BRIDGET: Hi, everyone. I’m Bridget. And as Katie mentioned,. I’m a product manager on the iQueue for infusion centers product. Let me go ahead and go over to the slides. OK. Let’s go ahead and jump in. So before we dig into measuring patient wait times, we should talk a bit about why they matter. First on the list– patient satisfaction. It’s no surprise that high wait times often lead to low patient satisfaction scores. This can have a negative effect impact on the overall [INAUDIBLE] of your institution. Next step– patient safety and staff satisfaction. A poorly planned day can lead to staffing bottlenecks, which in turn lead to longer wait times, staff burnout, and the potential for patient safety issues. Third item on the list– low operational efficiency. High wait times in the middle of the day are often driven by resource and staffing bottlenecks while those same resources and staff sit underutilized in the early mornings and the late afternoons.
Lastly, patient access. There are many activities that go on in and around an infusion center. And inadequate operational processes can result in wasted resource time, which in turn can limit patient access. OK. Now that we’ve covered why your wait times matter, let’s talk about how you can measure them. First of all, remember that if you can’t measure it, you can’t improve it. For infusion center wait times, we can break the patient’s wait times down into two key parts– chair wait and drug wait. Chair wait is the time spent waiting to be seated in the infusion suite. This is typically the time spent in the waiting room. For patients who show up early, we start the clock at the time of their appointment assuming that any wait prior to that time is kind of planned. The second piece is the drug wait, how long the patient waits for their treatment to start. This metric is mostly related to the pharmacy turnaround time, though it also has the potential to highlight other operational deficiencies, which we’ll dig into later. OK. So the first part of patient wait times– waiting to be seated in the infusion suite.
These wait times are most often driven by resource bottlenecks. The most obvious culprit– not having an empty chair or bed to put the patient in. When you have a day that’s planned to reach or exceed your physical capacity at multiple points in the day, you’re highly unlikely to not run out of chairs. At this point, the next patient can’t be seated until a resource becomes available. You’ve essentially planned for your patients to wait during that time while leaving your resources underutilized at other times of the day. So we can see a clear example of this on the upper right-hand side of the slide. This is a screenshot from the daily huddle tool within theiQueue application. That’s simply a tool that we use to visualize how all of the appointments stack up over the entire course of the day.
Across the bottom, we have the time of the day. So you can see here it goes from 7:30 AM to 7:00 PM. And then at the top, you see a line marking the units capacity. So here they have a total of 32 chairs. That’s what that line is showing us. That’s the total capacity of the center. When we look at the bars, these are telling us how many patients are scheduled to be sitting in a chair at each point in the day. And it’s pretty clear that there are two main points where the unit is scheduled to exceed their capacity. These are clear bottlenecks in the physical resources they have available. And the later part of the afternoon is under-utilized. So again, these bottlenecks here, these are going to cause some of those wait times that are driven by the lack of care resources. But let’s say that your chairs aren’t the issue. You scheduled your day to fit nicely within the physical capacity of your unit for the entire day– not an issue.
But the next potential bottleneck is staffing. If you have an excess of patients that are scheduled within a short period of time but you don’t have enough nurses to get them all started, some of them will end up waiting until there is a nurse available. Once again, it’s in the plan for a few patients to be waiting during that time. This can result in the nurses feeling pressure to move faster, which may end up in reducing the wait times but can decrease the satisfaction in the patient experience because they feel like their nurses are rushing through. And it can also create some patient safety concerns, which are obviously a big deal. [INAUDIBLE] again, we have an example of this on the lower right-hand side. This bottom table shows the bookings for the beginning of the day. We have 7:30 to 1 o’clock showing here. So those times are down the left-hand side. And then we can see the breakdown by the appointment duration across the top. Now, the colors [? themselves ?] relate to how these appointments were scheduled compared to theiQueue scheduling template. So that is probably not going to be relevant for a good number of you.
That being said, we can see that bookings total on the right-hand side. That’s what we’re going to focus on. Now, it’s a little bit small. But if read the numbers, you can see here that in the 9:00, 9:30, and 10:00 1/2 hours, there are seven to eight appointments scheduled while the 8:30 and 10:30 are just down at four and five. So this uneven scheduling can result in nurses standing around at the start, or end, or kind of at the different points in their shift while the mid-morning will feel stressful and will likely have higher wait times for some of these patients. So this is just an example. But let’s say that this center just had six nurses. It’s not feasible for six nurses to get eight patients started within a 30-minute period. So those three 1/2 hour periods are going to be quite challenging and are going to result in that nurse bottleneck that we’re talking about. Now, we’ve talked about the chairs and the nurses on their own.
But what happens when you have both a chair and a nurse available but they’re not in the same pod or in the same segment of your infusion center? So the more you segment your resources into smaller and smaller sections, you’re restricting the flexibility of the patient flow, and the more likely you are to end that’s in this scenario where you have an open chair and you have an open nurse but they’re not in the same place. The more you allow yourself to truly pool these resources, allowing nurses to work across a more loosely defined group of chairs, the more likely you are to be able to match up both resources for an incoming patient. So that’s a little bit of the connection between the first two points. OK. Now, let’s talk about the reality of these resource bottlenecks.
Either one of these scenarios is going to end up creating a backlog of patients that need to be treated, which, in turn, can end up having a domino effect on the rest of your day. When you’ve got a backlog, it’s very difficult to work out of it. In fact, in order to work out of a bottleneck, you have to have to pause, which may or may not happen depending on how the rest of your day is scheduled. For example, you might be able to work through the backlog of patients created during this section during the dips that we see here. But it will probably be a tight squeeze to work through all of those before it picks back up again in the afternoon and you hit that next bottleneck.
So the domino effect can have a major impact on your wait times for the entire day. It’s not just going to affect these patients, but it might affect these ones in the middle of the day, and it certainly will affect these, which, again in turn, can have an effect on these patients. So it’s that rolling domino effect that we really want to try to avoid especially when we’re looking at how you’re planning the day in general. So lastly, once everything is scheduled, it’s difficult to fix everything, right? You get what you get when you end up with the schedule. And on the day of, you’re not very likely to be able to have much influence over when those patients show up. And you certainly can’t do much about how they were scheduled. But the way that you manage the patient flow on the day of can impact the availability of your chair and nurse resources. You might have a standard policy on taking patients back on a first come, first served basis. But if a patient shows up very early for their appointment at a time where you’re anticipating a resource bottleneck, seating them early could end up. causing wait times for another patient that shows up on time for their appointment just slightly after that patient.
On a different day, you might have a patient scheduled during a bottleneck period that arrives a bit early during a time where the chairs and nurses are planned to be a bit underutilized. In this case, taking this patient back early could help you clear out some of the bottleneck by getting ahead of the game. These two examples show that having the context of what’s coming is more helpful in managing your day effectively than a strict, standard approach for the order in which to seat patients. All right. So we’ve covered the primary drivers behind the first part of the patient’s wait time. Now let’s focus on the second piece, the time spent waiting for treatment to start. So this wait time, probably unsurprisingly, is often driven by pharmacy bottlenecks. So let’s dig into what those tend to be. First off, we’ve got the pharmacy workload. This concept is quite similar to the nurse bottleneck. If you have more appointments scheduled in a period of time than the pharmacy can process, you’ve planned to have a bottleneck in pharmacy, which means that you’re planning for some of your patients to wait longer for their treatment to start. [INAUDIBLE] here is the pharmacy staffing, especially in terms of timing.
KATIE: Sorry, Bridget,. I’m going to stop you. I think your screen is not sharing anymore. Do you want to just try and re-share it?
BRIDGET: Yeah. Let me try. Sorry for the pause. We’ll get back in as soon as we can here. KATIE: All right. There you go.
BRIDGET: Great. OK. We’re at the pharmacy bottleneck. So we talked a bit about just the level of the workload over the course of the day. So if it’s uneven, again, it’s very similar to the nurse workload where you’ll have times of the day where the pharmacy can’t get through the number of appointments in the amount of time that they have. And so you end up with a backlog and a bottleneck on that side. And once again, you’ll have underutilized times of the day where your pharmacists are not as well utilized as they could be. So the second piece is the pharmacy staffing. So in terms of timing, you want to be careful that you are matching up the ramp-up of the pharmacy staffing at the same pace as the infusion center. So if you don’t do that, you’ll either end up with an under- or over-utilized staff in the morning. So if you bring all your staff on first thing in the morning but the infusion center can’t quite get going that fast, it’s not feasible for them to see a bunch of patients right first thing in the morning, then you’re going to have some pharmacy staff that are being under underutilized in the morning.
On the other side, if you just have one pharmacist for the first two or three hours of the day before you bring the second one on, then there’s the potential that that one pharmacist is going to be highly over-utilized in the morning because maybe the demand coming from infusion is much higher than what they can feasibly do on their own. So making sure that you’re careful about how you’re matching up the pharmacy staffing in comparison to the infusion center ramp up, that’s going to be a key piece.
KATIE: I’m still having trouble seeing your screen. So why don’t you swap it to me and we can display mine. BRIDGET: OK.
KATIE: All right
. BRIDGET: All right. Hopefully everybody can see the screen now.
KATIE: It looks good.
BRIDGET: OK. Now, we talked about the pharmacy workload and the staffing. And those are key pieces that really relate to the overall kind of workload for the pharmacists and how that relates to the infusion center demand. The second key pieces are really process items. OK. So first, let’s talk about batching. Whether it’s mixing, checking, or delivering drugs, any time you batch one of these steps, you’re building in unnecessary wait times for the drugs that would otherwise have been ready first. So instead of that drug being administered to the patient, it will sit and wait for the full batch of drugs to be ready to go. So while some of these batch practices are unavoidable– so for example, if you have a smaller satellite location that doesn’t have its own pharmacy, you’re likely going to need to batch your deliveries in some way. It’s just not feasible to send one drug over from a main pharmacy to a satellite location every time it’s done and depending on how your deliveries work. But the more you can create a steady flow of drug output, the lower your overall turnaround time and drug waits will be. So finally, let’s talk about pre-mixing.
So this is another one of those process steps. Again, for some centers, this really isn’t a feasible option. However, there’s likely to be a subset of treatments that can be pre-mixed, especially for larger centers where you get a bit of an advantage from economies of scale. So a few quick examples of treatments that could be pre-mixed– patients whose labs were processed and approved the day before, or patients who won’t need labs. Those are a great example of people that can have their drugs mixed and ready to go when they arrive. Another example is drugs with a longer shelf life and a high frequency of use. So in these cases, even if the patient that the drug was originally intended for it doesn’t end up being treated, you have a high likelihood that someone else will need it within its shelf life. So being able to identify what drugs kind of fall into that category can help you push more of your drugs towards pre-mixing.
Again, not all of them are going to be eligible. So in general, what advantage does pre-mixing provide? Primarily, it can help even out the pharmacy workload over the course of the day. So by handling the pre-mixing during the slow times, you’ll not only end up with better utilization of your pharmacy resources during these times, but you’ll help reduce the workload during the peak hours, which has the potential to eliminate the chance for a pharmacy bottleneck altogether. All right. We’ve talked about the– I’m actually going to go to this slide real quick. We’ve talked about the clear contributors to the two distinct parts of the patient wait time. But now we want to dig into the other operational factors that can impact your wait times. Which wait times these will impact really depends on how you’re handling these processes. So it’s not really clear if this is going to end up impacting the amount of time the patient waits to be seated or the amount of time it takes for the treatment to start. It really depends on how you guys are handling it individually. And we’ve seen it done many different ways at the number of different centers that we’ve worked with across the country.
So for labs– now again, there are a number of different ways that these are scheduled. We’re going to talk about two examples here. So if you’re taking the patient back into the infusion center, drawing the lab, and then having the patient wait in that same chair until everything is signed off and ready to go, you have the potential to create a chair bottleneck. Now, if you’re not a chair-constrained center, meaning you don’t really run into the issue where you don’t have a place to seat the patient, this process of taking the patient back and doing the lab in the chair, then having them sit there and wait in the infusion chair, it’s not necessarily going to create an issue for the infusion center in the current state. But it does have the potential to limit your ability to absorb at additional volume. In general, this process of taking the patient back and having them sit there while their labs are resulting is creating wasted resource time. So the patient will be waiting, taking up a chair that could otherwise be used to get the treatment or lab started for another patient.
Ultimately, this excess time will be reflected in the drug wait, which again, we think of as that the time from when the patient is seated to when the treatment is started. So if that time is including the lab, then that overall turnaround time is going to be higher. So alternatively, labs can be scheduled prior to the infusion appointment, either in a separate area completely or within infusion with the expectation that the patient will return to the waiting room in between. They’ll be pulled back, labs drawn, sent back out until they’re all ready to go and treatment is ready to start. For these appointments, a realistic lab turnaround time needs to be accounted for. An unrealistic window between these appointments can result in longer wait times for the patient and can delay the day’s overall operation, once again creating a backlog that will be difficult to work out. So it might seem patient-centric to say, OK, I’m going to schedule your lab at 10:00 and your infusion at 10:15. But in reality, if the lab turnaround time is closer to 30 to 45 minutes, then you’re setting the expectation that the patient is going to start their treatment at 10:15. And then they’re going to be upset that they have to wait for an additional 15 to 30 minutes before their treatment starts.
So sticking to a realistic turnaround time between those appointments is going to provide truth in scheduling both for the patients and for the infusion center so that they know what to expect in terms of when the patients are really going to be ready to treat. So moving on to orders– I’m sure many of you have discussed this internally a multitude of times. So it shouldn’t be a surprise that not having orders signed prior to the patient’s arrival in infusion can create a delay in the entire infusion process. These delayed patients often end up having everything ready to go right as the center hits the peak time of the day, which can create a resource bottleneck for the other patients and drive up wait times for those other patients as well. Additionally, unsigned orders often require additional nursing time to chase them down, time that could be spent treating patients. So again, we’re creating, inherently, some wasted resource time that could be used in other ways simply by having a process that isn’t as efficient as it could be. So for both of these operational factors, we often see this illustrated when we compare the scheduled chair utilization to the actual chair utilization.
So that’s what we’re looking at in this chart at the bottom. So this, again, is a screenshot from the iQueue for infusion application. Again, this is just kind of a visual. We like to look at the chair utilization by hour, buy time across the bottom, and then by chairs going up. And here, what we’ve done is we’ve aggregated a number of days in the past where we’ve said, OK, at each time in the day, what is the median number of patients that you have scheduled at any time? So that’s this blue line here. We also looked at a number of completed days and said, all right, what is the median number of patients that you actually have sitting in a chair at each time of the day? So what does your actual utilization end up looking like? So that’s the pink line. Here, we can see that there is a lag. There’s a clear lag between what is planned– again, this blue line– and what actually happens, this pink line. So either of these cases where you’re having some delay whether it’s because the labs aren’t turning around quickly enough compared to how things are scheduled or the orders aren’t ready on time, or aren’t in when the patient arrives, and therefore the patient doesn’t get seated until a while after their treatment was scheduled for– both of those end up in this lag. And that can carry out to the end of the day, which has the potential to create some nursing overtime. So we can see that here where they’re planned to be done by 9:00. But really, the last patient isn’t leaving until 10:00. OK. So jumping back up here, we’re going to talk about measuring your wait times. So again, if you can’t measure your wait times, you can’t improve them. So many of you are probably already tracking one or both forms of your wait time in some way.
And today, we’re going to jump into the iQueue application for a visual of what we’re looking at when we measure wait times. So let’s do this.
KATIE: I’ve changed it back to you.
BRIDGET: Great. All right. So do shout if you’re still having trouble seeing my screen. Hopefully, this will work. But we’ll make sure that everybody can see. All right. So here we have theiQueue application. So once again, this is just– we’re walking through the tool to show some of the ways that we visualize wait times. You guys might have different ways of looking at it. But we’re going to talk a bit about why these are kind of interesting ways to look at your wait times and what they can help you see in your wait times. The first thing that you’ll notice here at the top is this toggle. So I’m actually going to go to a different unit first. So we have a toggle here between infusion and drugs. So when we’re talking about the wait times, the infusion wait time– again, you can see this definition here. This is the one where we’re looking from the appointment time to the chair time. If a patient checks in late, we don’t count them as waiting until they arrive. Fair enough, right? So both of these pages do have that clear definition at the top in case you’re ever wondering what it is that we’re looking at.
And the first chart here shows the overall trend in wait times, including a few summary metrics. Now, you’ll notice that this chart has two lines, one measuring the overall average wait time. That’s this blue line here. We just looked at that. And the other one measures the peak hours wait times. Now, we want to call this distinction between the two values because it gives some color to when and where your bottlenecks might be. For example, when these two numbers are quite close, it’s not likely that there was a chair bottleneck, since that would have driven the wait times up in the middle of the day. We see a day where peak wait time was actually lower than the overall average.
So for example, here we can assume that there was some sort of delay during the ramp-up period, most likely a nurse or pharmacy bottleneck. So we can see that here, the overall average for the day was higher than the peak average. So not really a resource issue in the middle of the day. Probably something at either the morning, maybe the afternoon, but more likely in the morning. So other days where we see that peak above the average, that’s expected for a center if you are resource-constrained in the middle of the day. So I think a lot of you are probably familiar with the 10:00 to 2:00 peak what that feels like, and the fact that your wait times are often higher during that time. And this chart really shows the difference between a day where it was probably more even over the day– so again, these two lines are quite close to each other– versus days where the peak was probably quite a bit busier, where that gap is much bigger. OK. So if we scroll down, we can see the breakdown of wait times by their duration.
So we can see the different colors. The lighter the color, the shorter the wait. If they were seated before their appointment time, we tag them as early or no wait. And then we can see this darkest red is up to over two hours. So this breakdown can help you understand if you’re looking at a day that had some high wait times or higher than you were expecting, you can understand, is it that I had a lot of patients that were waiting kind of a medium amount of time? Or was it one or two that waited quite a long time, and that really drove up the average? So once you’re able to break that down, you can understand, all right, if it’s one or two patients, was there something wrong with the process of getting them in, there was something missing, maybe, where it was kind of a one-off and potentially some improvement in processes that you can eliminate that potential in the future? Or was it that the day was playing poorly and you ended up with this backlog, and that caused a lot of patients to wait a long time? So really being able to see this breakdown can help you understand, all right, what is the battle that I need to fight? How do I fix this and prevent this from happening again in the future? So down at the bottom, we have the breakdown of wait times by hour. So this is the median. And this helps us understand when and where the wait times. So here, we see a slight increase in the median wait time in the middle of the day, which again we saw bit of up here where those peak wait times were a little bit higher. And [INAUDIBLE] breakdown can really help us, once again, identify where the bottleneck are. So I’m going to go back up to the top and we’ll take a quick glance at the drug wait times for these guys. So once again, we have the definition.
And we have the same three charts where it’s overall trends, the breakdown by duration, and then the breakdown by hour. Let me go to a different example so we can see what it looks like when you have really high wait times in the middle of the day. So here we can see they get going pretty smoothly. And then they really hit this peak-y time. So this is some sample data of that middle-of-the-day wait time. But it is really clear that this is probably our resource bottleneck. And it’s probably worth diving into how things are scheduled in order to understand if the plan is to run out of chairs and/or nurses and, really, what can be done to adjust that? OK. So jumping back over to the slide, let’s get back over here. So once you’ve used these tools or whatever tools you have to measure your wait times, what can you do with that information? So we talked quite a bit about identifying the bottleneck.
So being able to compare the hourly trends by time of day, especially across the chair wait versus the drug wait, that can help identify where your bottleneck is likely to be. So in this example here, we’ve got some sample wait times where on the top, we can see that the wait time per hour for a chair, it really goes up in the middle of the day. Whereas the wait time for the drugs, that turnaround time for the drugs, is really flat over the course of the day. So it’s unlikely to be a pharmacy bottleneck. There isn’t a certain time where your pharmacy is much slower than other times in the day. But there is this time where you’re really ending up crunched, and you’re really ending up with higher wait times in the middle of the day. So again, that’s going to take some sort of a resource bottleneck that we talked about earlier. So once you’re able to identify the bottleneck, what can you do about it? Ideally, we want to be proactive and schedule strategically. So depending on what the bottleneck is and where your issue is coming from, that might mean that you adjust your pharmacy staffing to better match the demand coming from infusion. Or you limit your scheduling by hour depending on the pharmacy output or your nurse availability.
So being able to kind of level out that workload to make sure that you don’t end up planned to be resource-constrained during those times. We’ve also heard of strategies of really being strict about which patients are scheduled during those [INAUDIBLE] hours, those kind of 10:00 to noon, 10:00 to 2:00 time periods, really saying, look, these are the times that we need to be getting patients in who are going to the clinic ahead of time or who are a little bit more constrained due to other treatments that they’re getting that day. And therefore, anybody who’s doesn’t fall into that eligible category really needs to be scheduled at other times so that we don’t end up creating this bottleneck and unnecessary wait times. So being able to strategically schedule is a key piece to taking a proactive approach to reducing your wait times. Now lastly, we talked a bit about this earlier, managing your day effectively.
So if you’re able to execute thoughtfully by having a clear picture of where to expect your bottlenecks on the day of, you can help reduce the wait times by making smart decisions. So if a day isn’t scheduled well, you won’t be able to fix everything, right? If a day is scheduled to have bottlenecks, whether it’s chairs, nurses, pharmacy, et cetera, you are not going to be able to fix everything on the day of. But by arming yourself with the knowledge of what’s coming, you can at least mitigate some of the unnecessary wait times by using this information to help drive your decisions. All right. So that’s the end of our webinar today. I’m going to pass things back over to Katie so that we can jump into some Q&;A.
KATIE: Great. Thank you so much, Bridget. Now we’re going to open it up to you guys. On the lower-right corner of your screen, there’s a Q&;A box. Please feel free to ask all of your questions. Bridget’s here to answer anything. And we’ll just give you a couple of minutes to think through what you have to say. Oh, here we go.
OK, first question. I’m currently working on an infusion center that is experiencing high wait times in the mornings. And I feel that my nurses aren’t being utilized to their full potential. Any thoughts on what’s occurring?
BRIDGET: Yeah. So if you have those high wait times in the morning but you’re seeing that it’s not because your nurses are slammed, it’s probably one of those process issues. So there’s a chance that either the patients are there but the orders aren’t, and so therefore the nurses can’t get them going yet. It might be that the pharmacy staffing isn’t quite matched up with the demand from infusion. So maybe you only have one pharmacist but you have a bunch of nurses that are ready to go, ready to get their patients going, and that the pharmacy just can’t keep up with the number of patients that you’re trying to get started that early. Those are some key examples of iQueue where that morning wait time can fall into place and can come from.
KATIE: OK. Does the iQueue software interface with our EMR to automate the distribution of patients?
BRIDGET: Ah, OK. So this is a question about this Queue for infusion center software specifically. So the Queue templates, we do work with you to build them straight into your EMR. And then the schedulers will use the template slots to schedule your patients. So it is an optimized schedule that kind of helps you level load across the day. And it does account for the various constraints that we’ve talked about. So how many times do you have? How many nurses do you have at what points in the day? Any pharmacy constraints that you might have in terms of the number of drugs that they can spit out per hour– all of those things get configured into our optimal scheduling templates. And then we work with you to get those into your EMR so that your scheduling– again, this goes back to the point of scheduling strategically. The Queue for Infusion Center’s templates are designed to help you do that. So, again they’re built into the EMR.
KATIE: Is the scheduling mistake-proofed when using optimization templates or can human error affect this?
BRIDGET: Yeah. So there’s always a bit of the human side of things. So it really depends on what EMR you’re using and what sort of overbooking privileges you have set up for your center. So typically, if you have the ability to restrict overbooking to a set number of people, we’d recommend kind of having a gatekeeper to say, you know what, these are the appointment slots that you have to book as general schedulers. And if you really can’t find an appointment that works for you, then you need to go to person A and they can help you make a decision about what is going to be a good fit. So I don’t know that I’d say 100% that it’s mistake-proof. But really, we do a lot of work to make sure that the way that it’s set up in your [? EHR ?] is going to work well for your team. And then we talk to the schedulers. We make sure that we train them so that they understand how to use the templates well to make sure that, again, the strategic scheduling part of it is done well.
KATIE: For building templates, do you recommend scheduling to available nurses or against chair availability?
BRIDGET: OK. So this is a really tricky question. So I know a lot of you guys aren’t currently using the iQueue for infusion center’s product. And there’s always this question of, how do we schedule? Do we schedule to a chair view or to a nurse view? And the reason that that’s hard is because you really need to make sure that you have both. And that is a difficult thing to kind of visualize sometimes. So from our side, when we are building out templates for our iQueue customers, the optimization that we use does consider both. From your side, I’d say a general recommendation is think about where you tend to have a bottleneck and be really careful about that. So if you know that you’re constantly running out of chairs in the middle of the day, then that’s going to be a really key thing to keep an eye on. Whereas if you know that maybe you’re not as chair-constrained but your nurses feel like when it’s not considered well, they don’t have enough time between patients, then that’s going to be a big concern. Ideally, you’re going to be able to set up a way that you can care about both. And especially when it comes to, like I said, our templates, we certainly do want to make sure we’re considering both. But in general, it’s kind of pick a poison and, in general, focusing on the one that’s going to be the likeliest issue is going to be a key part there.
KATIE: You mentioned something called the daily huddle. Can you explain this feature more?
BRIDGET: Yeah, absolutely. Let me go ahead and jump back up to that slide. OK. So again, this feature that’s within the application, this is something that we use to visualize the day itself. So once again, you can do everything possible to try to schedule it well. But sometimes things happen where you had a number of appointments that were kind of added on two days beforehand and there was really no flexibility in where they needed to be scheduled. So you might end up with some bottlenecks planned in the day. What the daily huddle allows you to do is see where those bottlenecks are. And just knowing what’s coming can make a huge difference for the staff to understand, OK, we’re going to have a bit of a bottleneck between 10 and 11 o’clock. But we can push through it and then it’ll be pretty smooth for the rest of the day. Just having that information to be prepared to, again, make those day-of decisions and make the decisions about when you want to take patients back earlier versus when you want to try to hold off and make sure that you’re taking patients in the order of their appointments.
Things like that the daily huddle can really help you visualize and make those decisions clearly.
KATIE: How do you handle when a chemotherapy infusion chair time is schedule based on the infusion time of the drug but the patient needs electrolyte infusion added on? This is causing us to have extended unplanned chair times and longer wait times for others.
BRIDGET: Yeah, that’s a great question. So kind of the unexpected things, right? Ideally, when you’re scheduling, and when we build out the templates, what we’re aiming to do is create a bit of buffer. So part of level loading the day, both across your nurses and your chairs, by doing that, you’re allowing a little bit of wiggle room into your overall plan. So again, if you are planned to go all the way up to capacity and especially for an extended period of time, any wrench that gets thrown into the day is going to cause an issue. It’s going to cause that backup, that backlog, and again, longer wait times for others who are expecting that chair to open up for them, and it doesn’t because this patient needed something to be added on. So if you’re able to level load the day a bit more across the chairs, then you can help absorb some of that variability that is inherent to infusion. So when you have a little bit of buffer space between what’s planned and your maximum capacity, you allow yourself some wiggle room for those add-ons whether it’s add-on treatments to an existing patient that was already scheduled or a patient that’s an urgent add-on where they weren’t even planning to come in that day but they really need to be seen. So having that buffer space in that chair utilization that’s not only not planned to exceed capacity but is planned to be a little bit under is going to help you do it.
KATIE: Does the daily huddle assign chairs to the nurses or is this built into the template?
BRIDGET: That’s a great question. So it’s not within their daily huddle itself, but we do have a tool within the application. It’s called a nurse allocation. I would pull up an example, but I’m not quite sure that I have a picture or one that’s already readily available. But basically, what it does is it takes not just what the template is but what you actually booked for the day. And you can enter what your nurse staffing is before that day as well. And it lets you run an optimization again to allocate all of those appointments across nurses based on when those nurses are scheduled, when all of the patients are scheduled, what your typical amount of time needed to get a patient going is, and kind of considering that you want, probably, an even spread of assignments over all of the nurses. So that is one of the features that’s within the application. It’s a little bit separate from the daily huddle itself.
KATIE: How much buffer time is a good time? Because we don’t want chairs sitting open and turning other patients away for extra time being built into infusions.
BRIDGET: Yeah. So I’m assuming you’re asking about the buffer chair that I was referring to earlier. That’s really dependent a bit on your operation. So when we look at the data for the various centers that we’ve worked with, we can see different patterns in the way the day is planned versus how it’s played out. So kind of going back to this view, in the case of this center, we can see that there is a definite lag in what’s planned versus what’s actual. While the overall kind of height of it is roughly the same. It shrinks a little bit in terms of the number of chairs that we use. So for these guys, we want to be careful about how late we plan the day to go. We might want to pull in that planned end time a little bit so that they can actually get out at what’s considered on-time for them.
When it comes to how much buffer we want to consider at the top of it, so how many chairs of buffer you want to think about, that’s really where it’s looking at your pattern of add-ons versus no-shows, even your arrival patterns. So seeing, do you often have patients that are showing up late that might be contributing to a middle-of-the-day bottleneck because rather than showing up in the morning ramp up, they’re showing up right during that peak time? It’s really center-specific. We’ve seen some centers where they need more buffer because they tend to have more add-ons, and no-shows, and same-day cancels, and therefore they need to be able to account for that a little bit more clearly.
And we’ve seen other centers where we know that there’s going to be a little bit of shrinkage because they have more no-shows than add-ons. And so that buffer that’s built into what you’re scheduling is a little bit less key because it kind of happens naturally based on the way the day actually [? plays ?] out. So it’s something that’s certainly a good question to ask. And really, if you can dig into that in terms of your data and understand what it is that you guys usually see, then that can help you make some really good decisions about, all right, when. I’m scheduling, I need to account for x amount of buffer because I know that I typically get this many appointments that get extended or this many appointments that get added on.
KATIE: Great. So it looks like our time is up for today. A huge thanks to Bridget and, of course, all of you for participating. Keep an eye on your inbox for the link to this recording of the session as well as announcements for feature webinars. Also, please complete the survey at the end of this session. We always appreciate your feedback. Thanks again. Have a great day.