 So, hello everyone. I'm Githika from Hispindya and I'm here to demonstrate the MR application MR surveillance system built on DHH2. I'll just give a short presentation to give a background of the MR antimicrobial resistance and how we started working on this project. And then I'll move on to the application to show the data entry app designed for this project specifically. Okay, so as most of us know that MR is notified by WHO as top 10 health priority globally. And there are various global declarations being announced and the global action plan has urged all member states to implement the national action plans. But they had asked for 2017 and many of the countries are working on their global action plans on MR. And the key recommendation is to have surveillance systems, strengthening evidence and knowledge based through surveillance. So that is a big need for surveillance system for recording the MR. So antimicrobial resistance is basically when we diagnose pathogens or organisms in samples in human or animals or food samples which are not treatable by any of the antibiotics. So we check whether a pathogen can be treated by antibiotic, a group of antibiotics. And we are seeing that many of the antibiotics have got resistant to pathogens or the pathogen. We are not able to treat those infections leading to high rates of MR and leading to deaths because of the inefficiency of treatment. So as we see that this is the mortality and economic impact of MR where it's envisaged that by 2050 up to 20 million deaths would be there by a per year. Leading to 2 to 3.5% reduction of GDP. And so this is the figures of deaths that will be attributable to MR by 2015 which we see that the major chunk would be 10 million deaths would be by MR. I mean estimated with the resistance being increasing to antibiotics. So our study is to see whether at the lab setup what is the antimicrobial resistance so that we could accordingly help the hospitals in making their antibiotic stewardship policies to decide on what antibiotics should be given then to the patients to reduce the resistance. So in India there are a network of facilities, 25 facilities under ICMR and NCDC which are reporting the surveillance data but there is no system for reporting this data. So somebody is doing it in Excel, some are just compiling annual data due to which we do not have any evidence based patterns to see the MR prevalence. We do not know the consumption patterns for antibiotic and we do not know about in the hospital what are the hospital-acquired infections due to the antibiotic resistance. So we designed this MR surveillance system which we started in October 2018 and it is built on DHIs too in which we report, manage and analyze the surveillance data in two ways. It is used by healthcare facilities where their focus is on patient-based care and they are tracking each samples. While it is used by research organizations or at national level where there are more focused on each sample which is tested and the resistance. So in both ways the analysis of the data can be done, sample-specific and patient-specific. And then we've also been working with WHO team on the glass software and integrating it with glass and hornet applications. But I think the focus of today's call is to show you the data entry app. These are the different features of the app. I am not going in detail because I'll be demonstrating the same during the presentation. But I would just like to tell you the workflow that exists in the system. So once the user logs into the application, he sees his hospitals. It is at the lab setup, the user who's sitting at the lab will register the patient by entering his demographics. And then he'll collect the sample from the patient which needs to be tested. So he'll enter the sample collection details. Once the result is available, he'll update the pathogen which is reported and based on the pathogen reported and the sample. So say for urine sample equalize detected. Accordingly the system will automatically show the list of tests which need to be reported. So say for equalize only 10 antibiotics need to be tested. So only those 10 will be shown in the system, not all the entire list of antibiotics. So then the lab user enters the results for these tests and we calculate the resistance pattern. And accordingly, more results, more organisms can be entered if detected. So I will move to the demonstration now. And I think if there are any questions, we can take them after the demonstration. Is the data size two screen visible? Yes. Can anyone just confirm? Thank you for fun. So this is the data entry app MR data entry patient overview app. The same thing which is done as in the records entry and all all of that can be done in tracker capture also, but to ease out the data entry process for lab technicians because there will be different screens that they'll have to see and roll to different programs simultaneously, which is difficult for them to then enter the data. So we have designed this app to make users data entry easier. During the demonstration, I'll show you how what are the steps which are being reduced through this app to make the data entry easier for the user. So in this app on the first page, we have added some working lists. So whenever a lab technician adds a new record, he can see if after adding the record, he still needs to enter the result. It will be available in this queue. Once the results are entered, organism is detected. The antibiotic testing needs to take about two to three days. And after three days, the results will be entered for that case. So then he could just filter out in the pending antibiotic results and look at the patients for whom the antibiotic results need to be entered. Antibiotics result received is for clinicians wherein they could see the records for which entire testing is completed. So for clinician, it's important to see just the complete record so they can see those in this list. The same is done for Android also. So clinicians while visiting each patient in the ward can access their lists in the handle devices and update their notes. This is the follow-up option given because at times it will be a long list and the clinicians might want to come back to a patient again. So they could just mark or unmark for follow-up here. This is mainly through the Android that they are able to do it because they are using the Android devices. And then for the samples where no pathogen was detected, it was not infected. They will appear here as sterile samples. Now to add a new record, I'll go ahead. All of these are metadata in DHIs too. So we're using form names in the app. These are the attributes which we see in the first section. So this is the enrollment page in DHIs to terminology. So once I add these details, the patient will get registered to the sample testing program in the application. So I'm giving a registration number. I'm just adding the mandatory fields for now. Agender and these details I'm just missing. So as soon as I enter these mandatory details, the patient gets enrolled and I move to the next stage. So this was not visible before this panel. Now it is available. So this is one of the stage in the sample testing program where I need to enter this date of sample collection. And my other fields will appear. So in terms of DHIs to this is my event date and then I can see the data fields to modify this page. I could add or remove fields from my program stages and accordingly they will be updated in the section health. So I'm again filling in the mandatory fields, the department of the patient. All of these are mandatory because the analysis is based on analyzing across each of these fields. So patient location, lab ID, which is again unique as requested sample type. So now at this point, if I do not have the sample result, I can go back and it will remain saved as it is. If I have the sample result available with me, I can enter it right now as trial or a partition detector. Now when I say partition detector and I go next. So here I need to select what is the partition detected because accordingly next of the workflows will be made available. Now in DHIs to this is a separate program because there are entirely different types of antibiotics and fields needed. But for user, it will be very difficult to go back and then search for the program and then again select enter the same fields again. So we have kind of made this app wherein directly from the sample testing, they can now go to the specific pathogen which was identified. And now I can select from this list of pathogens to say if I select this one. I will, these details are automatically filled from the previous section of the sample type and lab ID. The location department is auto filled other fields I can fill as available. And now because I selected enterococcus fecalis in the urine sample, so then I can see the list of antibiotics which need to be tested for this pathogen. So these are the suggested antibiotics to be tested. I see two sections DD and MIC because these are two types of tests which are done. Some labs have MIC machines, some do not have those that's a higher kind of machine. Some have DD which is this diffusion, which is the conventional method and they are entering. So now if I add any result here you see this color coding this is for resistant intermediate or susceptible. So 30 means this antibiotic is susceptible and this can be given. So green means it's susceptible. If it turns pink that is resistant. So this is resistant if you give ampicillin it will not work for this bacteria. And similarly based on the results that you enter if you enter results in both then MIC results will override the DD results. So all of them have been done through program rules. So based on the breakpoint values the rules run and accordingly values are populated in data elements which are hidden in this form. And now for the same sample if one more bacteria is there I could say submit and add new isolate or I could just mark this as complete. When I mark this as complete this gets aggregated and pushed to the aggregate data sets where then all the analysis is possible through pivot table and data visualizers. If the clinician is accessing they can directly go to the clinician notes and access the result here. I mean enter the details of the notes here and just to show you one more thing because I did not mark this complete I don't yet see an option to see the report. So now when I mark this record as complete and I go back I can see this report button available. So once I've completed the antibiotic result testing I can generate a report for the patient which is which can be given to the patient or the clinician can use it to see. So in the same report more antibiotics and samples will come in then based on the data of the patient and all my different events will be available here. So in any case if the patient comes back after 2-3 months with same infection it will be seen here and accordingly I mean this is basically the listing down of all the events for the patient. And just to quickly show you in the tracker capture so all of this configuration is available in tracker capture and the same records can be seen here as well. Do you want I mean do you just want to see this or do you want me to show some dashboards or something so you could let me know. If there are any questions here? Yes, dashboards. Gittika, can you hear us? Yes, yes, John. There is some few questions. Thank you very much. Interesting information indeed. I want to ask you like few questions and maybe I will discuss few things with you regarding reporting on glass. Do you use, does the system enables you to extract information and to upload to glass on a priority pathogens? Does the system enable you to create anti-biogram at the hospital level? And I want to see the dashboard on what the analysis is the system is able to do for you to be used for IPC intervention for decision making for developing stewardship programs at the hospital level. Thank you. Okay, thank you. So I think for glass we had made the reports which are required to be uploaded on glass. So all of them have been designed and available. But there are discussions ongoing with WTDM because there was no, this was last year and there was no development interface available for glass. So we could not import them and test but they are in the same format which is given by the glass site to, you know, prepare the reports. Then for the hospital specific anti-biograms, so based on the hospital where we are working right now, they have some of the dashboards which I'll just show you and they have specific, they prefer preparing tabular reports on the quarterly data, which gives you a picture of top three pathogens, what were the anti-biotechs given. So I could show you those reports also quickly. So I'll just share my screen again. These are some of the dashboards. I'll just log in again. Most of them are tabular. We did initially have a lot of graphs, column and bar charts but for the hospital users, they prefer tabular. Some of the pie charts to see the isolation patterns of the pathogens are available. These tables show you department, location, sample-wise, what is the isolation patterns. To see the antibiotic susceptibility, non-susceptibility rates, we have this another tab, where in for each antibiotic, we are able to see what is the resistance pattern. So here you see how many were resistant, intermediate and susceptible. And what the hospitals are mostly using are these HTML reports, which give us a picture of the total number of isolates done and what were the susceptibility patterns. So they see both the percentages as well as the numbers. Similarly, we have another report. Here we have the top isolates along with the antibiotics, the location where the patient came from and then which is the group of antibiotics that they belong to. Similarly, we have these organism-wise and antibiotic-wise reports also. Does that answer your question? Yes. People are very happy. We'll go to the, we have three more other demonstrations. Thanks, Kritika. If you want to know anything about AMR, like there are Hispanic elephants from here, you can ask them. Get the contact details so that they can answer all the questions. Okay, so we'll go to the next presenter. We'll be Dung. We will show the custom app, which has been used in many office projects in Southeast Asia. Good afternoon, everyone. Let me, I'm going to introduce myself again. My name is Dung, a technical leader of Heap Vietnam. So today I'm very happy to introduce you to use some of our apps. Let us go to the content. So today we are going to present three apps to the first. The first one is the ICAPG and the OCH. And the next one is the Bowers of Units profile. So like we quickly go to the first one, this is ICAPG. So what exactly is ICAPG? ICAPG is a data collection app which combines the data entry and the event capture. So why do we need one app to combine both of the data entry apps in DHSU? So we have one use cases in Laos. We implement the dataset and the event program in the same DHSU instances. So sometimes users are very confused. For example, if I want to enter the data for the dataset, I need to go to the data entry. I want to enter the data for one program. I need to go to the event capture. So sometimes people are very confused. They don't know which app they will use for the data entry. So that's why we have this ICAPG. And the user can go to only one app to enter the data for both the dataset and the event program. So what it has is the support highly customized forms. So using this one, you can have more customizable forms including the data entry form and also the event capture forms. And this one also supports the event program completeness. So what does it mean for the event program completeness? That would be the same mechanism as in the data entry. When you select one program and all you need, you need to also select the period. And the people can also complete the program for that particular unit and period. I can explain this more later. And it also supports the data comparison between the aggregated data and the event. So what is this? In loud. We have one use case in loud. They have one program but they input it in both the data. They have the aggregated data for that program. And they also have one or another program for that same program. So they input both the data. So sometimes they need to compare the data between aggregated data and the event data. So in this app, we have some special mechanism, special customization to achieve this requirement. And the next one is the real-time program indicator calculations. So as we all know for each event program, we will have the list of program indicators. Every time you need to get the data from these program indicators, you need to go to the event report or pivot table to calculate the values. And you need to wait for the analytics. So for this, I capture whenever people select one event program for that particular unit and period. We will have the list of all the program indicators. And those are automatically calculated. And I could show you, show it to you. So for the next one, what's the difference between the data entry and the event capture? So whenever people go to the event capture, they can select both programs and assess all the selection. For data entry, we have in the data entry, we can only select the data sets. In the event capture, we can select only the event programs. But for eye capture, we can select both. In the list, we have the sections for all the data sets. And we have another section for the program. So that's the first difference. Okay. And the next one is for the program and data set selector move to the first selection on the control bar. So by this one, whenever people select one data set or event program, the next, the app will render the organization of the selector. And the organization unit list here will be filtered based on the selected program or data set. Okay. So by doing this, we can have more performance for the selector and all the organization unit, which are not assigned to that particular program data set will be hidden. So it can save more space and that's easier for the user to find their organization units. Yeah. Okay. So let's have a look on some other screenshots. As I'm showing here, you can see the first one is the data set and the program selector. You can see here we have two sections. The first section is the list of the data set. I'm searching for mch. This is our development instance for now. So on the top, we have the list of the data set. We have two, which are mch and we have many, many programs on the below sections. As you can see here for the selection, we have both data sets and programs. Okay. This is one of the examples. For the first organization unit hierarchy on your left, it shows all the organization units. Okay. But when people select one program, it's the example if people select the outline. And this program is only assigned to all the province. So you can see the organization on the right. All the facilities, the district are filtered. You can see only the list of the province. Yep. Okay. So we go through the first part of the capture, which is data entry. When people select the particular data set, you can see the form will be rendered here with some color. This is very high customizable form. So nothing special in here. We have the input fields, the mean, max value, the data history, the audit trails, the complete and run the validation just like normal data entry. Okay. Here, the next screenshot, we can see the mean, max, the command, history and audit trail. But see this one. For this example, I'm showing the forms for the data set EPI monthly. You can see some of the input, some of the table have only one input field. But you can see on the table child below, some of the field they have two input fields. And the first input field on your left hand side is the current value of this data element for this particular organization and period. And the input field on your right is the value form event capture. So we put the input fields side by side for the people to easily compare the value from the current data set. And the value from event capture for the same program. So for this one, one example, we have the EPI EPI program and we also have the EPI monthly data set. So people need to compare the data between these two program and data set. And how do we set this one up? Okay. So for setting this one up, we create validation rules. For the validation rule, we have the left hand side and the right hand side. In the left hand side of the validation rule, we will assign the program indicator. So this program indicator will calculate the data from that particular program. And for the right hand side, we will have the data element in the data set, which you want to compare the value with some value in the program. So I assign the program indicator to the left hand side and the data element to the right hand side. That's how we make the linkages between the data element and the program indicator. So basically, the value from the program, from the event program will be calculated based on the program indicator. And we need to link that program indicator to the data element that you want to compare it. So that's the whole point. Yeah, we move to the next part, which is the event capture. So whenever people select one event program, you can see this is the first screen. On the left hand side, you will have the indicators for the particular period and organization you need. So this list of program indicators and the program indicators belong to that particular program. It will be all listed here. And you can see on the right column, we have the value. So all the values here are calculated real-time using the data from all the events for that particular program, for the selected organization you need, and for the selected period. We will calculate the values for these program indicators using that. So that should be helpful for the data entry person. And on my right hand side here, we have the list of the events for that particular organization and period for that program. And I think we have the register event button and also the complete button. So that's a very special thing here. When people select the program, we also have the period selector. As we all know, the period selector is only available in the data entry app in the HSU. But here, when people select the program, we also have the period selector. That's how we implement the completeness for the event program. We also have the complete button on the top. And how did we set this up? So in order to implement the event completeness and real-time program indicator we need, the period selector is available. So not all the event programs are having the completeness functionality. Only the desired program. And how did we do that? First, we are going to create one dataset with the same name of the desired program. For example, you want to implement the completeness for HIV program. HIV positive program. You will create one dataset with also have the name HIV positive. And we don't assign any data elements to that dataset. And next, we will choose the period type as you want to implement the completeness for the newly created dataset. And the last step, we need to link the newly created dataset to that particular program. And how we do that, we will break one more custom attribute and we put the UID of that dataset into the program. That's how we link. When we do that, the period selector and the completeness button will automatically show up. You can see on my screenshot here, on my left hand is the completeness dataset. On the right hand side, we have one custom attribute. We will put the UID for that dataset into the program using the custom attribute. And so that program will have the same completeness functionality just like when people click on the conflict button, people cannot modify or add new events for that particular conflict and period. As you can see, this is a screenshot from the event capture for HIV. On the left hand side, we have the forms with some customized components. On the right hand side, we have the list of the events and program indicators. Okay, so next will be the OCA, OCA is done for offline capture app. It's a native application to be installed on the data collector computer. And again, in-law, not all the regions have the internet connection. So that's why we need to make one desktop app for all the people which is living in the region, don't have any internet, they can input the data. And whenever they have the internet, they can push the data into the data too. And the thing is the layout and the functionality of the OCA is exactly the same as iCapture. So people will not be confused when they switch from iCapture to OCA. The only difference between iCapture and OCA is the offline functionality of OCA. Yeah, you can see this is the one screenshot from the OCA. The layout looks the same, but on the top we have the data sync button. So whenever people have the internet, they just click on the sync button to push all the data which they have in their local computer to the HIV instance. Okay, and our last app is the loud off-unit profile. So the purpose of this app is the management of OCA across multiple instances. In loud off-unit we have three instances, the first is the HMS, the second one is the tracker, and the third one is COVID. And actually they have their own OCA, it's the same, but some of the OCA will be only available in HMS or only available in COVID, something like that. So we need one app to manage all of them. And for this one we can also manage the other facilities such as university warehouse, PPM, drop-in center, mass facilities, etc. Yeah, and as you can see the one screenshot from here we have the summary with aggregated data. How many facilities we have in this particular province for the army, hospital, center hospital, COVID isolation facilities. And this is the line list where we have the list of the facilities that we want to manage. Yeah, and here we have the filter. People can filter by facility category, type, ownership, and by service and managing more. And here's the detail page of one facility. We have all the information of the service week. This facility is providing. And on the right hand side we also have a photo of that facility and its location on the map. Yeah, that's all for the presentation. Any more? Any more? Yeah, any questions? Yes. First of all, thank you very much for sharing your innovations. I will call this to developing different apps. But here I'm just confused about the one thing. Since we are using Jacker Capture to capture each events and there we have the same data elements based on which we have developed program indicators. And then at the same time we also developed the data sets, which is updated monthly. So since we have a Jacker Capture on all events, I think we can summarize all the data from the program events to show the monthly data or the music data. And then by taking that rule, why the users need to update the same data again every month? Actually this was before where they had the full list because some people are assigned to do the event data, which is daily. And it's done by a couple of different people. And the monthly HMIS form is filled by HMIS people. And the event data, the EPI people are doing the event field. So what was happening was like we used to get two figures at the national level. And then just say, why are we getting two figures? And we told, why are you entering this data? We already have all the events. No, no, no, that's EPI. But HMIS people have to enter the data. And then to solve this problem actually for the health worker level at the facility level, just say you can enter all this data and complete the button. That means we have finished the data entry for events. So what the SOP was, first finish the your events and then let HMIS people enter the data. So then they can, like when they're doing the data entry, they can actually know what is wrong. So we are accepting both the data. But then they know, okay, there is some problem in these things. They haven't sent us the latest data. So then they can cross check with their EPI team. That was the reason why we had to include this one up. Because before, like we also just say, if you have event data, we can push this data inside. No, no, no, that's completely different. Because this HMIS is HMIS user and EPI will be entered by EPI user. That was the reason why we entered, why we had to create this one. Any other questions? Yes, thank you. I have a question with your presentation about OVA, an offline custom app. Is it available to install on Windows? And if we have to design something or programming inside the OVA, or just download and install. And one other thing, the question is, I see you when the data entry enters some records. And the user have to click the button synchronize. It cannot be automatically when they get a connection for example, in other province, the connection is not stable. So do we need to click manually or we can have another option to synchronize automatically when the internet returns? Thank you. Thank you for your question. For the first one, whenever the people install the OCA on their computer, it will be run on both Windows and Max and Linux also. So the first thing they are going to do is to just sync the metadata from the HL2. So that's the first step. We can say that for the first time they install the OCA app in their computer, they need to have internet. First sync all the metadata and also the data. But for the data people must select what is the time period they want to sync the data. But let's say in the setting of OCA, we have the setting of three months, six months, or two months. Because if we sync a lot of data from the HL2, that would be very, very, very slow and heavy. So whenever we sync the metadata from the HL2, all the programs, all the data sets will be on their local computer and they can start working on that. After the syncing, they can bring their computer back to their home and start entering the data without the internet. Okay, for the next one about the syncing, this is just one of the requirements. We can, yes, people need, from now, people need to click on the sync button whenever they have the internet. They click the sync button and all the data will be sent to the HL2. But we can, yes, we can also do, like, whenever the internet connectivity is available, we will sync on the data, push the data automatically through the HL2. We can also do that, yeah. No, no, but the reason why we didn't do that one, because like we wanted people to sync and if they synchronize fail, it will also show what all the different things for the end user. So there is a message button, okay, this has been synced, this has not been synced. And then they can attempt to sync again, that sort of thing. And then the automatic sync, we didn't want to do that one because like it's sometimes you have internet. You don't want to use it because like I want to use it for other things. And especially when they have this, they use the mobile internet and they don't want to sync even though they have internet. So that's why like we want to give the options to the users to sync. Yes, Dr. John, yes, we should have an option that we should automatically or manually. And because like the question is some data and 3.4, they offline for a week. So the data and 3.4 hundred record, so they need to click one by one, one by one and it will take long time. Sync is one time. So sync all the record together, you can enter. No, sync is one time. So all the data what is stored locally, all the data like if you enter for 10 different program and all the things, it will sync one time. So user have to click synchronize only one time, not one by one. This is only for events and aggregate, not for traffic yet. It's good to develop that app. But here we have a one Android platform, a blue stack. Right. So if you install blue stack in the window, we can install Android apps. And there we can use and write the house on aggregate and checker. And there is also the same thing. We can use the Android app offline and do the all the entities. And then when the internet is on the system, automatic sync sync to the online person. And then the system gives you a message. This TI are not seen with this either. So we have also that type of platform where we can use often Boston in the window. Yeah. Perfect. Any other questions? It's okay. So we have two more. So the next one. So any other question? The MFL or the, sorry. Now all unit. Okay. So the next one is from Sri Lanka. Are you presenting?