This deep-dive session led by Glytec CMO, Jordan Messler, MD, SFHM, FACP and VP of Customer Success, Dave Cooper, RN provides insight into the powerful capabilities of GlucoMetrics and how it can revolutionize your data analysis and decision-making processes.



Dr. Messler 

Hi, everybody. Welcome to day two of Glytec’s Time to Target conference. We're really excited to have this special customer-only session on GlucoMetrics, deep dive into our analytics, really a two hundred level type session, for all those out there, we're really excited to talk to our customers. You've been through our analytics platform, looked at our dashboards, I really wanted to take a chance to get under the hood. I'm excited to be joined by two of my close colleagues.

I'm Jordan Messler, the Chief Medical Officer at Glytec.

I also have, Dave Cooper. Dave, you wanna introduce yourself? 

Dave Cooper

Sure. Hi, everyone. I'm Dave Cooper, registered nurse, and the VP of customer success Glitec. Prior to joining Glytec, I worked in the ICU for about ten years and used Glucommander many of those years.

In my nearly eight years at Glytec, I've had the incredible opportunity of working with many of our different partner hospitals and many of you. So looking forward to discussing glucometrics and analytics today.

Dr. Messler

Great. Thanks, Dave, and also joined by Liz. 

Liz Kenney

Yep. Hi, everybody. I'm Liz Kenny. I'm also an RN-BSN.

But I'm additionally, the product manager of analytics here. Just quite the jump. I had a little bit of experience in the ICU as well prior to getting into technology, but there's nowhere else I would rather be. So I'm excited to talk through, some of the GlucoMetrics today.

Dr. Messler


So for, the next, forty five minutes or so, we have a few topics in mind. Thought we would take the first bit to really get a deeper dive behind our data. What where does our data come from? What's the data behind the data when you're looking at GlucoMetrics?

Dave's gonna take us through a case review using GlucoMetrics to really drive outcomes, and then we'll have a brief, discussion about what's on the road map. What's next? What other analytics can we add? And then we'll end our session with the Q and A.

So with that last piece in mind, please enter your questions into the chat. And we'll wanna make sure that we have time to get to your questions. Hopefully, we can get to most of them today. If not, we'll certainly respond after the session.

So for the first bit is the data behind the data. Really, a lift under the hood on GlucoMetrics, And I think it really will help you understand where our data comes from. And particularly when you're looking at other glycemic outcomes. There's a lot of different ways that you can get glycemic outcomes. Obviously, we wanna provide you data about your Glucommander population. That's most important to us is help you understand the value of Glucommander and where you can continue to drive improvement.

But it's not uncommon at your health system that you may have, glycemic data that your hospital delivers. And that's looking at your hospital population, your hospital outcomes, data provided, from your health system, from your hospital about various units, various areas. And some of you I know get other reports. The Society Hospital Medicine, SHGM, folks can sign up for their equip platform, get reports from them on glycemic outcomes.

We've heard a lot. We've had some great sessions here about CMS, glycemic metrics, eCQMs, Some are getting Vizient reports, and other reports from your glucometers. There's a variety of ways to get glycemic outcomes. And I show them up here as three apples because often, okay, I see a hypoglycemia rate from Glucommander, and then my hospital is giving me data and at initial pass, you might think that you're comparing apples to apples. And I wanted to talk through that you're really comparing apples to oranges, to perhaps pineapples when you look at some of this data, because the denominators in the populations, the inclusion, the exclusions might be different.

Now, certainly, you should be able to track the same type of data over time. If your hypoglycemia rates are improving with Glucommander, you'd like to see those same hypoglycemia rates improve when you're looking at hospital data. But you might not see the exact same number, right, if one percent in one population. It might be different than another. And I think the next few slides here will help uncover what we think about when we're looking at data and help you see when you're looking at other platforms where the data is coming from.

So we're looking at our pool for instance of hospitalized patients.

We're trying to understand those patients on insulin, particularly at Glucommander at Glytec. We have our SubQ and IV Glucommander. And we know about thirty, forty percent of patients in the hospital are on insulin.

There is a slightly larger subset to patients that are getting point of care This is a little bit larger. Maybe fifty percent of patients are getting point of care testing on blood sugars. We know that a lot of ICU patients are getting point of care done, even if the blood sugars are normal. I've been at health systems where for the first forty eight hours, almost every critical care patient is being monitored and many of them are now on insulin.

Once patients are started on steroids or tube feeds, maybe they're being monitored to see if they require insulin, or are just getting point of care. So there's this larger pool of point of care patients.

And then we know about twenty percent of patients that are getting that need insulin or in the ICU setting, about eighty percent in the non-ICU setting. You also have a group of patients in the, as well.

So you can categorize them by unit. For a brief second here, I thought I'd talk about ICU utilization. We know that the ADA guidelines recommend IV insulin critically ill with persistent hyperglycemia over 180. About twenty to twenty five percent of hospitalized patients who need insulin are in the ICU setting.

Of those ICU patients that are critically ill, probably about half of the meet criteria for IV insulin and it's certainly higher for patients that are getting bypass surgery up to eighty percent or even other surgical groups may be certainly higher than fifty percent yet only about ten to twenty percent of ICU patients who meet criteria are placed on IV insulin. There are studies that have looked at various populations. For instance, sepsis patients - thirteen percent of patients with sepsis who meet criteria to be on IV insulin are actually placed on IV insulin. So it's certainly underutilized for a variety of reasons won't get into that.

But about seven and a half percent of discharges could probably be on insulin. And then reality, again, it's much lower, probably a quarter to half of that. They're actually on IV insulin. So something to understand about the ICU population.

Alright. So we're looking at our insulin population, the point of care, a section in the ICU section, and the non-ICU, it's important to think about those unit breakdowns because if you're getting, for instance, SHM reports, you're not getting that data that's automatically generally excluded.defini

So you wanna consider how various reports are filtered out.

So of those ICU patients, maybe, half meet criteria often, even again, another half of that are actually on IV, not all the patients in the non ICU that are setting are on SubQ. Have these little green boxes for those patients that are just being monitored, and these are probably the more simple, simple BG cases, their blood sugars, you know, maybe at risk for, again, for needing insulin, but are simply being monitored. So we're having some of the lower blood sugars.

And then when we think about layering Glucommander, maybe you were picturing that, alright, that group that meet indications for IV, that group that meet indications for SubQ, are subsequently on Glucommander IV and SubQ. Although we know for our sites that have Glucommander IV and SubQ, there is a population I'll put here called “other”, that don't wind up on Glucommander for a variety of reasons. Sometimes it's a small segment, sometimes it's a much larger segment. And certainly, if you just have Glucommander IV, then this other segment is much larger in GlucoMetrics, our Glucommander treated patients, only include those Glucommander IV.

So if we look, further, thinking about these groups of patients that are placed on IV, Glucommander, placed on SubQ Glucommander, certainly has not just a bias, but those are the be be the patients that clearly mean indications to be on insulin. Those are certainly gonna by default initially have some of the highest blood sugars. And these other groups of patients that are just being monitored, were in place on SubQ Glucommander, are certainly more likely to have lower blood sugars. And when you think about the platforms and dashboards we have in GlucoMetrics, so the Glucommander IV, Glucommander SubQ, you could filter those populations, but that group is in essence the treated on Glucommander.

And then we have this dashboard, if you've been diving into our dashboards that we have as treated, untreated, dashboard.

As a reminder, this untreated is those, is our attempt to find a population with persistent hyperglycemia.

The definition is in essence that patients are having multiple readings over one eighty over the course of a few days, and they have not been placed on Glucommander throughout their hospital stay. So we're really trying to get almost the baseline population. We're trying to get a similar population.

But I wanted to highlight that, generally, they're not. They're not, this is our attempt to try to get that at. If you look at the treated untreated, the first place I like to look in that dashboard of what admission blood sugars are, and generally you'll see something like this. So those patients are treated, you know, against the bias.

Clearly these patients meet indications for basal bolus and are on SubQ. They tend to have a higher initial blood sugar than the untreated. So they're not really the same populations, and we're really a disservice to begin comparing those populations.

The other reason they're not really the same population, sort of a under the hood, under the hood, a really deeper dive in is that when we talk about Glucommander, or treated data, we're only accessing that information while patients are on Glucommander, at least in current state. This is what's happening. So a patient, another way to look at it, a patient's got this gray bar of a hospital stay that come into the ICU. We got initial blood sugar. Hopefully, you're getting them on Glucommander IV or SubQ promptly, then while they're on Glucommander IV and SubQ, we have that data that we're analyzing.

And then if you stop Glucommander early, not perhaps optimally using Glucommander SubQ, then there's some blood sugars where perhaps they're really normal after Glucommander that we're not capturing. That part of the stay, again, at least in its current state.

So I wanted to highlight that because then there's a segment in addition to the Glucommander entry BGs in the data, there's some other BGs that are not being captured in current state. All that to say if you're using our treated untreated dashboards, they're not really similar populations, and it's not really a good strategy to compare their outcomes.

It's something that we've, you know, learned over time.

Some sites, it may be fairly helpful if you're under-utilizing Glucommander, but particularly when you're using it more, particularly if you're just IV Glucommander, it's not really a comparable populations. So and that's that vein we're sharing with you today. They're not the same groups. We're gonna temporarily change how you access the untreated report over the next month.

We don't have a set date just yet. But over the next month, you'll be talking to your customer success team. They will have access to this data still. So you can still get access if you're using these reports now.

We want you to still have access to the reports, but we think there's a real opportunity to give you better information to really enhance this updated, make it more valuable. So we wanna take the time, to do that. So it's really valuable, to all of our sites. So you'll still have access.

You'll just have access in a different way. And more information to come, on that. We wanna make these, really helpful. And you really need that context to make them helpful.

So if we're looking at, another category, let's say the CMS glycemia measures, another thing about apples to apples, good to understand as you're thinking about, you know, what your eCQMs for hypo and hyperglycemia will look like when those are, some of you may be gathering now, but when they're ultimately again, they were they're sent to CMS, in in February, March, and they're actually reported next October.

So if you have Glucommander IV, for instance, patients on insulin recognize that if you're only using Glucommander IV and you're looking at hypo data, it's just the segment of the patients that require insulin. If you're using Glucommander SubQ, it depends how much you're utilizing it to really get the story of what your CMS measures will look like. If you're using off Glucommander SubQ optimally and really getting up to that eighty, ninety percent of non-ICU patients that meet criteria, then Glucommander IV and SubQ data may tell you a pretty reasonable story of what your CMS measures will look like for the whole hospital.

Certainly, we wanna tell better stories and we're actively working on it. So if you look at these pools of hospitalized point of care patients, if we had ordering data from hospitals, which we're, which we're working to see how we can add that to GlucoMetrics. Then we can tell more precise stories. You can have the story of Glucommander, you can have the story of patients not on Glucommander begin to filter those out. And that's what we're thinking about for the future. How can we tell better stories of those patients that are on Glucommander, not on Glucommander, capture all the data we want, maybe even begin to filter things down to different populations.

Such as CT surgery. So all things that we're thinking about, to be able to tell better stories. And for, obviously, if you're only IV, you're really getting a limited story. But even for those patients not on Glucommander for IV only sites, we think, there's a future state where we can tell much better story. Alright. So the bottom line for this piece is that treated Glucommander, which is our primary focus, really understand what's the value you're getting for putting your patients on Glucommander, seeing that seventy to ninety percent reduction in severe hypoglycemia for Glucommander IV seeing that fifty to seventy percent reduction, for severe hypoglycemia when you're on Glucommander SubQ. How can we tell you that your story better that you're seeing that value?

So we wanna continue to do that. Certainly, if you have higher utilization of IV and SubQ Glucommander, then you're gonna get a more comprehensive story of what's happening at your health system. As mentioned, that untreated story in current state, the intent again is to find that similar population that's not on Glucommander, but in essence, it's not really the same population. A baseline population is really what we wanna compare to.

So in the meantime, we're changing how you access it. You can still have access you report, certainly currently, it hasn't changed. Probably in the next month, it'll change. And at that point, you would just simply contact your customer success manager.

If you want those, while we work to really enhance and update it. And if you are using Glucommander, GlucoMetrics, treated data, and you're also looking at your hospital data reports you're looking at vision data or the eQUIPS database, keep in mind that they're they're different populations at times. They're different filters, they're inclusions and exclusions, even how you calculate some of these outcomes are slightly different.

You know, remember, and I didn't go through it all today, but we report as percent BG's as a denominator as percent patient days as percent patient stays.

So you wanna make sure how the denominator is created. What's the population, and you really can't compare the exact numbers. Again, if trends are coming down, for hypo and hyper rates in one data set, they should also be coming down in the other data set, but you can't compare the exact numbers. And then we talked a bit about CMS. We didn't go through details. We had a great talk yesterday from Dr. Maynard about the details of eCQMs but they'll have other inclusions, exclusions that we don't include in current state, but I'll showcase, a little bit later in this presentation, where we're thinking about, being able to share a CMS type dashboard that will eventually include some of those, get closer to how CMS is calculating.

So hence not always apples to apples. So keep that in mind when you're looking at other data set. And I hope that really is helpful to get a deeper dive, a bit under the hood, maybe spark some questions, put those in the chat, and we will try to get to them at the end of this session. So now I'm glad to be able to hand off the next section of this presentation, to Dave Cooper who will give us an outstanding case study using glucometrics.

Dave Cooper

Thank you very much, Dr. Messler.

There are a number of different ways that you can use GlucoMetrics. You can see how different areas are trending, how good outcomes they have. You can also compare to other Glytec sites by looking at the Glucommander average, or you can do comparisons within your own system by looking at like either facilities or like units. So not only does GlucoMetrics allow you to understand current state, but it can also help you identify where there are opportunities for improvement. So where you can focus your optimization efforts to get those outcomes that you desire.

So I wanted to share one potential use case that we've seen from GlucoMetrics. And it may not apply exactly at your organization, but hopefully it can give you some insight into ways that you can dive into the data. So for the purposes of our case study, we'll say that I'm a nursing system leader at an organization that has two different facilities, and I'm interested in improving our system’s hypoglycemia outcomes.

So I'm starting on the patient days dashboard. Although you could probably tell a similar story if you use the patient dashboard and used patient as that denominator, or if you wanted to look at it in terms of percent of BG's actual BG events and use that as the denominator. But I'm gonna look at percent of patient day outcomes.

And before I get into the data, I'll quickly orient you to this dashboard. At the top, you'll notice there are some different tabs or workbooks that have a little bit different information.

Right here on the top left, I can see my total number of patient days, and then I get some system outcomes right below that. And then below that, I have my filters, and I'm just leaving my filter on the default for the last twelve months. So I can look at my look at all the data for the last twelve months.

Up here at the top, is a way to compare the previous two months, the previous two quarters, or the previous two years, And you can see how those metrics have changed. So it's a really quick way of identifying how you're trending for percent of patient days less than forty or, percent of patient days in range or your hyperglycemia greater than three hundred.

Moving down to the center of the screen. I get my outcomes, hypo, and hyper outcomes by facility. And this is where I wanted to focus in. So for our demo, I only have facility named by number. So facility five twenty three and facility two ninety two. But one of the things that sticks out right away as I compare these two facilities for their hypo outcomes is when I look at less than forty, they both have pretty similar outcomes.

But when I look at their less than seventy hypo results, now I start to see some significant differences where this facility five twenty three is around eight percent and facility two ninety two is around three point one four percent. So it is you know, this facility five twenty three has over, double the less than seventy hypoglycemia is the facility two ninety two. So that's what I'm gonna dive into in a little bit more detail. The other thing I'll point out before I move on is that I do have the ability here to see the Glucommander average. So I can tell that facility two ninety two is below the Glucommander average and facility five twenty three is well above the glue commander average. So that's the comparison to all of the Glytec customers that use Glucommander.

So I'm gonna go ahead and move to the facility workbook now and get a little bit different information on this workbook. I'll point out down here in the center. I have this great scatter plot, which really gives me and this was what Dr. Maynard talked about yesterday.

The balancing comparison of the hyperglycemia greater than three hundred and the hypoglycemia less than forty. And you can also get a quick snapshot of where the Glucommander average is for those two metrics also.

And up here at the top, I have a table for my percent of patient days outcomes by facility. So it's some of the information that I visualized on the overview dashboard, but it gives me a little bit more detail. And then off to the right, I have my percent of patient days outcomes by target range, and I'm gonna come back to that in just a minute.

But I'll point out here that I can see that my total treated patient days at both facilities is almost the same number. So they almost have the exact same number of patients at both facilities. And like I mentioned earlier, if I'm looking at severe hypoglycemia less than forty, I have pretty similar outcomes. But as I move to less than fifty four, now I'm starting to see a difference where one is two percent, the other one is point six. And then for less than seventy, we're noticing that difference I saw on the overview dashboard. So I'm gonna go to the next workbook, the unit tab, and dive into this a little bit more.

I'll note when you go through different workbooks within a dashboard and GlucoMetrics, whatever filters you set stick. So I haven't changed my filters. I'm still in the last twelve months.

On this workbook, I can see those outcomes at a system level over here on the right. So this is a quick way of knowing what the system is capable of. So I know my system has less than seventy outcomes of five point five eight percent. So I know they're capable of that.

Over here in the center, now I start to see the breakdown by unit categories. So where are we using Glucommander and one of those outcomes for those different categories? So the top area where Glucommander is being utilized is the ICU with six thousand eight hundred and fifty two total treated patient day. So because of that, I'm gonna go ahead and filter for just the ICU so that I can focus just on the ICU.

Now, when you go down to the center of the page, I can see my different specialty areas. So different types of ICUs or different types of ICUs that are using Glucommander. And I can see that cardiac surgery by far has the most number of total treated days. So they are definitely high users of Glucommander and I get those outcomes for my cardiac surgery areas across the system. I can see that the cardiac surgery in general is at six point zero three percent. Well, down below this table, I have the breakdown at a unit level and because this is a demo environment, my units are numbers, but typically you're gonna see those units labeled with what type of unit they are. So for the purposes of our demo, I know that this two ninety two point two five is a CV ICU.

At one facility. And I know this five twenty three point five unit is a CB ICU at another facility. So I'm gonna go ahead and filter to just those two units and look at the information that way.

Okay. Now that I've got two CV ICUs. So we’re similar patient types for both of these two CBICs. So you would expect similar outcomes, right?

But and they treat a similar number of total patient days, but look at the difference now that is driving some of those differences we saw at the facility level. So when I look at less than forty, this, CV ICU in the two ninety two facility has zero in the last twelve months. If I look at less than fifty four, it's point three three. And then when you go up to less than seventy, those differences become even more stark because I have one unit that is about four times better performance when it comes to less than seventy. Than the other unit.

Now we know with hypoglycemia, there's a number of variables that can affect like renal issues, liver choose critical illness, sepsis, you know, lack of a dextrose source. But those, you know, you could investigate those things, And that's probably a good idea, but that would require some chart audits.

There are however some insights you can get in GlucoMetrics without having to do those chart audits.

So next, I'm going to move over to a process metric that we have in GlucoMetrics, the blood glucose measurement, time unit, dashboard.

And we know that when you are treating patients with IV insulin, it's really important to check that blood glucose on time because you don't want the patient to get more insulin than they need for longer than they need. So this dashboard is a really important process metric to let you know how your staff, how your nurses are doing in checking their blood glucose is on time. So the first thing I'll point out when you go to this dashboard is that on the top left, you can see the average response time for the whole organization, the whole system. And so we know now that the system is capable of achieving almost seventy two percent.

That means almost seventy two percent of the time they're checking blood glucoses within ten minutes of when they're due. We do, allow ten minutes past the due time. And then twenty eight percent of the time, they are delayed.

Because I'm focused on those two CV ICUs, I'm gonna go ahead and filter to both of those units again.

Now when I apply that filter, and let's just see how both of those two units are performing. Now remember my system, the organization was at seventy two percent response time, average, BG check time. Now if I look down here on the table on the right, I get the results for both of those units. And you'll notice that one unit, the CV ICU that is in the five twenty three facility. So this is the same facility that had the worst outcomes for less than seventy has an average of fifty three point three percent versus the two ninety two facility has an average, VG timeliness seventy nine point three percent. So one is above the system average, and one is below the system average.

And those differences are pretty, stark. So just to recap a little bit, I've started at a system level looking at two different facilities.

I narrowed it down to the ICU, then I narrowed it down to some of my high utilizers, the CV ICUs.

I saw some differences in outcomes at both of those CV ICUs, then I was able to go into this blood glucose measurement timeliness dashboard, and now I see a variation in process metrics between those two units also.

But before we wrap it up, there's one other thing I wanna look at. There is another process metric that's available to you in GlucoMetrics. If you, I'm going back to my patient days, dashboard, and I'm gonna go to the facility workbook. And I mentioned it at the beginning, but we have this percent of patient days, glycemic outcomes by target range. So how's that a process metric? Well, that gives you insight into ordering information. This tells us how the providers are ordering Glucommander for the different patients. Now I can't see when just looking at this which area is using which target range, and I remember my filters stick on the dashboard, so I still have it filtered to just those two units.

But if I filter down to a specific unit, then I see that one unit, one of these CV ICUs is exclusively using the hundred to one hundred and forty target range, and the other one is using the one twenty to one sixty target range. So the outcomes that you see here, by target range are consistent with the outcomes that we saw by both of those units. One is, you know, considerably higher rates of less than seventy hypoglycemia.

So to wrap it all up, I was able to identify some very different outcomes around hypoglycemia.

I was able to identify some process metric differences. And in working with the Glytec CCSM, the hospital was informed that this outcome for the one hundred to one hundred and forty is much higher than, Glytec is average across different CV ICUs that are using that same target range. So we know that even it's not a target range issue per se. So the hospital leadership decided that they're going to focus on improving IV BG timing first. And then after spending some time trying to improve that process metric, we'll see what the outcomes are. We'll see what the results are and maybe then evaluate whether a target range change is necessary.

And as part of that, follow-up, the leadership was is able to set up subscription so they can get that process metrics and the outcome metrics push to them via email on a monthly basis so they can see how they're training to achieve those goals and achieve that goal of reduced hypoglycemia that they're after.

So I hope you can see within just a few minutes into GlucoMetrics, it provides you a vast array of data that you can dive into to not only understand the current state, but to really help you focus your optimization efforts and achieve those outcomes that you're after.

Now I'll pass it back to you, Dr. Messler.

Dr. Messler

Thanks so much, Dave. I really was a terrific case study. I think you could see as Dave goes through that case study how getting those kinds of insights can really take a lot of time, chart audits, if you didn't have access to this type of data. Now we know there's other processes, other things that may contribute to hypoglycemia, but Dave walking through the data showed how a site can get some low hanging fruit to really drive change, follow-up after they work on their BG timeliness, and then see if that solves a problem or if there are other pieces that they have to, look for, to understand their hypoglycemia rates.

Alright. I thought I'd, we thought we'd continue, discussion about GlucoMetrics with Glucommander, really talk a bit about the metrics that we have available and why we have these metrics available and what's coming next.

The space of metrics in any field can get quite complicated. You heard Dave mention a few times about process metrics. Generally, when you're dealing with these metrics, you're thinking about outcomes, we're showing your hypoglycemia or you're in range, you're hyperglycemia range. But if you really want to improve, you need some of these process metrics like ordering data, utilization, BG timeliness, three great process metrics that we have in current state, and I'll share a bit some other process metrics that we're thinking about in future state.

Structure measures are those ones like do you have a committee. You have those policies and protocols. You have other pieces that help enable the outcomes that you want around glycemia, the outcomes, the process we talked about, and you saw those scatter plots that we have to try to illustrate the importance of balancing, looking at hypo and hyperglycemia. And then there's numerous ways to present the data, which could be, you know, quite challenging to see how we can get the right visualizations to get the insights quickly. So when you're dealing with data, we think about taking this data, how do we process this into information and really getting the far right side?

How do we get the insights? Like you saw about BG timeliness in this case study? And getting those steps that we can really make good decisions just through the data. So you don't have to spend months auditing charts, months to try to understand the value and the processes that you might need to improve.

And that's really what we're trying to think about.

As you think in current state and your health system, particularly around glycemia, often they're underwhelming reports. Often don't have too much information around outcomes and processes at your health systems. We hear from sites that they may only have just a handful of measures, maybe just severe hypo, moderate hypoglycemia, often just presented in one format such as percent BG's. And again, we know CMS, Society Hospital Medicine recommends looking at patient day and patient stay, which are more complicated to calculate. And often very few filters. We've seen some sites that have more robust dashboards, but often very difficult to get to access to this kind of data for a variety of reasons.

So we've been thinking about data, and I wanna illustrate just to remind the variety of metrics we have, and we know sometimes it can seem like a lot, and that's certainly a goal of ours to continue to show the value and really tell better stories. But we've got numerous metrics that we provide in GlucoMetrics. About utilization, utilization over time. Again, thinking about the various ways that the complicated calculation, so we share the data as a percent patient day stay and percent BG's.

Most of the clinical value is around that patient day. Some of them can really be insightful as a patient stay, and particularly when you're talking others about the number of patients that had a less than forty. It's sometimes easier to understand. We know CMS again is showing that patients stay less than forty.

The patient day greater than three hundred. So you could see how the dashboard should be, even combining some of these. We're giving those outcomes by target range, the BG Timeliness, saw in the case study, we also are able to share with you average admission and discharge BG for patients on Glucommander, the average BG during the hospital stay. Number of BG's, And then even outside of GlucoMetrics and Glucommander reports, there's other ways to get to some real time data about patients with a variety of filters, time to target hypoglycemia recheck So really a lot of of data information, and I hope you see in the case studies how to really get those insights.

And numerous filters around date ranges, treatment types, unit facility, category specialty to really get those insights.

So some of this can be a bit overwhelming. So what are we thinking about next? We wanna do a better storytelling with the data, and I'm sure and a variety of areas throughout your hospital.

You may have a lot of data and it's hard to get those insights. So we're really thinking diligently how we can improve the storytelling with the data to really understand the value you're getting with Glucommander IV and SubQ, how you can translate that data to actionable insights. So in a few clicks, you could see alright unit a. If I just improve BG timeliness. I might reduce my hypoglycemia.

How can you do that without having to do numerous chart audits?

I'll show you a preview in a second of our simplified overview page. We've got a GlucoMetrics overview page currently.

We really wanna distill the information into meaningful information. So we have a GlucoMetrics hub page we'll share with you that is really trying to give bottom line metrics, improved analytics flow, to take you to the pages more easily through clicks.

We wanna partner with sites soon to really develop a better baseline data story, mention that that untreated story we want to improve, and really we want to tell a better story prior to having Glucommander, so you could really understand that the the the impressive hypo and hyperglycemia reduction that you're having while using Glucommander.

CMS metrics. We know, our sites, you're thinking about those eCQMs.

So the less than forty patients stay, the greater than three hundred patient day which have a variety of other inclusions and exclusions.

So we're working. The analytics team is working hard developing a dashboard, those glycemia insights, and I'll give you a, version one draft of that. Wanna improve the hypoglycemia story. That BG timeliness in a story that Dave told is one process metric contributing to hypoglycemia.

We're working on developing a story around SubQ, utilization, basal bolus, and we know patients with renal issues are at risk for hypoglycemia.

So we've been able to share, for instance, with sites in the past, that when you're using custom ordering, you're using perhaps not perhaps the not the right target range the right total daily dose that you want or a target range that's different than you recommend that you have a hypoglycemia rate that's much higher when when using what's recommended to start patients with renal issues. How can we tell that story? A hyperglycemia story that may exclude that first day that you have that greater than three hundred and recognize that almost every day afterwards, I probably can get that hyperglycemia greater than three hundred close to zero percent. And again, as mentioned, improving that untreated story. So those are things that we're thinking about to give you a preview of a couple of these. One is this GlucoMetrics hub.

I won't dive deeply into this, but the premise here is to really give bottom line information about utilization over a time period a month over perhaps comparison to the prior year. Those glycemic insights that are most important, the hypoglycemia less than seventy, greater than one eighty, patient days. You can get a run chart over that. You can see in the middle on the right, and then some other snapshot metrics.

And then we're hoping within this, well, two, a couple of things - we're gonna minimize the filters to get that right information and then be able to click out of this. So you want more details on utilization. You would click through here and get right to the utilization page. You want more details about moderate hypoglycemia.

You'd click through here and get right to that page. So those are things that our super superb analytics team is thinking about. We got a draft CMS dashboard that we've been working on. Here's a screenshot of that.

And again, these will all be dynamic, like you saw when Dave went through the case study to get insights with hovering as well. But how can we really tell you that? Alright. The less than forty patients day, the greater than three hundred patient day.

This is a draft for the hypoglycemia portion of the future eCQMs and what a future version of this might look like. So really thinking hard to, you know, respond to what we're hearing from our sites, what kind of data is informative, really to distill the information, show the value, get insights around the CMS dashboard as well.

And if you need more resources, we'll, again, we'll be here in a minute to answer some questions. But just as a reminder, we have a lot of resources about GlucoMetrics. Your customer success manager can help get you access to our GlucoMetrics hub, where you have your variety of tutorials, and information to really deep dive into what is available in glucometrics.

So our our amazing analytics team, Liz, our product manager, a shout out to Andrew, April, and So Ling, and and others on the analytics team really have been working hard to make a product that's useful to really show value and drive insights and really continuing to improve really exciting for the work that's been done and will continue to be done.

So for that portion, from Dave, myself and Liz, we'll stop here. And, I think we have time for, some questions. And I'm seeing in the chat here, a question.

I think, sites are asking, how do we get this… be for you, Liz? How do we get access to GlucoMetrics for folks that don't have access yet. 

Liz Kenney

Yep. So the first step would just be to reach out to your CCSM and they could get you started on that process us.

Dr. Messler


I see another question coming through. Do my hospital executives, entire glycemic management committee need access to GlucoMetrics to see these reports?

That's probably, Liz, you wanna take that one too? 

Liz Kenney

Yeah. I can take that. So, anybody who wants access to GlucoMetrics within your org is more than welcome to get a login, and an account we can get that to them.

If they don't necessarily want the account, they just want to receive reports.

There's a subscription feature that we offer but, they would only be able to receive that if they were an account holder. You can still provide those if you are an account holder to anybody within your organization that you so choose.

Dr. Messler


Dave, there's a question here that says will Glytec assist with reviewing and interpreting the data with our facility.

Dave Cooper

Yes. Glytec would be happy to talk to you about your data. I would say just reach out to your clinical customer success manager.

Dr. Messler


I'm seeing some other questions. So here's one that, someone was asking about patient days as opposed to percent BG's.

I think I can say a little bit about that.

So as mentioned, there's a variety of ways to look at glycemic data where the denominators, the percent BG's. So why do you use that data? That's often the simplest to calculate, but often really doesn't tell a very good story. There's a lot of factors.

You got patients that are in the hospital for a long length of stay. There's a variety of reasons where it's really not informative. We had a session yesterday, catch the data detectives where we talked about how percent BG's can appear to be a very low number. But actually hiding since the denominators can get so big, actually hiding a numerator that can be a thousand less than forty where it looks like, oh, point one percent.

And, evaluation of GlucoMetrics and a variety of studies showing that there's more value patient days, accounting for some patients that can have numerous BGs throughout the day like on IV insulin, and and why the value of, really making that denominator patient days, can be more clinically relevant.


Let's see here. I think we've got time for another question.

I can read one off here if you guys see one. Let's see. There was a question.

I think we're running out of questions. If you guys see one here, 

Liz Kenney

I saw one that I had sort of alluded to, for how to receive reports on a regular basis. So just to kind of go back to that, we do have a subscription feature that account holders can click on. So you can subscribe to it on whatever cadence that you would like. And it will come directly into your inbox.

Yeah. There's really some great tools within our GlucoMetrics to be able to, right, push out dashboards. You've done some filtering. You like a certain dashboard to push that out into your inbox regularly. And then others that have access to glucometrics, you could even share things instantly.

Dr. Messler

Mhmm. Fantastic.

I think that's, I think all the questions that came through.

From Dave, Liz, myself, really, really appreciate the hard work that you all are doing. I hope you find getting into glucometrics as fun and as exciting as we do, really discovering new insights with the data and, wanted to let you know that we really continue to work hard on how we can improve these data stories so you can really show to your institution, the value getting out of Glucommander, and then continue to improve those processes, to really optimize Glucommander as well. So thanks again for your time. Stay tuned for other, fantastic sessions today at day two of our Time to Target conference.

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