Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
From Case Costing to Profit Forecasting
0 Playsin 3 hours
In the third installment of our case costing series, HST Pathways’ Chief Innovation Officer, Gavin Fabian, breaks down what “mature” case costing actually looks like in a real ASC (and why, done right, it’s pretty boring). The conversation covers why so many initiatives fall apart at the cost-data level, how to clean up your item master and documentation workflows, and practical steps to move from retrospective spreadsheets to automated case profitability forecasting. Gavin also shares where AI can help plug data gaps, where forecasting should live in your scheduling workflow, and how to use these insights to drive surgeon alignment without sacrificing culture.
In the data segment, we discuss a custom metric from HST’s demographic benchmarking report: OR Minute–Case Gap (by Sex). You’ll hear how this metric is calculated from a 5.3 million–case dataset, what national patterns look like in specialties such as Cardiovascular and Spine, and how ASC leaders can use these insights to challenge assumptions about block times, staffing, and scheduling — so days run closer to plan.
Resources mentioned:
Who’s on Your Schedule? Demographic Trends and Benchmarks for ASCs
Who’s on Your Schedule? Exploring Demographic Data for ASCs (Podcast)
HST Pathways 2025 Client Award Nominations
Brought to you by HST Pathways.
Recommended
Transcript
Introduction to ASC Industry and Case Costing
00:00:00
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, 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.
Understanding Mature Case Costing
00:00:22
Speaker
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 Case Tabs, which many of you are likely familiar with, and is now the surgery center care coordination and scheduling product within an HST's platform.
00:00:40
Speaker
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.
00:01:03
Speaker
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.
Data Segment: Demographics and Procedure Analysis
00:01:18
Speaker
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.
00:01:43
Speaker
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.
00:01:54
Speaker
I hope you enjoy today's episode and here's what's going on this week in surgery centers.
00:02:06
Speaker
Hello, Gavin. It's so awesome to have you on the podcast today.
Challenges in Data Documentation and Hygiene
00:02:10
Speaker
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
Speaker
Sure. 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.
00:02:32
Speaker
That's now the scheduling system 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
Speaker
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.
Automating Profit Forecasting
00:03:15
Speaker
And then how would profit forecasting extend that process from hindsight to foresight? I think mature case costing 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 month, you can kind of lose track of the process and you may have employees that are difficult to manage to continue documenting.
00:03:45
Speaker
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 and in general, the 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 ah good set of data to forecast on the revenue side, but data hygiene on the cost side is really everything if you want to have accurate feasibility screening at the center. So
00:04:23
Speaker
That makes sense. So having data trust and data hygiene is critical to having the right inputs. So in order to get there, what do you recommend teams do? 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.
00:04:49
Speaker
I've developed a tool, as you know, that forecasts 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 yeah what ends up happening is that inaccuracy ah because of poor data what what motivates the center to take three months and start getting it
Centralizing Data and Governance
00:05:16
Speaker
right.
00:05:16
Speaker
and 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.
00:05:28
Speaker
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. 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
Speaker
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.
00:06:00
Speaker
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.
00:06:15
Speaker
The second category of miss on the cost side is the way your inventory or practice management system categorizes supplies and implants.
00:06:26
Speaker
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 effectively their cost.
00:06:42
Speaker
It really matters in the way you forecast profitability if the implant cost is actually offset by ah reimbursement. And so those are the two areas is like not documenting supplies because it's perceived as kind of inconsequential, but it's not really.
00:06:59
Speaker
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.
The Shift to Automated Forecasting
00:07:20
Speaker
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?
00:07:41
Speaker
I mean, I I've really only seen it live within a practice management system. I think there are systems that can support 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.
00:08:01
Speaker
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?
00:08:21
Speaker
And if there's accountability top down, it creates like an incentive or 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
Speaker
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
Interpreting Forecasting Models
00:08:52
Speaker
profit?
00:08:53
Speaker
Yeah. 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, ah 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.
00:09:35
Speaker
And on the revenue side, typically the center is referencing contracts, and 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?
00:09:55
Speaker
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.
00:10:15
Speaker
Oftentimes the centers will have certain payers that they know be problematic and then they run the process for ah 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.
00:10:32
Speaker
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.
00:10:51
Speaker
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.
00:11:03
Speaker
On the revenue side, the system doesn't need to dig up CPT codes from past cases. They can just look at your actual contracts and see what's scheduled.
00:11:14
Speaker
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.
00:11:27
Speaker
And so it's 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.
00:11:40
Speaker
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% And that's where automation can 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.
00:12:16
Speaker
Like in the example I just shared, you just can't do that ah with, ah you know, unless you want to hire like a staff of five extra data analysts, which no one has bandwidth
Practical Application of Forecasting Models
00:12:26
Speaker
to do. So somebody gets these models, they have three potential scenarios, right?
00:12:31
Speaker
How should they be interpreting that data and then making decisions based on it? Or should they be at all? Yeah. At least when we talk to centers and implement the tools that we've developed, we compare this to like a hurricane forecast.
00:12:46
Speaker
So everyone's familiar when there's a, let's say a hurricane in the Caribbean and everyone wants to know like, how's it going 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.
00:13:07
Speaker
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.
00:13:21
Speaker
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 get $9,100 to $9,300 in revenue.
00:13:36
Speaker
You can be pretty confident that it's going to be around there. If the models completely diverge, where one model saying your reimbursement is going to be $5,100 and then there's another at $13,000, something's going on.
00:13:50
Speaker
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 model's ever going to understand.
00:14:02
Speaker
Your materials manager may know after talking to the doctor and the striker 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.
00:14:16
Speaker
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?
00:14:28
Speaker
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,
00:14:48
Speaker
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. And maybe that one that you needed to zero in on was going to make or break your month. you really needed to cash that, right? Yep.
00:15:00
Speaker
It often does, right? Like in that example, like you're $7,000 off on a case, like that can really be material. Absolutely. So that kind of leads me to my next question and 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? Yeah.
00:15:22
Speaker
Forecasting models are helpful if they are applied. and visible early in the journey of getting a case scheduled and performed.
00:15:33
Speaker
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.
00:15:49
Speaker
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 it over, but it's not formally on the schedule yet.
00:16:03
Speaker
it gets automatically forecasted. And only when the center ah approves the case does it actually hit the schedule. The second place I've seen to be applied successfully is after an initial screening, it goes on the formal schedule.
00:16:21
Speaker
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,
Effective Communication with Surgeons
00:16:38
Speaker
but it's been flagged.
00:16:39
Speaker
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?
00:16:54
Speaker
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. How does one take this information and then translate that into insight that can help change surgeon behavior?
00:17:10
Speaker
How should this data be communicated to them from your point of view? 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.
00:17:25
Speaker
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.
00:17:58
Speaker
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 you still using Arthrax? It's five times more expensive than the other physicians.
00:18:18
Speaker
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'm i'm more cost efficient. So if you put the data in the hands of it the doctors and ideally share how other physicians at the center are performing those same procedures, it can really drive more efficient decision-making.
00:18:48
Speaker
but you got to give the doctors the data. And you can't give the doctor like 2000 row Excel spreadsheet and expect them to like go and run the filters and report on it. Like you got to have a dashboard that's easy to engage with that just puts it right in front of them.
00:19:04
Speaker
something simple and straightforward. Of course, when you start to make those metrics transparent and so 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. Yeah, but You want to be thoughtful and think through all the ways those conversations can go sideways but before you have them.
00:19:25
Speaker
I've seen it play out in all sorts of ways ah that are often unexpected. One example is it 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,
00:19:43
Speaker
They saw that actually the hot shot 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.
00:20:05
Speaker
There's, i mean, obviously you're going to have physicians at the board who see everything, but if you're going to compare physicians, hip replacements, show the doctors that do hip replacements. You'd probably don't want to compare a GI physician with an orthopedic physician.
00:20:24
Speaker
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.
00:20:40
Speaker
You may find one doctor whose knee replacements are less profitable than another doctor, but If you look at their cost, maybe 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.
Implementing Data Tools in ASCs
00:21:02
Speaker
So you want to be thoughtful and 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
Speaker
That's a great point that there's some 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? Where you're definitely looking at and a very different type of payer mix for sure. Exactly. I think this is probably the hardest thing to accomplish at a multi-specialty center, right? Like when you have spine dedicated center with three ah ORs and three doctors who kind of all went through the same training, like yeah it's often easier to just arrive at apples to apples than center that is doing, you know, 800 cases a month across like six different specialties.
00:22:14
Speaker
It can be tough. 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?
00:22:33
Speaker
i think that on the surface, everyone wants to have good data have real-time forecasting right when cases hit the schedule.
00:22:44
Speaker
And so on the surface, whether ah 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,
00:23:03
Speaker
you should have an idea of, are you going to lose money or make money on surgery? So it's easy to say yes oftentimes, but it's also easy to just think that the tool is 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.
00:23:27
Speaker
And unless the entire team is bought into, we're going to put the case here, then this person's going to review it. Then add an administrator to approve it if it meets these thresholds. Like there needs to be workload design when a new tool is implemented.
00:23:46
Speaker
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.
00:24:03
Speaker
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,
00:24:20
Speaker
modify that workflow if it's not working or we need to make course corrections. But I think workflow design and some iteration in the first couple months is really important before things often fall apart.
Improving Data Hygiene for Better Operations
00:24:32
Speaker
That's a great point. Technology for the sake of technology is usually not a winning strategy. I would add, like, before you get started, you want to be clear-eyed about the quality of your data.
00:24:43
Speaker
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 you're like, that's not right.
00:25:08
Speaker
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 of months.
00:25:26
Speaker
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. Right, right. 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
Speaker
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.
00:26:01
Speaker
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.
00:26:14
Speaker
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.
00:26:33
Speaker
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.
00:26:47
Speaker
But I think that the 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.
00:27:05
Speaker
So for example, we've introduced and in our tool, a chatbot named Kaya and Kaya is trained on the entire code base of the tool, as well as all the support marketing materials. So if you ask, for example,
00:27:21
Speaker
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?
00:27:37
Speaker
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 and 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.
00:28:02
Speaker
There's all sorts of tools that that are going to reduce the need for documentation just across the board. That's exciting. Awesome. Well, thank you for that. And then i have ah one final question for you. What's one thing that admins can do this week to improve their surgery centers?
00:28:21
Speaker
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, whatever whatever it may be, improving the quality of your cost data will just serve all sorts of purposes.
Conclusion and Demographic Benchmarking
00:28:46
Speaker
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. Yeah, thanks for having me. Of course.
00:29:03
Speaker
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.
00:29:20
Speaker
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 time frame that began in Q1 of 2020 all the way through Q2 of 2025. So five and a half years of data.
00:29:45
Speaker
The data set 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.
00:30:06
Speaker
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.
00:30:18
Speaker
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.
00:30:30
Speaker
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?
00:30:42
Speaker
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.
00:30:53
Speaker
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?
00:31:07
Speaker
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.
00:31:18
Speaker
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.
00:31:31
Speaker
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.
00:31:43
Speaker
They're a little under half of the cases, but comprise 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.
00:31:56
Speaker
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.
00:32:13
Speaker
And then they stop there. But if one group is consistently taking more minutes per case, then your staffing and scheduling might be built based on the wrong assumptions. Second, it's meant to be a signal and not a verdict.
00:32:26
Speaker
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.
00:32:39
Speaker
The OR Minute Case Gap by Saxmetric 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.
00:32:54
Speaker
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.
00:33:06
Speaker
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.
00:33:21
Speaker
Then for each one, you're going to want to calculate case share, OR minute share by sex, and then compute that gap. And then you're going want to look for double-digit gaps, around 10 points or more, where one group's OR r minute share is significantly higher than their case share.
00:33:37
Speaker
Anywhere you see those big deltas, that's your shortlist for a deeper review. And 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.
00:33:50
Speaker
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. It's titled, Who's on Your Schedule?
00:34:01
Speaker
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.
00:34:19
Speaker
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.
00:34:31
Speaker
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.
00:34:45
Speaker
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.