SOFIA DEMARCO: Good morning, good afternoon. This is a big topic, so let’s jump right in. Today we are going to try to untangle the infamous linkage between clinics and infusion. So we’ll start with admiring the problems just a little bit and talking about why it’s a hard one to solve. And then we’ll get into the various parts of the solution– what comes up on the infusion side, what could come from the clinic side, and how both templates and scheduling approaches play important parts in getting it fully right. And after that, we’ll spend just a little while talking about what you can measure in terms of your scheduling and making sure that you really are doing the right things to make sure that the right space is reserved for clinic appointments for those patients coming from clinic on a daily basis.
We believe at Lean TaaS that it takes complex optimization algorithms to solve the infusion scheduling problem in general, and especially to get the coordination right between clinics and infusion. It all starts with the concept of matching demand and supply. So you have an inherent challenge in arrival patterns that have to be matched every day with supply and infusion. We’ve got patients coming from clinics and other patients coming as well. Sometimes it feels that many patients get off the bus at the same time and come to the clinic. And then you’ve got a lot of variability in it. So in that world of demand that changes, variability, lots of folks coming from clinic at the same time. You have to somehow match the supply correctly to the demand. We know some prominent experts here of matching demand and supply.
Uber is a great example of a company that uses algorithms to make sure they’ve got the right supply of drivers in every location at every moment. You don’t have so much flexibility in an infusion center, but there’s still a lot that can be done to get the demand and supply matching right. Optimizing connected services– that it isn’t just limited to clinic and infusions, but that’s a major challenge that many of you are probably facing. You also have labs, you also have [INAUDIBLE] and other things that may be involved. But with all of them, you’ve kind of got the same challenge of making sure you’ve got flight paths that are convenient for patients and yet still possible to execute within each of those different nodes– within the lab itself, within clinics, within infusion, et cetera– consistently every single day. An example for an industry that’s got this very much right is the airlines. So you’ve all had the experience of taking connecting flights, and somehow, even though you’ve got many incoming flights and many outgoing flights, most of us are able to get a connection that works most of the time. We hardly ever miss our flights, and most of us don’t end up stuck in the airports for all that long. So it is possible, but of course the airlines have an advantage of having very, very good on-time arrival performance, which isn’t necessarily the case in the health care setting. If we look at the universe of connecting flights within oncology, this is what we’ve got for the most part.
We’ve got patients coming into labs who may go to their oncologists and clinic afterwards or from lab to infusion. They might just go to their oncologists and then infusion. You’ve got radiation oncology in there most likely, so you’ve got all of the various ways patients that their either labs first or oncologists and clinic first. You’ve got the pharmacy as well that’s linked up with infusion. So it’s a pretty complicated web. Each of these nodes or bubbles here on its own has its own challenges around supply and demand. And then, of course, getting that connection to really work means that each is relying on the other to have good on-time performance. So we do think that the connecting flight analogy is a good one. Like flights, each node here relies the on-time performance of the preceding node.
And managing that correctly in a world where you don’t have perfect on-time performance means that we’ve got to be really thoughtful about what that connection time should be. Of course, we want to make sure that each patient doesn’t have too much wait time built in to their itinerary. At the same time, if there’s too little, it’s very likely that they’ll arrive late to the next node and helping create demand patterns that don’t work well there. Zeroing in on clinic and infusion now, we often get asked whether there is an anchor appointment and which one it should be. In other words, which appointment truly gets priority– clinic or infusion? So either way you cut it, you end up with a potential issue if you think of an anchor appointment as being a pure driver of the next or the preceding appointment. If the clinic appointment is scheduled first, then what happens is, you might be placing an infusion appointment a set amount of time after that, and just letting the infusion of schedule get built really at the mercy of that demand pattern coming from clinic completely.
In that case, it’s very difficult to build a truly optimal schedule within infusion, and you’re very likely to end up with too many patients coming at the same time. Similarly, if the infusion appointment is fully locked in place and you just back up to the clinic appointment, you’re going to have the same problem in reverse. So doing that, it’s unlikely that you’ll end up with a clinic schedule that’s planned in a way that both your clinic rooms are utilized well, you don’t run out of space there, and things line up properly for providers and all of the choreography that goes on around them. So the ideal becomes to look for a coordinated option that truly works well in both schedules. Meaning, actually see, before locking down either appointment, that you’ve got a connection that works.
So we’ve seen this work in places where schedulers have access to pull up the infusion schedule and the clinic schedule at the same time and place them side by side. That’s the absolute best option. However, when in doubt, it would be the clinic appointment that takes priority. And the reason for that is that in infusion, you’ve got more fungible resources. Meaning, you have a set of infusion chairs, and you have a set of nurses, many of whom could take multiple patients at once. Meaning, they may be responsible for up to four patients simultaneously or even more than that in some cases and who can cover for each other a little bit. Versus in clinic, you’ve got a small number of rooms most likely per individual provider’s clinic. And you’ve got just one provider, maybe two if you’re lucky and have an APP helping out as well. So given that, you are more resource-constrained in clinic, and you’ve got a better chance of working it out across the resources you have in infusion. So when in doubt, the oncologist’s schedule should take priority.
But truly the best option is to consider neither appointment as the anchor, but actually look across both schedules and find an option that will work well in both places. Now let’s talk a little bit about making this connection work. We believe that solving for linked appointments completely takes coordination both on the template side– meaning the way your infusions schedule is planned– the template you’re working with to guide your scheduling, and also the specific scheduling decisions themselves. On the template side, your infusion schedule needs to be planned and set up to adequately meet the demand from clinic. So the goal is to set up the infusion schedule so that it can accommodate patients coming from clinic exactly as they do come, to model the infusion templates around that.
However, that may not work perfectly in 100% of cases. And in some cases, it may be necessary to reshape that clinic demand a little bit. More likely, the higher number of linked appointments that you have. So depending on the amount of coupling and decoupling that your center does, we’ve worked with some folks who have as few as 20% linked appointments. Meaning, if you look at any day in infusion, 20% of the patients would have come from clinic on the same day. We’ve worked with others who have as high as 80% or even 90% linked appointments. So the higher your percentage of linked appointments, the more likely it is that the clinic demand needs to be reshaped somewhat in order for the infusion schedules to work. When I say for the infusion schedules to work, what that means is that you’ve got the resources in infusion to actually serve all of the patients coming at any given time. You’ve got a chair, you’ve got the nurse, you’re able to get the drugs in that patient in a timely manner without overloading any of those resources and causing potential delays or a suboptimal experience.
So the way to do that in a nutshell is to use clinic demand patterns from a historical data set to drive the number of infusion availabilities of each length at each time throughout the day. So getting the temples right, that’s the first big step, but it doesn’t solve the problem for you completely. Next, you have to actually fill up those temples by matching the right patients into the right scheduling options at the right time, allowing the clinics demand patterns to take priority. So a couple of sub-points with them that– you’ve got to schedule a right-sized gap between the clinic and infusion appointment– not too long and not too short. And then there’s a real question of prioritizing the demand that’s coming directly from clinic in the midday time frame. There are multiple strategies around that that we’ll walk through in a minute here. OK.
On the template side, the idea here is to use those clinic demand patterns as the basis for your templates, but still to optimize the flow in infusion. So here at Lean TaaS, we base infusion scheduling templates on an optimization algorithm that we use that’s configured for each center we work with. And one of the inputs that it takes is your historical scheduling. We call that a heat map, and you can see an image of that on the left hand side. Essentially, it helps prioritize giving you scheduling options for the right length of appointments at the right times to match with what your demand patterns naturally look like. So we take that historical data as an input. And for folks who also send us clinic data so that we can identify which are the patients who came from clinic on the same day, we can narrow that heat map down to a more specific one just focused on not clinic-driven demand.
So the image here in the middle is where we focused in a little bit more saying, OK, let’s start with those patients who came from clinic and the times that they have typically been arriving in infusion. And let’s make sure we’ve got the spots on the infusion schedule that accommodate those patients. So it takes a chunk of historical data, looking at the pattern there of how many patients are coming from clinic at what times. Probably most of you struggling with this issue notice that day after day, you’ve got kind of the same challenge around when patients are arriving from clinic. It may be different by day of week.
If you’ve got a different set of providers working on different weekdays, you might find that Wednesdays are particularly challenging because of the [INAUDIBLE] population that you have something between 10:00 and 11:00 AM or something like that. So these patterns do need to be surfaced by day of week individually, but we can zero in on that focused part of the demand really on the patients coming from clinic and we can use that to influence the availabilities on the infusion templates. In this image here, all the way on the right hand side, this is an example of what an infusion template would end up looking like that’s trying to accommodate the clinic demand you see in the middle. So this is all, again, in the camp of building your infusion templates to accommodate the demand of patients coming from clinic as close to the raw patterns as possible.
So taking in the raw patterns and adjusting them just a very little bit to get the right spread for the infusion operation. Since this is all done as part of an optimization algorithm, we are making sure that all of the constraints you have on the infusion side are met, even though the clinic demand is being accommodated in there. However, it’s not always the case– if you’ve got a lot of linked appointments, for example– it’s not always the case that it’s possible to accommodate the clinic demand exactly as it is in infusion. It might be that on certain days of the week, your clinics are truly sending just too many patients at the same time. In that case, the thing to do is to create scheduling options in infusion based on an additional piece of crazy math in the optimization that says, OK, we can’t give every patient the exact perfect flight path. So let’s spread out those flight paths just as much as we need to in order to fit the demand within the resources that infusion has available.
With that, you’re starting to get into the zone of giving some patients a slightly longer connecting time than you ideally would. But the benefit of doing that is that then when the patient gets to infusion, you can guarantee that you’ve actually got the resources there to take care of them. Otherwise, if you give them the short flight path, but when they get through infusion you don’t have a chair available or you don’t have a nurse or you can’t get the drug on time, they’re just waiting anyway and that’s not a positive experience. So the starting point is to accommodate the clinic-based demand exactly as it is. But if that is truly infeasible for infusion, then we would look at creating those options in a way that are just slightly more spread out.
This is a data science problem. It’s folded into the optimization math. That can minimize total theoretical wait time between clinic and infusion. And the translation of that is to say, if you can’t give every patient a perfect flight path because that would truly overload your infusion operation at certain times, we can still create a template that works for infusion using that fancy math to come up with exactly how much each flight path needs to be elongated so that it actually works in infusion. There is another option there as well, which is to go back to the clinics and ask them to make a change.
This is usually a pretty sensitive topic, and so at Lean TaaS, what we try to do is as much as we possibly can on the infusion side to come up with templates that work there and do accommodate the clinic demand patterns as close to their raw form as possible. However, if having done that, we’re finding that creates some flight paths that are just too long, another option is to go back to the clinic and ask some providers to spread out their schedules a little bit differently. Depending on the size of your organization, there may be many, many providers that are feeding a particular infusion operation, and this makes this a daunting task. The recommendation would be to identify a small number of heavy hitter providers and focus in on them. So we’ve got an example of that here, where you can see at 10:00 and 11:00, you’ve got more patients coming from clinic– 18 and 16– than the true infusion capacity of more like 14 and 15 at those time. So digging into that, if we look at who is sending these patients to infusion. We split that demand out by provider– we’ve got provider A,. B, C, D, E, F here– we can see that provider. C is the one sending the most patients at that time. So the recommendation would be, rather than trying to coordinate across potentially many clinics, actually just focus on a few who are sending a large number of patients and see if there are strategic adjustments that can be made just to their schedules. So, again, the bigger your institution is, the more providers you’ve got all sending patients to infusions, the more complicated of an exercise this is.
And it’s really not the starting point. It would only be the recommendation in case we’ve already done everything we can on the infusion side, and we’re finding that truly getting all of the patients into infusion means spreading out some of those flight paths a little bit more than the institution is comfortable with from a patient experience standpoint. That would be the time to go back to the clinic. And if you do, the goal would be to touch as few clinics as possible by focusing in on those providers with the heaviest load. Now let’s talk a little bit about the scheduling side of things. So imagine a world where you’ve got the perfect infusion templates in place. There are options there that work for every single patient, giving everybody a reasonable flight path. The next challenge is to actually book your real-life patients into that template. The starting point on getting that right is to actually define an appropriate booking window. You need some flexibility, so that’s why I’m saying a booking window rather than just a set amount of time after clinic to book the patient an infusion. So you need some flexibility to align patients with the scheduling options you’ve got in infusion. But you want to keep that reasonable. The bigger the window you choose, the more opportunity there is to fit patients into your infusions schedule in multiple ways and to help balance out your resource utilization that way. But you should put some kind of a maximum on it.
That would be determined by what your standards are for the patient experience. Depending on the size and complexity of your institution, you may have very strict standards around that or an expectation that patients are seen almost immediately in infusion after their clinic appointment. Or you may be experiencing significant wait times in infusion and be comfortable with planning a slightly longer window– for example, 90 minutes or two hours– so that you can guarantee that you’re always able to deliver on that in infusion. The window to target is short enough that the patient experience is good, but long enough that the patient can be seen reliably on time once they get to infusion. So with that, the minimum time should be the amount that’s needed so that with the observed on-time performance of your clinics, you’ve got a good shot at the patients actually getting to infusion on time. This becomes a bit of a Goldilocks problem. Too long and your patients are waiting, too short and your patients are late to infusion, which causes operational challenges there. So we really need that middle just right option. And what that option is does actually vary from center to center, which means we’ve got to get into the data to define what it is. But the result should be a clear guideline given to the scheduling team. OK. So we’ve talked a little bit about the upper end of that scheduling window. It should be defined based on institutional standards around patient experience.
So the short end, we need to get into the data and do a little bit of math on it. So the trick there is to have both the infusion and the clinical data together. You need to start with getting the clinic appointment time and the infusion arrival time for each visit over a chunk of time. With that, you can calculate the actual time between the start of the clinic appointment and when patients arrive in infusion. And then you can bucket the patients by how long they took. So in order to get a number where you’re pretty sure most patients are going to arrive in infusion on time, you could go for something like a 75th percentile in that difference between your clinic appointment time and your infusion arrival time. What that 75th percentile really means is that 75% of the time, your patients would arrive in infusion on time. If you went for a median– for example, the 50th percentile– then half the time, your patients would arrive on time to infusion. And half the time, your patients would arrive late. That’s still a lot of late arrivals for infusion to accommodate well, which is why we recommend going closer to a 75th percentile. Recognizing that that still means that 25% of the time, your patients will not quite arrive on time in infusion. If you go beyond that to a 90th percentile or the maximum, then you’re starting to get into the territory where actually a chunk of your patients are still waiting probably longer than you’d like. So to recap on that, the max window between clinic and infusion is really a decision around how long you’re comfortable with patients waiting. And the minimum is something that should be figured based on the reality of the on-time performance of your clinics and what those real observed gaps actually are. Once you’ve got the right guideline around what that window should be for placing patients on the infusion schedule when they’ve got a clinic visit prior, the next thing is to define your strategy around reserving space for your clinic demand in infusion. So as I’m sure you are all aware, almost every patient tends to prefer your mid-day time frame for their appointments.
Those peak times of day, usually between 10:00 and 2:00, could also be 9:00 to 1:00, something like that, are very popular and often for very good reason. So even putting the clinic demand aside for a moment, you’ve got patients with transportation issues. You’ve got patients who are very sick and who can’t reasonably get to the infusion center very early in the morning. You’ve got patients who need to come at certain times because that’s what works with their jobs, et cetera, et cetera. So a lot of constraints that patients face that are really very legitimate. At the same time, if you give every patient their preferred time, you will have every patient coming between 10:00 AM and 2:00 PM. And that’s not feasible for inclusion, and it’s not a reasonable way to run the operation. So how do you balance that out? First of all, it’s important to proactively get patients who are a good match for the non-peak time into those earlier and later parts of the day.
We call those the shoulders of the day, as we look at infusion templates because you’re ramping up and ramping down during those times. So the kinds of patients that you can proactively target to get into the earlier morning and later afternoon would be non-oncology patients, patients who actually prefer to receive treatment early or late because they may still be working, patients who are coming in for planned supportive care, and any other patients who don’t have clinic visits or who are having their clinic visits the day before. So that’s the first step is to proactively try to fill up that space that can be less popular with all of the eligible patients you can get in there. The second piece of it is to define some specific strategies around protecting the middle.
So on the stricter end of things, you can actually go as far as saying, we have a no-fly zone for non-linked appointments. Meaning, between certain hours– maybe it’s 11:00 and 1:00 or maybe it’s as much as 10:00 to 2:00– we are not going to schedule any patients in the peak times who don’t absolutely have to be there because they have a clinic appointment or because they’re one of the noted exceptions that we’re going to allow in. On that front, it helps a lot to have very clear guidelines for the scheduling team around the exceptions that can be made to a no-fly zone. Because as I’m sure all of you are aware, the scheduling job is very difficult, and those patients with– those conversations with patients are tough to manage. So having a clear set of guidelines releases the stress for schedulers just a little bit because they don’t feel as personally responsible for every single negotiation.
They know what the standard is and what that decision is. And with that, they know they’re being fair to everybody. So having a clear list of what the exceptions are is a good thing to do, especially if you’re in that more strict end– towards that strict end of the spectrum. The less strict things to do with the something as simple as offering those eligible patients off-peak times first. So since your midday is going to be the most popular time for pretty much every patient, it’s important to do what you can on guiding patients to the shoulders of the day by offering them those options, rather than jumping to what time would you like to come, which is inevitably going to be 10:00 or 11:00. So strategies are one thing. There are also some things that can be done within the template setup to help protect certain areas. So a couple of things that we’ve seen folks do successfully in the past, one is actually setting up specific resources or blocks within the templates that can be really set aside or reserved for linked appointments only.
The other thing that can be done is to set set up time release blocks. Which means that they wouldn’t be available at first for general scheduling, but if they come available only a few days before the treatment date. This can help with linked appointments, especially in the setup where you’ve got some schedulers that work with the templates through an auto search type of setup and others who are maybe in a more supervisory type role who have the ability to go in and to place patients directly on the schedule. Those more advanced schedulers or folks working the schedule would have the ability to book into even the reserved blocks before they’ve been time released. So that’s another strategy for kind of reserving that space, having some blocks that only become available for general scheduling when it gets closer to the date of service. So depending on the size of your organization and the size of your scheduling team, it might be enough to get this right primarily through guidelines and training with the scheduling team. That would be a better fit usually for smaller organizations and smaller teams.
If you have, for example, only three or four people who do most of your scheduling or even fewer than that in some cases, then really training those folks on the appropriate guidelines becomes a very good strategy. With a larger organization and potentially a very large scheduling team, some folks have even up to 50 people touching the infusion schedule in one way or another. It becomes more helpful to have your strategies around protecting the middle of the day fully baked in to your scheduling system. So we’ve got examples here from Epic, but you can do this in other systems as well. So having strategies is one around guidelines and training for your scheduling team, around what you do physically within the templates in your EHR. The next thing is to actually measure how you’re doing with protecting that midday time for linked appointments. The first part of that is to truly know what flexibility you have to schedule infusion-only visits during those peak hours. And that, again, differs institution to institution. There’s not one answer here. Depending on how many clinics you have and what that demand looks like, depending on your percentage of linked appointments, depending on how tight you are in your infusion resources, it could be that you have almost no flexibility in the middle of the day to schedule any non-linked patients. Or it could be that if you open up the door completely and schedule anybody in the midday, you’re going to run into trouble.
But you actually do have a fair bit of flexibility and you can schedule half of your appointment options– you can give away half your appointment options in the midday frame to patients who only have infusion that day. So the first thing is to actually know how much of your midday space has to be reserved for linked appointments. And the second piece of course is to measure that. Measure your actual scheduling against what that guideline is on how many patients you can schedule with non-linked appointments in the middle of the day. So looking here at an example of linked appointments and non-linked appointments scheduled in the key times of day and comparing that to the infusion capacity at those times. An analysis like this is what you need to truly understand what flexibility you have for non-linked appointments in the middle of the day. The way to do this would be to look weekday by weekday, because, again, with a different set of oncologists practicing each weekday, you will have different demand patterns most likely. And then to see for each weekday, how many linked appointments are being scheduled within each hour, and compare that to the infusion capacity at that time. So looking at this chart here, we’ve got the infusion capacity within each hour– meaning, how many patients can be reasonably started an infusion within each hour. It’s got that mark that’s a little line in each of these boxes. So if we look at the Monday row here, the infusion capacity in the 9 AM hour is 17 appointment starts. The second step is to see what the difference is between what you’re typically scheduling for linked appointments and your infusion capacity. T
hat gives you the number of non-linked appointments it’s actually OK to schedule in that time frame. So in this example here, on. Monday, on average, there are about 10 linked appointments being scheduled in the 9:00 AM hour. And the capacity in infusion is to start 17 patients in that frame. So with that, there is space to schedule something like seven non-linked appointments. On Thursday– that’s the other orange box we’ve got down here– the picture is different. So you can see both. Mondays and Thursdays have a good chunk of linked demand– I should have said that’s the blue colored bars in here. Both Mondays and Thursdays have a fair amount of linked demand, but the Monday linked demand is much heavier in the 9 AM hour than it is on a Thursday. So on Thursdays, if we look closely here, it’s only about two linked appointments that are being scheduled in the 9:00 hour. And so on Thursdays, it’s actually quite reasonable to schedule a good chunk of non-linked appointments at that time. Really, up to 15 will fit within the total capacity. So it’s not obvious what the amount of flexibility is. You may feel it based on how much trouble you have forming your infusion schedules and not running out of space. But it’s important to actually do this analysis of what is our linked demand, and therefore, how much remaining flexibility do we have if we still need to fit within the constraints of what infusion can actually handle. Once you have a handle on what those numbers are– how many more non-linked appointments– it’s OK to schedule on different weekdays during those key hours. You can go ahead and measure that. So this is generally a retrospective analysis, where you can look back and say, OK, are we meeting those targets on how many non-linked patients we’re scheduling in those key hours of the day.
That’s really to measure, and then you can go into re-education if you need to with a scheduling team. Again, the scheduling job is a really difficult one, and it may be necessary to kind of keep reiterating what those targets are and also refresh on the strategies for matching the right patients into the right slots all of the messaging around inviting people towards a more flexible time of day for the non-linked patients especially, et cetera, et cetera. And so measuring that and managing to it is key. The other thing that you can do is proactively look forward and see for the upcoming days– the days in the next one or two weeks– how many non-linked patients do we have on the infusion schedule in those key times? And is that in the range that’s going to work with our linked demand as well? Or are there already too many non-linked patients on the schedule? Are there enough that we might need to move some of them before we get too close to the date of service? The iQueue web application has an analytics that can help with that. It’s called the Booking. Patterns Analytic.
And you can look at future dates within that, and you can use a little filter– I think this is a little small to see– but we’ve got a filter in here where you can select just the non-linked appointments or just the linked appointments. For this exercise of looking forward, it would be a question of selecting the non-linked appointments so that you can see where you have how many of them coming up in the next one or two weeks as an input to actually maybe going and looking at rescheduling some of those patients. That’s not ideal, of course. The best thing is to have the right templates in place and the right guidelines in place so that you aren’t ending up with too many non-linked appointments where they don’t actually fit. But not relying on perfection, it may also be helpful to look forward and see the next one week or two weeks what is our pattern of non-linked appointments. How many do we have already on the books, and is that going to work with our linked demand as well? Or do we need to actually proactively try to reschedule some of those non-linked appointments at key times? Coming to the end here.
We’ll just do a quick summary of what it takes to get this linkage right. First of all, we need to acknowledge that it is truly a coordination between clinic and infusion. It’s not generally right to treat either appointment as the anchor appointment that drives the placement of the other. It’s much better to look across both schedules and find an option that will work for the oncologist and will work in infusion, while still having the right kind of flight path in between. That in order to have consistently flight paths that work well in order to find those coordinated options on your schedules, it’s important to set up your infusion templates so that they either accommodate the clinical demands exactly as it comes or as close as possible. Meaning, just spread out the infusion options a little bit more and create slightly longer flight paths so that the pattern can be accommodated within the infusion resources. By exception, it might also be necessary to go back to some key clinics and adjust those templates a little bit. So that’s not the starting point. A lot can be done just on the infusion side by optimizing the infusion schedules around the clinic demands or spreading out that demand just a little bit. But by exception, if you are truly imbalanced, especially on certain weekdays, it might be the next step to go back to clinics and ask a couple of people to change schedules around. In addition to getting those templates right, you’ve got to fill them in with putting the right patients in the right spots.
That means a thoughtful approach to how you do that, between a set of guidelines for your scheduling team and potentially even reserving space within infusion at certain times physically in your scheduling system. And again, the right strategy there depends on how your team is structured for infusion counseling. Who all can touch your schedule, and who all has what permissions? And then finally, you’ve got to see, is our approach working? Are we hitting the targets of how many linked versus non-linked appointments we should be scheduling at certain times? So to do that, you’ve got to define those targets based on looking at your patterns of clinic demands that come to infusion at certain times. So you could actually know how much flexibility do we have to schedule non-linked appointments. Then you’ve got to see, are we matching with those targets? And there is also the proactive steps you can take of looking forward at the next week or two weeks to see if you’re in line with those targets coming up or if you can still take some action to get in line with those targets. A lot of this does rely on data analysis and looking at that data. So for those of you who already use iQueue, if you’re not sending the clinical data to us, we should get it. So we may come to you with that request. Because in order to make sure that you’ve got the absolute best flight path in place for every patient, we have to see both the clinic and the infusion records. OK. So that’s the summary for today. We’re right about at 45 minutes in, and we’ll spend the next few minutes here taking questions.
MODERATOR: Awesome. So we had a few questions coming at coming while you were speaking. A couple people had asked for the slides. And just so you know, we will attach the PDF of the PowerPoint to the landing page where the video can be accessed. So when you get the email after the presentation, within about 12 to 24 hours, just look at the bottom. There’ll be a button that says Presentation. The slides will be there. One two-part question is, is your schedule compatible with Epic? And then, does it takes lab time to run into consideration?
SOFIA DEMARCO: OK. The iQueue schedules are absolutely compatible with Epic. Epic has a lot of flexibility in how it gives you tools to set up templates. And we’ve got a lot of folks using the iQueue templates plugged in to Epic. So that’s a clear yes on that one. And depending on your scheduling practices, we would work with you to get the templates set up in the right way, the best fit for you. But Epic is great and flexible, and it can take in the iQueue templates. The second part of that was about coordinating with lab as well. So the connection between lab and infusion– in a way, it’s like a mini version of the same connective problem between clinic and infusion. With the difference that your lab resources tend to be a little bit more flexible. And since the lab appointments are very, very short, it tends to be a little bit easier to flexibly squeeze in the number of patients that you need to.
But that’s all within a limit. So one thing that it might be necessary to look at in building out your infusion templates, if you’re finding that you have challenges around patients getting their labs first and then coming to infusion, arriving on time, and all of that is to almost do a mini version of the same exercise of looking at which are the patients that are coming to lab first and what are those on time arrival rates actually doing. So for example, if you’ve got a lot of patients who are doing labs on the same day, if you’re not doing a lot of labs the day prior, you may need to have slightly fewer appointments on the infusion schedule first thing in the morning if you don’t realistically have enough patients that you can plug into your infusion schedule right at the start of the day who wouldn’t need to go to lab first.
And so this is a common thing. It’s not quite as hard to sort out as the linkage between clinic and infusion. And generally, it’s helpful to– if you’re not getting good on time performance of patients coming to infusion from lab, it might be that the solution is more on the side of bulking up your lab resources at key times, rather than adjusting infusion schedules. But at the same time, when we start building out templates for you, we would look at the reality of how your day is going. So like I mentioned, if it’s not realistic to ramp up as quickly as you potentially could in infusion because you won’t have enough patients who are coming to infusion without going to lab first, that’s something that we would need to work around.
MODERATOR: OK. So just a reminder, if you have a question, you can use the. Q&A widget, which is to the upper right hand corner of your screen. Another question we’ve gotten is, do you recommend decoupling appointments?
SOFIA DEMARCO: OK. This is a touchy one. The decision to decouple appointments is largely a clinical one. And so we don’t make a recommendation on that. Now, it is true that the more decoupling you have of your clinic and infusion appointments, the easier it is to manage the infusion schedule. That’s true. It gives you more flexibility. However, you can run an infusion operation very, very well while having many coupled appointments. So it really kind of depends on how constrained your infusion resources are. If you’re getting to the point where it’s just not possible to accommodate all of the patients’ infusion who are coming from clinic without giving very, very long flight path, and you’ve gone back to the clinic and tried to spread out those demand patterns a little bit, if it’s still too much, it might be the time as the very last resort to consider decoupling some more appointments. But generally, if you’re in the range of kind of 40% to 60% coupled of appointments, that’s very much something that can be accommodated in infusion. So if you’re in the 40% to 60% range, we wouldn’t really consider making that recommendation at all, honestly. It would really only be if you’re seeing very, very many linked appointments that we would even consider it. But even then, it would be the last resort recommendation after we’ve spread out the infusion schedules, elongating some flight paths maybe just a little bit, and going back to the clinic to spread out some of that demand if needed.
MODERATOR: Another question we received is, how has this improved delays?
SOFIA DEMARCO: OK. So the way performance– how much it’s improved delays has depended with our customers on where they are to start with. Some folks we’ve worked with have gotten as much as a 50% reduction in their infusion wait times. Others who haven’t had as big of a wait time problem have had a smaller reduction than that, but maybe they’ve been able to squeeze in some more patients as well. So there is a variation there. But generally when folks go with these optimal scheduling templates, especially when we’re incorporating the clinic demand patterns in them explicitly, we can get a pretty good reduction. So something in the 10% to 30% range would be pretty typical. And we have seen up to a 50% reduction in wait times in infusion.
MODERATOR: Another two-part question is, how well does. iQueue work with Cerner? And does or could iQueue link with the staffing scheduling system as well?
SOFIA DEMARCO: Right. On the Cerner side, yes, we’ve got a set of customers who use Cerner, and iQueue works just fine with it. So in Cerner as well, you’ve got a lot of flexibility on different ways to set up your templates. And the way we recommend setting up templates, based on duration and spreading out the start staggering them and also spreading them throughout the day, that can absolutely be done in Cerner. On the staffing side, we start with an input of your typical staff schedule, and we use those as a constraint that feeds into the templates to make sure that we’re not giving you more load at any time than what your staff can handle. Actually, it’s a roadmap item for us to take in data from your actual people manage– or, how do I say that– your payroll system perhaps or Cronos, whatever you’re using to track the hours that folks are working is something that we’re starting to do with a couple of institutions right now so that we can start to also track your patients per nurse actual. Not just what we think you’re doing based on schedule you have provided at appointment times. And also kind of measure the changes in that over time. So that’s something that we are working on incorporating right now.
MODERATOR: Does it work with Medatech?
SOFIA DEMARCO: Yes. So generally, you can work with any scheduling system. Because all we need to get the templates in place is to have a concept of duration and to be able to specify start times. And really, that’s a pretty basic concept or a set of concepts. Where the magic comes and is in the optimization that says, this is how many patients of each length you should special each time. So the math happens on the iQueue side. We come up with a set of templates. Those templates look pretty simple, and they only need you to have a concept of duration and for you to be able to specify how many appointments to start at each time of each length. So we’ve yet to see a scheduling system that we can’t work with.
MODERATOR: Last question. I think we’ll have time for is, what are typical scheduling windows between clinic and infusion that you’ve seen work well or would recommend?
SOFIA DEMARCO: All right. So the smaller your institution is, the tighter you’re probably able to get these windows, just because of less complexity and less variability overall. So we’ve seen generally on the shorter end something like a 30 to 60-minute window work well for some institutions. For others, it would be more like 45 to 90 or up to 120 minutes. That’s a pretty good range that tends to work, giving you both a certain amount of flexibility to get patients into the infusion schedule in a way that works well for infusion and still maintain pretty reasonable flight paths overall.
MODERATOR: Great. And for those of you who may have more questions or more questions on consideration, you can reply to virtually any email you get from Lean TaaS, and there is a human behind that email address who will be able to forward it to the right people or answer it right away. Also, if you need more information, there is a phone number and an email address at the top of your console. And you’re welcome to reach out to either of those at anytime. Thanks again for joining us for today’s webinar and have a marvelous day.