 Alright, just to note first that this session will be recorded. Everyone. And so welcome. Welcome to the participants. Thank you to for being here. Thank you to the other presenters and big thank you to Alice and the other organizers for putting together a lot of work for this week long seminar for DHS to for the annual conference so big thank you there as well. So this is the session for LMS vision and use cases for DHS to. So I'm Brenna horse the LMS technical lead working closely with George McGuire who's the LMS technical advisor. We both began in the core team here at the University of Oslo in January. And in that time we've really tried to consolidate the work around stock management and logistics which has been actually going on for quite some time with DHS to it's not necessarily new, but we've consolidated a lot of that work and then what what is perhaps somewhat coming with an approach with a vision and with a lot of input from different stakeholders which will present throughout and to come with a best practice and a way that we can best optimize DHS to then for stock management and logistics management. All right, so without further ado, I will move to the agenda, and just also quickly to say that you can interact with us through the chat on zoom or through the community of practice which Alice shared a link to so you can post your questions there and we'll try to get to them. Secondly, that this session will be immediately followed by a LMS expert lounge. So we'll also share the link to that where you can connect to a platform where we can interact continue the discussions and everybody can kind of mingle and connect. All right. So agenda, as I mentioned will present to you the LMS vision for DHS to go through some functionality use cases and also talk about triangulation of health and stock data which is an important aspect of this LMS use case. And then we're very happy to have also some of our technology partners also on the call and presenting. So we'll go to M supplying Craig drown will be presenting their platform and some of the work we've done with integration. Extra thank you to Craig it's he's based in New Zealand so it's past midnight for him there so big thank you that he's participating with us here now. Thanks for that. We'll also talk about Medexas with pair cons live and Landry met again. We'll also talk about our work together and then open boxes with Kelsey Nagel and Justin Miranda who will also talk about some of the work we've done around this sort of approach to using the chance to and then integrating with these LMS platforms. So without further ado, let me get into the presentation then on end user stock management system for effective logistics management and improved health service delivery. So here we want to emphasize again that for DHS to it's the end user aspect that we were focusing on and then that it should inform your logistics management or supply chain management, but also the quality of the health service delivery. So let's start by quickly going through an as is or perhaps a maybe scenario, which you may identify with in part or totally but at least some aspects of this should be something that you can relate to, depending on your experiences within logistics and stock management. So quickly, just to represent here then within a country or program you would have a central and perhaps a regional or district warehouse where you're distributing medicines and health products. So this is the level of distribution. So this is the point of care where you're actually dispersing and consuming. So either a hospital, a health center or community health worker. And then of course patients were then receiving this, these medicines are treatments. You then have data being captured so you have your data full starting at that point for at the end user level. So that's being shared upstream paper based or God forbid you're using Excel, you're then capturing this data and sharing it somehow to then inform decisions demand planning forecasting and so on. Now again you may identify with one or more of these but I'll just go through a sort of a number of issues that may arise. And first it's touching on the availability of medicines and products where you need them at the right time in the right quantity. And to avoid any overstock to avoid stock outs, and that you have these available and perhaps having a paper based system is not the most ideal or efficient for that. And then their paper based or also Excel based is not perhaps the ideal you're not having readily available data upstream to inform your, your resupply and also you may have a challenge with having access to historical data to be able to analyze what the consumption has been and to inform future decisions. So it may also be done in a siloed way so it may be specific programs are using a system may be using a, you know, a digital system and it may be working fine but it's not something that will cover globally all products within a specific organization or within the Ministry of Health. And there may be a plurality of systems so maybe the persons working on the distribution or on the healthcare are needing to use multiple systems. They may create complexity and confusion and lack of overall visibility insufficient integration among systems may also create some level of an inefficient or lack of overview insufficient data to provide this visibility for the end to end supply. And as I mentioned before also complexity for the end user if they're having to use multiple systems or even lack of systems or systems only for certain programs. So first use case for DHS to. And that again, keeping our same structure here for health product distribution, it's then using DHS to at this end user level using a mobile device to then capture this stock data directly within DHS to then move away from having any kind of paper based or Excel based kind of reporting and would help with having offline capacity also with the DHS to mobile app. You would capture data digitally and sort of once and for all, you would be able to build dashboards and analytics, both for these users and users at any level that have access to DHS to would be able to have this data, as soon as you put into the system and you can create then indicators and and to follow on your stock management. We also have the capacity to have a product catalog which will be relevant for that end user, and also some cold chain management possibilities. This is in the case that there's no integration with an upstream system. So here just to quickly clarify that the upstream system would be a full scale full fledged logistics management system or an ERP that's maybe being run at a national level at a central level. And downstream would be at this point of care at the hospital health center or the community health worker level. So then if DHS to is working on its own, we can still provide this level of functionality. Before you have your data available within the system, no matter where you are, so long as you have access to the DHS to platform. So this is sort of a first step or first use case for using DHS to for stock management. The second use case is similar. So using it at this end user level, it's providing all of these benefits, but then we're discussing or we're then proposing an integration with this full fledged upstream system so you would have what the system that you're using then for your supply at a central level could be connected with DHS to data can be shared between the systems and the benefits would be that you would need to collect less data at the end user level. A lot of the data could be deduced from the, from the data being produced in the ELM is and you can provide also forecasting and planning stock replenishment. It would inform order management and you could even do deduce a batch and expiry management for each facility based on a deduction. So this would be the first expiry first out principle. So there's a lot more, let's say, features that can be used, and there should be a more simplified level of data capture for the end user, given this model. All right. So this is sort of a second step, both of these two first steps are also to specify that they're based on reporting. So this would be daily weekly but ideally monthly reporting of stock levels and whatever other logistics data you're capturing so this would be monthly reports which are then synchronized and shared with the, with the ELM is. And then we come to the use case three, where we then have the same integration but then we're looking at using the tracker program which would be a transaction based system so you would be capturing every transaction. Using our tracker app also mobile app with the same offline capabilities. But then here now you're really only recording stock that's issued. We would use barcode scanning for ease of use and all of the other, let's say data points and indicators could be calculated between the systems, you have this integration with the ELM is. This is actual real time data so for the monthly reporting of course it would come, you would have the data available once it's input and reported on but now you're recording every transaction. And this is up to the minute data, let's say. And of course, in the upstream system you would have indicators automatically calculated automatic alerts for shortage and stockouts, and all of the other benefits mentioned previously, beginning with the digitization all the way down to as I mentioned the automatic alerts. We have the third and the most ideal use case that we foresee. And, yeah, kind of the standard that we would try to work towards quickly to summarize then these proposed functionality between the systems with the same idea of upstream and downstream system here from left to right. We start at the, we're on the right column at the facility level at the point of care, then it's using DHS to for your stock management, order management performance management dashboards and the cold chain product catalog and eventually tracking trace capability. So this is within DHS to mobile app. So the upstream system in the full fledged LMIS you would have your full functionality, and then some of this data would then be shared with the HS to as mentioned before so you demand planning and forecasting so that at the facility you can see. All right, based on the numbers that they have for my facility this is the order that I'll be receiving, you know within a week's time or however time it is for delivery, and they can have an overview of what's happening. The data would then be shared back to the HS to. So it's not simply capturing data in the HS to, but it's mainly that and sharing upstream but also having indicators being calculated and some information shared back into the, to the end user to also inform them inform their decision making quickly on the architecture then and we will not spend a lot of time this is definitely something we can elaborate more on and discuss more with our interoperability and integration team and also in the expert challenge but just quickly to show that within the sort of again generic ministry of health structure. As we foresee DHS to use that all levels for your health management information capturing analysis and visualization for DHS to it's really only at this end user level that it's being used. So you can have your national full scale full fledged elmi a solution, and we can work on an integration one to one with that, or that there may be an interoperability layer something already existing, which we can also connect through and there's already existing examples of these that have been done previously so both are quite attainable solutions. Moving on, and I think this is stages for implementation or as we see it sort of digital evolution. This is, first of all, how we go from the as is to the one of the use cases one two or three. We kind of see it as as a step ladder to getting to that third use case so first it's working on an assessment phase, conducting and as is analysis and making that as real as possible to the specific context identifying the workflows and systems and also limitations, then identifying an ambition level of where which level you want to attain and how much resources you have in order to reach that, and then developing a roadmap for for implementation. So you can proceed transition phase where perhaps you would first implement this first use case simply digitizing the facilities, having users using the DHS to mobile app to capture stock, and doing some pilots and developing some dashboards and seeing how that improves supply and demand and then eventually moving to a full implementation working on an integration with an upstream system, and then eventually moving from the report based system to the tracker transaction based system. So this is sort of a phased approach to a stepped approach, where you would start with the assessment and then move through the use cases. And one thing also perhaps to specify here is that again when we're speaking about the facility level or the at this end user level it may well be a health worker, not a pharmacist or dedicated logistics person capturing the stock data. So having a health person is already using the DHS to mobile app to capture some health data simply input some stock data is also a way of reducing potential issues and making this more of a fluid transition to capturing also stock at that level. And it's one of the benefits that we see. Now I'll quickly the next two slides I'll just quickly show the report and the transaction based systems. And then immediately after George will provide a quick demo for us for this report based system so this is more or less a screenshot of the data entry form. And George will go through with us and this is for the use case one and two as I mentioned previously. This is really defining or a few different data points that you need to identify before inputting your values. And then you have the tracker, the transaction based system where you would then we could build a product catalog. And then go through our code scanning to identify items and then go through a few steps for resupply distribute items to patients counter stock on hand, capture any discarded or wasted, and also stock redistributed to other facilities. Now this is already a functional program, and we can use it as of now. What we're looking to do is to optimize it to make it a bit more user friendly when we're dealing with, you know, 100 plus 200 plus items at the moment we highly we would recommend it for maybe for a specific program if you're dealing with 10 or 20 items. And what we're looking to do and you'll see that in our way forward is to optimize this to be able to use it for as many items as you might need to manage across programs. So now let me just hand over quickly to George who will do a quick demo before I continue with the presentation. Okay. Thanks a lot. Right now. My name is George McGuire. Working in Oslo as a LMS technical advisor. Can you see my screen is it here. Yeah, we see that. Okay, great. So I make it 120% so it's a bit larger. So I don't know. Some of you might not be very familiar with DHS to or with logistics. So this is a usual data entry form in DHS to where you can select the country and your organization units or healthcare facility. We have different prototypes but we are basically looking at the, the data entry form layout using individual data elements which is a conventional way that DHS to works. And one thing that is very important you can see the simplicity of the screen so we are really limiting the data that we want to collect at the end user level to the absolute minimum. We don't want to burden a staff as Brenner said which might not be dedicated logistics staff so healthcare workers with logistics tasks we want to make it as efficient is easy for them so that they can spend as much time as possible. And that's what we do in healthcare for the patients. So, as in DHS to this is assumption is that this is a monthly reporting form so you can select the months so let's say I'm reporting on June. And we have the different logistics data that we want to collect as individual tabs. Also, this was basically elaborated together with the World Health Organization to limit the number of stock data to their absolute essentials that cannot be that are not available elsewhere and cannot be calculated. We have the stock received the stock that were issued or distributed commonly called consumption. Then we cater for the situation where you might have stock that you are sharing with another nearby facility because they have a shortage or because you have x stock, x stock, then hopefully rare but it can always happen that you have to discard stock because it was damaged or maybe because it is expired. And then the main point for the storekeepers is to carry out their monthly stock count. So you will hear have a list of data elements so these are your items or commodities for which you're calculating for which you're recording the stock data. And all you need to do is basically once a month I deal on a mobile device directly. Next to the shelf in the healthcare facility in the hospital and the clinic, collect the data, let's say I have 45 bottles of povidone. As I will count my stock and enter the data and then count my tetracycline and then my amoxicillin tablets, let's say 13,000. So you see, there's really this is replicates the workflow that you normally have as a storekeeper. So once a month you will count your stock and go through all the items, count them and enter the stock on hand. So this is as simple as a get as it gets you just have a table with the items and the quantities that you enter. And then, if you have, if you find that there's a discrepancy on your stock card or your bin card, so that the stock on hand that you count it doesn't match with the stock issues and stock receipt and hopefully that is rare the opportunity of entering a stock correction, which could be positive and negative to adjust so that you're calculated and balance matches with the actual physical stock on hand. And there's also possibility in DHS to envisage on the dashboard to calculate any such discrepancies to have an analytics dashboard which will notify the storekeeper of any items where these stock balances don't add up to the release and stock count. And then stock out days is commonly recorded as a is a performance indicator. So this one will have to be taken from manner records but it can be entered into DHS to to have a record month by month record. So this is very briefly on what it looks like again to stress the simplicity so it should be easy to teach anybody quickly, including non specialists non logisticians. I will briefly present some dashboards that are already available from the double it show metadata packages and which will be the the model for our future. So on the will go fairly quickly we can always discuss details in expert lounge. So on the left side you can see a map of this fictitious country where you can see the facilities that had at least one stock out, have at least a stock out on one item. Then the next map here with the with the blue and the green dots. I think it's really interesting. As panel pointed out you can use DHS to even if you're not integrated with an upstream elms system. So if you have the, if you're only collecting data to healthcare facility level. It doesn't benefit it's just isolated data, but actually this dashboard that was developed by Scott from a lobby is really powerful and is in use there. Here you can see the coverage time so that's the number of months that your stock will last if your consumption does not change in the future that's just an assumption. You can see for example you have this facility here fly hospital gateway, which has a shortage of this PCG vaccine. And now the important point is that at the same time, the program manager healthcare work at this facility can also see on the map the blue points which are facilities which actually overstocked and chances are that one of the stock facilities will be able to share some stocks with a facility that has a shortage until they receive their resupply from the district or the provincial medical store. So I think this is really interesting facilities which have access stock can see where they can share the stocks and if you have a shortage. You can obtain hopefully stocks from a nearby facility without having to prompt an emergency order to your district stores on the table next to it you can see basically the same. The same data as a table so you can see fly hospital gateway PhD which has zero stock on on this of this vaccine. And then if you take. Again, if you look at the stock availability which is the, the main indicator that logisticians are looking at because the main objective of a logistician is to ensure that all the goods that are needed for the patients and all the containers are available at all times and to avoid any stockouts, you can see for this PhD PCG vaccine, the percentage of facilities. In this case, there's 79.5% of the facilities overstocked and 19% have a stock out and 9.5% are under stocked. So you can imagine there is. I mean the DHS to analytics is very developed you can make a huge number of different charts in all shapes and form. I will show one more from from the malaria dashboard that is also available with the WHO metadata packages, and that is where you can see the different stock data that I showed before that you can collect on a monthly basis. This is a time series over the past months. So you can say this is trivial but imagine that your storekeeper, you probably have monthly records or you have the stock on hand at the end of every month indicated with a red pen on your stock card. So here you can actually go on the table, and you can select any item and you can see for each of the months in the past for the six or 12 months in the past, your stock on hand your stock issues, the stock receipts which is very useful for analysis. And you can do that without having to re enter your data into an Excel file processing it otherwise. Okay, I think with this hand it back over to Brennan. Thank you. Thank you so much for that George. Let me just get back into the presentation here. All right, so that was great that's pretty much the report based data capture and then also some of the analytics. I'll just take a step back and just go through this then triangulation of health and stock data and that's partly what George already presented with these dashboards and part of the power of what you can provide when you have the stock and health data in the same location. Before we speak about triangulation we're talking about the synthesis of two pieces of data to address relevant questions for program planning and decision making. So then the focus to triangulate data from routine aggregate health reporting so the regular health reporting and then bring that together with the stock or logistics data report. Again, as George mentioned based on WHO, but also UNICEF and CDC recommendations, and then that this is some of what I'll present is also based on tests conducted by our implementation team on the visualization and dashboards in different countries. So I won't go over the same ones that this is what George also presented previously but showing then a national level overview where you have over stock and under stock. And also the same comparison here with doses given versus stock used to compare then the health to the stock data and see if there's any large discrepancies. Here evidently there's an issue with perhaps data capture but then you can analyze further to see what the issue is. There are some wasted rates on the far right in the chart where you have color coded there and yellow green and pink then is showing sort of acceptable or unacceptable rates, wasted rates which you can then dig deeper to find the issue. And then, again, another example of the stock levels and also by facility. And then this ability to identify stockouts and redistribute stocks. And this can be done for a number of items as you see here on the same overlay on the same maps having different items with their availability. So quite a lot of possibilities. Now from a specific example from Togo this is where they also use then the dashboards to identify under immunized children bringing together some population data and looking at vaccination rates to identify regions where you had higher or lower vaccination rates for children for specific antigens. And here another dashboard showing indicators as a proportion of children immunized per antigen. And this is a ratio so you're trying to be as close as possible to one and identifying where you have more or less efficient rates of vaccination. You can look for them conduct corrective action based on the output either checking for data entry or check if stock inventory data is correct. Checking how doses are being tallied and then also monitoring and monitoring facilities that are having issues that are having a, let's say less efficient administration of vaccines. And this is the type of information that can lead then to corrective action and I think that's one of the points that we really want to emphasize is having dashboards data and indicators that are leading to action that will improve and not simply to just show and report which is fine but also to lead to corrective action to improve the quality of services. This is an example of cold chain equipment monitoring so this is from Molly and Togo where there was a testing of this with manual input of temperatures and simply capturing where you had an alert where you had temperatures, going outside of parameters, and then dividing that into specific regions to see where you needed to do some assessment and conduct some check of the of the quality of the equipment. Here is the same using the map function so then you could have with the different color coding you have some very small nearly insignificant dots there just showing where you might have had a single alert over a certain period. And here are some larger dots signify larger issues where you need to identify a solution and sort of dig deeper to find what the problem is and what the solution is to remedy the situation and avoid any losses of vaccines or other stuff. And here are final chart showing alerts by region and here. It was surmised by this graph that more or less the more central closer to capital. These regions were these sites were the less alerts would be received worse the more remote sites you had a larger number of alerts. And this is just a way of showing that over time, which sites perhaps need more attention and are more prone to having an issue with your cold chain and where you might risk than losing vaccines and other valuable stocks. A summary, and a way forward to say that the main then priorities for for George and I, working here now with the team around this and with the others within the core team is really to finalize an LMIS metadata package for aggregate reporting so it's having a best practice and a package of data and indicators that can be used for the stock data management. It's improving and sort of fine tuning that tracker transaction based system, which we showed previously to make it more adaptable for then managing hundreds of items, and having a really the full potential to manage you know stocks across programs. And to facilitate integration for both of these approaches so having design documents and standards, and, and really guidelines for any implementers looking to to use any of these solutions that we're proposing. We're looking to bring together stock and health data to provide an opportunity for in depth analysis as we showed a few examples here, but it's looking to then improve and build on this because this is really only scratching the surface of what is possible when bringing this data together. And to even lead have it lead to certain things such as predictive forecasting where you have clinical health data informing resupply so that if you have a no break you can also adjust your supply to meet that coming need that you identified through your, your surveillance activity so it's really bringing together data from diverse sources together to then inform decision making. Generally, we've seen and it's been shown that these dashboards can be relatively easily developed and used by stakeholders for data monitoring analysis, and that it does lead to corrective action and we want to continue to build on that. So that requires some effort but it's not. It's a very possible and fruitful endeavor. The specific project which we're working on then is also the automated temperature data monitoring tool. So using Bluetooth sensors and with that we're looking to move away from manual reporting of temperature and go to a automated solution with sensors. And of course if there's any potential partners interested to support or pilot please contact us and we're very open to cooperate on that as well. So we have developments in a few of those are tracking trace capability and also anti counterfeiting, where we would look to add these functionality dysfunctionality also for the end user level. All right, so those are the some of the projects we're working on and some of our immediate and more longer term priorities. I also want to mention here that we're involved in quite a few with quite a few different partners and some key ones here is to first mention our hisp network. So they're really the first line of support and implementation that are present in different regions globally. They're directly ministries of health and even working in the ministry of health in some countries to implement and develop further DHS to and they're really our first line of information and that are really informing the decisions we're making. I think this is if any of you were in the previous interoperability session. It's sort of informing that aspect and I think it was the first principle where a lot of the needs and developments for DHS to and what's led to a lot of the successes to is having that grassroots and country level need at the forefront so when the needs come from the country, we know that we're making a good decision to invest in that feature and functionality. So our hisp network really help with that. Also collaboration with the WHO global fund Gavi and others. A lot of the indicators and standards that we're working on is directly with WHO working groups. And of course, with some funding from global funding Gavi to help move a lot of this work forward and also some of their technical expertise so they're also very great collaborate collaborators. And then we participate in the open HIE supply chain subgroup and that's where we're looking to then define and contribute to developing the standards and conventions that we can use for integration where we're talking about multiple systems, working together. And then two documents which you also have used quite repeatedly in addition to sort of academic papers that are there in the academic literature. It's the country guidance and selecting LMS from global fund and Gavi and then the target software standards from Gavi as well but I think endorsed by global fund and these are two key documents which we've used especially the target software standards where we're looking to then fulfill all of those requirements in an integrated scenario we're using the integration with an upstream system we would meet all of these requirements outlined in the document. And then lastly a key partnership that we have is with the Stella Center of Excellence, which is bringing together different partners and different organizations and for then our involvement really to improve supply chain of health products and improving this collaboration within the field. And this is a then partnership encompassing University of Oslo Novartis University of Basel and Swiss Tropical and Public Health Institute. So a big thank you there as well where we have both expertise and some funding coming through this, this cooperation and this partnership. These are our contacts, if you want to reach out and we're very open and looking to collaborate with others and any one of the topics which we've mentioned. We look forward to you engaging with us also in the expert lounge following this session. So please don't hesitate and we will share these slides in this context, also in the in SCED. So now I will transition to Craig and M supply, who will present. So Craig, over to you. Thank you, Bruno. And yeah, I learned a lot from that as well. Thank you very much. I can share my screen. See how we go. Looking like that might not be allowed. I think we're able to pass on. Normally you should be allowed, Craig, you are co host. Okay, thank you. Let's try this at center the green button perhaps. Yeah, it's just showing no. No desktops available. Bring up the same slide set that I think to you. Let me bring that up just a second. If you're able to just use that as something going on with zoom here it's just getting an error message when I'm trying to share. I will share and I will go through just let me know when to switch. Okay, I think I need to reduce 10 minutes down to about five minutes to leave enough time for the other presenters. We'll go super fast and please excuse the rush but you'll get the slides for later consumption. Okay, thank you. Yes, onto the next one. Thank you. All right. Yeah, so we're the M supply foundation. So we're transitioning our work over on first of April this coming year to have everything done through the foundation. So very similar aims to the DHS to project and we're also transitioning all our software over to be an open source. Next things. Okay, so actually up to about 35 countries now and I think we're on to our sixth and seventh languages for M supply mobile. So thank you. So, Bruno covered this pretty well. So I don't need to repeat all the details. So you had three different scenarios so we described it pretty much the same that the first scenario. A little bit different in this case. And this is used as the next slide mentions in Laos and in Tonga and we've got Tim on this day just in development at the moment. Can you go back a slide. Thank you. So at this point, you've got all your data in M supply and DHS to is acting as as an HHS and you're picking and cherry picking selected data that you want to want to make available in your DHS to system. And so that's been running in Laos for maybe three to four years now and I think has been very successful. I would say the success is not really just about the systems it's due to a great local team who have paid a lot of attention to training and a lot of attention to data quality. And without that, whichever system you're using you're not going to get very far. Thank you. So, yeah, so that's that's one way of doing it and we can move on to the next one. So, at this point, so this is what we've been had a bit of fun in the last few weeks, working with the DHS to team on where you're using at the facility level you're you're capturing period periodic data using DHS to SAP or via a browser that goes to the DHS to and then on a schedule M supply can pull that data down and use it to fulfill orders. So, oops sorry that last one, the last, the red arrow should actually say physical supply of goods back to the facility. So that one's got a break in a little bit of a break in the chain there from data completeness and that we don't have a way at the moment to inform the facility of what has been supplied so I think we could work with the DHS to team on that in the future. Okay. Thanks for. So, here's a here's a quick example using the same data set that was used in the previous presentation so there's a periodic report. And on the next slide, that's on a schedule being pulled into M supply, where it's able to be used to fulfill an order. So, and there's, I think one of the one of the great things about this sort of variety of models is that you can take into account all the external factors in the country like what software are they already using, what's their local capacity, and what have they been trained in and you can put those pieces together in a way that provides the best outcomes for the country. So it's great to have options. Thanks Bruno. Right. So this was the third, the third, in our, in our second example, the, the, yeah, making the point that this, this step is still manual supply of stock and we need to work together on how to actually link the systems up to for supply verification. Thank you. Yeah, so a couple of points there that still still need solving. As we mentioned, updating the facility with incoming stock. And I think the other issue is, as the DH is to team work to something that's based on a transactional model, obviously requisitions have have the issue that you're getting a monthly snapshot of what your stock was and you don't have visibility in between your reporting periods. And Bruno's very kindly said, take a couple of minutes to say what M supply does so we'll go through very quickly. I think our, our main unique uniqueness is that we're very much vertically integrated from end to end. So write down to patient dispensing and these days with a mobile app also for COVID vaccine dispensing. And the other big feature that we've has often saved countries we would say is the fact that it was offline first so it works very well with only intermittent internet. And this is our mobile app which is open source at the moment so data synchronizes from this through to DHS to via our own cloud server. Here it is showing patient dispensing in a mobile app so it's a full stock tracking patient directions right right through on a named patient basis. We've just done a vaccine dispensing for it's been used very successfully in Tonga in the South Pacific for their COVID vaccination program. They're expanding it out to use for other vaccines it's been so successful. Bluetooth sensors and let's we have WHO qualification for those and that's integrated into the stock management. Thank you. We've got our own dashboards as well so Laos make good good use of saying some data we just need locally and that for the supply chain teams and other data we need in DHS to. Thank you. Myanmar is the country with a lot of challenges these last few months. They do have very large national program and stalling in supply. Thank you. Initial designs for open name supply. We're underway and we will have new desktop open source public trial by the end of the year. So, thank you very much. Much appreciated. All right, thank you for that we'll go quickly then to pair and landry from indexes and I will share them your slides as well one moment here. So can you hear me. Okay, yes. Yeah. So, first, thank you to Ben and Craig. Well, so I have a lot less slide. But anyway, what I'd like to talk about here is that we are working together with DHS to on a concrete project but also on a principle you would say. If you take the first slide. Yes. So what you start with saying is we are having an elms. And it has the standard functionality of an elms, including cold chain management, but I'm not going through that right now I want to try to explain what we're doing together with DHS to. So the situation today in several countries, and also in a very concrete country we're working on, as you got, of course, sentimental store, which will usually have some sort of an ERP. You have a district store, you've got health facilities and you've got community health workers. And now these. I was likely to be reporting on paper to health facility level. And it is not now these days actually pretty frequent you have the health facilities and district store reporting into a DHS to system. And you may also have a national dashboard of some sort from DHS to or from the AP and or, or another way so this is quite normal right. But there are challenges also. So, there will be periodic logistics data reported, but it's not a transactional system. It's not an elms. There's lots of things you cannot do. That's so that's a common situation so what we are doing together with DHS to with our software indexes is because we just run the next slide if you move to the next one. Yes. So, I will try to explain it so what we do here now is we, we are. Well, one of the big challenges we're getting an elms out there is not so much to set up an elms because that's technically done, and if you're done several times. But it's actually to roll it out to make it happen and make it be maintained, because you can take any country you might talk 1000 to 4000 health facilities. And if we include the community health workers, you may be talking 10,000 of those. And in the health facility level, you will have maybe a quarter of the personnel changing every year. So, if you start putting out a piece of software that needs training, you will need to retraining and retraining and follow up and so on so forth. So this whole operation is actually the tricky part. This is what cost is not so difficult in a way to put up a piece of software in a few health facilities to train people make it work. And this is the big deal. This is the problem. This is a challenge. This is why it's not out there already. Now, some places, many places. So the thing is what we are going to do now. Here is we want, we're putting back on the paper systems as far as possible. We want to obtain the direct digital data from health facilities and community health workers via the existing DHS to interface, which they anyway are reporting on both health facilities, some places also community health workers. So they will anyway be putting this into a DHS to screen, we can add to that screen screen we can change it can do things to it but we're not changing the interface. Not really. And we're keeping it absolutely as simple as possible that reduces our training costs that reduces the meaning maintenance cost that makes it more easy to make happen. Now, so, then we move the data into details to, but then we installed our elms named Medexis and move the data into that one. So we can do the transactional things in that elms is sort of the same as Craig described a few minutes ago. We would like to have one. Whatever. But anyway, we would like them at district store level, we would not limit to that then there they we will have them using the Medexis with the full functionality. We can change equipment so on so forth. But health facility community health workers, they should stay on the details to screens. So that's the, that's, this is the task we work on. And now we are having a dashboard here also in this table. That's because you often have a national dashboard already. Obviously Medexis has a dashboard, but we don't change the dashboards, we want to move the data from Medexis and into their dashboard. Now we have a very concrete project going on, which we are really going to scale up I hope very soon. And so we have done these some of these phases which Bruno was mentioning so analyzes start discussions so forth and now we're going into the people hopefully very soon start the pilot for real. And that's in Mali. And in Mali, we're having a national dashboard already on money. Sorry, we're having all health facilities reporting at DHS to we do not have the community health workers with pointies and so they're filling it in a large paper form. We would want to reach a situation where they fill in a, they use a tablet and put data directly into a DHS to form so we kept to that data. Yeah, do you mind if we wrap up so we have some time for the open boxes team as well. We're just at the end and we can definitely continue this in the exercise. That was really I haven't had those two slides. I think Mexico across we're trying to integrate yes. Thank you so much pair and we'll continue in the experts lounge with Medexis as well. Bruno I think we can go a little bit longer. Since we are going to the expert lounge just after this. Okay, thanks Scott. Yeah. Do you want a final final words up here. No, it's on. Okay, the message. Okay, that final word is what we are doing here is getting an ELMS in there. But what we're really focusing at is keeping down the costs of implementation of maintenance and making it actually having big scale by using the existing DHS infrastructure by minimizing the extra work. That's the thing and minimizing the cost and making it as simple as possible and trying not to turn things up down but maintain was there. That's it. Okay, thank you so much parent thank you for mentioning then our work with Molly and I think that's a very good example to show that again with our approach we're not looking to push the use of DHS to for stock data but we're looking to make sure where it's already being used, both for health and for stock, as in Molly, and then integrating with a system like yours which is able to do the, again the upstream functionality so thanks again for that. So over now to Kelsey nagle and Justin Miranda from open boxes. Go ahead and thanks again Scott so we can go over a few minutes since we'll be going into the experts not for LMS. So, do you want me to share my screen I think I. Oh no, let's say this. Oh yeah, I think I can do that myself. Can you share. Yeah, let me. Okay, that will be best I think. So this can everyone see the screen. We see that. Okay, great. I'll basically just go over. I think everyone else has made really great points about sort of the benefits and, and, you know, why you would choose to use DHS to at the end user level. I'll go through a couple of the benefits but I did both Craig and pair brought up things that I hadn't thought of, or at least detail them a lot better than I did. But I'm just going to go through sort of the quick data flow of how, how we sort of went through our peripheral concept for this so first of all, open boxes is an open source cloud based or on premise. Inventory management system warehouse management system sort of covers use cases, both for inventory and warehouse and also for some some ERP but it's, it's clearly not aligned with them with one single use case. Features include purchasing and inbound stock movements that are, you know, source from donors, being able to receive and put away stock, creating stock levels to allow you to do reorders based on your stock levels, and also replenishment from downstream systems. Obviously, they would be able to enter their data if they so choose to use open boxes otherwise open boxes is more likely to be used in a distribution center to distribute their stock to the to the downstream consumers. We also have product catalogs which are essentially used as formularies for, you know, for most of our sites, and we have the beginnings of demand forecasting within the system where we can. We record all of the requisitions that are coming in from the downstream users and how we fulfill them and whether there's been a stock out and things like that. So we have a pretty good idea of what the demand signal is downstream, and we can provide both detailed information and sort of a an overview of what kind of stock is available at the distribution center. So, yeah, so as I said I just wanted to go through like what I saw or how we implemented the proof of concept, and it starts with this awful screenshot that I took. Actually, a photo of a screen that I had to take because I couldn't get Android to cooperate with me on taking a screenshot. I just saw the, the really nice UI from the DHS to mobile app. So you know what it could do this is not giving credit to that. But basically, you know, the community health worker or pharmacist or whoever is entering their monthly data into a form within the mobile app. So that data is being sent up to DHS to and, you know, is recorded there, and can be validated and edited, if necessary, per period, and then the open boxes or whatever LMS you're using would go and grab that data and pull it down. In our case, we are, we deal with everything is at the transaction level. So this data needs to be mapped to a transaction within our system so you would say we would go and pull. You could see we've got stock issues, redistributed discarded corrections and stock on hand. And here you can see that we've mapped a transaction type for each one of those so we can record, pull the data from the DHS to and actually put it into the data here. And you'll also notice that we have a receipt here, which is something that we can then push back to DHS to in, you know, as a, as a, as a, an aggregated data for aggregated value for the month for all the receipts that came in. In addition, we could push other data like demand, demand values or forecasting values, any number of things we could, we could push back to DHS to as well to sort of provide a better, I guess, picture of what's happening at that downstream consumer. And, and that would allow you to build whatever, you know, dashboard reports and things like that that you would need. The one thing that we didn't really explore it but that I've been excited to work on is how we would use DHS to at the distribution level. In our depots and warehouses to push sort of that aggregate level up and stock outs and things like that. And we haven't explored that yet but that that's something that I'd like to look into in the next couple of months. So in conclusion. These are the benefits of this kind of implementation. You would, you would, you know, improve the service delivery by allowing the distribution center to forecast the needs of the downstream consumers and push stock based on consumption rather than letting the downstream consumers to what I need. Please give it to me. Sometimes, if there's not confidence in the supply chain. You might get, you know, an over, there might be an over forecast at the, at the lower levels and so they're going to be asking for double, because they don't want to run out in the next month with a system like this where you've got all your downstream consumers entering their data and sending it back up to DHIS to and being pulled into the LMIS. There's the ability to actually know what the redistribution situation was like what the, you know how much was issued consumed, and it gives a better picture and would allow for this. If necessary, about sort of a push replenishment system, which is great. In addition, it increases data quality because it sort of bridges the gap. We have from a distribution center. You know, let's say our distribution center in Haiti we send out to somewhere on the order of 12 different sites, and we don't actually know what like of that stock what was consumed what expired what was damaged. Things like that. And having this information sent back up to to the distribution center. It gives us a way of reconciling the difference between, you know, what was issued and what was consumed and say okay well we might need to, you know, have some initiative here to deal with expiring stock. We can't send anything that's like expiring within three months, for example, down downstream and things like that so. And the also a point that was brought up is the, you know, reducing deployment time reducing like the maintenance that is involved. We've been working in Haiti on on with open boxes for about 10 years, 11 years, and we still don't have open boxes is not being, you know, being used at that at those lower levels in all locations, and that, you know, is due to internet issues or just we don't have the staff to have to have somebody, you know, log in and record all these transactions. So, given that like, it would be great to have a system that could easily be used offline could be used, you know, by by pharmacists by by any, you know, staff within the hospital to enter the data, you know, they're already entering it, entering it for their monthly like ministry of health reports so it isn't a huge burden to add, you know, or to replace that system with this DHS to like mobile data capture, and I think it's a, you know, very simple way to capture the data and report it back and and it sort of. It's, it gives us the visibility that we need at the lower levels are the downstream consumer level that we need. So, let's see, for next steps, we've got a roadmap. We're hoping to work more with. Anyway, I'll leave this up. These are some of the things that we're looking to do data mapping, real time data flows like either having web books or using event streaming or something like that. And decoupling the systems right now where we've got a pretty tight coupling between the apis of the systems so that's about it. This is sort of the picture of what we would like things to look like but you can replace open boxes with any mobile MIS you can, you know, use whatever you'd like to for product catalog open boxes could be used for the product catalogs, you know, open MRS as a candidate for electronic medical records. So we just want to work with all of these groups to build this, maybe create reference implementations for for organizations build up the metadata packages things like that. And that is it. And I found out yesterday that it's national let it go day today in the US. So I don't know if anyone needs to hear that but that's right. Thanks a lot Justin sounds like an American holiday. All right, so everybody I think I shared the link to the gather town where we can connect and continue the discussion so if we all meet there we can discuss in plenary one on one and just continue this, these talks. All right. So thanks a lot to all the presenters. Craig Justin pair and George as well and hope to keep the discussion going and again feel free to reach out to any one of us I'm sure we'd be happy to to engage. Thanks again.