the board.
[00:28:08] Alex Larralde: That’s exciting. Awesome. Well thank you for that. And then I have one final question for you. What’s one thing that admins can do this week to improve their surgery centers?
[00:28:21] Gavin Fabian: Oh, check the quality of your cost data. And where there are gaps or things that don’t look right, put in a plan to get good cost data hygiene because it’ll pay dividends all over the place. Whether it’s when you’re trying to negotiate better pricing, whether you’re trying to forecast your cases, what, whatever it may be, improving the quality of your cost data will just serve all sorts of purposes.
[00:28:45] Alex Larralde: Fantastic. Well, thank you so much for joining us. It’s a pleasure to have you on today. Learned a ton from you, and I hope everybody listening did as well. So yeah,
[00:28:54] Gavin Fabian: thanks for having me.
[00:28:55] Alex Larralde: Of course.
[00:28:55] Alex Larralde: For today’s data segment, I want to zoom in on a custom metric from our latest demographic benchmarking report. And that metric is called OR Minute-Case Gap by Sex. And before we get into the math of that, I want to give you some additional context on the report it came from, because it’s the first of its kind from HST.
So, Will Evans actually did an episode on this back in October where he goes over the findings in great detail. That’s a great accompaniment to the report. If you haven’t seen it, I suggest that you check it out. But what we did was look at 5.3 million cases across 635 surgery centers over a timeframe that began in Q1 of 2020, all the way through Q2 of 2025, so five and a half years of data. The dataset is definitely representative and comprehensive, and we used it to derive the insights you’ll find in our report.
Our goal here wasn’t to create clinical guidance and instead provide descriptive benchmarks so you could get a clear picture of how case volume, OR minutes, and dollars are distributed across specialties and patient age band and biological sex. Ultimately, we want you to be able to hold up your own data against these benchmarks and see where you’re similar and maybe where there are some differences or deviations to dive into and understand a bit better for your own center.
Inside of that bigger benchmarking report, we developed a set of custom metrics to help you see those patterns more clearly. And one of the most useful, if not a little bit nerdy is our OR Minute-Case Gap by Sex metric. So, what is it?
At a high level, it’s just a way of asking within a given specialty, does one sex use more OR time than we would expect based on their share of cases? That’s it. It’s that simple.
And so, here’s how you’re going to want to calculate it. Start by looking at one specialty at a time and then do three simple steps to get this number for each specialty. First, you want to calculate case share: what percentage of your cases are male versus female? Second, you want to calculate your OR minute share: what percentage of your OR time within that given specialty belongs to male versus female patients?
And then for each sex, you’re going to want to subtract case share from OR minute share. If the result is positive that sex is using more OR time than their case share would predict. Their cases are longer or more resource intensive on average, and if it’s a negative, then they’re using less OR time than one would expect based on their volume.
In the national data, a couple of specialties really stood out to us. In cardiovascular procedures, women made up about 44% of cases but took up about 54% of OR time within that specialty. That’s a positive 10-point gap for women. They were a little under half of the cases but comprised a little over half of the OR time. Then in spine, we saw the mirror image. They were around 43% of case volume, but closer to half of the OR minutes again, showing a positive gap, fewer cases, but more than their expected share of time.
Now, why did we include this metric in the report?
First, it exposes something that raw volume counts alone hide. Most centers think about case mix by sex in terms of, well, about half of our cardio cases are men and half of them are women, and then they stop there. But if one group is consistently taking more minutes per case than your staffing and scheduling might be built based on the wrong assumptions.
Second, it’s meant to be a signal and not a verdict. The report doesn’t really tell you why women in cardio and men in spine are driving more OR time within those specialties. That could be procedure mix, comorbidities, surgical approach, positioning, imaging, you name it, it could be a lot of different things.
The OR Minute Case Gap by Sex metric is just there to put a spotlight on where that imbalance is, so your clinical and operational staff know where to dig in and find out more.
Third, it gives you a more realistic foundation for planning and expectations. If your data looks similar to the national benchmark and say women in your cardio specialty have a positive gap, and you’re still building blocks as if a case is just a case, it’s no surprise that some days might run long.
So, what can a center actually do with this data?
In the report we treat this as a more advanced metric. Once you’re comfortable tracking OR minutes by specialty, a practical way to start would be to pick one or two high impact specialties, usually Cardio, Spine, or Ortho. Then for each one, you’re going to want to calculate case share and or minute share by sex and then compute that gap. And then you’re going to want to look for double digit gaps around 10 points or more, where one group’s OR minutes share is significantly higher than their case share. Anywhere you see those big deltas, that’s your short list for a deeper review.
Just like the rest of the demographic report, this isn’t about labeling any group as harder patients. It’s about aligning your operations with reality, using de-identified national benchmarks for millions of cases as a mirror for your own data, so you’re not surprised where your time is actually going.
We’ll link to the full report, titled Who’s on Your Schedule? Demographic Trends and Benchmarks for ASCs, in the show notes, if you are interested in checking out the rest of the data, which I definitely encourage you to do.
Also, I wanted to give everybody listening who’s an HST client the heads up that we are currently accepting nominations for our 2025 Client Awards. We give out five, three are based on nominations, two are based on data. So, I encourage you to head on over to our website. I’ll also include this link in the show notes — but nominate yourself or a peer. You’re doing incredible work, and you deserve the recognition.
And that wraps our show today. Thanks so much for joining us for this episode. I hope you enjoyed my conversation with Gavin, maybe learned a thing or two about patient demographics within ASCs. But in any case, we’re so grateful every time you take a few minutes out of your week to spend with us. I’ll see you again next time.