Michael Besedick - Precision Staffing - Or How I Learned to Stop Worrying and Love Statistics

This episode of the Anesthesia Economics Podcast was recorded live at the Anesthesia Economics Summit in Charleston.

Michael Besedick, Managing Director of Information Products at Medaxion shows how a dynamic, data-driven approach is transforming OR staffing by blending schedules, contracts, and historical performance into adaptive coverage plans that learn and improve daily.

Hear a real-world example showing how smarter shift design, staggered starts, and flexible relief roles can reduce error rates, cut premium pay, and unlock 8–10% productivity gains while aligning staff coverage with actual demand.

Explore the full episode page: https://www.medaxion.com/michael-besedick-precision-staffing-for-anesthesia-case-study

Read the complete case study that's available for a free download: https://www.medaxion.com/precision-staffing-in-anesthesia-moving-beyond-schedules-to-data-driven-coverage

Jeff McLaren introduces the speakers and panelists whose discussions were recorded live at the 2026 Anesthesia Economics Summit.

Watch on YouTube. Learn more about Medaxion's solutions.

Transcript

Dr. Strangelove Analogy

SPEAKER_00 0:05

Thanks everybody for having me. Yeah, so if you uh you guys recognize uh Peter Sellers up here, um I thought Dr. Strangelove was appropriate because it's been a game changer for me and and something really

Traditional Scheduling Limits

SPEAKER_00 0:17

fun to work on. And as I'm sure everyone in that's in this room can appreciate, it's a really tough problem. Um, you know, there's a lot of different ways to do this, but results are mixed. I mean, if you asked anyone in this room, they could probably put a staff uh staffing plan together, but you never really know what you're gonna get. And the first category is you know, tradition really has reigned supreme. It's it's what's driven behavior and how you assemble the schedule for years and years and years. And, you know, some practices have this, particularly private practice groups have been well equipped because they've both looked at the staff and the schedule and looked at the PL and have intuited what the gap between those two things are.

OR Schedule Volatility

SPEAKER_00 0:54

As the industry shifts, you're gonna see that experience kind of erode and not necessarily seeing the same level of performance as you uh as you know, you go from one paradigm to the other. You know, OR schedules are you know, they're either flawed or they go still quickly. You can have a great guess and you know, get get the direction. You might use a block schedule, but everyone knows here, you know, any given day, you're looking at a 20 to 30 percent add-on rate in most acute care settings, and it's it's only gotten worse in the years that I've you know been working in this industry.

Tech Tools & Misalignment

SPEAKER_00 1:25

So what are you left with? Well, you have some technology tools that are on the table. You have uh, you know, workforce optimization tools, schedule tools, sound great, but who set these tools up? You know, was it the provider preference in setting up their schedule? Is there an alignment with what the hospital's goals are? Those are also in flux. After you go through the configuration stage, it's you know, you're already on to a different paradigm.

Dynamic Staffing Vision

SPEAKER_00 1:51

So, what is a better solution? Well, I you know, I personally think one is something that balances your coverage and your productivity dynamically. So something that can learn, something that can adapt to new inbound information, something that is not static, that takes you months to produce, and then as soon as it's produced, you walk away and it's and it's gone. So the other thing that you need is you need a plan where you can actually take action. So once the schedule is produced, well, what do you do with it? So I understand what my coverage needs are. What am I actually going to change? Who am I going to hire? Who am I going to fire? What open recs and what can I open or close as a function of this? And what we've seen in this kind of dynamic approach is usually like an 8 to 10% pickup, depending on the scale.

Tools in the Toolbox

SPEAKER_00 2:36

It depends on where you start. But this has real results, and I'm going to walk you through actually a real example of how this can work for you day to day. So there are three kinds of tools that you have in your toolbox if you want to solve this problem. You have experience, you know the facility well, or it's very small, it's something that you can just understand for yourself. You have a contract or a budget, something that you've set up with your group, or you're using OR data to actually figure this out. Um, really gonna touch on these two because it's hard to put experience and intuition on a screen. Um, but you know, these are really the two predictive tools that you have in anticipating what's gonna happen in your day.

OR Day Example

SPEAKER_00 3:16

And let's just walk through an example of what this looks like. So this is a real OR, this is an actual day, a schedule. You know, this is uh, you know, something that you probably look at. Maybe it's not you know necessarily ganted out. Maybe you're looking at a sheet of paper, maybe you're looking at something on your phone screen where you're looking at rooms and you're looking at cases in these rooms. And I put it, you know, the three o'clock, five o'clock, seven o'clock cut points, those are the common places where you know you're gonna um really design your staffing around. You need to pay attention to because those represent your eight hour, ten-hour, twelve-hour shifts. You know, you see some staggered starts here, some strategies that we talked about employing here. And you're gonna convert this into a rooms running tool. We've

Contract vs Schedule

SPEAKER_00 3:59

all seen this. You can see up at the top right, there's my Gantt chart. And in, you know, in the middle here we have our concurrency, your total number of rooms running, you know, needed by hour of day. And you know, this is kind of the first step in visually understanding if I'm a director and I need to put a staff together. Okay, here's, you know, I need this many people working at three, I need this many people working at five. And you can see here, yeah, you know, the schedule looks great. We're all we're gonna be done by five. You know, how often does that happen? So the the other tool that we have up here is the contract. Two predictors in anticipating what your schedule is gonna look like tomorrow. So you really have two paths forward here. You have your schedule, you have your contract, right? You know, which one are you gonna choose? Are you gonna default to the contract because that's what you're obligated to do? They're not even close to the same. So, you know, are you gonna average them? Are you gonna take the fudge factor approach? You know, and this is very common scenario. You're overstaffed during the prime hours, but you're understaffed in the evenings, so you're doing premium pay, but you see this underutilization from seven to seven. It's just a very common thing that we that

Predictive Model Innovation

SPEAKER_00 5:05

we see. So, what's what's an alternative? Well, this, you know, everyone's shown up, uh, you know, their prediction for rooms running analysis, but there's something unique about this approach this approach here that you know I think is really kind of innovative. And if you guys have used Chat GPT or a large language model, it's not exactly like that, but the tool functionally works the same. You're integrating not just the schedule, which is the purple line, to get the green line, you're also integrating historical information and you're actually integrating your last prediction and how right or wrong that prediction was and altering that. So you can see here there's clear divergence in the afternoon. You know, so how do we modify our plan to actually cover this? You know, here's the discrepancy here. You can see there's obvious differences in that divergence. Here's the discrepancy here between the schedule. We have to reconcile this red area, right? And that's real money every single day. And which path that we choose, you know, you know, you're gonna be beholden to the consequences of whatever that choice is. So what do

Adaptive Staffing & Results

SPEAKER_00 6:08

we do? Well, after we've made our prediction, this is the other new component about this, is we can actually take that line and construct a real staffing plan that covers it in alignment with the shifts and the shift preferences that we have within our organization. So you can see here, you see this box, and kind of more straightforward here, we have an alignment of, okay, we need this many eights, we need this many tens, we need this many 12s, and then we also have a what we call a relief shift, and this could be a person that comes in that maybe gives lunch or break relief starting at 11. They work till 7, another eight-hour shift, or a swing shift, someone that comes in late, you know, talking through one of the strategies there for um potentially having staggered starts. These are all configurable things. So if you wanted to make the person that came in at 11 be totally clinical and not necessarily and reduce your room coverage at that point or reduce the amount of rooms that you needed to cover at that point, you can absolutely do this. And it's a very flexible model. You could change what the preference is on the day of week, for instance. So it's entirely up to you, um, you know, and and and you can change them in real time. So it's, you know, I I think this is pretty innovative, you know, and it essentially it adapts day to day. And, you know, we have to ask ourselves the questions well, how did we actually do? Well, we can model, evaluate each of our models against what actually happened on the day. So what I'm showing you here is the blue line is what actually happened. So I know there's a bunch of lines to keep track of, but here's our schedule, here's what actually happened on the day. Diverges in the same way that we would have expected. Here's what actually happened, and here's our contract, and here's what happened, and here's what we predicted to happen. I don't know about you, um, but they look a lot closer. So, what's what's the improvement? You know, conveniently that 30% error rate, pretty much in line with the add-on, you know, and this is just one random example that we plucked. And our contract actually performed even worse. And if you can imagine a model that does this and learns every day, incorporates things like vacation, weather patterns. You know, if you had a weather event like Nashville had, and uh seeing an extreme dip in the number of cases that you have scheduled, your model would adapt to that change. So the the important part of this is that dynamism and not necessarily getting stuck in doing the same thing every day and hoping for a different result. So, like I said, you know, this is a you know a pretty important development for us in giving our clients a tool that can really adapt to um you know the day-to-day effects um, you know, uh of these changes. Um this is this is really the flow. You see up a 2% or a two-fold improvement in your error prediction rate. This translates to real money. And this is a very simple schematic of how it works. You start with your coverage estimation and then you can devolve that coverage into specific shifts, and you can do this day to day looking as far in advance as really you want. Generally, we we like to look you know about a week in advance or so. And uh, you know, it's it's real dollars here. So