 always here. The section on tractor for clinical use and supervision. I'm Mike Ross, the product manager for Tracker. I come from the public health side. I've worked in public health since 2007. My first large project was about collecting data for malaria that was coming from clinical records. And my sin was that what I ended up designing was a quarterly survey that was done in 18 countries every year, which was a terrible way to get the kind of data that was needed. It consisted of teams going out into the field and randomly pulling patient files to capture all of this data and then submit it as a survey. It never fed into the HMIS. It was basically data that was going back to the donor level. It was not really being used extensively in the country, and it costs over a million dollars a year per country. So since that time, I have tried to change my ways and focus much more on trading systems for countries that will actually serve the country's needs and not just at the decision making level, but all the way down to where the data are actually generated, which is the clinical level. And it's a driving belief on the product management side of Tracker that you can collect all of the data you need at the point of care without turning it into a massive burden. And so that's the underlying philosophy is to try to make something that actually benefits the people collecting the data, provides them with the kinds of tools that will make their job easier rather than more difficult. So I'm just going to do a tiny intro here to give us all on the same page of how we're talking about Tracker in terms of clinical use. So I'm creating some slides here from GFF they were presenting to the World Bank. I asked this question pretty much every time that I present is Tracker and EMR. So I'm just going to show you at least how somebody else decided to describe Tracker versus EMRs. I think everybody's familiar with EMR, and you know what the EMR is, and you wonder whether collecting data through Tracker might also make Tracker an EMR. But at least under the definitions that they used here, they distinguish between Tracker as a category, not talking specifically about the HS2 Tracker. But in general, there's a category of data collection tools that can describe those Trackers, which are much more targeted than an EMR. So the concept thing that in the kinds of lower level health facilities, community health workers, kind of the periphery in where a lot of the services are being provided, those are areas where they're not likely to be able to use an EMR. The EMR may not be appropriate. It's a much heavier tool. Whereas at least how we see both public health programs being monitored and managed and funded, it's often more disease specific. It's health program specific. They at this moment in time have an initiative of funding and a cadre of health workers for malaria, and they're ready now for any malaria disease. And so the idea behind a Tracker is that you can build a clinical facing tool for that specific program, very lightweight, easy to use, can capture the required data elements there, can be used for things like decision support, while also reporting out the chain. So always explicitly in the kind of use of this clinical data, we like it ideally into the reported requirements as well. So a little bit of a distinction, but also thinking that in those areas, the next group that sees this malaria system would be the TV group. And they think, oh, we want one too. So you add on TV and then the HIV group wants to get involved in the emergency. And after a few of these, you start to see you created a full shared health record at that site, or at least at that level on the system. And in that sense, it starts to look a lot more like an EMR, but you built it kind of one step at a time. And hopefully you're feeding it into a shared health record that is also being used at the hospital level that is being used to link to the EMRs. And that's kind of what we're thinking of that the directions that many countries are going. We've seen an explosion of this kind of use of EHIS-2 tractor in the last couple of years. We have 85 countries that are using tractor on a larger national scale, and many of them are using this kind of clinical phases today. Just wanted to say we keep in mind the few recommendations that so far have come out of WHO with regards to digital interventions for health systems. And these seven or eight are the ones that we think specifically tractor is targeting and able to provide a solution for. In fact, maybe the one that we're talking about the most today and that is most on our minds as we design tractor is this kind of combined recommendation that is digital tracking of patients combined with decision support and targeted client communication. And you can see in this recommendation we're talking about data, data that goes up, we're talking about decision support for the person that's collecting the data, and we're talking about information going to the client for the patient itself. So we really do have in mind this that the entire universe of who needs this data and where it comes from we're trying to support them in a single application which would really reduce the reporting burden while also the increase in quality of care, supporting the team of care, and enabling the patient to have more ownership over their data. My last slide before I turn it over to colleagues to share what they're actually doing and what they're doing with tractor is to say that this space is very challenge. This report, some of you may be familiar with, came out a couple of years ago. Talking back over the US, it's spent $36 billion on EMRs and it's been a big small fail. And I loved this quote that was included because this sounds like it could have come from any of the people that I'm working with all around the world. Every single idea was well meaning, potentially a societal benefit, but the combined burden of all of them being conditioned simultaneously to make office practice basically impossible. I've heard the same story all around the world over and over, clinics that only have the date and see patients because they spend the rest of the day filling out reports and giving all of the data collection that they're supposed to do, nurses that are being given multiple devices from different health programs trying to report everything through these in a way that is not useful to them at all. And so we again keep this very much in mind as we're designing a factor, trying to make it a purpose built tool that can be very tailored to your specific use space and match the workflow that the clinician actually wants to use and can collect the data that will be useful again up the chain and also how to do the patient itself. So with that, I'm going to stop talking. I'm going to turn it over to Ann from the Reed Institute of Public Health who's going to talk to us and make about the use of Tracker for anti-natal care and applying the digital adaptation here in WHO. Hi everyone, my name is Ann and I'm here today on behalf of the Chief Executive of Public Health and I would talk to you a little bit about the Tracker for Campaigns and Civilization Attitude. Okay, so the WHO has published anti-national guidelines for digital health performance in their team and interventions for different health domains and that includes how to do delivery and start from health assistance. So there are however a lot of challenges related to Frank's monitoring of guidelines adherence and non-standard contents of the DHIs and also the lack of any digital research and evidence specifically related to DHIs and so to ensure countries can effectively benefit from digital health investments and DHIs are designed to facilitate the accurate reflection of the WHO guidelines within the digital assistance companies are adopting. So the DHIs are operational and software neutral and they're also an attempt to standardize the health content for the DHIs. So the evidence-based policies on the health systems interventions for anti-natal care is helpful. It's also known as the E-POSIT. It's a research project for ANC in Uganda which is conducted in collaboration with the University of Austin, the University of Bergen, Mathias University in Uganda, Kisbyganda, and Sysmark and Kings College in London. And the project was funded by the Research Council of Norway to act age. So in this project we have the ambition to assist Uganda with better evidence to improve essential services for pregnant women and their babies. And so the project is twofold. It's supporting Uganda in adopting two WHO guidelines in line with national guidelines and policies and providing recommendations on anti-natal care for positive pregnancy experiments and recommendations on digital intervention for health system strengthening. So we will provide these through implementation by building local innovations on the WHO and the DHI for ANC. So the DHI is part of the SMARC guidelines. It's also known as the National DHI for health information systems and interventional science by conducting cluster-involved control trials of the transition from four ANC contexts to eight ANC contexts. So the DHI is part of the SMARC guidelines. These are a comprehensive set of useful digital health components for instance interoperability standards, code libraries and algorithms. These transform the guideline adaptation and implementation process to preserve fidelity and accelerate uptake. So they inform guideline developers, technologists and countries in their transition from paper-based systems to SMARC digital assistance. Yes. And these also aid the countries to more effectively and accurately adopt and benefit from the WHO health and data recommendations through the digital assistance, which is the last bit. For this particular DHI for the ANC, their requirements are based on systems that provide the functionality of digital tracking and decision support, including some other components for data elements and decision support budget. And this documentation will be used in this project with the DHI to include international WHO recommendations for ANC and your type of tracker to improve the government health system informing evidence-based policies and other initiatives. So hopefully this will result in better overall health care for, for example, other cases. Thanks for that set of feedback on this project. My name is Brian Adano. I work with the University of Oslo as an implementation advisor mostly on the DHI to metadata packages. But for a number of years, I've been working with the Ministry of Public Health on adapting behavioral care guidelines into DHIs to tracker modules for clinical care. And so I'm going to walk through a bit of the process of adapting this DHI that we did into a global and generic DHIs to tracker modules. So within the DHI, you can see that it has a number of components here. The one that was very helpful for us was this 2.5 of some core data elements, because these are essentially spreadsheets that describe an ideal, an ideal system for collecting data on antial care expressing the WHO guidelines. And so you can see here that these core data elements are described for something like gestational age. What are the types of ways that you can collect gestational age? And then also some of the descriptions, definitions, it also includes things like ICD-10 codes and glowing codes, options and option sets. And so it's a very rich resource for people who want to adapt WHO guidelines for a local context. So if we were going to actually use this in a situation like the Uganda trial that we were describing, what are the actual steps that we take? I mean, imagine here that you have apologies for the other slide, but you have a matrix where you have local guidance and local adaptation, and you have software diagnostic guidelines like the WHO, a DAK, and very software specific templates you can call them. So you're trying to adapt this global guideline in a generically expressed way, the DAK, into a locally context, local context of Uganda healthcare setting with a DHIS-2 application. So the process I would describe today is about transitioning from the DAK into a DHIS-2 system. And we use this the DAK core data elements, they also add user profiles and user personas, as well as business processes and workflows that were included, as well as the L4 of the smart guidelines, which is these open SRP demo application, which we could use as a template for contributing the DHIS-2 program. So the DHIS-2 mother's program that actually was produced through this process, it's been about 18 months so about application. We have 17 factors, we have to do seven different program stages in 76 total sections. That's a very large DHIS-2 tracker program because we're trying to adapt this for a clinical context. Not only do we have a number of different data elements that were incorporated into the DHIS-2 system, but we also have a number of programmable projects for clinical decision support that are part of the DHIS that we could try to express in DHIS-2. So these are 260 programmable as well. As I mentioned, the open SRP module was very helpful for getting an idea of this workflow that is expressed through this WHO DAK, and we were able to actually sort of mimic a little bit of the work for things like program rules. So in DHIS-2, for example, we have, we see like the a bit called the registration data elements in the DAK, but these are actually tracked into the attributes. So you can enroll a client in the system, and the first thing that you want to do before you can enter any data for this client is do a quick check. You want to make sure that this woman does not have any danger signs, that means that she needs to be referred to an emergency facility or to a high level of care. So we can actually say, hide these other stages by program rules to make sure that there are no danger signs for this patient, and then they can open up all of the other stages that would be involved with it. And you can see that there's a number of different sections as well, like moving on to profile and history, and there you see the same data elements that were in this DAK module for calculating gestational age. And so these are now in a DHIS-2 program, which we plan to make publicly available and for others to use and adapt. But there are a number of different challenges that come with this process of just taking this global generic slide sheets and then actually translating them into DHIS-2. So the first is like deciding what is a TEI attribute and what is a data element? How do we actually describe what is in the first visit of this stage versus a routine visit? Do we want to have it separate and see one program stage or make it repeatable? And then I mentioned a quick check. But one of the great benefits of having this DAK was that we could do both imported metadata by CMC. There were additional technical challenges of saying they made your field maps are exactly the same into DHIS-2. So this is something that you kind of have to learn by doing. The other big one was there is no multiple select option in DHIS-2, so finding a workaround for that was a challenge. But one of the key things I want to bring up here is that we would have had these same challenges with adapting the guidelines if we had actually used a fire adapter, for example. If we actually used the L3 guidelines, which are the implementation guide of fire, we would have had many of these same internal discussions about how do you model multiple selecting DHIS-2. We would have had the same questions of how should we do a repeatable program stage, for example. And so a lot of these topics that we had, a lot of these topics that were brought up during the transition from CSV into DHIS-2, we had to find workarounds. So for example, I mentioned that the CSV file included blowing ICD-10 codes. And there's no field in DHIS-2 to include the ICD-10 code where you can update a custom attribute for all the data elements, these ICD-10 codes. So now if the country were to adopt this program in their own context, then they can find some other ways to use these ICD-10 codes to integrate with other systems. So that's one point to work around. I did mention earlier about like the fire implementation guide and also this, the L4 that's called the template software or code, sorry, the executable code is called. And these are helpful to sort of get a sense of the type of structure that you can actually have with your program. And it's just another expression of these WHO guidelines. The one thing that we weren't able to do, and this is kind of how it relates to this session, is that if you're actually going to create a global version of a clinical tracker system, the one thing that's very locally contextual or clinical care algorithms. So there's a lot of work that goes into developing program rules and workflows for something like pre-eclampsia for a loan. And so if we're going to build a global generic system, and then go into the local adaptation, maybe we can just skip that global set, have a sort of skeleton architecture, and then the local country, the country partners can go program rules, for example, here's like an example of preeclampsia and severe hypertension based on their own country guidelines. So this is one way in which we're trying to bridge the gap between global guidelines, local adaptation, and also a generic expression of software standards and a DHI to implementation. So I'm a bit over time, but thank you, and I think I'll do the next session. Now we can take questions out of this. Just a warning, we are likely to run over time on this session, but the next thing is lunch. So if you'd like to stay with us, we want to make sure that we give the presenters enough time. There's also a community of practice link where you can be posting questions now, and we'll be trying to answer throughout the session. So we'll try to get some questions, but yes, coming over to Leona. Good morning. Hi everyone. My name is Leona Rosenblum. I'm here representing the Country Health Information Systems and Data Use Project, which is a USAID funded five-year program looking at data use and health information systems globally. And I'm here today to talk about digital awareness to support supervision. I think this session is about Tracker, and we're really excited about some of the existing implementations of Tracker for supervision, but also wanting to sort of plant the seed in all of your minds that while we often think about Tracker for the clinical health records, like EMR sort of approach, that it's actually also a powerful tool for supervisors and for monitoring health reports or performance, providing feedback, lunch, journal, data-driven tracking of performance. So that's where we're heading. Just a quick overview of what I'm going to talk about today. Just explain a little bit more about what CHISU is, and then talk about this activity that we're working on. And then I'm really going to spend most of my time talking about the framework that we're building out around digitizing support and supervision. Talk a little bit about what's already being done, what couldn't be done with the ATIS-2 and specifically with Tracker, and then about next steps where I hope maybe some of us can all collaborate in the future. So as I mentioned, CHISU is a project that's really focused on country health information systems. We're trying to support countries to have actionable data available that is being used to drive health decision making at all levels of the health system. And we do that by looking across four objectives, which I think are well-blinded. I reckon what we're talking about here this week is strengthening governance and the enabling environment, increasing availability and interoperability of the data and information systems themselves, increasing the demand and use of health data and information systems, and then strengthening organizational capacity of partners and post-market governments to do this work going forward. The activity that I'm talking about here today is around, is looking at developing a framework and some guidance to documentation around how countries can look at digitizing and supporting supervision system. So what we've been working on is a landscape assessment, looking at all the different ways that support supervision has been digitized in a variety of contexts so far. We've done that through a docking interview and a lot of conversation, including with colleagues here at the University of Oslo, which is how we had the idea that this was actually a relevant topic to submit an abstract for for this conference. And then we're working on this framework and guidance document. So I do want to be clear that this is about, we're putting together guidance that will hopefully be a useful way for countries to think about how to digitize either components or the whole of a supported supervision system. We are not developing any sort of application out of this. We think that countries have a variety of clinical and aggregate data systems already in place and which pieces of that and how to digitize them together in a way that will be interoperable and that will meet country needs is going to be different in every context. So we're putting forward that framework, that skeleton of how you can think about it and what to keep in mind when you're looking at digitizing one piece and where all those other parts might need to fit together. But just to be clear, we're not putting out an application. So what are we talking about when we say digitizing and supporting supervision? I have a feeling that if I asked her a joke man, so everyone here who had heard of the term supported supervision, you will probably all raise their hands. But we find that it's actually quite hard to find an additional sort of formal definition of supported supervision. I think what we can all agree on is we're talking about a set of processes and activities that involves a health worker, their direct supervisor, and someone who's managing a health program facility or project. So we've got these three personas who are involved. Depending on the level, you might have a community health worker, their supervisor might be at the health facility and their manager might be at the health facility themselves or at the district. At other levels, it might be a health worker who's at a hospital and their supervisor from the district level and manager of the program might be at the regional or national level. So there's a variety of constellations, but in general, we're looking at the interplay of these three actors. And when you think about what is supported supervision, it involves these three actors that we talked about conducting a variety of activities, right? There's a preparation, planning, and budgeting process around supervision. What facilities need to be supervised? How much does it cost to get there? Where is the fuel coming from? Which ones have I been to recently? Which ones are really hard to get to in the rainy season? So I better go before it starts raining. There's also a reporting process both before and after the visits, looking at data that has come in, looking at data that's missing, looking at the data that you collected when you were out at your visit. When you're actually there at the point of care or at the facility with the supervisor, the health provider, it's a mixture of direct visit observation, inspection of the facility, interviews with the health provider, sort of spending that time with the provider in the facility, getting information that isn't available to you for next day at your office in the district. And then based on that sort of information gathering and observation, there's sort of immediate points and follow-up points of feedback that can be part of this process, right? On the doubt training or pressure content that can be shared with the health worker, there can be follow-up. Oh, I'm noticing that there's a stockout. I need to follow up with the supply chain person responsible at my level. There can be problem-solving, coaching, and other sorts of support and joint consensus building between the supervisor and the supervisor. And so when you think about all of those pieces, I think what has jumped out to us as we are looking at this, there's digital interventions that can help with all of these things, right? And so next slide. Sorry, I'm going to do that a little bit here, but we're trying to talk, think through what are all of these individual digital health interventions that can support all of those tasks that go into a supported supervision system? So, thanks for coming, sorry about the formatting. But so if you're looking at in the planning and reporting and preparation process, you know, pulling in data from other systems so that you can prioritize which facility to go to based on low performance according to their aggregated health service delivery data that's already available in your DHS2-based HMIS system, right? That data source can help you in the planning. When you go out to actually do a supervision, having your data in, having the health worker that you're supervising be a case and tracker where you can be tracking their performance over time, you can see where they were strong, where they were weak last time, what were some of the issues last time, what were your follow-up actions as supervisors? Did you take them? What were their follow-up actions as a health provider? Did they take that? As opposed to what we see often on paper systems or on less developed digitized systems, it's a single snapshot, right? It's a piece of paper checklist or a capture checklist, but it's not longitudinally tracking over time and you lose that follow-up and consistency piece. There's also a huge opportunity to look at digital training materials and interact both for the supervisor to bring with them to the facility to look at together with the provider or to push out to devices after the session to help follow-up on areas of weakness. I'm not going to name every single of the green slots out there because there's a lot of different points. The general point that I want to make is when you think about the whole of the supervision system with all this component system on the left side, there are a lot of different entry points that you can digitize and punches tend to be taking pieces of them at a time and I think that's probably the most appropriate way to bring any of this forward, but thinking of it as a whole as you start with, okay, we're going to build this large two-piece management system, but where will we eventually want to pull in data in from? What kinds of follow-up actions will we want to be able to track? It's a helpful process. So I wanted to just, you don't have a lot of time, but I wanted to just flag one example that's already being done using VHS to your end as a specific tracker, which is the HNQIS system that was developed by BSI, but it's being used or was being developed as well with support from University of Oslo and is running in many countries already and they have built out components that help with planning, assessing, improving, and monitoring, so that's how they've broken out those four categories and a little blurry and I have to see, but this is pretty, the yellow one is looking at the checklist to watch what happens during the counseling session and the testing session and did the health worker actually accomplish these activities and then the blue one is showing actual scoring in terms of quality of care, so it's based on the data that you're encountering, while you're observing when you're on a fair visit, you're actually in real time providing a score and then you can share that right while you're there with the health worker. There's a lot of documentation available on this particular system and I think there's a lot of work going on to make it available as a core HHS2 product as well and to make it easier for countries to take it on, so if that's something you're interested in, I'm happy to help send you to the right folks too who might be able to help with that. I'll just wrap up by saying we're going to have a draft document of this framework to share with health worker consultation in the next one or so and we really love your feedback, your thoughts on whether this is helpful framing, how we might be able to use it and then next year we'll be working with countries that are already developing different types of support supervision digital tools to look at those in the context of this framework and think about how those tools might fit into a larger digitized support supervision system, so if you're, if any of the countries where you're working might be interested in participating with that in that activity, please let me know and we're happy to cooperate, so thank you and thank you very much. So our final speaker is Ruby Jeremy Dore which I probably didn't know about, it's pronounced correctly, it's finished with 80 and had troubles with this slide so we re-accommodated for this session so please stick around come so we can hear the full presentation if you're able to and again if you have questions please try to find it on the community of practice the link for this session we'd love to interact with you there. Hi everyone, my name is Samuel O'Neill, I'm from CUNHELY, it will be more easier to make this presentation in front of me, so thank you my mother there, so unless you have a short time and I will meet you in front of my show English, I will join you today, so we are going to talk about the TV tracker here from the database to the DHIS2 tracker, so what we did, what was the training, which step we used to go forward and where we are right now. Okay for the overview for the presentation we are going to talk quickly about the ABC TV program overview and we're going to talk about the TV program digital transformation, we're going to talk about DHIS2 towards sustainability, the challenges and some key programs extension. That's okay, you can understand me. So it's the reality of the TV program overview will help the high TV providers in America and in 2004 there were 12,097 pulmonary and proformin cases in Haiti and then that's what the reality, the mortality is high, 58 for 100,000, this is the big fact that there are only 40. The national TV program works to control material resistance and TVHR and co-election in doing so close collaboration with the district and the national network of laboratories is needed. The measure of the program is starting information, legal information is available at central report that is from program implementation and the monitoring of the activities. So what is the reality right now in Haiti? The TV data were only available after each quarter and were of two points. What we did every three months they called off the facility with the register to have a meeting and to discuss about data and about the program. So at the national level we have the contact of the data, the TV data everything. So we don't have enough time to make the data consistency. So we don't have enough time to report and to show data at the national level for this is in Haiti. That was the reality. So what is the main project when we make the TV tracker? It's to improve data availability. Improves overall TV data quality. Improves the availability and quality of the TV program monitoring indicators. Decrease data pollution towards 20 costs to claim the registered movement in Haiti is a pro country. And increase the use of TV data to improve passenger management and self-delivery. What we did? First of all, we like I might say truck as you know in Myanmar. So we select the register. What kind of the register that we are using on the TV program? Does it call that the TV doesn't catch on the phone? So when the person have a tuberculosis, criminally positive or negative or extra criminally tuberculosis, they give to the person the card in the contact for the monitoring, for each one of them. Okay, I think they run it in English, right? Yeah. So they use the pharmaceutical sensor to be registered. In French, it's a form of a surgery. So in English, it's a form of a surgery. Okay, to register each index case. And we have a register for each contact. So if we have a case, we have to track all the contact. So we have a register for the contact. And for HIV TV, HIV connection with the HIV population register, in which is registered the population alienation. Okay. And it's for HIV connection. And at the end of the day, we have a special register for mid-register. So what we did is the configuration inside the LHX2 for the four registers. That's the reality. So we have to start all the register, we have to do two questions. It was very, very difficult. And then what was the difference? So for the world, we have to understand the norm and protocols and the algorithm of the program. Because we have to do a stage inside the LHX2. So for that, we have to work with the peak energy is the program, the national program that managed the HIV program, HIV, and then at the end of it. And UPP is the immunity responsible for HIV transmission. We are working. So we have to work together to understand the norm, protocols, algorithm, and so on and so forth, and to create a stage inside the LHX2. We make the configuration of 14 registers using the LHX2 tracker. And the direct care and treatment protocol are used to make the configuration inside the LHX2 tracker integrate the link between all different registers. That means when I have a first stage in the stage inside the sensitive registers, so I make the link between the contact registers and the HIV person, HIV case, I make the link between the HIV prophylaxis registers and I make the link between the mid-resistant register. That means I don't have to for each person to identify. So with the system, I track the person and I track the contact and if the person is HIV person, I can put him directly on on the IMH technology. And if the patient takes drugs and develops some resistance to the drug, I can track him and put him on the multi-resistant resistance. So that's the process. It wasn't very easy automated because we need advocacy. And what we made a trend, a trend of health care to the use of the TB tracker for personnel. Medi-Train is quite, it was very easy to make it because at the facility level, the nurse, it doesn't, the nurse was very poor person. So train nursing, nursing at the facility to use tablet in terms of the test. You have to train, we train, we train, we train, supervision to the vision. So what we made, at the national level we put data center. No, it's not the data center, it's called center. So every week we have to call directly the nurse at the facility level to see what's all the data. Why do we don't make the data out here? And then we make the monitoring, it was micro monitoring, it wasn't easy, we make it. And then we made training at the disability level, they make the coordination, the supervision of all the facilities. And we buy tablets with the USID support and CDC. And we have tablets with SIM card, with internet to give to the facilities to deliver the data directly. And then we continue data points assessment and nation learning meeting. So even though every week we may call to know what happened at the facility level, everything more, everything more, we have meeting, special meeting with the, with all the facilities are disagreeable to make the data assessment, to measure the data consistency and so on and so forth. So that in terms of technology what we did at the, we have the TV pressure for the TV server, okay? And we use the dash tracker to have analysis dashboard, data for laboratory entries, data for broadcasting, data for the reporting, data for lab entries, data for this manager, and data for passion. So here is the purpose of we have a lot of partners. So we have to make advocacy, to make coordination, and sometimes to find partners to follow the needs of the management. It's not easier, it's a fight, we are fighting. So what is the reason it's not? So an increase in number of passion are not being monitored through the system. From 30,000 in the second quarter of 2019 to more than 74,000 has helped with the thing. All 203 TV sites are electronically monitored in passion and data for the decision making for government management to be dual forecasting and reporting purposes. They are one of the active users of the system and to be 40 non-useful users and 90 for anyone in international organizations. That's the tendency of the number of cases of TV password node and track between 2018 and 2020. So this is the engagement in terms of sustainability. So we've all worked in the discipline to see, to assess the data consistency, see how the nurses feel the resistance and what the data issues are making on the system. It's at the national level of the data consistency and we assess the data and make feedback at the discipline and facility level. This is the data model that we create using the dashes, the DHS tracker, TV program. What is the challenge? We need for what is human resources capability at facility level. What I say, at the facility level, the nurses for the TV program is not very young person, so we have to train in the discipline. After the facility, we learn more internet connectivity and quality of coverage on the area. On the countryside, even though we have a company which think up, so we don't have a good connectivity in there. So we use the Android version of the DHS tracker that means we can make the data entry without internet and go on the area as connectivity in the other track. It's that easier, so we're community. Making a set of data quality and data access with the program of internet connectivity and the capacity of the human resources at the facility level to the data entry and that after the data we have on the system. The coordination of the six observatory all in the facility data quality. And the use of the TV data at the facility level for decision making as a challenge in training for the secure context and for security issues. Sometimes it's very difficult for the nurses to go to the facility level because there are, again, the security violence and that after the performance on the system. For gravitation, and the first one is the ability to track TV vaccination in the children, Mr. J. Ability to track TV fashion, COVID-19, vaccination status, and the question of the community health information system. We are using the tracker to the, the chase community health information system for the community-based information system for the community health workers, okay, they get the tablet for the workers and make the data entry. So at the workers, the community health workers can give drugs to the patient, right? So we have to make delay with the community health information system and what we are using at the facility level because the tracker for TV are using at the facility level. So we have to work to make this design. Include management dashboard for head of the TV program at the security level in the fast distribution, testing and work ability management. Increase the number of the TV point of service with direct access to the TV tracker for patient management. Integration of the TV tracker data and to the catameter. Catameter is a map of all the facilities that are available at facility level. Okay, so we have to make the track to the tracker and with the mapping to see where the services are available and what is the reality for each facility. And it was to be tracked analysis, visualization and interpretation of data from decision-making. I think that all, I hope that my English is well been clear. Ha, ha, ha. Ha, ha, ha. Ha, ha, ha.