 Hello everyone. Let's wait another couple of minutes to allow people to join and then we'll get started not to this time. But yes, let's wait another like two or three minutes and then get started. I would say we have quite a lot of people already. So why don't we get started? I'm going to put the camera on just to say hello to all of you. Hope it's not too dark. Hi everyone. I'm not going to keep on the camera just to not be too heavy on people who are following us. In some cases it becomes a bit a bit too much putting the camera on. But just wanted to say thank you for joining us today. And and the one of like it's a it's one of the of course of the Gavi series of webinars. And the main reasoning behind these webinar is is is mostly the idea that we have our two kids we have done a whole series of webinars around the building blocks of these toolkits, be it the LMIS, be it the EPI surveillance, the tremolation dashboards, the micro planning and such. And we have reached quite a quite a maturity of these toolkits. And and so we wanted to have like a moment of reflection a little bit and and share the work of the two countries in particular have have carried forward in how they managed to take these building blocks of their of their routine health information systems and and study better and and implement them with the idea of wanting to use the data that these building blocks are producing not just implementing for the sake of implementing. So we thought that would have been an interesting an interesting topic and hopefully an inspirational topic also for the audience here today with us to see how once you implemented or could be the next steps for countries to really start up taking these these data and try to like really use them for decision making. So the the in my introduction will be a very general introduction just to like given the idea of how much the use and the and the uptake of the building blocks of the immunization toolkit have grown in the past few years. You see here like this is like a very quick snapshot of like the countries using the HS2. And we see here that 85% of the countries that are actually using it have integrated immunization data in their DHS like national interest system, which is fantastic considering like the situation that we were in like more or less three years ago. We have seen also particularly also with with COVID that that are really given the push that was needed to put more effort into routine immunization data and and kind of use the idea that immunization for for COVID was really the final push for them to really invest into it. We see also that like out of these 46 countries that have integrated is protein immunization data into the national system rely exclusively 1989% of them rely exclusively on the HS as there's that main data source, which is something that we truly try to encourage in order to create a reliable routine health information system that becomes like the repository and like the source of truth of your data. And the main reason why is also because in the long run, once you start having like stable, reliable data, you also want to start triangulating this data, be it with a bit with surveillance, be it with nutrition, be it with malaria, be it with like anything that could be relevant for your programs and for your activities. So it's it's great that more and more countries are truly starting to use DHIS as their main source of truth rather than scattering their data all over the places. And of course, when we talk about immunization toolkit as well, we're also talking about surveillance and we're still working on surveillance. Of course, there are like a lot of systems out there, but more and more countries are actually looking at DHIS too as a successful way to integrate their surveillance data into their into their national systems. So again, having this integrated system where you can truly analyze and use your data in order to make actions. Here is like the some of you might have already seen it. It was the old version, though this is the most updated version that I have of the immunization toolkit as it is today. Some things were already there. Some things are a little bit more new. Hopefully we will move more towards a proteinizing also the side of COVID-19 because nowadays most countries have already taken it. But we have like quite a lot of LMIS new new new tools and and and uses. And of course, we have like a work quite a lot on data triangulation. And on the API, we have like also updated with the latest upgrade of the of the of the API package. We have also integrated the visualizations of the that used to be purely for the immunization app also in the routine dashboards that can be used like on a daily basis. Of course, that's very dependent on which person of DHIS to you're actually using. So of course, the immunization app is still available together with the other apps that we of course know by now. But in case you have like the right versions that can be where the analytics can be used for the purpose. Right now, the immunization outputs can also be seen in the in the routine API dashboards. And so like as of now, as I said, we have reached quite a maturity of this toolkit. And and we've seen that the toolkit has given quite a lot and has been using quite a range of activities during COVID, but also during during routine routine data, especially for EPIS surveillance, we see it here. We have like plenty on of of impact stories be it in our website, but also in the community of practice. And of course, thanks to the community of practice, our in general, DHIS communities continuing to improve, giving us feedback for for what we have in the in the toolkit, but also suggesting to each other new way of of implementing and avoiding for example, barriers, which is one of the things that we would like to also share today with you. Here, I just put a compilation of since we always share these slides with you. But for those who among you who might have not like joined all the webinars and they are not aware, we have like, of course, the DHIS immunization page within our website. But then we also have a YouTube playlist that is few fully focused on immunization. And I pasted the link over there. And in that in that in that page of like, let's say playlist of YouTube, you have also all these webinars that we that we hosted for this quarterly webinar, actually, like meet up, let's say. And they are a wide range, you started from, of course, the COVID, but then we went on, of course, of course, also quite a lot of micro planning and mapping tools. And and also under the other other components of the immunization toolkit. So here, I also pasted like in the page, the English and of course, the French, the French links for you, in order to like, if you want to catch up or if you want to know more about other implementation or other components of the toolkit. Looking ahead, we have like, of course, reached this maturity, but we would like to like, of course, work more with what this toolkit is today. First, we continue enhancing and improving and continuing updates in content, be it from from our SMEs, with our partners, the WHO, UNICEF, CDC and such, but also, of course, announcements from the actual support tools and the platform itself to continue like analyzing this data, collecting this data. Of course, what we would like also to really focus nowadays, once we have like now these established building blocks that a lot of countries have already taken, we would like to really start focusing on improving the capacities of of using this data and analyzing this data for decision making. So be it for like supporting like the analysis of like and the finding of zero those children, more evidence-based solutions based on, for example, microplanning or better triangulation of tools. And of course, the final goal is the idea of having an integration of data throughout the different type of toolkits, not just the, of course, immunization toolkit, but anything that could be related for countries to truly start using for real this data and start making decisions based on real numbers and reliable numbers. As I said, we also try to foster continuous improvement and it's also coming from lessons learned from implementations. And part of these lessons learned from implementation is also on the analysis of the data used their countries are doing. So we are really trying to like gather as much information as we can from the real implementations rather than now pushing out a lot of like new content and new and new toolkits. Of course, there will be the routine upgrade. Of course, there will be some some some inputs that are coming from implementations that could be incredibly useful for for other countries to uptake and could be integrated into into the general the general toolkit. But we really try to like get information from from you all towards us to either integrating in the guides that we have out there, or at least to like suggested also improvements to our partners. So what we are having today, it's actually two big implementations and two big sources of data use that we have. And it's quite a lot of work. Then Rwanda and Tanzania are done on the use of data related but not just limited to the immunization toolkit. So actually, I don't want to take any more time because I think that the most important people today are here with us. And we have here from Rwanda, Jean Paul and from Tanzania, Wilfred and and yeah, I mean, honestly, I would just leave like the word to to Jean Paul and and yes, the the Mike is yours, Jean Paul. Just tell us everything. You thank you very much. Thank you, Vito and thank you for all who is joining webinar. Maybe Vito just without of you let me share my screen and continue from where your vision ended. Yes, please. I can. Let me know whether you can see my screen. We can just put it on. Yeah, exactly. Thank you. Good. Is Vito was saying this is the use case of Rwanda where we have been collaborating with a hospital in Tanzania, but also we have some specificity in use cases. But we wanted for this webinar to share with you how we have been collaborating with the means of health and other partners to enhance that utilization and effective the highest implementation. For us, specifically, we would share with you the use cases. The first one will be how we have implemented the scope at both national and some national level. This return facilities level for them to be able to use data by visualizing the key indicators using this application and talk with data through this application. The second use case will be how his program has been collaborating with the MOH and other partners to try and let to create a kind of merging or communication between different instances, one for immunization and the other one for vaccine preventable disease for them for anyone who is using vaccination immunization data to be able to come up with tangible evidences that will be used for planning and decisions. So I will be trying to take you through the summarize key points here, background methodologies that's conclusion on next step for the two use cases. Background, as you may know, the health information systems, they play a crucial role in addressing the health challenges in developing countries and improving healthcare. They write, but apart from that, the systems, even though they provide the massive data, they are often of low quality. We still have that part of data, but also they are often not used effectively for decision making. And then there is lack of coordination for different stakeholders where we see some fragmentation of the systems and that may lead to limited availability of targeted data at local levels. And sometimes you would see that much attention is currently directed towards the interventions it's national. Yet data, they are corrected at subnational levels. But some of subnational levels at facility level, they think that data is being corrected just for some meeting to national levels. And that normally should be used at subnational levels to create improvements in terms of performances, but also that we lead to quite care. Then there is a lot of data that's being corrected at facilities and reported upwards. That's what I'm saying. Yet it's being corrected and being submitted in her system hierarchy without being used to make decisions at some national levels. And then here that's where I came in the SCO-CAD intervention that resulted from the I mean the collaboration between different history groups, ISP Tanzania and ISP Rwanda, which are both engaged in the implementation of this SCO-CAD and HIS and the HISP in general. Yes, a big inclusion of the intervention was just to strengthen the data use at some national level by improving the availability of key indicators and data from the national looting HIS to local health workers and managers. And the intervention focuses on the implementing districts and facility level SCO-CAD for the expanded program on immunization API in Rwanda and the other HIS programs. Of course, the intervention of the implementation of the SCO-CAD though this program was the key during the piloting, but currently the SCO-CAD is being used by other programs. Then this SCO-CAD is presenting a way to present key performance indicators in tabular form colored in traffic light signs such as green arrow and red. These colors they are being referred to because, you know, these traffic lights if you see green, you know what to do if you see yellow, you know what to do if you see red, you know what. So from this logic we configure this SCO-CAD so that if performance is scored in green, yellow and red, decision makers and technicians will be able immediately to talk with that and decide what to do. Then the SCO-CADs were designed to provide information support to looting connection meetings by highlighting achievements against targets and neighboring comparison, comparison and peer review between the health facilities. In a background, in Nwanda, we have these connection meetings through which clinicians and machine makers and health facility managers they are meeting on regular basis on mantra and coterie and discuss about the indicators. So this SCO-CAD is now being used as a tool to discuss API coverages, API indicators and decide what can be the action according to what data through the SCO-CAD is showing. These are the descriptions of the SCO-CAD and about the methodology of the intervention. This approach of implementing the SCO-CAD has been done the framework of research. I mean, action research where we started by developing and implementing the SCO-CAD followed the action research approaches by engaging local users in diagnosing the current environment planning, selecting the indicators and implementing the media actions, correcting, refracting on the outcomes and lessons learned during the implementation of this SCO-CAD intervention. And the key faces, one was the planning and stakeholders engagement where we have met with different stakeholders to define what can be the indicators program by program starting from the administration and other programs and then decide what should be the scores and the range of scores up to the design and capacity building and the SCO-CAD and also post the program. Monitoring, the second step was designing and pre-meditation and designing the SCO-CAD for the limited immunization. We designed the SCO-CAD that has different indicators from different programs. But after we came up with a specific SCO-CAD for limited immunization for immunization programs to be able to monitor their performances using this specific program for the API program. And then SCO-CAD for the a big discussion of the SCO-CAD for API where one of the SCO-CAD for API was using this range of scores. Like for example, one between 0 to 60 was not good performance. So it has to be enumerated. If one of the API indicators falls in this range will be enumerated and sooner makers will be easily identifying this failure and decide what to do and why. And for example, between 0 to 80 its scores, this performance is not on track. Then 80 to 95 progress made but more efforts and then 19 to 150 targets performed risk. So all these ranges relative to colors and show makers are able to use data referring to what SCO-CAD is showing. So these are all about steps and I just want to go about the post implementation and evaluation to strengthen the SCO-CAD is another step. You know, after implementing now we are using the SCO-CAD we are monitoring how the SCO-CAD is contributing to data use and we are trying to strengthen the use of SCO-CAD and its implementation. Through different programs. Promoting local data availability. This is one of the results of the implementation of this intervention. It's very contributing to the promotion of local data availability analysis and the use through the SCO-CAD. These are the explanations and the second result is that they are helping to promote these are the conclusion of the first point. Then it's also incurring the interventions with local practices like this intervention monitoring which is a key activity that promotes data use at different health center levels. This results in the underconduct monthly meeting as a background. Then data managers distributed using this SCO-CAD to present in the connection meetings where they show the status and they come up as a multisciplinary team that is attending this data use sessions came up with actions using this SCO-CAD. And the other result is that there is kind of innovation through actionary research from this implementation of this SCO-CAD. Then if I can summarize the next step for the implementation of this SCO-CAD we are collaborating with our partners and programs that the Ministry to strengthen the operational SCO-CAD is used at facet level and we will keep documenting the connection meeting process because you may see that the way the process the way facetes are conducting the underusing this SCO-CAD is not yet harmonized. So we are now trying to document the connection meeting process so that we can come up with a kind of guiding document that will guide all faceties during the connection meeting so that they can use even this SCO-CAD in a harmonized way so that it can give results in an efficient way. Then documentation and publication of SCO-CAD implementation user stories to facetate other countries other organizations for them to be able to implement this kind of interventions. Then continuous capacity building for SCO-CAD users for them to be able to efficiently use this intervention. So it's conclusion in some ways to say that though we are having a big data in our systems, we need to keep using these kind of interventions to help users to come up with I mean to have easy way of visualizing data, discussions, trigger discussions and come up with a kind of actions then this profession also facetated identifying the information needs of data managers at local levels and structured the interventions to address their local needs. That was about the the SCO-CAD and then we have another intervention where we have matched, we have created different instances for them to be able to talk to each other that is data is to data triangulation, pulmonization and vaccine preventable disease. I wanted just to explain or to take you through about the roadmap for implementation and our findings. I will also use try to summarize this intervention in these points bug-run methodology, results and conclusion on next step. I have my correct brace if you are on on this call, can you take on can take over and continue? Brace, are you there? Yes, please. Yeah, I consider brace is raising hands but it's not talking if I think I can continue. Okay. Maybe brace really compliments. So as a background we I call it public data is a key, as you know, to time and from decision-making. So multiple electronic systems in this WHO Afro countries were there. So we need new methods to help these systems to talk to each other. Yes, brace, can you take over if you now can pick? Yeah, thank you. I was not co-host so I couldn't do it myself. Okay. Yes, thank you. Yes, hello everyone. Thank you for being present and I'll be working through the current implementation of this year's data translation. And as I've seen some people already posted questions in the chat, so free to keep doing it and the colleagues will be answering them going forward and we shall also have a session for the Q&A. So yes, for the data translation, I will just give you a small background. We have been having high quality public health data in Rwanda and most of our instances designed within DHS-2 that includes immunization, vaccine prevention for diseases and they are all configured in DHS-2. However, all this data is separate and used in different systems and we realize that programs we are making decisions based on the only data but not able to triangulate the different informations. So in a way to conceptualize and utilize all the available data effectively, data translation based in DHS-2 was conceptualized so that we can be able to automate the communication of the systems. Next, please. Thank you. Yes, and then part of the methodologies we used to be able to initiate this idea was first we adopted the global guidance, which we can always find on TrackNet and also DHS-2. If you Google, you can be able to find it and if you need those links, please feel free to get in touch so that we can share them. In addition, after assessing the global guidance, we did have to create a roadmap, a roadmap of activities. And initially, the first one was doing a data mapping. Data mapping included one to identify the sources of information with which we have, we have the aggregates of surveillance. We have the individual best surveillance as well. And then we have the expanded immunization registry, all of these which are in DHS-2. And then in addition, we have the civil registry state of the years and then also the vaccine logistic management information system. So at the end of the day, these were the systems that were going to be the source of data that creates the data triangulation. And then in addition, we needed to understand what will be the integration, what do you have with the data be exchanging? Some of the data is collected on a daily basis. The other data is collected on a weekly and some data is collected on a monthly basis. So we needed to be able to understand how will the data be communicating and how best on what regular basis. And then obviously the next step was to configure and design the visualizations. Please next. Yes. Thank you. And then once we had identified the sources and we were able to understand how the systems will be discussed or integrating between data or communicating, this was the final integration model. This is the overall of how it works. So you have the integration model in between that gets information from disease surveillance, pushes it to immunization for the selected key performance indicators and then the selected indicators from immunization are also pushed to the disease surveillance. So these indicators we have a list of them. There is a global standard one for from CDC and then you countries based on the country's resources and also what the country needs to be able to see. So the idea is HMIS vaccine data are pushed to the DHS to immunization. And then whatever information is needed by disease surveillance is also pushed to disease surveillance. Yes. So eventually we shall be having two dashboards, one within disease surveillance and one within immunization. And each one of them shall be able to accommodate for the different data transformation. Next. Next. Yes. Thank you. This is one more detailed integration module. This is specifically for let's say for the dashboard of disease surveillance. So we assume the vaccination officer will be entering individual data within the EIR. Once it goes into the cloud, our DHS systems run analytics at around 11 p.m. at night. And once the analytics run, the indicators that have been configured to fit the dashboard will be generated. And that means by finishing the immunization dashboard will be updated automatically. So now goes to the indicators that are supposed to be pushed to disease surveillance. The integration module will be running at 2 a.m. So that means if the analytics run from 11 p.m., they might end around 1 a.m. So we pushed it a bit faster that in case the analytics are still running, it does not interfere. So once the integration module runs at 2 a.m., it's able to send the selected indicators to IDSR or disease surveillance. And then once disease surveillance receives those indicators, it also runs analytics on the next day at 11 p.m. And then now we are able to generate a data-triangulated dashboard, which has both disease surveillance information and also immunization data within the cell dashboard. Well, next goes again. Thank you. So one of the key diseases we are following up in is acute flaccid paralysis, measles and neonatal tetanus. These are the ones that are vaccine-preventable diseases that Rwanda currently follows up. And then we have that divided into different groups of dashboards. One is the immunity gaps and then the program performance. So the first one has a specific kind of group categories of indicators, one being assessing the vaccination status or coverage among the cases of those diseases mentioned, both by age group and by vaccination status. Another thing is measuring the drop-out rates for the different measles, polio and orthodipathy. And this helps us to also be able to the help they mean the disease surveillance program to be able to identify if an area has a lot of diseases. Is it because of the low vaccine coverage that it is? We shall be seeing a couple of examples and I'll be able to elaborate more. In addition to immunity gap, we have also assessing the zero dose and immunized, which is currently using estimated population from the statistical house. But however, the sphere of years will soon be also co-provided permission for us to use the CRVS-based data. Currently, we're using estimated population. But within two to three months, we shall be using CRVS. We believe that data for CRVS is accurate to 85 to 95 percent. However, they wish it for it to be a little bit higher or to stay at that level for the next how many months before using it. So within program performance, we have assessing access and utilization of immunization services. We need to compare coverage and drop out rates. This will help the immunization program be able to better monitor and also understand the best locations to perform their campaigns and ETC. So this will allow them for a better planning with an informed decision. Also, in addition, there is a great you're able to identify data quality and discrepancies. One you'll be able to see is where you're comparing aggregated data and individual data. So if they're reporting at the end of the week, they're reporting IDSA aggregated weekly data. If they have reported five and individual has only two, that means there is a lack of information in the system. So this is going to help both the national and district teams, which are the initial implementation levels, they'll be able to now monitor the data whether it's correct before having to wait for the end of the month when they're supposed to actually submit their reports. Another thing is surveillance system. We're able to to assess the sensitivity of the detection, both for measures and the FB. We're also able to do a representiveness and the time around time for the difference analysis. Next please. Yes, in addition, we shall these have not yet been configured, but we shall also be doing surveillance co-functions, which is detection of measures in terms of the source of transmission. There are these variables are not yet available, but going forward, they shall also be implemented. And then, obviously, a complete measure of investigation of suspected cases. So as soon as all districts have been implemented, these indicators shall also be added to the dashboards. Next please. Yes, so I'll be showing you a few of the examples. As I mentioned before, we prior to the implementation, we had to adapt both the global standard and also the country's needs. So on the left, you'll see the global standard on the right. You see what Rhonda has created. And on the right side, this is what we have as immunization status of confirmed music cases by age group. And then we have MR-1 who have identified as music cases desegregated by their vaccination status. This is all the visualization. The current one has also unknown and unknown and also not vaccinated. So that shall also be added as desegregation. So one thing to mention is this data is from individual data. So I know for a fact that there is a challenge between the HST users on how to properly present individual data as stacked bar. That is also something we had faced as a challenge, but we did it by converting them to indicators, sharing them to another instance and then returning them as data elements. For those who wish to get more details, feel free to ask more detailed questions. Yes, thank you. So this is also not clear, but this is for comparison of zero dose and I mean, it is the PT coverage and the PT to coverage. So as I mentioned, this graph in particular is using estimated population. However, in the next two to three months, we expect to also be able to compare what is the coverage for estimated population and what is the coverage for CRBS based population that next place. Yes, so that was immunity gap. This is program performance. So on the left, as mentioned again, this is global guidance and then on the right is for the Wanda's implementation. So please note, this is this is not real data. This is fake data. So this particular visualizer is going to help the surveillance team. We have a surveillance team at the national level that is supposed to be monitoring the reporting. So that their job is to always monitor this and be able to identify if if we have more data within the weekly reports, then I mean, individual data is not being collected or reported on time, which voids the the thing of which voids the purpose of the system for emergency alerting and VTC. So once when a disease is identified as a misuse, there's a selected team of people that receive SMSs. So this particular dashboard will be helping them to to monitor if diseases have not been reported so that they can be able to follow up. Yes, next place. So as I mentioned, we did face some challenges, not quite big, but most of them were technical because most of the systems have already been implemented and they're working. All what was needed was to find a way to help them communicate with another through integration. So in terms of data accuracy, we had we have to wait for CRVS as I mentioned for each to reach a favorable percentage according to the Ministry of Health. And then also the vaccine logistic management information system was recently implemented, which was developed from scratch as well, was recently implemented. So they're still waiting for it to reach a level of confidence and then they can allow us to configure the indicators based on the vaccine and the vaccine stock data. So as of now, we do not have VLMIs in the dashboard. So in conclusion, the outputs we have finalized the dashboard as we speak right now, we're actually going to be carrying out training of trainees for the next three days and also be able to visit the field so that we can understand how best will these tools be utilized, which levels shall be the you know, which shall be used at national level, but how best can they help the district not only in decision making, but also in monitoring of disease surveillance. And then also, obviously, the outcomes we expect from this implementation is improved collaboration of data sharing, especially through between programs. Now they're going to have an advantage by the disease surveillance is able to understand what is the coverage for the MR MISO vaccine. Is that is this why we're seeing a lot of cases is this place not having good coverage. And now they have it in real time on their dashboards. The difference of hours of reporting is approximately 24 to 36 hours. So that will help them to make real time decision making as much as possible. And then also improved the routine use of data national subnational this especially to to their team of organization. They do have campaigns now they will make them with a more informed decision on which areas are most affected with diseases and which ones need the intervention of the program. So yeah, we are this shall help us in the impact is obviously as I mentioned reduced incidences of VPDs and deaths and then increase efficiency management that is by better planning through informed decision going to improve coverage in the future because now they will know what are the best places to go which places need our attention to see on the part of the visualizers in the program performance also include being able to track the immunization schedule if someone was supposed to receive a dose at path within one week and they receive it within two weeks then we are able to tell that there is delayed immunization schedule. So all these these visualizers will be able to support the teams in decision making but also also a quick decision making because they'll be having them ready at hand. Next please I think I'm done. Jean-Paul next please. Oh yes thank you. So as the next steps as I've mentioned for the next three days we're going to be performing national training for the next three days. The district training will be at probably in August and then we shall support the monitoring and maintenance of the dashboards in the first few months so that we can see how they're being utilized and also help them to know what is the best way to use them which levels should they be used and also obviously we shall be sharing a documentary since land one from the implementation status. What does it take to develop this? What does it take to implement the data triangulation when your country has different systems and then we also obviously have to share a use case once it has been implemented for a while. Sustainable coordination collaboration and user training all these will be kind of the supports will be providing to the Ministry of Health specifically both programs. So we have been working with multiple stakeholders the Ministry of Health, RBC, which is the implementation party and then his Tanzania WHRCDC at length and University of Mexico. So thank you to everyone that helps us. These implementations are not only helping in better planning and decision making but also showing the potential by DHS too in the health interventions. Thank you very much. Over to you Jamfo. Right, thank you my colleague Bres for this good presentation and we thank you everyone who has been following these two use cases from around the side. There is a lot of there are different use cases we could be sharing here but due to time I think we would be having different sessions now take over and continue. Otherwise for participants feel free to chat I mean these chats but also at the end these documents. Thank you. Over to you Vito. Thank you so much Jamfo and Bres. It was really really good to hear your advancements but most importantly I think that also our audience here today is really engaged and they're really like interested in like how you overcame some limitations of course but also how you addressed some key points. There are some questions there already if you and Bres would like to already address some of them. I really hope we can have like some time for Q&A so like without further ado I actually like pass it on to you Wilfred and with your summary of like a little bit of like the experience of Tanzania for data use. Thank you. Thanks Vittoria. Can you get me there? Yep. Great. So hi everyone. My name is Wilfred Signoni. I'll be here presenting experiences from Tanzania in particular on enhancing that utilization and effective GHS to implementation. I'm part of his Tanzania part of also his network being part of DHS to implementation for the past 15 years implementing national scale health information system in our country Tanzania but also in other countries. Victoria can you confirm that you can see my screen? Yes, yes, yes. Great. Yeah, so my presentation is kind of outlined into these six particular areas. I'll talk a little bit about the background. Talk about the overall objective because we are actually sharing a little bit of our experience for the past two years and specifically on different interventions which we have done in collaboration with the Ministry of Health but in collaboration with different partners to enhance data use in different levels. So I'll talk a little bit of a background. Why are we doing that? Our main objective or main goal that in terms of the interventions as well what kind of interventions we have done. We have kind of split them into three, three, four interventions and then of course one of the lessons lens which we have done we have uncovered through this implementation of these different interventions but as well as what are we looking for in terms of the next step. Now as we all know health information systems are really key in terms of is actually information system health information is actually building block into their whole systems in terms of supporting effective and efficient delivery of health services and in particular in law and developing countries. These health information systems help us to generate timely, reliable and complete information and this information I actually use now with the data managers to make informed decisions at the different levels. However, we see that there's a bigger challenges in terms of these systems are facing. I think one of the key challenge which we are facing most of it is the low data quality. People are not really trusting these data which are coming out from this routine health information system. We have multiple or fragmented systems operating there with minimum to limited horizontal data sharing and of course as you know as stakeholders are not really aligned, coordinated and we have this parallel initiative in the end of course bringing burden to our health workers. Of course this in the end kind of results into having data massive generating massive data but of course these data are really not being used by the decision maker and some of these data have been used yes at the national level but of course if you go at the sub national level there's none too limited use of these data for the local actions there at the particular level. Now as we all know the HS2 has been kind of implemented the last check I think it was more than 80 countries and it's been used as the national HHS in these particular different countries in Africa Asia and some of the American continent as well. So understanding the aspect of you know how can you improve this data use and in particular in the HS2 implementations quite kind of interesting. So our overall objective for the past two years or more or less what we've been doing as a team has been more or less kind of looking how can we work with the Ministry of Health in terms of strengthening data use in routine health information system through implementation of local interventions. Specifically we were more or less looking about how can we come up with these interventions geared to our enhanced data use promoting that availability disseminating information out of these routine information systems so that the decision maker can have this information but also other stakeholders can have this information and use this information and not only introducing this particular interventions but we wanted to learn from that both challenges best practices but also what is the best options in terms of adopting these interventions and how can you scale these interventions across the country regionalized and maybe also globally and of course providing recommendation on the best step of how to do that. Now as I said I based in Estonia Estonia is part of the global history network as you know our aim is more as to strengthen health information systems we work in Tanzania but also we work in other countries to support their national health information system for example in Somalia Eritrea South Sudan and also in Zanzibar as well we've been working also with other partners in different countries to support their information systems in different contexts. This is kind of a picture of our team having a little bit of resting time after working 24-7. Yeah so let's jump directly into the district into these local interventions before I go there just a quick point is that as we introduce these interventions we have been working closely with the Ministry of Health making sure that they are leaving this particular process owning this particular process and also catalyzing this process and pushing this process we have been also working with different partners both local and international as well locally we have this Ministry of we call it ProLab where they actually are managing the local government within the country but also we have been working with different implementing partners in the ground to kind of complimenting our interventions which we are doing. We've also been working with the University of Oswald at the center to kind of implement learning the some of the interventions which we are doing but also as our colleague from X Rwanda have said we've been working with them also to implement some of these interventions which I'll touch base a little bit because they've touched base already but also to kind of do comparison or comparative study in terms of what has been happening in Rwanda what has been happening in our country and can we analyze this informational lesson learn from these particular kind of interventions. As I said these are kind of experiences from the last three years which we have been working within the national health information system. Now first intervention which I'll go through is the district of Excellency so what is the district of Excellency? We kind of came up with this idea in terms of coming up with a learning environment where different approaches and technologies can be tested within this safer environment and of course kind of gathered learning and also of course push ups some recommendations. DHS2 in Tanzania was kind of adopted in 20 by December 2013 it was national scale in the whole country and since then we've been kind of implementing different interventions at the national scale. However we've been lucky in terms of area where we can have small scale interventions where space where we can have intervention implemented in a small scale learn about what is happening there and also after that kind of pick it up and also scale it to other regions. So we designed with the Ministry of Health and also local government to designate two districts in one of our regions actually the capital city of Tanzania the Dome region where they designated two districts as our district of Excellency of course one being the rural setting another one being urban setting. The idea is we kind of introduced some kind of interventions in this particular area and then of course learn from that and of course the focus of our main focus is more or less how can we enhance that using particular at some national level and of course immunization being one of the key topic. So our approach was kind of straightforward we identified some thematic areas where this district of Excellency will be kind of engaged. One was data management information using I think this has been one part which have been really focused in terms of understanding what is really going on in the ground at the local level at the facility level at the district level what are the data use practices what are the gaps which are there and how can we improve those routines which are there and what kind of practices can we introduce to kind of add or enhance the data use at the local levels because the second thematic area is the digital innovations how can we use digital tools to actually help how can we improve the DHS too to actually help data use at the local level build the capacity and specific down that I use and also research in documentation. So before we conducted this we started we started this project we conducted baseline assessment where we kind of understand the formal and formal and also some intangible processes which are there in terms of managing routine data and also disseminating and use this data at the local level actually at the all these particular levels once we kind of gathered the input from this we first sit down with the user and come up with the a low formulating some interventions working with them so that we can improve the data use. One of the principles which have been using is making sure that we are building on top of the routines which are there not really kind of starting parallel processes rather than building on top of what there have been the processes which are there which we are working. So while few interventions which we have done so far one is more or less kind of work with the health facilities and the district level to actually kind of come or develop some denominators for health facilities to facilitate that analysis and its dissemination this is one of the gap which we saw in terms of you know population data being at the level of district however was quite difficult to actually have this population denominators at the health facility level so we've worked with the team in terms of coming up with the best way of formulating these denominators for a facility to improve or to catalyze that analysis and dissemination we've been working with the team also to develop some health facility and district level dashboard based on the local use local needs build the capacity one of the biggest gap which we saw was that mostly health facilities were reporting they were reporting that upwards more or less not using this for local actions so it was more or less important to actually build that capacity in terms of management of the data review of the data analysis and dissemination and particular use of course an additional part while we saw some quarter review meeting being conducted at the district level involving all health facilities we actually agreed to add to introduce monthly data routine data use routines which will be creating a platform for user to engage discuss and also interpret the data which they have and I think this has been kind of really sound in terms of improving the data analysis within the district and health facilities but also getting people engage in discussing and understanding their data um this project is something which we have started last year quick preliminary results which we have already seen so far is that these are the use of district health facility dashboard is quite of helping users to actually kind of you know a build or come up with these local actions we have also seen now health facility people being capacitated and also they now using DHS2 actually to analyze their data making some action and making some recommendation also making follow-ups into their facilities where they've seen there's an issue but also we have seen also by using by capacitating them then of course the aspect of dissemination sharing best practices follow follow best practices in how they're using the data is becoming quite sound a good example which we have seen in one of the district the Bahia District Council we have seen how the district malaria for a person have actually been using the data within the DHS2 to really actually pin in pointing facilities where whereas a little bit of a high cases of malaria go there of doing intervention then also monitoring that after conducting this intervention this particular best practices is also scaled now not only to malaria but also to other programs such as API and etc so that has been the first intervention which we have been engaged the second intervention is a little bit more on the technical end where we have been deploying developing also deploying health portal the health portal this is kind of a standard platform where the Ministry of Health is actually sharing its data reviewed that of course from the routine health information system so basically the Ministry of Health is collecting all this data within the DHS2 but there is a kind of a tension at some point where most of the stakeholders wanted access into the DHS2 but also there are also feeling that there are some stakeholders who were left behind or left outside because they didn't have credentials to access these information which are within the DHS2 so there was a need to actually kind of opening up the data which is there to the public so that they could actually see this use it and also build more trust in the data which is coming out of the routine health information system so we actually developed this portal and of course one of the biggest program which we are kind of instrumental was also EPI in terms of what we call it IVD so they've been quite instrumental in terms of guiding us providing some indicators what indicators needs to be kind of designed populated and of course being part of the whole process of populating this particular health portal this portal is actually accessible online if you go to check the link there to mysporto.mox.go.tizet you can go and find all these information where different different data from routine health information system and for our case DHS2 actually kind of pushed on a quarterly base you could actually do your analysis at the national level regional level up to the district level and of course into a different period so you actually bringing the functionality of the user you're empowering the user to actually analyze this data and have access to this data publicly yeah so like I said building this health portal was quite essential and it gave the team the Ministry of Health also ability to enhance data access and also increase transparency into the data which is being used to make information to make decision making at different level now people are more confident in terms of data which is being collected being processed but also being used for decision making because this is something which is also available to the public and public can actually look at it can actually scrutinize it and actually give feedback on areas which needs to be looked at which is also being able to make this information available to decision makers some of the decision makers they don't have time to actually go into the DHS2 and inter credential they need this information at their hands so having this portal was kind of a deal breaker in terms of opening access and reaching we say reaching those stakeholders which we have not reached in the beginning but of course the whole aspect of using easy visualization to actually inform users as you have seen most of these users who actually were outside who didn't have access to the DHS2 they need digested information they need information which is kind of new process so it was also an experience in terms of how you go through it and how you design the visualization and how you make these a good output so that the user can understand the data which we're actually presenting in addition to that while we the web portal was there there was also additional demand in terms of having a mobile application to access this and this was more or less in a case that where you have decision makers they want to access this information they don't need to go to their computer in search they need to have it on their mobile phone and this was a challenge which was given to us in terms of you know can we have this a web portal in our mobile application and both in Android and iOS and the team has been working with the Minister of Health to actually develop this mobile application for the web portal making sure that all these indicators are actually integrated and as I said IVD and of course the global toolkit which has been Victoria kind of pointed out at some point has been instrumental in terms of guiding us how we structure these particular indicators and how do we communicate these indicators to the decision makers at different levels yeah so apart from that second interventions we have also been working in terms of developing and implementing data use apps our team here at East Tanzania has been kind of quite instrumental in terms of building data use applications namely scorecard and bottleneck analysis these applications have been instrumental in terms of promoting data use and also making sure that data data managers and also stakeholders who are not really conversant with the the health programmatic they could actually follow and monitor the performance of indicators through the color coding I think my colleague John Ho has also talked a little bit so I'll touch briefly on on these particular apps as as he also mentioned scorecard is just a a tool which help you with tracking key performance through these traffic lies codes red means danger you need to kind of improve yellow means you know you are performing but not yet within the target which you have set of course green means you're performing but of course you need also to make sure that you are maintaining that performance in that particular color color coding so these are kind of easy digestible visualization which data managers decision makers and politicians are easy to read understand that there are some area where it's there are some challenges and we need also to kind of address these challenges based on certain performance issues which I've mentioned there of course we have also BNA application this BNA application is helping out in terms of you know a building evidence based planning where you could actually monitor the the performance of your indicators but also coming up with these particular areas of of features for example you could monitor the what exactly is the bottleneck or what are the areas which is actually contributing to either low performance of your particular indicator either if it's a graphical accessibility if it's a human resource if it's a commodities if it's an initial utilization and continuous utilization or effective coverage for this particular particular implementation I'll talk a little bit more about Scorecard because out of these two or three apps Scorecard has been the one which has been quite or extensively used here in Tanzania but also in other countries as my colleague mentioned in Rwanda but also globally so I'll talk a little bit more about that in terms of how it has helped improve data use at different levels one of the key Scorecard tool Scorecard tool which we have used in Tanzania is building an Adam and C.H. Scorecard this is more or less for Martino in child health and it is being used to monitor these indicators as we know that immunization or EPI is actually part of there are some indicators all they actually one of the contributing indicators within the Martino and child health program in general so we build this we have this our message Scorecard which is actually monitoring these indicators and this Scorecard has been quite instrumental in terms of bringing changes but also holding people accountable in terms of what in terms of you know improving service delivery but also allocating resources areas which require these particular resources on the picture there it's our current president in Tanzania kind of showing or launching the Martino child health Scorecard right from the data which is being generated from our DHS too so how have we been using this Scorecard so far I think the most important part it has been used as accountability tool where it is holding not only service providers who are delivering services but also leaders in in their area you know in terms of you know making sure the resources appropriate resources are located into specific areas the areas which are needed and also making sure because this is also something which is shared publicly then also the public is also holding service providing their leaders accountable in in in their performance as well in addition it has also increased transparency and also kind of increase the prioritization of the resource in terms of making sure key areas kind of place with the resources which is needed so it's actually kind of helping out in terms of making informed decisions when it comes to distributing resources but also how these resources are being distributed in addition it has been used in terms of monitoring and tracking program performance as I said RMCH but also we have other programs like TV and nutrition we are using this particular program based on their use we have also identified some challenges but practices how it's been used how best we can deploy how can we configure these indicators how can we manage these indicators in the in their performance so that a clear communication is coming out from from the scorecard to the user because for example if you share this scorecard with the politician they will actually see the red and when they see the red they will kind of you know hold people accountable so we were actually at some point Advocated that not only you're sharing this scorecard but you're also sharing some guidance in terms of how how what are the necessary steps to actually address some challenges which are there but also what does it mean when you see these particular calcodes key success factors in using this particular scorecard I think there are so many but I've tried to kind of mention just a few here one is how the tool has been used in terms of management tool the manager using them to actually monitor performance of their health service provider but also in different departments within the organization so this tool has been used now in the management meetings where people can actually assess themselves and assess the performance of their department it has also been very instrumental in terms of how this tool was launched at the national level but it has been able to scale at the regional and also district level local government level and now the managers and health workers are actually using this scorecard as a monitoring tool in the day to day or at least monthly to monthly progress it has also been used in terms of integrating itself within existing management processes and of course as I said before used at political level to rally mobilize resources but also to disseminate properly these resources to areas which really demand and and require those particular resources quite useful in terms of how it has been used in dissemination public sharing advocacy I think that has been quite quite quite good and now of course we have tried to also document and evaluate the implementation of this scorecard and what are the lessons lens which we plan from that and then the last interventions which I'm going to talk about touch base is on the research and dissemination while we are introducing these interventions we also look in terms of understanding why why these interventions are working or not work why these interventions are working in this particular location and why it's actually effective in one location and how can we really scale these particular interventions and also we have been trying to document and disseminate some of these interventions so that not only it is knowledge which we are gathering here not only us which will be aware of it but also the community as well so one of the key aspect which I've been doing is you know researching data use we have been collaborating with local and international research institutions to actually bring about researchers students to come and investigate further and document of course these local data data use practices identify the gaps which are there providing recommendations and also be part in terms of bringing change within the routine health information system as you can see we have been partnering with the universal law bringing the researchers students here within our context going to the field understanding the local practice which is there for example you know these world dashboards how can we transform these world dashboards into the DHS2 district dashboard facility dashboard how can we you know make sure that the health facility are using DHS2 to analyze their data use the the output from the DHS2 to as an input in their management meeting but also in their quarterly review meeting so there's a lot of work which have been doing conducting this research as I said at the local level with the university of the Islam the students there but also at the global level as well with the university of Oslo but also other partners who we are engaging on the focus is of course as I said identify these gaps document them and also kind of using an action action research kind of bringing change and also learning from these changes which we are conducting we've also been instrumental or pushing in terms of documenting and share best practices my colleague Jean-Paul touch base in terms of the initiative which have been working on on the health facility to scorecard at the facility level where we have implemented it in Rwanda and of course our team in Tanzania has been more instrumental in development but also trying to also see how we can implement it in Tanzania and we have been trying to document whatever we are finding these two sites comparing them kind of do comparative study but also see what we can find as a best practice this is a paper which we have recently published in the ISD Africa conference which was actually conducted in few months ago I think it was end of May early June where team from his Tanzania team from his Rwanda but also Universal also kind of come together and analyze the data which we have kind of collected from both sides and also see and publish from that particular data so I would actually urge you to kind of go in the ISD Africa conference proceedings look at this particular paper making data talk using proper for strengthening that they use at local level where we have kind of outlined some of our findings which we have seen in our implementation so far so out of these interventions what have we learned there's a lot of lessons learned but I will just summarize this into a couple of points I think the important part which we have seen in all these interventions is that there's a need to have an ownership of the Ministry of Health taking leadership of that and coordination as the Ministry of Health has been kind of instrumental and focal in terms of guiding through these interventions it has been quite of I would say helpful in terms of opening doors in terms of reaching out to the stakeholders in terms of learning together and of course in terms of owning these results which we are getting from that so I think that was kind of important in terms of having a partnership a long-term partnership with the Ministry of Health and also going together and doing these interventions together having these using existing resources standard tools I think it was quite important as we deployed these dashboards scorecard it was really necessary to actually tap into what these global tools or standard tools were there I think Victoria touched a little bit about this if for example EPI toolkit for feminization but also there are other toolkits there which are there which are quite important in terms of guidance the indicators how do you design how do you use it how do you interpret these the information so I think tapping in or leveraging these resources is quite important another thing we should have learned is that as we introduce new interventions in particular for data use is actually important to build on top of local routines which are there important is that you shouldn't really create parallel data use routines rather than build on top of that learn from what is happening and then of course try to institutionalize them as you go forward another thing of course it was important to build capacity there was a huge gap in terms of data use at the local level so it's really important to build capacity and don't assume that capacity data use capacity is actually the same all up through all levels so I think it is important to build that capacity but also not at the capacity at the national level it's not necessarily the same or the what you are training at the national level it's not necessarily what is needed at the local level so I think that adjusting and understanding their needs and also providing that capacity based on their needs is actually quite important yeah engaging the user in terms of you know designing your tools developing testing even the interventions which are you are actually creating is important so that they own it but also they actually they become champions of your particular interventions digital tools using digital tools to actually help analyze the data understanding the data is quite important the use of scorecard as I said the use of BNA the use of these user centric dashboard was kind of important in terms of promoting data use at all levels within the routine automation system but also there was a need actually to create a platform where users are coming together to review their data clean their data and of course discuss the data so I think this was also something which is important that are creating these routines so that people can talk can communicate can engage within the data was quite important of course as we promote that they use we need also to look about infrastructure so I think this is also something which we really need to emphasize in terms of you know devices internet at the facility level where they can use this information next step we are looking forward for the more data use experiences one we need to be using of course our district of extensive site but also using our different interventions we are we are hoping that we are continue to test different interventions different solutions different routines so that we can learn and also see how we can scale also these solutions which we we have we also are promoting and building capacity of data use at sub-national level we understand this is quite important so that we can have these local actions so we are actually looking forward in terms of building more capacity at the local level in terms of you know data management data review data analysis data use data dissemination etc and of course use these digital tools which you have within the DHS2 ecosystem to promote the data use we look forward of course to engage more on the research and different efforts in terms of understanding better these the different data use practices and how do we anchor these practices and scale them across the country so I think this is quite important the aspect of denominator of health facility this is also something which we are quite interested in terms of how we can come up with these different proxies for example or different ways of coming up with the denominator for health facility and of course continuous presentation will keep you posted in terms of different lessons learn best practice which we have and we hopeful that in the learning from what we have learned but also reading some of the articles we are writing some of the publications which we are providing on the community but also on different levels of course so that you'll be kind of aware of what is happening in Tanzania but also in our colleagues in Rwanda but also in general in community yeah let me stop there thank you very much for your attention Victoria back to you thank you so much for it it was actually like I mean we have to thank you all so like if we have the school house at the end of the day and and it's great to see like what you have implemented and how things are going there are actually a few questions about school cards per se that I want to take advantage to ask you but of course Jean-Paul and Blais like if there are also other things to add and like uses and such specifically for Rwanda please jump in so there were like a few questions I'm merging a bit like bits and bobs from different type of questions so like for example when you are we're talking about school cards if they were able to email school cards directly to inboxes if you have had and if you've seen any improvement in performance after the introduction of the school cards and at the same time how have facilities reacted to it and like if there have been like any in send cable or like even backfire I would add with this like a new kind of like accountability and like these kind of like a flagging of like being able to perform you know and and let's get started with these ones and then we can we can dig in a little bit further thank you Vito maybe I can jump regarding the school card and how let me start by whether after the implementation and the problem until this school card whether we are now observing some changes of course they maybe let me start by saying that yeah you know and there is an existing forum where on a monthly basis all facilities gather just in what is called monthly connection meeting where facilities are together to discuss just about the indicators there are some selected indicators and the attachments which are called key performance indicators and those performance indicators they are discussed on a monthly basis to see how far they are comparing to what they want to to be and where there was last month so of course we are developing this kind of solutions to enable the I mean to support the existing initiatives so the school card now has been produced in the existing forum so that it can be used as a tool to easy to give them the easy of visualizing that and be able to discuss and find where there is a good performance and where there is a need of improvement so comparing to how the station was before the introduction of this school card now we are participating in the connection connection meeting to see how it is being used and what is its contribution and for information is that now we are correcting the user stories where both health facility managers and data managers that are confirming that before the introduction of school card they were not even able to discuss about data cause you know most of our people not most but some of our people who are involved in data management and M&D and mostly for facility leaders they are not those people who are familiar with statistical formulas so by using other statistical formulas and the fact that his facility managers and other decision makers they don't have enough time to go and analyze those formulas they were not able to have that time analyze and come up identify where there is a need of improvement by using school card now they are just finding that it's easy to identify by just the presentation opening presentation it's easy to identify where there is read asking why and bring together MIT disciplinary team and come up with action so they're confirming that at least now they're able to discuss about the current status using the findings of visualization from the school card that's the mid-term impact if I may say then we are now facilitating that process and now we are helping them to evaluate not only taking actions from the visualizations but also monitoring the actions taken and evaluating whether those actions taken are now contributing and creating changes where now those leads that has been presented last time are now turning in yellow or in green by implementing the actions taken from the previous connection meetings that's how now we are now facilitating and then say the other question was whether facilities and the staff are now adopting those initiatives without maybe requiring other additional motivation I think the workforce who is involved in this data management and M&D and his facility management they are self-motivated because they were struggling to have that easy way of using data to come up with solutions and now the school card is being used as a tool for them to use that to talk with data to manipulate data and come up with those solutions and the second thing is that the fact that we are leveraging on the existing we are leveraging on the existing interventions like connection meeting connection meeting was there before the introduction school card so now the school card is joining the other existing strategies to improve data use that's why for workforce they are self-motivated of using these tools and there is no any objection for them to use these tools and they are very happy for these tools that is now enabling them to use data and come up with solutions over to you I think now Dr. Wilfried you can compliment I think we should call you have kind of touched well the tool has been kind of instrumental while you can while we have seen actually both positive and negative impact of the use of school card in terms of decision making taken by decision makers and in particular politicians when they are faced with you know making decision based on seeing what they've noticed on the scope there are kind of different uses different kind of cases which we have seen in Tanzania where these decisions were made based on of course information seen or at least information gathered from this scorecard both at the politician level and as you understand this also affects a lot in terms of the technical aspect as well that's why I think at some point I also mentioned in terms of we actually learn in terms of not only presenting this scorecard but also supporting it with additional information so that the decision makers understand more in terms of how they're making the decision based on the information which they are gathering out of this scorecard I'll stop there and allow more questions we're one minute over but like all I wanted to ask like just to clarify because I assume that that could also trigger some kind of like worry for like data security and such but when you were mentioning about the portal Wilfred like they were asking if like the users that access that portal are able to actually download the underlying data and use these data to actually perform some extra analysis if they wanted Yes Victoria the portal is is kind of a public portal which is accessible by everyone I think I shared the link there anyone who has a link can actually go and access the portal analyze the data which is there but as well as download the data which is there for additional or at least a further analysis it was actually a difficult decision to reach in terms of sharing this information I understand the worry in terms of data but also kind of worry how people are going to use this data but I think it's also you actually need to I mean I would say the Ministry of Health weigh in the positive and the negative aspect because as you try to promote and make sure that your data is available you're actually improving transparency in terms of you know whatever you're collecting this is what we have this is the status which we have in the country and we need to actually kind of work hard to level to improve areas where we have gaps or standardize somewhere but also it actually encourages health workers in terms of seeing what they are what they have been doing becomes now public you know becomes now visible to the public and that also adds more urgency in terms of making sure you collect quality data so that the data which is used is actually used appropriately because it's also kind of you know both age you know in terms of you can produce this data and then people can use to actually shame you at some point so I think it's that it was we actually coin it in a positive way in terms of encouraging health worker to be more precise in terms of what they're collecting and also be aware that what they are collecting will be access publicly of course you don't allow all the data to be accessed publicly I think the first point was you know to come up with these key indicators key programs and key indicators which we want to share and I think that's linked with the point which I mentioned before in terms of leveraging in terms of standard tools which are there standard documentations which are there where kind of guides into one of the key indicators which we need to share with the public either linking it with the you know national indicator plans but also global ones such as SDG etc and of course in top of that you need also to have a really rigorous reviews processes to make sure that wherever data you are pushing outside it has been analyzed this has been reviewed by your team national team technical team so that that all the data which is put is actually of high quality so that you don't compromise of course the trust which we build for the people to access those particular data let me stop there and if there's any other additional question thank you so much for the glorification well for it especially like we're a bit over time so I don't want to take too much time from from our participants but one thing that I could definitely say is if any of your questions have not been answered or if you want to have like more information from from our presenters today I would suggest you actually ask for the questions in the in the COP where this link where the I mean the information about this webinar was posted to begin with and and we can definitely follow up there I would actually and ask Wilfred if you could if you could attach to the to the to the to the COP post it seems like there is like a bit of problems with access understandably because it's from Drive so like maybe if you can just like post the document directly in the COP for people to to to check it out I think it would be great and we try to answer as many questions like either here now or like in the chat but again please feel free to like continue the conversation in the COP and if not like also like I mean your email directly with our presenters and yeah I mean I would like to thank you all to for having joined us today and I hope you had found like a funny interesting and most importantly has like triggered some question and and some like use cases that you could implement either in your country or within your user of of data with with the HHS too and and yeah thank you so much once again and until the next quarterly webinar and just so you know there is like a French version of this webinar on on on Thursday so for those who are not here today or like not speak English there will be exactly the same but in French that's next Thursday I mean this Thursday sorry okay then thank you so much and yeah until next time cheers just a follow-up from that as well the recording for the session will be available shortly afterwards on YouTube if anyone wants to re-watch thanks very much yes thank you Grant and thank you Blaise Wilfred Jean-Paul cheers everyone thank you Victor just a quick question Victoria the PPT slides will also be available on the COP right yes everything will be available over there and everyone can access it over there then thank you very much everyone thank you Victoria cheers bye thank you guys thank you you have me too I have been too much thanks this one it's all over we need to put it back okay