Hello everyone. Well I want to thank Dr Miller. That was an amazing, incredibly informative presentation. Now I'm gonna go through a step by step approach for launching and maintaining a C G. M and slash PGP based management program and the family practice and specifically how do I work with my available resources and what do I need to know to incorporate C G. M. And a G. P into my practice and clinical and patient workflow. My name is Diana Isaacs. I'm an endocrine clinical pharmacy specialist. I'm also the continuous glucose monitoring program coordinator at the Cleveland clinic, endocrinology and metabolism institute. And although a pharmacist I do have a scope of practice with a collaborative practice agreement where I can prescribe both new technologies and medications. In terms of our learning objectives for this presentation, we are going to describe barriers to incorporating C G. M. Into family practice settings. We're gonna discuss real world strategies to overcome these barriers to see GM use and family practice settings and then I'm gonna go through what's called the identify configure collaborate framework and how this can be used to address many of these common barriers. So I love showing this visualization of just how impactful C g. M is compared to maybe traditional B g. M blood glucose monitoring And what you're seeing here are four dots four times when a person is doing a finger stick. And of course we're lucky if someone comes in and they're checking for finger sticks a day often we don't have that much information but if I were to look at this I would say okay these are all appear to be in that target range of 70-180. Now, what happens when you put C. G. M. On the person? Well now you see everything that's happening in between those dots and in this case we see that there's undetected hyperglycemia. But what is especially alarming is this undetected hypoglycemia? And unfortunately this instance is very, very common often in our quest to help people reach their A one C targets. It's common that we escalate therapy. We might go up on the long acting insulin not realizing we may run into over base realization where someone's getting too much long acting insulin and actually experiencing hypoglycemia. Which can be dangerous. In addition to uncovering the glucose patterns glucose variability, hypoglycemia. We know there's tremendous benefits in the real time data for patients. So all of the C. G. M. S have the ability to show the current glucose reading as well as the trend arrow. So this is so impactful because you can imagine if someone is getting ready to go in a car or go exercise to drive that seeing that arrow and understanding if they're going to be dropping in the next 15 or 30 minutes they can take action versus if they don't know or they just did a finger stick and saw 108, They may think it's safe to do these activities. Most devices also have the option to set alerts where you can actually have an alert in a certain high or low threshold and then some also have the option to have predict alerts which can be really helpful and so not surprising there's a lot of great outcomes that we see with the C. G. M. S from randomized controlled trials and some of those include things like reducing episodes of severe hypo and hyperglycemia which lead to decreased emergency department visits and hospitalizations. We see that there's increased time and range and reduction in A one C levels which contribute to better long term outcomes. However, we also know that there are barriers to implementing C. G. M. U. S. And clinical practice. I think the data is clear on the benefits but it's not always so easy to implement a new technology into clinical practice. I've split this up into three different areas of barriers. The healthcare professional side, the patient side and then systems itself. In terms of the systems, there are logistic issues even though it doesn't seem so complicated figuring out the logistics of a new workflow can be challenging a big barrier that we face is the integration with the electronic medical record. There are different platforms for all the different C. G. M. Systems. Yes there are some that will incorporate multiple but there's a lot of different data platforms and ideally we would have all of this stream into the electronic medical record where it would go into flow sheets and we could just everyone would be seeing the same information, the current state is not that there are some systems that are starting to integrate but currently what we're usually having to do is log into a separate system and then paste that information into the EMR which is obviously adding different steps to that. Also in terms of the health care team, patients receiving their care from different services and everyone having access with these data platforms, there can be different clinic user names and passwords and different clinic sites and making sure we're all able to access that same information and then therapeutic inertia. Just sometimes it's easier to just keep doing what we're doing just rechecking that a one c every three months versus now having all of that new information can be a lot to incorporate. Now on the healthcare professional and patient side there definitely can be tech aversions anytime you're adding something new that can feel scary or that can feel hard on the patient aspect. We do see that some patients really, they don't want people to know they have diabetes and so even the act of wearing a sensor can feel uncomfortable. Especially for example it's a sensor that's one in the back of the arm and in the summer they like to wear a sleeveless shirt so that can sometimes be a barrier in terms of cost and access, we know that sometimes the cost is difficult for people to afford or on the flip side the access in terms of figuring out on the healthcare professional side. Well how do I prescribe this and then once I prescribe it what type of education and training does the patient need And how do I make sure the person gets that training and then what do you do with the data? So interpreting the data and being able to do it efficiently in your workflow versus spending an hour looking at a report and then on the patient side to understanding what the data means because patient visits even if they're coming in once a month which most aren't coming in that often but still they're spending the rest of the month by themselves looking at the data and so understanding what it means can lead to more action and more improvement and time and range. We also know there's unfortunately a lot of disparities there's disparities and diabetes and we see disparities in C. G. M. Use. I've listed some statistics here. So 65% of black and hispanic patients compared with 79% of white knew that Medicare helps pay for diabetes testing supplies and self management education. So it's kind of like well why why is that barrier? They all have that benefit But why do less? Underserved minorities know about that. A retrospective chart review showed that 30% of black and 32.5% of Hispanic patients initiated C. G. M. Compared with 54.3% of white patients and then among Medicare beneficiaries who acquired a C. G. M. Device in 2020 and that was over 3000. There was a significantly lower proportion of C. G. M. Use by black and Hispanic beneficiaries 30000.5 and two point 9% respectively compared with 91% white and then other 5.6%. So these disparities exist and we have to ask ourselves why and are we possibly contributing to these disparities? So I don't mean to be all doom and gloom. We have solutions for these various barriers and one potential solution is really adopting what's called the I. C. C. Framework which stands for identify configure, collaborate and this is a framework that was originally developed by the Association of diabetes Care and education specialists to really overcome common barriers to technology use and therapeutic inertia. And there's really three steps to this. The first identify is identifying the right technology for the right person at the right time, understanding that there are differences between devices and so we don't just want to throw the same device that every person we want to individualize it based on the person's unique needs and desires. But that's not enough. It's not enough just to give a device to a person. The next step is to configure that device according to the unique user preferences the treatment plan and their support and that can include things like setting customized alerts and reminders and then collaborate. So we take the rich data that we get from these devices and we talk about it we have data driven conversations that include shared decision making and that can include medication adjustments and lifestyle interventions to really optimize the time in target range. When we think about choosing a glucose monitoring device, there's definitely several considerations that come into place. One of them might be the frequency of sensor change. So devices range from 7 to 14 days. There's also an implantable one that only has to be changed every six months. So that may be very important to a person how frequently they have to change it out cost of course is going to be a big one. There are certainly differences in terms of cost or in terms of insurance coverage and what is paid for compatibility is a big one. There's actually a lot of mobile applications that work with some devices and not with others. There's also insulin pumps and connected pens and depending on which one they work with certain C. G. M. Sensors. So knowing that can help individualize the choice, some people are very concerned about the size of the sensor and smaller. Fortunately they keep getting smaller and smaller but that is an important consideration as well as the sites for example that they can be worn although in clinical practice often we do use off label sites which can work well for for people and then accuracy of the sensor. Certainly in some conditions like dialysis. Sometimes we worry about the accuracy and we might need to do more finger sticks and then different devices have some differences in terms of the predictive alerts, how the alerts work. And so that customization can be important for people. And I've listed a couple examples here. So the first person says you know I don't want to have something attached to me. That might be a good person for the implantable sensor because it's very easy. There is a transmitter that goes on top but it's very easy to remove it. Or alternatively this might be a person that just sticks with blood glucose monitoring and truthfully that that can be okay to the second person says well if I could see more information I think I'd feel motivated to take my meds and eat healthier. So clearly in this situation the person really would benefit from C. G. M. And then we can ask some follow up questions to determine what might be the best device. There is a great tool to help you help your patients identify the most optimal device. This is called diabetes wise Pro so there's actually a diabetes wise that's for patients and this is a site that I share with my patients all the time. Often I will introduce the different options and then say hey go to this website, check it out for yourself. There's actually a questionnaire that they can fill out that will help guide them to what might be a good device for them. But now there's a professional version where HCPS can go to to basically get free unbranded, non biased device information. And what's nice about this is that it's not industry funded, not that there's anything wrong with industry funded. But the idea here is that it's offering all the options in a very objective way to learn more about that and match the device to your patients to determine the best the best one for the patient. There's also steps for prescribing because sometimes that's confusing. Like what things do I need? How many sensors is there a separate transmitter? And this really helps us. So thinking about this I. C. C. Framework, this is really helping to fulfill that identify step. And then I just included a screenshot here. So you can see this is the device library and you can actually click on different device details to learn more about it. So I put it on the overview but you can look at affordability and access, data monitoring options, the data viewing options, duration and so storage vision, auditory dexterity issues. And then there's even additional patient considerations that you can click on like if someone has a very active lifestyle or someone wants a fewer finger stick. So a device that has less calibrations. All of those are kind of options to go through on here. So we talked about identify next step is configuring according to the person's unique needs and preferences. And here is where we can set different high and low alerts. We can possibly set rise and fall rates depending on the device. Free frequency of reminders. Um And also sharing data to so many of the devices. If you're using a mobile the mobile phone app you can choose to share it with friends, family caregivers but some people want to share in some don't they want to keep their data private. Um And I've shown an example here too because these alerts could drive people crazy if they're not customized correctly and that's probably what I see people discontinue C. G. M. It's often because they're alerts are just not optimized. So for example having a high alert that is set for 200 that for many people is just too low especially when you're starting see Gm it's really easy to hit 200 every time you eat. And the last thing you want is someone has a bedtime snack and their C. G. M. Is going off all night long. So Really working with a person and these are things that may change over time as you increase time and range and get closer to the targets. We may set that high number lower but initially I like to keep it high or I like to even turn it off at first and then similar with the low alerts to. I've had many times we'll use 70. But I have some patients that really want to know earlier at 80, even. So we can really customize those for people. And here's some examples of uh that configuring. So in terms of sharing data, the first example says well I want my wife and kids to see if I'm having a higher low blood sugar so they can help me if I need it, especially if I'm out of town on business. And it's really nice if a person goes out of town they can still share with people from all over the country or the world even to be able to see that and check in on them. The second example about alarms, sleep is really important to me. I heard C. G. M. Buzzes and beeps all night. I do not want anything beeping at me during my sleep. I have always been able to feel my lows. And so in that case we might set that low really low or even turn it off one note is that when you do use the mobile phone apps there generally is a 55 alert that you can't shut off. Although usually if someone hits 55 you would want them to know that. So that's okay and then reminders. Well I get so wrapped up in what I'm doing. I forget to check my glucose or take insulin. I could use a reminder. So in this situation we might set it to remind to go ahead and look at that glucose value to see what it is or a reminder to bullets with insulin for mealtime. The third step of this I. C. C. Framework is that collaboration. So and I wanted to highlight this quote that comes from the american diabetes association standards of care that says no device used in diabetes management works optimally without education training and follow up. And so this is really really important that we don't even though yes the devices are not so complicated. We don't just want to throw devices of people we want to provide that education so they can use it optimally and increase their time and range. I've included an example here. This was a real patient of mine of course I changed the name and that's not her real picture. But this was her report. And basically she was given a C. G. M. But not educated on her glucose targets and she'd been wearing it for three months and then came to see me. I was not the one who originally started her on it. And this was her report. And so she's 2% in the target range. Her G. M. I. Which is that estimated a one C. Is 12.1%. She's averaging 3 68. But the problem here is that that was going on for three months and she did not she did not know that this is not what her targets were. She was actually very engaged with the device. This is a scanning device and she was scanning very regularly but didn't have the insight to know this is not the goals and she should reach out to someone on the team to address that. And so that that is the sad part here. Not getting that. Education to do that. So speaking of that there's actually 42 factors that have been described to impact glucose levels. And this is part of the fun of the data interpretation is figuring out kind of like a puzzle well which of these factors might be impacting glucose and how can we address it? And definitely the common ones food wise, right carbohydrates are going to increase glucose exercise we generally expect often will decrease it. Insulin medications will decrease it. But it's actually much more nuanced than that. I mean there's even impacts from fat and protein on glucose, alcohol can really go both ways depending on the timing and the type of alcohol and even activity levels depending on someone's fitness. The type of activity it sometimes might initially rise and then fall all this to say that it involves a discussion and spending that time collaborating and kind of figuring out the puzzle is part of the I guess the fun of it. The fun of the C. G. M. And figuring out who on your team would be best suited to do this. So speaking of that the what the data really allows us to do is to be able to make adjustments to be able to modify therapy to increase or decrease doses. And what we find with C. G. M. Is that the technology itself is helpful. Studies do show you put C. G. M. On a person there will be slight A. One C lowering but really to get better to get increased time and range and get even more A one C lowering is also coupling that with the medication adjustments and that review of data. So this is an important component to think about well who on the patient's care team will review and respond to the data and we understand that it's not realistic to expect that one person is going to do everything I know in my system the provider visits are like 20 minutes long. So how in 20 minutes are you supposed to assess all the different conditions, health maintenance and then now review C. G. M. Data. So I encourage you to do is think about who do you have on your team that may have more time to be able to do this? Do you have pharmacists on your team? Do you have diabetes educators or diabetes care and education specialists that can help dietitians nurses? Um Every team is a little bit different but thinking about who is available who can discuss and even if the person doesn't have it in their scope of practice to make direct medication adjustments often they can come up with some very valuable insights then share that with the prescribers to be able to facilitate those medication adjustments. One tool that I particularly really liked and I was involved in creating. So I've got a huge bias here but is the data model for going through data with a person, there's essentially five steps. The first is you've got to obtain the data right, You've got to download it or log into whatever respective system to be able to get it in this step. I like to orient the person with diabetes to what it means. And no I don't go into crazy details but at least for a person like Camille at least understanding well what is time and range and what is the design higher goal which for most people is 70% or more in that target range after that. The next step when you're looking at a report is really assessing safety that's looking for hypoglycemia. And that first example that I showed with the B. G. M. And then the C. G. M. Tracing how there was hypoglycemia overnight. That's something we would want to address right away because that's a safety issue. And then also that often leads to rebound hyperglycemia, more glucose variability, worse outcomes. So we want to address that right away. The third step is actually spending most of the time discussing time and range and that's focusing on the positive what's working well. So even in ca meals report which was almost all above range there was still 2% in range. I would focus on that 2%. But the idea here is we focus on the positive and try to replicate it because often in diabetes people feel judged, they feel judged by their numbers. They may be hesitant to share their data because of that. And we want to really remove that. We don't want people to feel that way. We understand manage diabetes is hard work. Let's celebrate the successes and see what we how we can learn from those. And then the next step is areas to improve. We're not gonna ignore when there's hyperglycemia and there's opportunities to increase time and range. It's just not the emphasis of the whole conversation and then we take that whole discussion together. Come up with an action plan through shared decision making and at each step I really try to express that these are numbers. This is information. This is not inherently good or bad. It's just data that we're using to help the person with diabetes and I know you're going to get more into the data in the next section of this presentation. But I just wanted to highlight this is an example of a report where I started working with the patient and um the A. G. P. Is showing that there is a significant amount of hypoglycemia there's 7% time spent below 70. And the follow up through working with This data model with the person and adjusting making medication adjustments was a much more flat what we call flat, narrow in range and that time and range 86% and now only 1% of the time below 70. So you can see how working with a person can really be effective. The other part that I really love about C. G. M. Data and with these respective data platforms is it allows us to utilize remote monitoring and really take advantage of population health initiatives. This is an example. This is taken from library view where I've shown our patient portal and I can organize it by different metrics. For example like the percentage of time spent in target, the target range of 70 to 180. I could choose to organize it by the time spent below. So time spent in hypoglycemia, the coefficient of variation is glucose variability. I could organize it by that. So I have a number of ways that can organize the data and then I can proactively reach out to patients who are not meeting their goals to try to work with them to increase their time and range and help them to get to their goals. There are also building codes associated with remote monitoring that you can take advantage of if you're utilizing systems like these. So the last part of the presentation that I want to really focus on are some additional barriers and solutions to overcome those barriers that we see in practice? A really common one is my sensor fell off early. What do I do? So my advice is have the patient called the company directly to get replacement. The companies are great about offering replacements and that's going to be a lot easier than trying to send a new prescription in and and get additional sensors covered. But also being proactive about solutions to help it stay on better. I mean hot summers where it's humid and there's tons of sweating. It's not hard to see why a sensor might not stand on the full 7, 10 or 14 days. Fortunately there's a lot of products out there like skin, skin tack or master cell to put underneath the sensor to help it to stick better and tons of fun. A beautiful overlays with great colorful designs and sparkles and all kinds of things that you can put over for it. There's also clear ones for people that, that wanted to be a little more discreet but there's tons of different options out there and so encouraging use of these and sometimes it takes a little trial and error to find the best combination also just cleaning the area very well using alcohol underneath, making sure it's completely dry before inserting the sensor can help with success. Another very common barrier is how do I order this and I would encourage you to go to the diabetes wise professional site because there is information that helps you there. But also understanding is it pharmacy or does it go through durable medical equipment DME? Because a common mistake that happens is you send the script to pharmacy then they ask for a prior authorization. The P. A. Gets rejected and then you just assume the patients didn't have coverage for it when actually they did. It was supposed to go through DME, the pharmacy didn't tell you. And and so that is a recipe for disaster. So knowing which route and unfortunately it does vary a little bit on insurance. Generally. Medicare prefers to go through DME uh many times commercial can go through pharmacy but it depends a little bit on the plan and the sensor type. So my advice is actually to form relationships with the representatives from the companies in your area and they can be that resource and they can just tell you so you're not like spending all this time trying to figure it out yourself. And if you can identify someone on your team to have those relationships and have those people's contact information such as a medical assistant or an administrative assistant that can work out really really great went to check glucose. So this the guidelines actually say every person with C. G. M. Even if you're using a type that doesn't require regular finger sticks or calibrations should still have access to a meter. And the reason why is because there's certain instances Where it's still desire to check a finger stick, one would definitely be if a calibration symbol appears on the device with library generally the 1st 12 hours it's recommended to verify with finger stick before making a treatment decision. Um with other devices or sometimes optional calibrations. If it's running off, you know, it seems like it's further off from where it should be. And then some devices require calibrations like the ever since and the Guardian require calibrations. The G6 has that option. Um But with all devices, regardless if symptoms do not match what the device is saying, that would be a time to verify with the finger stick technology is not perfect 100% of the time. So if symptoms of someone feels low, even if it's saying they're not they should do a finger stick and then the other instance is interfering substances. So we do know that there are interfering substances with vitamin C. With library acetaminophen for example, with Guardian hydroxyurea with Guardian and G six. So if someone is taking those substances, finger sticks should be verified because that can cause false increases or decreases in the glucose values and then patient asks you is my C. G. M accurate. I don't understand it. My finger stick is 10 points different than the C. G. M. Which one do I trust. So having some education up front about the fact that nothing is exact and it's expected there's going to be a difference between the two. It's in we call it a unicorn when they're the exact exact same number. But there's also this concept called lag time where C G. M. Is measuring interstitial fluid and B G. M. Is measuring capillary glucose. So if a person recently ate food, they recently exercise, give themselves insulin, it's expected it's going to be a little different. And a great analogy to explain this to patients is the roller coaster or the train where let's say a roller coaster is going up a big hill. The front of the car is going to be higher than the back of the car. So like if someone recently ate their finger stick glucose might be 1 70 maybe the C G M is trailing a little bit behind it saying one 30 but there's an arrow going straight up. So that is expected generally when glucose is stable. Like in a fasting state we do expect it's going to be more similar but honestly even within being within 20% of each other is very, very normal and the devices are all of them are, you know, designed, they're accurate enough to be able to make treatment decisions off of. So with that I talked about lots of information. Just a few summary points I want to share. See GM has demonstrated many improved outcomes but to experience maximum benefit people with diabetes need education and training on the devices and the health care team needs to be trained on how to use the data. The identified configure collaborate framework is a tool that can address many of the common barriers to see GM use and there's many ways that the family practice team can help with C. G. M. Access initiation, education and collaboration of data to ensure optimal use and maximum benefit and here's some additional resources I just wanted to share to get more information, especially that diabetes wise website that I had mentioned in the presentation. And with that I want to thank you so much for your attention. I'd like to go ahead and turn it over to Dr Eugene right now. Who's going to talk about the glucose pattern insight report. Thank you.
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