 Hi, everyone. Thanks for joining us during us today. I find it a bit ironic that I'm presenting on uncommunicable diseases and I'm competing with the sugar and caffeine that's outside right now. But yeah, we'll go with the people that we have here now. I'm very excited for our presenters today to learn a bit more about NCT use cases in country. Yeah, I will just click this here. My name is Brian O'Donnell. I'm a DHS to implementer at the University of Oslo at this center, where I work on the mobile health content and standards team producing packages, for example, and providing other DHS to technical assistance. But I have a few introductory slides to go through. But I will hear shortly from some NCT use cases from both Nigeria and Indonesia. And I will present them in due time. So first, just to set the scene, I want to leave some space for questions. Well, I apologize if I go through this a bit fast. But a sustainable development goal 3.4 said that by 2030, we should reduce by one third premature mortality from noncommunicable diseases through prevention and treatment. I put up this slide here so that everyone is on the same page about what noncommunicable diseases are, how they differ from infectious diseases. And I also understand a bit of the burden. So this is a world burden of disease by cause around the world, using a disability adjusted life, life years or dailies. Sure, most of the people joining us are familiar with these terms. But to understand globally that cardiovascular diseases and cancers are the leading causes of death. But also if you were to break this down by region or by country, then the burden of disease by cause for lower middle income countries, for example, it's actually going down greatly for infectious diseases such as or neonatal disorders or intraday infections. And it's increasing over time for noncommunicable diseases. So you can see that each year more than 15 million people die from an NCD between the ages of 30 and 69 years. And 85% of these premature deaths occur in low and middle income countries. I also bring up this point because while NCD's burden is increasing around the world, our health information architectures were largely designed around infectious disease programs, right? So we still have these different programs for HIV, TB, malaria, collecting data in their own silos and for specific use cases. But as NCD's increase their burden globally, we might see a shift in health information systems towards more focus on burden of NCD's and providing care for patients with hypertension and diabetes or other types of respiratory and cardiovascular disorders. So there was actually a NCD hard talks put on by the WHO just a few weeks ago that said so far the global response to NCD's is a test that we have failed. So it's quite a large step to say that we are failing a test and we need to get back on track. And while tobacco use is down, almost all other indicators for NCD's are on the rise. And I think that we will see a large increase in investments for digital health into NCD's over the next short to medium term because of that. So what are we doing at HISP-UIO as in our partnership with WHO around NCD's? Well, there's an integrated NCD package for HMIS that we are working on with the WHO NCD program, including the heart's indicators of the WHO technical package. And since 2020, we've been putting together a global product for NCD's for collecting data on NCD's at the facility level. And that included a rapid landscaping of the HISP network, which revealed that really the NCD indicators are not included in many HMIS systems that we work with. And furthermore that a lot of the key NCD indicators rely on a longitudinal follow-up of chronic diseases, which are quite challenging to implement. So oftentimes you might find yourself in an aggregate system or an event capture system for infectious diseases. But then for for these chronic diseases, you might need a much longer time horizon to capture data for patients with hypertension, for example, right? And one of the things I'd like to put up there as well is this WHO HISP-N Resolve to Save Lives partnership in Bangladesh, which is piloting, collecting individual level data through another application and then importing it into DHIS2. So all of this talk about mobile health applications being used to track things like blood pressure or diabetes. How can we actually integrate those tools into the health management information system? We also have a rehabilitation package, which is related to NCDs as well, which has been recently published. I won't go into a lot of detail on that, but just a short plug if you're interested. A number of countries have expressed interest in finding a standard set of indicators for personnel density utilization of different products around rehabilitation. But really this session is about learning from country use cases, and that's kind of the same approach that we're taking right now with the metadata packages and our collaboration with WHO. So we have a number of different work streams for informing this package development. We'll hear today from Joseph about the Resolve to Save Lives work on the Hypertension Screening app in Nigeria, and then they also work in Bangladesh. We're also talking with the World Diabetes Foundation about their work around a digital diabetes compass and exploring possible uses of DHIS2 tracker around diabetes screening. And Hispawanda also has a couple of programs that are of interest if you are in the NCD space, particularly around integrating a DHIS2 tracker with the desktop application called CANREG-5, which is used to analyze cancer data and is used in a number of facilities in different contexts. There's also a national NCD screening program that Hispawanda is doing around a number of these different NCDs. But with that I will turn it to our first speaker to give a presentation. Mr. Joseph Odu is a technical advisor for monitoring and evaluation at Resolve to Save Lives, a country office in Nigeria, and he will be sharing a bit about the Hypertension Screening application and the program there. So can I stop sharing here? I don't want to share a screen. So, sorry. Joseph, can you share a screen? Yes, I'm about to do that. All right, thank you. Yeah, we can't see your screen yet. You've added earlier. Yes, I'm trying to get it up and, okay, found it. Great, thank you. Can you say it now? Yes, just put it on presentation mode. Okay, great. I like your presentation because it set the stage for mine and I think most of my background has been taken care of by you. So thank you everyone, good day. And like Grant said, my name is Joseph Odu and I'll be speaking to this presentation titled DHIS2 at the Scale of Hypertension. Lessons learned so far in Nigeria. I'll be speaking on behalf of a team from the Federal Ministry of Health in Nigeria, the National Primary Health Care Development Agency, WHO Nigeria Country Office, HIST Nigeria, and Resolve to Save Lives. And our presentation will take these formats, the background of our projects, our approach for the deployment of DHIS2 on our project, the lessons we've learned, and also our next steps. So the Nigerian Hypertension Control Initiative, which was formerly known as a National Hypertension Control Initiative, was bettered in Nigeria in November of 2020 in Kano State and Ogun State. Those are the two pilot states for this initiative. Kano is in the northern part of Nigeria, while Ogun is in the southern part of Nigeria. This intervention was first piloted in 12 facilities for over a year. And during this time, this initiative was led by the Federal Ministry of Health and the National Primary Health Care Development Agency. I'll talk about their individual roles on the next slide. And during this period, the management of data was primarily paper based, which was in alignment to the national management information system in the country. And why was this initiative bettered? It was to reduce the mobility and mortality from cardiovascular diseases at the primary health care level in Nigeria. For those of you who are familiar with the Nigerian Health Care System, management of cardiovascular diseases such as hypertension is primarily the focus of tertiary facilities and secondary facilities. It was not within the remit of primary health care facilities. However, looking at the burden of hypertension and cardiovascular diseases in Nigeria, where one out of every three Nigerians has uncontrolled blood pressure and also cardiovascular diseases being responsible for 11 percent of the deaths in Nigeria, of which hypertension is the major risk factor. The Federal Ministry of Health thought it wise to bring the management of hypertension closer to the doorsteps of our households in Nigeria, thereby creating an enabling environment to capacitate primary health care centers to manage hypertension. That is one of the major successes of this initiative so far. We've worked with partners to develop a national hypertension treatment guideline and we've also ensured that there is access to high quality comprehensive hypertension diagnosis and treatment using the WHO HATS package. And we've also ensured the availability of validated sphignum anoritas for measuring blood pressure and also availability of drugs. We've also worked with partners such as his to ensure that there is a functional information management system to cater to these hypertensive patients. So who are the players in this initiative? The initiative is led just like I said earlier by the Federal Ministry of Health. We are responsible for the policymaking duty in the health ministry. Then the National Primary Health Care Development Agency who are responsible for the day-to-day running of primary health centers in Nigeria and technical partners are WHO and also his WHO takes so responsibility of providing technical support in service delivery and drug management while his supports with data management resolve to save lives at the funders and also provide technical support to the initiative. Currently we have over 13,000 patients being managed with the help of the HIS-2. We stick out from 24 facilities. Currently we have 104 facilities using the HIS-2 to manage their patients in both Kano and Ogil States. This is a big fit for us and we're excited about this. But before this achievement, what was the case? Just like I mentioned, the prevalence of hypertension in Nigeria is about slightly over 31 percent and so imagine managing patients about 30 percent of the population with a paper-based system and these patients will manage throughout their lifetime as we know hypertension management using the HIS-2 is a longitudinal process. So this has been envisioned to lead to reporting burden at facility levels. In Nigeria we have a National Health Management Information System that manages data for most of the programs. However, we still have some standalone management systems. Our PhDs are understaffed and some of most of the staff are not so motivated. Now, bringing a hypertension treatment program that is being piloted that requires them to use new tools that will be trained on, seem to look like a burden, and also took away from the time required to manage patients. As a result of the increased workload, this affected the quality of the data that was submitted at the end of each reporting period and also delayed availability of data that's been processed for decision makers to inform programs at both state, national, and at the facility level. So these were the reasons why the partnership that's called implementing this National Hypertension Control Initiative decided to come together and see how best to make life easy for the healthcare providers at the facility level to ensure that there's high quality data for decision making and to ensure that we have a good picture of the hypertension burden in Nigeria. So what did we do? We held several meetings. The Federal Ministry of Health, WHO, NPA, CDA East Nigeria, and Results Saved Lives first met to conceptualize the idea and see how best to present this case to stakeholders for their buying. And having had successful meetings and fend off our plans, we met with the Department of Health, Planning, Research, and Statistics in the Federal Ministry of Health, worked with them to come up with blueprints for the deployment of DHS2. First of all, we held a co-creation workshop to agree with implementing partners, development partners, healthcare providers, and even beneficiaries on the indicators we wanted to showcase on this platform because we had simplicity at the back of our minds. So we focused on the WHO hat indicators, following the co-creation workshop, and also because the Federal Ministry of Health was looking towards an integrated program, we agreed on indicators for both hypertension and for diabetes to be deployed through DHS2. Then the prototype was developed by HISP and UIO, which we then took to the field in November of 2021 and showed the healthcare workers. We visited six facilities, three in Oguna and three in Kano, where we talked to 19 healthcare providers, gave them the tools to use in collecting, registering new patients, recalling patient data manually, recalling data using a QR code, and also for entering data for follow-up patients. We observed that on the average, it took about four minutes to capture data or register a new client and a minute to enter data for follow-up patients. So these learnings and all the findings we got, we transferred to HISP and UIO to use for upgrading the device. Following the upgrade to the device, we trained a critical mass of stakeholders at the national level where we had a tiered level of training. So we had a TOT, we trained staff from the Ministry, Federal Ministry of Health, we trained staff from the WHO, National Primary Healthcare Development Agency. And when we got the critical mass of trainers, we rolled down to Cascader down to the states where we trained the states and also the facility staff. Following the trainings, we then deployed DHIS-2 in our initial 24 facilities, 12 in Kano and 12 in Ogun. Following the deployment and a form of secondary pilot, we then rolled out to all 104 facilities on the project. In the design of the DHIS-2 tracker for longitudinal tracking of patients on the NETI, we learned from an existing electronic data management tool called SIMPL, used by RTSL in four countries, Sri Lanka, Bangladesh, Ethiopia. And I think that the fourth country doesn't come to mind now, where they are managing two million patients on the SIMPL app. We asked and tried to find out what was the reason, what were the success factors. We found out that the fact that the app was fast and easy to use, enabled its front uptake. And why was it fast? Because it was tracking very few critical indicators, therefore not inundating healthcare workers with a lot of data elements for data entry and also ensuring that there was priority indicators available for decision makers. And also the fact that the SIMPL app was able to work offline and looking at Nigerian context, where we have operational challenges like limited power supply to ensure that the devices are always charged and unstable internet connectivity. These were the features that we ensured that our DHS2 app had. And we worked with the HISTING to transfer these learnings from SIMPL to our DHS2 tracker. So what did we learn? We've stratified our learnings into two. The first are the potential inhibitors. So we've come here to share with colleagues who intend to use DHS2 tracker for long snort tracking of hypertension cases. That it is very important to have a well taught out and coordinated plan for transitioning data from paper-based tools into the DHS2 platform. Prior to deployment of DHS2, we had about 7,000 patients that were already on the program. And you recall that management of hypertension is a lifelong process. So it is very important that we transfer the legacy data already captured on the paper-based tool into DHS2 so that we can recall or healthcare workers can recall them during subsequent visits and provide appropriate care and potential continuum of care. So one of the lessons we learned is if this handshake between the paper-based tools and the DHS2 tracker is not well coordinated, there will be several technical glitches when the migration has been done and recalling patients when they visit for follow-up will be very challenging. We had some challenges such as this at the beginning and one of such is during deployment we had DHS2.5 version. So it was a version control crisis and that version promptly told us whenever there was a sync error if the data was uploaded. However, when some of those, when the new facilities came on board and they had 2.6, 2.6 was not as, for one of a better word I would say sensitive to picking some of the sync errors and we've been able to identify some of these errors and with the help of his Nigeria and UIO we are trying to sort out these challenges that we faced. So we would like others who want to use DHS2 for this kind of programming to ensure that this handshake is almost flawless, if not flawless, to ensure that you don't have such technical challenges. Secondly, we have the enabling factors. The fact, the lesson we learned from Simple was further buttressed because when we deployed DHS2 considering that we have minimal data elements to go, to be imputed into the device and courage healthcare workers to uptake, I mean to use DHS2 more than we had expected. They were only collecting data on the patients' demography, only on patients' medication and blood pressure, very minimal data. Other data elements were either automated or considered not very important for this and so we made their life very easy. Also, traditionally in Nigeria, when programs use electronic tools or devices, they procure these devices for the healthcare workers. However, during our user acceptance testing, we tried to disrupt this process by engaging the healthcare workers to see if they would allow us to use their devices. Most were excited to do this on the condition that we provided data which we're happy to provide. However, I would like us to know that this was not without challenges because some of the healthcare workers have devices who do not have enough storage capacity and this was a challenge. However, it saved the program a lot of costs and it's an area that the country is really interested in exploring to see how going forward we would leverage devices of healthcare workers based on the learnings we get from this. Another, the last lesson was the active engagement of stakeholders as a very veritable tool in ensuring the success of our implementation thus far. Some of the challenges we had were promptly surmounted because the states were part of the design of this program, they were part of the intervention all through and they took ownership of it. So they supported us in ensuring that most of the challenges we faced were overcome. So overall, these are the three enabling factors that we've learned so far following deployment of GHIS-2 from October 2022 to dates. We would also like to share some of the next steps that we have. We are currently working to ensure that we document all the lessons that we've learned in this process and we hope to share this with UI, OHIS and WHO that this process is something that is scalable, is something that other clients and other countries and other programs can use and we are also going to continue to advocate for the seamless transition to 100% electronic data management of NCD cases where there will be no use of paper, there will be sole use of GHIS-2 because we've seen that it takes the healthcare workers four minutes to collect data and register new patients and one minute to enter data for new, I mean for revisit patients as against using paper tools that are prone to several challenges like we all are familiar with. We would like to show you the interface of our GHIS-2 tracker so you see that it just gets the patient's name, date of birth, I mean the patient's date of birth is automatically calculates the age, the patient's phone number and address so that we can follow up, there's a QR code that can be scanned to recall patient information, the blood pressure reading measurement, the drugs and then the dates for the follow-up visits. So this is what our GHIS-2 tracker data captures, then this is what our dashboard shows, it shows us the number of patients that have been enrolled, it shows us the number of patients that whose blood pressure are controlled and also shows us our cohort control over time. Thank you and I look forward to further engagements. Thanks a lot Joseph, I want to leave a time for the team from Indonesia to present next but is there anyone who has a quick question and then maybe take more questions at the end? Okay if there are no questions then we can proceed to the next one and Joseph if you can hold on in case there's any questions towards the end. But we're now joined by our next speaker, Dr. Guardian Lai Sanjaya is a senior implementer of GHIS-2 from the University of Dajamada in Indonesia and he will be presenting on the use of GHIS-2 Tractor for Disease Registration. So Dr. Sanjaya, off to you. Yeah, thank you Brian. Hi everyone, can you hear my, yes, the screen is already shared, can you see the screens? Yes, we can see your screen. Okay great. Mia and my colleague, Phi Phi will present our works experience experiments to use the day as to tracker for disease registrations and this is really to support clinical research involving different hospitals, especially in Indonesia, third level of hospital in Indonesia and what we want to share is related to these registrations that defined by the clinicians which is relatively different with for example like HIV registry or malaria registry or immunization registry and we try because we already implement the IES2 since 2017 and we would like to use the IES2 as a clinical data management software to facilitate the clinicians to collect the data related to the disease registrations, specific disease registrations and several I think the step is almost similar with others implementation in many countries and we want to specifically share our implementation challenge that maybe we can get input from all of you that already implement this the IES2 as a disease registry. I saw that Rwanda and also other country already developed the similar functions of the IES2 for disease registrations and this is disease registrations which is when we talk with the clinicians they normally call it clinical data management because once the data is actually collected from the medical records either electronic or non-electronics many of hospitals in Indonesia also still are using non-electronic this but not all the data from the medical record is collected for the specific disease because patients with cancer for example they can come to the hospital because of the COVID-19 or because of others disease so the data also can be collected prospectively since the patient comes firstly come to the hospital for example for asthma registry but also can be retrospective data collections in cancer registry for examples they collect data two years after the patient first diagnosed so they cannot collect the data for the first time because the first time is still under diagnosed and still waiting for the treatment waiting for other things so that's why the in cancer registry is most likely is retrospective data collections and sometimes also the disease specifics data is beyond the medical records they can they usually also need to collect the primary data from the patient for example like the risk factors and environmental factors and etc as additional from the medical records and they have also very specific very systematic data quality checks for example like cancer registry the enumerator to collect the data the data manager will review the data and after the data managers and it has to be reviewed by a minimum two of clinicians before it can be used to to be analyzed and to represent populations they need to collaborate with different hospitals even though that the data is collected in the only in the one hospital so they actually have a site visit to different hospitals and collect the data related to for example like cancers and once the data is collected in other hospitals they return it to the main hospital and collect the data in these main hospitals and this is the way in one of the this is registrations in Indonesia but the others requires other hospital to collect data directly from their hospitals not not the way that the plus part mentions and sometimes because the outcomes the output of the analysis is to see the outcome of the interventions and sometimes they need to have specifics analysis for example like survival analysis where the data should be imported but maybe imported in a specific time of period and then they use it to they use different tools to analyze the data using the statistical tools and we saw that the IES2 as a clinical data management software have many opportunities it can cover the server client for multiple centers and even it can accommodate the continuous data collections for example like cancers even though that the data the firstly is collected in the this years and the outcomes can be collected in the maybe another two years or another three years so the clinician I can also monitor through the descriptive analysis that they can do it by themselves it is self analysis and the data the data can be extracted into the Excel format for example or text format that they can import it into the statistical tools to be analyzed more appropriately so that's I think the IES2 give lots of opportunity to to be used as a clinical data management software but we are facing several challenges of course related to the implementations the step for the implementation I think almost similar with other many other countries or many other developing partners to implement the IES2 we start to identify the data elements using the standard form from the global registry disease registry with specific disease registry and then of course there are several local SNITs that we need to accommodate and they are very happy that we can modify the forms modify the option sets directly to the IES2 and then very dynamic data elements that can build from the IES2 and of course we can demonstrate we demonstrate to them and ask for that input to improve the forms and also the data elements this is the registration this is registration that we develop in collaboration with the different departments in faculty of medicines and also subject to hospital it is third level of hospitals in in Yogyakarta province and it's not in the same times the implementations it start from the COVID-19 in children's last years start in April once we show the benefit of using the IES2 and then their colleagues from different sub departments asking about how about epilepsy in children how about asthma in children and so on and we always say that okay let's do it and then showing them about this is the benefit and this is the data you already collected and showing the dashboard or analytics they can conduct by themselves even though this is a descriptive analysis and we starting from a teaching hospitals a third level of hospitals in in Yogyakarta regions because they do have a network with other hospitals in the province and also in other province and they start to involve other hospitals especially in asthma in children's they we are collaboration with the pediatric associations that covers egg teaching hospital centers in Indonesia so basically they they already have the data manager to collect the data or animerators to collect the data and they can see the data by themselves and they can analyze the data and they can yeah it's easy for them to to use the electronic base it's not very we very minor capacity building to with them because they already familiar with the difference electronic form so it's easy for them to follow the the day as to data collection tools and this is examples of the dashboard from the COVID-19 in children's and many other dashboards we develop and they can develop by themselves especially for the asthma and cancer registry what are the challenges during the implementations once synthesizing data from the medical record this is from their part they are not from the the day as to all the technical part because not all the data that already in the electronic and non-electronic medical record it is should be inputted into the clinical data management software so basically some maybe some some of the information should be synthesized by themselves and they have to interpret by themselves which particulars data elements it's sweet that to be collected into the the day as to and the teaching hospital because we work with teaching hospitals tertiary cares where they receive a patient from multiple district and maybe different province as well and when we want to create the visualization in in maps we have to process apparently so we have to put we have to extract it into the aggregate data and then we put it into the province districts levels data entry forms but this is only imported the data not not much effort that to conduct this kind of analysis the clinical outcomes analysis requires specific data query to prepare clean data and important into the statistical tools sometimes they ask about what are the clinical outcomes six months after the first diagnosed for example because each patient is diagnosed in different times and we have to calculate when is the six months when is the one years of the of the data so of each patient data so so it's mean that we have to manage the data very carefully to provide the clean data to the clinician so they can analyze the data using the statistical tools and we have of course limited technical capabilities amongst team members especially we know that there is a we can use the our software into the day as to but up to now we haven't tried that and maybe one of you have experience working with that and very appreciate if you can share also with us how to conduct that part and in conclusions basically the s2 can co-force all the requirements number of clinicals data management even though that is only force but we sure that even though that we add more disease that the s2 can accommodate the requirements all the requirements of the data elements and clinical data management tools should be used partly from electronic medical record because not all the the data from the medical record is should be collected into the the disease registrations because only specific data and and maybe sometimes it's only a specific times data should be collected in the clinical data registries and of course there are many opportunities to extend the functionality of the s2 that we need to learn small and our our challenge is how to facilitate the clinical review process which is more than one steps from the enumerators data managers and also two clinicians should be reviewed the data before it can be done it can be used for analytics yes i think that's our presentations and thank you for the opportunity to share our works in indonesia thank you so much doctor sanjay for that very interesting use case um do we have any questions from the audience or online or if you're on zoom you feel free to type it into the community of practice or zoom no questions i can i can ask a couple and make it maybe try to start some conversation that way um doctor sanjay you mentioned some of these uh advanced statistical analyses that you're also doing with the disease registry um could you uh describe the process uh for performing that type of analysis that you're currently doing and what type of analyses are most essential for your disease registry yeah uh one of the uh analysis is related to the survival analysis and um they want to see the outcomes of the uh treatments yeah uh of specific disease uh disease happens in the cancer registry because the cancer registry actually already have their own systems using the channel like five i think similar like uh rwanda uh but it is a standalone applications that cannot be accessed by uh many users in the different organizations so uh the requirement is how to uh export the data from the first uh uh first times the patient is diagnosed until the uh what's uh the clinical outcomes appears uh for example like um yeah uh it can be that it can be recurrent it can be uh uh many others uh outcome that defined by the clinicians so we have to collect one by one uh we have to uh collect all the data uh i mean import the data into the spreadsheet and then we uh use the spreadsheet to to uh uh to get the specifics uh data that need to be calculated in the survival analysis not all the data uh uh because uh is uh uh used for for uh uh statistic analysis uh because we only uh choose uh specific data and specific point of times uh when the uh the the clinical outcomes uh appears uh that's we cannot do it in the uh the as to at the moment maybe we can we can because we cannot because we don't have capacity to do so uh so that's why we extract the data into the uh excel sheet or or uh comma spirited value to to uh clean the data again great thank you for that yeah maybe that's the next step that we could uh work on in the future together um anyone else with uh questions maybe for the resolve to save lives team um yeah it's the end of the day it's the fourth one i won't uh hold many people for the uh experts lounges which are coming up next but um maybe uh joseph could you um share a bit more about the um the the process now so um did the trainings that you did at the um for these care providers um it's great that they're very speedy we said like one minute for doing follow-up visits four minutes for enrolling a patient but how do you manage uh or balance that um need for speed with data quality and are you finding any data quality issues with this uh very quick enrollment process thanks abrian so um we are um used to the fact that in Nigeria the healthcare workers are transferred from one facility to another and there's also high staff attrition as a result of maybe getting better paying jobs so one of the things we did with hisp and the federal ministry of health was to ensure that the training first of all was done in a cascaded manner to ensure that we always have people with the capacity at the national and state and facility level then also we developed um sop standard operating procedures that we've provided to all the healthcare providers that will guide them in ensuring smooth data entry into dhs2 then all the states have uh WhatsApp platforms and we have staff embedded in this platform that helps to answer queries on the go and then almost every um every week we look at the dhs2 platform and then pull out findings and try to probe deeper to find out why a particular facility is not reporting or why they are not um entering data as and when you so these are measures we've put in place to ensure that there's continuous oversight and quality improvement great very interesting uh process seeds and i can't wait to see the the documentation that's written up that have a review of the process of deploying the dhs2 tracker like this um i uh i think everyone's blood sugar is running a bit low right now um so any more questions great i look forward to hearing more on the community of practice uh let's give it up for our speakers and um thanks again we'll be in touch