and have been managing DHS implementations since last few years. So we are organizing this level one academy for DHS to track use along with the team at University of Fossilov, Hispindya and his Sri Lanka. As you all know, the academy contains three events. So we have the webinar one today and the webinar one webinar two scheduled tomorrow. The webinar one will focus on the latest DHS to traffic after features and what other features are in the pipeline so we'll be discussing that during the presentation today. So we'll have the more formal introduction the official opening where we'll provide you with the academy, the introduction to the course instructors and of course we'll have a round of introductions from the participants as well. We'll be introducing the platforms that will be using for the academy for the next five days in the coming week model and DHS to. We'll have the use cases that we're using in the academy during the course, which you'll be looking into evaluating and then of course using that for all exercises and the final examination. We'll have an introduction to the community of practice also tomorrow so we'll have the COP coordinator guessing will join us and give you an overview of the COP and how it can be utilized to express potential in terms of sharing concerns related to your DHS implementations. We'll introduce slack and we'll have one our presentation on the tracker data model, just to revise the concepts of the basics of track data model before we go into the further details of track use from the first day of the academy that is Monday 26th. So, so these are the main areas that will touch today and tomorrow. I like Michael fellow facilitator Pamod from health history Lanka to introduce themselves and take over the webinar proceedings. Pamod over to you. Hello, good morning. Good afternoon, everyone. I'm Pamod from history Lanka. So looking forward to spending roughly around one week with you with this DHS to day tracker data use academy for the Asia region for your 2022. So as Sarah already mentioned, today we will have the first webinar. And this is about introducing the DHS to tracker and the new features to you. So let me share my screen first. So, in this session, we will discuss and give you an update on what DHS to tracker is for those of you who have been using DHS to tracker. And for everyone who is kind of new to the DHS to tracker, we will make briefly mentioned what the DHS to tracker is and then, of course, we will provide you with a feature update of new features which are available in the DHS to tracker. So I must mention that we will be using some technical words in DHS to be and I mean, pardon us for that. For some of you, they may be slightly new, but don't worry, we will be discussing about all these features and the terminology and the data model about the tracker in the upcoming days. So first of all, let us look at the objective of today's webinar. So what we will be doing is first to describe what DHS to tracker is. And then we will try to understand how DHS to tracker can be modified or customized, and then describe some examples of how DHS to tracker is used globally across various domains. And then also we will describe the features of the DHS to tracker. So we will be mostly taking examples from the health domain such as tuberculosis and surveillance in explaining these various scenarios today. But during the academy, you will get exposed to various other use cases from all over the globe. And we can also further discuss of some use cases which you are currently working in while you are engaged with your respective countries. Right. So first of all, what is DHS to tracker. So in DHS to the tracker component allows for the collection analysis of identifiable individual and longitudinal data. This means we can create unique shared records which are related in several different services to the unique record that we are tracking. So what we mean by this is for example, if you think of the health domain, we can think of a scenario where we register a person in the system in the DHS to instance.  So for example, in a lifetime of a person, he may be following services in immunization program, maybe tuberculosis program and if it's a female, she may be enrolled with the antenatal care program. So all this information related to this respective program can be recorded, captured and analyzed in the DHS to platform at individual level. It's not only individual level, we can further move one step ahead and we can aggregate data produce aggregate analytics based on this individual data collected in the DHS to platform. So to do this, we have to use the DHS to tracker component. Some of the features which are available in the DHS to tracker include scheduling visits for various services and sending automated reminders based on these schedules and tracking missed or upcoming visits and creating reports displaying both individually identifiable and aggregated data as I mentioned before and support for data quality and decision support during the data collection and sending notifications and alerts based on data within each individual event. And in addition to collecting data, we can also analyze the data in DHS to tracker. So while using the tracker or the data collected at individual level is automatically aggregated based on predefined parameters. So meaning like if we collect data supposing in immunization program, we collect data at individual level and we are able to analyze and produce analytic outputs such as tables and graphs and maps at each level of the hierarchy. So for example, if we collect data at health facility level of individual patients, we are able to produce analytic outputs and dashboards at district provincial and national level. So both individual and aggregated tracker data can be weaved and analyzed within DHS to using the built in analytic tools such as dashboards tables charts and maps. You can also export your data to analytics platform of your choice. So that means you are not kind of locked into the DHS to platform you can always export the data at individual level and analyze it with some external tool. Data ownership is a main feature that is available with the DHS to so DHS to tracker contains granular sharing settings that allow system administrators to define which organizational levels groups and individual users can access specific kind of data stored within the tracker program. And hosting for each DHS to instance is handled by the owner organization. So for example, the Ministry of Health of a country is in charge of hosting the data so they all have the ministry will have complete ownership of the data hosted in their DHS to instance. And you can also define your own parameters for data storage in accordance with your local laws and privacy concerns. Whether you host your DHS to instance locally within your country or in the cloud, no outside entity, including the DHS to software developers can access the data, unless that data is specifically granted by the owner of the database. So if you do not provide access to a particular user, nobody outside of your organization or your information system will be able to access data, which is stored in your DHS to instance because we will be while using DHS to tracker will be collecting and analyzing data at individual personal level. This is of a major concern and we just want to assure you that. So tracker component just like the DHS to aggregate is customizable. So what do we mean by that the information and the workflows that are defined within the tracker are completely customizable. So to facilitate this we have a number of standard digital packages which are available as a starting point that can be freely modified based on local context. These packages which we will be discussing a little bit more in detail in upcoming days are a collection of metadata. So in case if you for example if your ministry wants to set up a collection of TV related data in your country and you don't want to start from the scratch and you are more interested in establishing an information system according to the globally accepted standards. So what you can simply do is to set up your DHS to instance and then install this metadata package which comes bundled together and once you do that you can have and have a DHS to tracker instance up and running in no time. But of course you might have to customize it based on your country requirements and these configurations that we mentioned as digital metadata packages are based on inputs from various partners including DC, UNICEF and GAPI and many other partners. So DHS to tracker is currently in use in more than 88 countries across the globe. So you can see the adoption in the map onto your right and these countries and organizations which are already using DHS to for aggregate data can leverage the existing infrastructure to implement tracker programs without the need for an additional software platform. So if your country already has a DHS to tracker implementation, sorry, if you already have a DHS to aggregate implementation you can use the same network and hosting infrastructure to set up your DHS to tracker component. You won't be actually needing any additional information when you are starting up. But however, when you scale up, you might need to expand your resources based on your requirements. So there are several core applications and features related to DHS to tracker and some of these the applications which will be using are listed in this slide. So for data collection, we will be using capture application tracker capture and Android capture and to produce outputs. We are using charts tables maps and dashboards. So every release of DHS to involves some updates to these tools. So some of you may already be familiar and maybe using these tools. So let us discuss some of the main updates we have seen lately in some of the tools that we have mentioned here. So first, we will discuss briefly about tracker tracker, sorry, first we will discuss briefly about this capture application. So the DHS to capture application is used to register new track entity instances and enroll into programs. So track entity instances is again a technical term which will be explaining to you during the academy. So you can think of, for example, a person. So the capture application helps you in registering a person and enrolling into various health programs that you have already configured in your tracker. And in addition, it leaves the tracker dashboards and and also supports adding and updating various events. And it also supports searching for track entity instances and listing and filtering track entity instances in tracker programs. So what you are seeing here is the DHS to capture application and you can clearly see in the screenshots. That this application integrates events and tracker data in one application. So historically, if you have used this application, it uses supports collection of events. First, not the tracker data, but now it supports both the tracker and event data capture. But however, in the latest version, I mean, this is a work in progress. So while it is supporting tracker data collection, some of the widgets that you may be finding in the tracker capture application, which we have been using to capture DHS to tracker data such as the relationship and referral widgets are not currently incorporated in the capture application, but they'll be included in time to come. And the capture app also supports pattern based ID generation as you are seeing on the screenshot on your left. So here, for example, in generating the unique ID, we can configure it so that it supports generation of the ID based on predefined patterns. And in addition, it also supports the detection of duplicates during the registration that you are seeing on your right. So if there is a possible duplicate record that is found when you are entering or registering a patient, it will always prompt you. And what you are seeing here is a screenshot from the tracker capture application, which you have been historically using to collect DHS to data related to the tracker, like for example, patient level data of patients we are following up in health programs. So the DHS to tracker capture application supports reviving the track entity dashboards and also reveal indicators. And in case if you want to provide feedback to the user based on the data collected on that patient, it is supported. And it also supports management of all the enrollments, meaning like if a patient is having enrollments in multiple health programs, the tracker capture application supports that. And of course, managing features such as relationships and entering event data for tracked entities are supported in the DHS to tracker capture application as seen here. And the capture application of course supports comparing repeatable events during the data entry. So you are actually seeing two different views, one in which you can actually see the various events in a repeatable program stage as columns, what you are seeing on to your left. And on to your right you are actually seeing the tabular data entry feature where you will be able to see all the events of a repeatable program stage in a tabular fashion. All this we will be discussing during the course of this academy. And the tracker capture application also supports metadata and data sharing. So what we mean by data sharing is it will control who can access registration of individuals. And then of course you can control access to specific program stages like data I mean collecting of data related to some sections within the program we can restrict who will have access to that particular section. And then of course we can further control who will be able to access the data in analytics application. So you are seeing here some of the screenshots related to this access control. So for example, here we try to register a person in a tracker in a tracker program and for some reason, if that person is not having access, it will prompt you saying you don't have access to create an event in the current selection. So likewise, we can kind of compartmentalize the access within your tracker program so you can give separate user groups, user roles access to a particular section within your health data collection. So here also we are trying to show you how we can kind of restrict access. So for this ANC registration now in the standard form that you are seeing here, the data entry and registration is all available to a default user. But in case if the access is restricted, you will be seeing that the fields are grayed out and you will be seeing an icon like this where it prompts saying that you are not able to edit the data. So restrictions such as this is available and fully customizable in the DHS2 tracker. And the tracker capture application also supports features such as persistent top bar while entering data. We have seen like when you are entering data on a particular patient, it really helps for us to see some basic information such as the information we are collecting as attributes for that track entity or the person to be displayed at the top bar so that we can quickly reference have a look at and also clarify the which patient we are entering data especially in the context of entering large number of data of related to persons. For example, it could be COVID immunization where you really have to clear bulk of the patients in a very short span of time. It can come in very handy to have this the top bar. And in addition, another security feature which is available in the DHS2 tracker is the is the audits. So what we mean by audits is that. So whenever supposing in an example they are health facility user wants to access data related to a patient which has been collected by another facility. So this may not be a standard practice. So in case if a person of another health facility wants to see some events which have been or data that is captured from a different health facility, we can always configure DHS2 tracker in such a way where whenever they want to access, we can define whether we are totally blocking that and we don't kind of let that person access data outside of his facility at all. Or else if we want to let that person access whenever they do that to provide some reason before accessing the data. So this is the concept called breaking the glass which will be mentioning during the academy. So this kind of granular security settings are always available in the DHS2 tracker and this keeps evolving based on the requirements across the globe. And what you are seeing here is is the audited values. So for example, we can configure the tracker where we keep a kind of a track of what happened to a particular data item. So in case if you want to see like who updated or made a change to a captured data and keep a log off all these updates and changes, we can always do that. And what you are seeing here is this feature called audit history where we can keep track of the data value who made changes who deleted who updated all these can be captured in the audit history. Right. And while capturing data there is always a feature available to shift between several data entry modes depending on program requirements and user preferences. So here what you are seeing is two different capture user interfaces. So one to your left is what we call the timeline data entry where we are we are having these boxes at the top where each book represent a particular event. And on your right you are actually seeing the feature called tabular data entry where like here you are seeing the data entry interface as a table format. And we just have to click on the row header to toggle between the various events. And here again like we are showing another interface so but I want to highlight here is like we have different user interface to support capturing data as per your convenience. Right. So another feature which we have seen in latest version of DHS2 is the user assignments of events and custom working list management. So while capturing data now we have the provision to assign a particular event to a particular user. So what you see on the screenshot number one here is where we do this assignment. So in this particular example the lab result we are assigning to a particular user. Right. So once we do that when that particular user logs in from his account he will be able to see a working list of the events which are assigned to him. So this way it will be much easier to manage workflows related to users and we can have a clear and less cluttered interface for the user when he logs in and also DHS2 supports creating relationships. So this was a very handy tool especially during the COVID-19 surveillance where like especially in the case of like contact mapping and contact tracing we had a requirement to relate one person to another. Right. So this is what we mean by relationships so we can create relationships in DHS2 where a particular person can be linked to another member. Right. So the capture interfaces supports these relationships so we can create a relationship while we are capturing data and the analysis related to the relationship is some work in progress. And I'm very hopeful that you will be able to see many exciting features in analyzing and visualizing these relationships in upcoming versions of DHS2. And the DHS2 supports enrolling a person in multiple health programs. So here we are seeing a contact registration so a person who is kind of registered in the case-based program we can always register that person in contact follow-up program. So here what we support is to kind of register a person in multiple health programs. Right. So this way when we are managing the life cycle of a patient we can at a given time have a patient enrolled in multiple programs or actually in no programs at all. Right. So this is a kind of a very handy feature so that we don't have to you know like duplicate creating or registering the same patient all over again. We can pull up the records of that patient and just keep enrolling that one to a new health program. And in addition the DHS2 also supports creating indicators. So indicators are basically calculated values and we can display them on the fly while we are capturing data. So for example if we collect the patient's date of birth we can always calculate the current age and display it in indicator widget while we are capturing data. And of course the DHS2 tracker supports scheduling visits and tracking their status. So for example we can schedule an upcoming immunization visit for a patient and we can also track who is due for a particular date. And in case if there are dropouts we can easily track them because we have this schedule date functionality in DHS2. In addition DHS2 supports reminders sending reminders to health staff as well as beneficiaries or patients based on these scheduled visits. Notifications can be configured to be sent out as emails or system generated messages within the DHS2 platform or even via SMS. So sometimes when you want to send out emails and SMS you might also consider using an external email server or external SMS gateway to facilitate this feature fully. And the DHS2 tracker capture also supports some criteria for decision support and data quality while capturing data. So for example what you are seeing here is when you select a particular vaccine type and select the particular vaccine manufacturer it supports auto display of the batch number and the expiration date as well as total doses required. So here we are actually trying to kind of facilitate decision support as well as reduce the data entry error that can take place by automating, populating some of the fields. So these kind of functionalities are available in DHS2 and these are fully customizable based on your requirements. And in addition there is also a feature available to show some feedback. So this feedback can be something such as a warning or an error where we can kind of do a real time validation of a data that is entered by the end user. So in case if we want to do a validation and we identify there is some error in the enter data we can definitely prompt it. And in case if we feel that the data entry person should check the data again we can give us some kind of a warning there itself so that we can support. With this feature we can definitely support minimizing the data entry errors that can happen during the data capture. Right, so now that we have discussed about various functionalities and features available in the web interface of DHS2 capture related to tracker let us now focus on what is there in the Android. So I'm going to hand over to my colleague Saurabh to take it forward from here. Over to you Saurabh. Thanks for the screen. Alright, so we'll continue with the DHS2 Android features. So as similar to the web application and the features that we discussed right before the DHS2 also supports an Android application which works with all three data models aggregate event and tracker data and works both in online as well as offline modes. So the app is tailored to set mobile data collection interfaces both work on a normal Android phone and as well as an Android tablet. And with these new features which have been coming in with each release offline analytics has also been introduced. Where you can where you can see the offline analytics within the tagged entity dashboard. So the various features will discuss for the Android app so on the screenshots which you see here so you can buy the sharing settings of the program that you do on the web version. You can do. You can assign these programs to the user and the user will have the access to different programs which may be of both event as well as tracker type and then you can also give them access to different data sets. So all the metadata to which the user has access to which can be data sets event programs and tracker program they will be listed on the homepage of the Android application once the user logs in. Then in the event programs we are currently supporting I can set base the data entry where when you're defining your drop down menus or your option sets you can assign you can assign an image or an icon to the option and then you'll have these pictorial data collection screens can also be defined. So currently it is applicable to event programs only but moving forward this would be also applied to the tracker programs. So as we saw that in the web version we had defined some program rules and. Logics to apply or automatically push the application data into different fields based on the previous value selected so the same. Program rules which are defined in the web version also works seamlessly on the Android versions however when you're configuring these program rules you need to ensure that you put the available considerations into account to design programs in a way that they are compatible both on the web version and the Android version. Given that a lot of health programs work in hard to reach areas there is always a need to have a digital way to collect data which is not relying on you on the internet connection so the web version we know that needs constant supply of internet to be operational. But wherever when the health worker is on the field or there is a lack of internet connectivity the health worker can switch to the Android application where a local database is maintained in the device which is synchronized with the copy of the metadata and as new data is added to the Android application. Whenever the connectivity is whenever it is available the data can be pushed to the web version from the Android version. So the data remains within the device and depending upon a manual sync or an automated sync which can be configured the information can be pushed to the web server which was collected offline using the Android device. Next similar to the web version we also have a intricate search and duplicate detection because we need to ensure that we're just in unique beneficiaries and not not creating duplicate records. So here you can search for a respective beneficiary through putting in values for different attributes. So you can put either one attribute or combination of certain attributes and then the app will search the records which are available in the device and give you the matching results in case no results are found with the search criteria then you can proceed towards the registration of the person. In certain use cases we got the feedback from the community that the search function may not be required. Therefore, now there is an option that you can define in the workflow while configuring the your program and the Android implementation whether you need the search feature as a compulsion or you can directly move towards creating new profiles. So you can configure these settings using the Android settings app and then the settings are applied globally to all the users who log in into the application. As we saw in the web version we have a drag entry dashboard similar to the web version the Android app also supports the drag entry dashboard where you can see the vital details of the beneficiary you can go into the registration data make some updates and then you can see the list of all the events or all the services the beneficiary has been provided with. And you can add new services schedule new events and you can even make a referral of the case to another health facility which provides similar services for the programs. The maps feature has been introduced in the Android app and it has been enhanced since last two releases. So when you're registering new beneficiaries are creating new events, you can collect the specific coordinates of the beneficiary and also associate them to the events. Those events or the coordinates which you collect can be plotted on the map, which you can see in the Android app itself. So you can see the individuals those who have been registered in the application and they have coordinates available. You can capture the location in two ways one is capturing the exact coordinates, but then you can also use collect the polygons from or an area from where the patient is getting registered so both the features are available. So when you're making selections you get both the options, whether you want to choose coordinates or you want to choose a specific polygon area so both can be done. So on maps recently they have been new features which have been introduced specifically the use cases which came across during the COVID pandemic. So when you're using a Android application, the DHS Android app is now integrated with Google Maps so you can take the beneficiaries location from your device and you can see the current distance between your location and the beneficiaries location if you're collecting data by specific households. Then you can see how far you are from the beneficiary household and if you make you need to make home visits then you can follow the location and reach the beneficiary based on the information which is available in the Android application. The use cases were specifically introduced in the COVID period where the linkage between the beneficiary and the health facility suffered so a lot of impetus was given and using features where we can provide continuous care at home by ensuring that the user is able to connect his location and the beneficiaries location using the Android application and its integration with the Google Maps. Then another feature is that you can collect images using a Android device or you can also read QR codes or barcodes for registration searching. So during the COVID vaccination examples we saw a lot of countries were generating these QR codes for on their vaccine certificates and they had set of information embedded. So the features introduced where you could scan a specific QR code and then the system will search for that specific beneficiary and give access to the record without the need to do a manual search by entering the specific attributes you could just scan a QR code. And if the QR code has the system ID embedded then we'll just search for that system ID and give you the the record. There have been use cases where QR codes have also been used for registration purpose so if the country follows a master patient index and they have issued these MPI cards which have QR code support. Then you could fetch the information which was embedded within the QR code into the registration form and proceed with the registration process moving forward. This was also piloted in few countries using program rules and it was pretty successful so with the advancements which are happening with use of QR codes and barcodes. The app is also enhancing the support to get to use and support different QR codes for patient information plus the barcodes for all stock related scanning which needs to be done specifically for the LMIS use cases. In terms of the user interface continuous improvements are being made. There are multiple rendering options now available which includes different types of data selection mechanisms which could be as you see on the screen could be vertical radio buttons it could be horizontal radio buttons you can do check boxes you can have options so if you have options around five or six options which you don't want to show as a drop down but you want to show it as radio buttons then you can configure the look and feel and you can decide that in what way you want to show your specific options. But of course there is an attached limit if you have more than 10 options then of course it takes a lot of real estate when you talk about horizontal vertical selection so it will not turn the option set or the data element appearance as a radio buttons if it exceeds the set number of options so that has been already put into account. Then you can have these available the custom working list filters where the end user can generate working lists so there are two levels at which this functionality has been implemented one is you can filter the events by the events which have been assigned to you so in the web we saw that we can assign different users to certain events. And the same can be done on the Android application so then the user can generate the events which are assigned to him for further follow up or the events which have been assigned to anyone which has access to the program and health facility. And then you can also filter events by different timelines. What are the events due today what are the events in a week and how many events are still unsynchronized how many are synchronized so you can put some additional filters and create different working list filters for you to use. Now we have local offline analytics supported in the Android app as well. So you can when when you're creating your web dashboards, you can define what charts you want to see on the Android application and what dashboard or what group of charts you want to see, which you have added on a dashboard on the Android app as well. So the data which is populated on these charts are basically from the data which is available in the device so whatever number of records you have collected on a device it will take up that data and create these charts for you. The charts are at two levels one is the level of individual where you can plot these information such as weight, the vaccination status of important doses the evaluation of high devaluation of weight, and then you can also see the comparison between and different Z scores which are there for nutrition monitoring so this is one example where you're plotting data for one individual and then reviewing the evolution of the data with that specific individual. But now you can also have your indicators data populated locally on your devices so you can configure your Android offline analysis on the web version and circulate with the users so we'll during the course of the academy we'll see how we can how we can configure our Android specific analytics and can use that in the Android application as well. Then in terms of the overall visualization tools available in DHS to you have your data visualizer application, which has now, which has which is a combination of charts and the table table function. So from version 2.37 onwards the table table app has been merged with data visualizer so all the use cases all the features which are available in the table table app and now part of data visualizer. So within the visualizer you have different charts available where you can create single value numbers you can create your trend lines across the years you can. You have the maps app you can create different application different geographical analysis of data, you can create patient and even clusters where you can do these clusters and then you can put additional filters and do styling to see data. Which has been entered for different events and how it can be further styled and differentiated into different categories example so if you want to see the total number of malaria cases registered by gender so you can create these. case clusters and then you can differentiate them by the color so for example in this. We can see that there are two colors available so one of them representing the male cases out of the 1487 cases reported in the geography and the blue one is for the female so you can create similar clusters and add more styling to the clusters that you created. So we'll see how you can use the maps application to analyze tracker data both patient specific and also related to the indicators during the academy sessions. The event reports app is currently being worked upon to be upgraded to the new line listing apps so there are a lot of new features which are under development and the new line listing have has been introduced for beta testing from version 2.3 at onwards. And so they went reports app has been rewritten on a modern and user XP for to provide modern user experience and to improve on the technical stack which was being used before. So it is the design language is in sync with the data visualizer application and it supports the track entity analytics where you can generate your line list from across the program. Using the attributes the patient specific information and the data elements from different stages. And you can create your line lists and even aggregate that information on the fly, depending upon your requirements. So this app is compatible from version 2.3 at onwards, and a lot of continuous development is being done on this application to put in all the use cases on the requirements that we've received for tracker line listing app from the community so this is currently working progress. So now we just have a look at how we can visualize tracker data. So one is you can have a look at the patient specific information using the event reports or the line listing app. But you can also aggregate that information in form of program indicators and indicators and you can also aggregate the patient information through event reports. So for example you want to see the distribution of COVID doses by COVID vaccines by different doses and gender then you can create these event reports or you can create your program indicators, and then you can visualize that data using the analytics application. So all the tracker data which you're collecting using events or the tracker capture apps you can aggregate that information through event reports and you can create program indicators to use those program indicators and indicators as part of your analytics application and put them on the dashboards of further analysis. So with use of the standard option sets which are defined for your different data elements, you can since they're codified so you can quickly aggregate that information and see that information through event visualizer or you can also use the program indicators for this. So the standardized responses which you collect which may be different age groups, you can see different types of vaccines or if you are looking at different case awareness program for HIV and TB you want to see quick data for patients currently on treatment or you want to see the outcome of TB treatment then you can easily aggregate the standardized responses which you collect for the TB treatment status or HIV treatment status so that could be easily aggregated and you can use the applications available to aggregate this information. So I think we can also create indicators using both tracker and aggregate data. So many times we see that for individuals in the collecting data in tracker but then our denominators and our population estimates or our program targets are part of the aggregate data. So when you're creating your indicators you can pull data from all different sources you can pull data from the program indicators which are aggregating data from your tracker capture or event capture or you can pull your aggregate denominators or estimations from the data sets in which you are storing the information say annually or whatever is the frequency for storing your estimates and targets. You can combine when creating your indicators you can combine data from all three sources and builder indicators. So the program indicators will give you a total count of say the people who have taken the first dose of COVID-19 vaccination. And then if you have a specific targets defined then the age attribute allows you to do an aggregation based on the age of the individual and then you can create your program indicator where you can count how many people have been given those one and who lie between the age of 35 and 54. And then you can divide that by a specific data element which stores the target of this respective population age group and then you can get these coverages in terms of percentage. So when using tracker it doesn't mean that you cannot combine that data with the other data sources available. So based on your requirements you can define your indicators which can pull data from multiple sources. And you can view that data in same report in the same output. So in the example you see here, people receiving first dose 35 to 54 that is a program indicator and that is coming from a tracker program. The population estimate of 35 to 54 years that is coming from an aggregate data set. And this helps us to do the total coverage where we see the dose one coverage is an indicator which gives you the percentage of population the eligible population who has been vaccinated for those one. So you can combine these program indicators together and data elements together to have your combined analytics from different data sources. Now since you're collecting granular data using tracker events. It is very easy to apply different disaggregations to the raw data. So, in case you want to analyze the same data, but between different age bands. So you can create your different age bands using the legends and you can assign those legends to your, your age attributes. So if you want to see the cases of say COVID-19 cases by agent sex you can define the age bands accordingly and then you can disaggregate the information by different legends which you can define and then you can see the data in different combinations. So, given that this data is granular you're collecting data for each case then it's easy for you and the system to generate these combinations and give you the data disaggregations as per the requirements. Now, as we discussed, you can review the tracker data across multiple program stages. So the event reports in the latest version as well as a new line listing app allows you to create tables which contain data from different program stages across the development. So for me it is important to see that the, for example, the person who was registered with COVID-19 or a person who was registered as a suspected TB case and then what was the lab result which was taken up for that specific case and what was the overall TB treatment outcome or health outcome in terms of COVID-19. So now this data gets collected across different forms, but in the output you want to have a combined table which can give you information from each of these events which are created for this respective case. So when you're creating your event reports or you're creating your tables, you can pull data from these different stages and create a combined table. In terms of the, if the lab, if your program stage is a repeatable stage then it will take up the data from the last, the latest stage which was created so that you have the latest information for that specific patient available in your tables when you're creating those tables. So you can review the data across multiple program stages in the select data elements which can belong to different stages in one table so that you have a combined overview of the case over different services which were given to that specific patient. So one way is to look at the latest data for that program stage in case of a repeatable event. But you can also see the data for all the events that the patients have taken. So for example if I look at the vaccination data then I want to see all the vaccination events which have been reported. So I can do an event report where I can select the event and the data elements which I want to review. So I can see the set of all the events, all the repeat, all the events which have been given for repeatable stage. Those also can be grouped together or if I want to see the data for specific events compiled together then as we saw in the previous step we can do that. We can combine data from different program stages and create a combined table but we can also see for a repeated stage you want to see all the events then you can do that as well. So both the features are available in the latest edition of the event reports and the line listing app. Then we come to the maps application how it can be used for visualizing the tracker data. One way is you can use your thematic maps. You can create your program indicators or indicators and then you can plot those program indicators and indicators on the maps app using the thematic layers where you can do these heat maps where the legend can be defined based on the indicator requirements. But then if you're collecting individual data then based on the coordinates of the patients and the events that they collect in the system you can create these clusters where you can see out of the total events suggested or total patients listed in the system. How many male patients how many female patients you can also do it specific clusters you can do clusters based on for example if you're tracking malaria cases you are looking at the the causative organism. You can look at how many cases were PV how many cases were PF so depending upon your analysis needs and how you want to create these clusters you can select your data elements port filters and then use styling on the maps app to create these patient clusters or event clusters which can be put on the dashboard as well and can be downloaded as images to be used for your data review presentations etc. Then during the pending a lot of requirements came for contact tracing and then contact tracing is very frequently done for different programs, more or less for TV as well. The track identity layer and the features introduced to use relationships in the maps application where you could identify the index case. As the red dot as you see here and then to how many other cases has the index case building to so you can create. You can map your relationships on the map based on the individual location which has been entered and if relationships have been created with that specific case so the red dot you see here is the index case. For example, that was say one of the TV cases that were identified one of the covert cases that was identified and then if the person has come in contact with five of the individuals and they are registered under the contact tracing program. Then you can based on the relationship which has been created you can plot the the the map to so one index cases related to how many individuals and what is their location so you can create these type of maps also in the maps application. Then in the pandemic. There were many requirements where features were not available of the shelf using the highest room because it came as kind of an unexpected event and then the country started using the highest for different use cases and then there were many requirements coming for vaccination certificates to be generated from the system. If the as to what was being used as the system for data collection. So, there were many custom applications developed during the course of last year. Some examples which you see on the screen. There is the vaccination certificate up which was generated for one or two. There you could generate the vaccination report and the certificate right at the point of data entry and provided to be the beneficiary based on the vaccinations that the beneficiary had taken. So this was the second example is from his Western Central Africa group where they created this travel pass, which was required for the, the beneficiaries to travel across the country. And since they've been using the as to as their main vaccine to go with vaccination system so they generated a custom app to generate these certificates and travel passes through the DHS to system. Then they were also working done on establishing these COVID-19 relationships network model. So there again, they were custom apps developed using the R software where you could create these relationship diagrams between the confirmed index case and the contacts and then you could be also able to segregate the the status of the contacts as confirmed or suspected. So you could create these network diagrams to see how the the pandemic had spread in terms of one person, one person infecting other individuals in their specific vicinity and then you can also put filters for different case status, different arguments, the distance of the closeness or between us these parameters already defined and these algorithms are defined so an app was developed using our software which was pulling data from DHS to and was able to create these network models for further COVID-19 analysis. Then if Sri Lanka worked on a COVID-19 relationships app where they were able to plot the the these diagrams based on the registrations that happened in the COVID-19 case surveillance program the confirmed cases and the the contacts which were registered during the surveillance exercise so they could see the blue dots which you see are the index cases which are confirmed cases and then the person they came in contact with are shown in purple so the greater the person has the number of relationships with their contacts then the greater was the size of the circle and then you could see the you could see each individual case what the case details were and then you can also see that specific record in the tracker captured application. So performance was one of the key issues that was identified when DHS to was pulled into a COVID-19 management systems be it case surveillance, be it vaccination given that the scale at which the data volume rose during the pandemic, a lot of focus was given to improving the performance of the system so version 2.35 and above saw many changes in terms of the performance where many countries reached out to the core developers at the University of Foslo and reported the performance issues of the systems and then there were many changes being made in terms of how the data is maintained how the API is working how the system responds to the different user inputs which are given by the end user while entering the data from 2.37 onwards a lot of performance based changes have been done where we have shifted to a sequence based ID schema which is also being recommended to be practiced wherever you are generating automated IDs from the system is recommended that you use the sequence method rather than using the random numbers so using a sequence number it was easy for the system to generate these IDs in bulk and then allow users to enter as volume of data. So wherever you have if you're currently in a large scale implementation or planning large scale implementations in future ensure that if you're generating asking the system to generate automatic IDs for you then it is recommended to follow the sequence based ID schema rather than the random ID scheme generation. So a lot of focus was given on getting the feedback from the community when we're using DHS for large national COVID vaccine campaigns so the server team and the developer team and the interoperate team and the University of Foslo interacted with different his groups who were implementing DHS tracker for COVID vaccine campaigns and they used to analyze the data in terms of response time what is the volume which is currently hitting the system and how the system is handling the response during the peak times. So we saw that during the peak times when the data until it wasn't full flow there were many instances which were facing 25,000 requests per minute for data input and which included registering new clients, adding events for vaccinations, generating vaccination certificates and rating reports. So all kind of things were going on simultaneously. So a lot of performance improvements were made documentation was done and these documentations were released for the community as best practices to implement and manage large scale implementations in the course of the academy will discuss these few aspects and then the global documentation which came out as the guidelines for managing large scale implementations specially for tracker those documents will also be shared with you for further reading and for further information and questions. So these are few statistics which were obtained from the field in countries where the tracker program was used in large volume. So these case studies available on the DHS to website the links are embedded in the presentation as well so Bangladesh last year data. Measles rubel immunization campaign where they had more than four lakh reporting sites and 35 million vaccinations happen all that data was reported by DHS to Sri Lanka had the entire vaccination program managed through DHS tracker where they had pre imported the the eligible beneficiaries using the country electoral roles and they had a volume of 60,000 vaccinations per day and more than 16 million people are tracked using DHS so a lot of learnings came from these implementations and the performance improvements which were done simultaneously. They are now part of the global core releases so these implementations really helped us to understand how DHS to go be further improved in terms of the performance. Another example was in Rwanda where they had around 1.5 million people registered and more than one lakh people were getting registered per day and then they have these set targets where they want to achieve the vaccination coverage is by July 2020 was 7 million so Rwanda has been using DHS to for all the COVID related programs so case surveillance contact tracing as well as vaccination and everything has been done on large scale and the learnings that came from these countries have now been implemented for in the global course so that could be all in all countries. So these organizations looking for doing large scale implementations can benefit out of the data. So there are some stability in performance considerations which have been come out of the whole exercise specifically during the COVID-19 implementations. So if you are planning to use DHS tracker for a large scale implementation ensure that you are on version 2.36 and above 3.7 had the largest impact in terms of the overall performance parameters so we recommend that you start if you are already in an implementation then you should update to 3.6 and above suggest it is 3.7 and if you are beginning new then you must start with the most stable version available at present with the performance enhancement. We see the analysis of the response times which were done for different type of metadata objects and processes the users were doing using DHS to came across that the user program indicators was taking the maximum response time because they generated on the fly and to load them in a dashboard page was affecting the overall system performance. Hence as a immediate recommendation it was shared that the program indicator should be used as minimum as possible on the dashboards since that was causing performance issues. The team is working on reviewing the the way the program indicators function and load so we'll have further improvements moving forward. Another alternative which was suggested was to aggregate your tracker data on the fly and move it to the data sets to different scripts or the global team also worked on the functionality which they call is tracker to aggregate. So if you wanted to do the analysis then without any performance issues then in many countries they were mechanisms made to push the data from tracker aggregated into the aggregate data sets so that the analytics could come from the aggregate data sets and that was not hampering the data entry and the other processes which was happening in tracker captioning parallel. So these alternative approaches were also documented and tested in certain countries to see if these models can be used for large scale implementation in countries. Then the third recommendation was to limit access to dashboards where you use program indicators to last number of users and also using that specific dashboard as a landing page because we know that dashboards are the first point of entry in the system. So therefore recommendations were made that the landing page dashboard should just have minimum information which could have your important information which you want to share with the user some important contacts documents etc. But all heavy dashboard should not be part of your landing page for the first dashboard that you create or which is your by default landing may should be very light in comparison to the other dashboards because if simultaneously thousands of users are logging in then a lot of resources of the servers were going on to loading that specific dashboard which is not needed. Hence the recommendations to keep your landing page as light as possible so that people are able to log in very easily and the server also doesn't chokes up because of the huge load and loading heavy dashboards which are set up as your first specific landing page. All the additional fixes which were done for performance they're already part of the last three supported version 3637 and 38 so the advice from the development team is to stay current. If you are using 3435 for your big implementations, the recommendation is to immediately upgrade to 363738 so that you can get benefits out of the stability and the performance updates that have been made over the last two years. Specifically we're supporting implementations such as COVID-19. So do focus on upgrading your VHS to instances to these last three supported versions to have the latest support available plus benefiting from the enhancements which have been made. So each year, his groups and the University of Foslow team have been working on the platform prioritization process. We have that process currently ongoing with the University of Foslow team where each is presenting their top five priorities for each of the DHS to components platform tracker analytics Android interoperability security. So these this roadmap prioritization process runs on an annual basis and the requirements are taken this year will now contribute to version 3940 and 41. Now, earlier we had multiple releases in a year now that has been restricted to two big releases one in April one in October, so that there is enough time for development. Enough time for development between the two releases and a lot of features and bugs could be fixed and introduced during the, during the, the final release. So earlier we used to have three releases for here now that has been reduced to two releases per year. So the DHS to roadmap is now part of the DHS to websites you can see the roadmap diagrams and you can see what features are planned for which release and then you can plan your upgradations based on the roadmap time then which has been designed based on the features which your implementation needs. As promote mentioned, the tracker support is now available in the existing capture app. So the basic tracker capture features of creating events scheduling events, adding data entry program rules, creating patient summaries, all that has been created is now available in the capture application, the relationships, etc not available which are being added to the new capture application. So at present you will have the tracker capture application working as before but then you also have the capture application where you can use that application as well for your new programs, which are not so complicated do not need relationships feature and these advanced widgets right now so it could be used. Now all the functionalities of tracker capture have been divided into different themes, which are tagged entity centric dashboard post program analytics improving duplicate record handling batch entry of records in terms of bulk imports, the performance and improving the UI all these themes have different user stories associated and the work is being done parallelly in all these teams to to ensure that the capture app covers all the functionalities and the design experience which was missing from the old track capture application. The new capture app which is available from 2.38 onwards will will be further developed and all the features we've introduced, which already part of the default are captured and the new ones which are being discussed and requirements have come from the ministries of health and the communities and the partner organizations have used the HIS to for their data collection and management. So these are some screenshots of the new web application so you could see the person dashboard with the enrollment overview where you can see one person is part of how many programs you could see this person is part of the TV tracker program the Malay registration program and has had inactive enrollment information campaign and she is not yet part of the child program any other programs which are in the system so in one screenshot you could see the the patients involvement in the system in terms of in how many programs the patient is registered. You could see the notes from all the programs so wherever there are key set of information available which could be important for the patients and the clinician to consider when you're enrolling this person into new programs such as allergies to penicillin mild asthma and any other key information that could be shared across programs so all the users will have access to this information. So you'll have a person dashboard which gives you an overall picture of the person with respect to the system not associated to one specific program but giving you the entire patient history. As per all the programs which are available in the system. Then this is the enrollment dashboard where you can see the data for the patient for all the events that have been created for this specific person, which includes different program stages which could be of both repeatable non repeatable nature so you can see the lab reporting visits. How many lab events have been carried out for this person how many have been overdue how many are scheduled and you can see information for the key parameters captured in each specific event. You can see the errors in the warnings here so if in your program you have defined warnings for. For your program in terms of data quality and further decision making then those warnings and errors will be showed on the right hand side in these boxes so that they're clearly visible and demarcated from the regular data entry. And then you can also see your indicator widget so the key indicators that you're defined for specific patients you can see that. So this is how the new enrollment screens would look like in the new capture application. Duplicate handling is also being improved upon with what's available for new capture up you'll be able to see the possible duplicates of this person. You could further review this person by going into the dashboard of the specific person and if this is not matching to the existing person then you can always save that as a new person in the program that you're registering. If now if you found that person as a possible duplicate then you can always and that was identified at the time of registration you can always flag that profile as a possible duplicate and the once it is marked as a possible duplicate then this the user will see a flag on the top that this can be a possible duplicate. And they should be checked and if this is not found as duplicate then this can be marked as unique so you can mark your profiles which you feel maybe duplicate profiles and then you can further classify and make corrections for this specific use case where you have duplicate records identified. Then in terms of the modes of data entry in the current tracker capture up you have to timeline and tablet data entry. In the line listing in the new capture application you can also make use of line listing data entry so you can do the updates one by one for data elements in line format. So that is that is also being introduced. So you can have you'll have both the the section wise data entry as you see in the default tracker capture up but then in the new capture up you can also see a line list data entry where you can do the entry for one person in one line and fill all record the information available for that specific person and event. In terms of deduplicating the records. There is a lot of work which is going on on improving the the logics on how to identify duplicate records. Which are being done through either including a fuzzy search and matching or matching against a combination of attributes so my first name last name data but mobile number could be searched together to give me a better filter results and a background job is also being introduced for finding potential duplicates. And since we saw in the previous slide that you could flag case as a duplicate case and keep it for review. The second step here would be the merging of these duplicate records so work is being progress in progress to introduce the merging of duplicate records by the system would suggest an automatic merge and then once you confirm that the identified duplicate records will be merged together into one single record. So the functionality for the deduplication is part of the API is now in 3738. The front end team is working on user interface where you can identify these duplicate cases, and then potentially move towards the merging exercise in the future releases which will be upcoming. So I guess that was my last slide. So, so I think the objective webinar was to introduce to you the basic structure features which are valuable and the features which are now part of the Android, and I'll be currently developed based on the feedback that we have received from the the community the users through Jira and different platforms through different his groups. If any specific questions, you could put them on the chat in the zoom. If you are already on Slack then you can put questions there as well. If you'd like to ask questions right now then you can please raise your hand and ask specific questions in general for traffic or in specific to the implementation as well. So we have around 30 minutes available so if any questions please feel free to raise your hand and as those questions will try best to give up responses as much as possible. So thank you for your patient listening and we can move it with a quick Q&A session before we close the first webinar. Thank you. I guess it's it may be a bit overwhelming for the first day for some of you if you're not too familiar with the tracker, but I assure you like will be going very slowly in the upcoming days explaining you the basics on tracker and I mean how are we actually going to use the different tools available with the DHS to track a component so I mean I will be sincerely hope by the end of this Academy you will get a better idea about how to use the tracker for your routine work. Any questions you have. Please feel free to ask. Yes, hello. Can you hear me please. Yes, we can hear you please go ahead. Okay, thank you, Mr. from work for giving me this opportunity to ask some questions. My name is I mean I'm a chance it from Congress, but actually I haven't implemented DHS at two years, but I've been studying the fundamentals and I'm looking forward to, to expand my knowledge on DHS to however I have some two questions. My first one is, I've been seeing I've been studying like can also DHS to support on iPhone it's only 100 up that's my first question. Secondly, I think towards his India. I was mentioned that there is a backup of the data I mean the data captured 100 up will be stored in a local database. So I'm asking what format could it be, either CSB or DHS or any format, and if it's in the DHS to format, how can we transform it into any other format for business and the data set and I normally use a Python and are. Can I also at least be systems and can I also transform the DHS database format to one of those formats. Thank you. Yeah, thank you so much for those questions. So about the first question regarding iPhone so it's like this. So at the moment, the native Android native tracker capture application on mobile is on Android that is mainly because like, we see a lot of Android based implementations in most of the countries that at field level because it's mostly a capture application the primary focus of having this mobile application is the data capture. But having said that we see a lot of requests coming from many countries, especially at the kind of, I mean higher levels for the data managers administrators and technical experts that they need to kind of visualize data mostly on Apple devices. So right now, we don't have a native approach, but then again, one approach is that DHS to the web version is now more and more compliant on mobile devices to be used. So for example, you can actually log into the DHS to web instance, using your mobile. Even if you have a Apple device, you can just use a inter browser login and there are a lot of like I mean like this weaving of dashboards and sometimes even data entry is possible using that so that's the kind of a tentative approach but of course, this has been really considered and I mean so many requests we have received with regard to this. So probably, I mean sometime down the line there is a chance that they might consider having, but of course as of now we don't actually have a separate application for that. So Rob, you want to add something for that? No problem, that's about it. Thank you. Yeah, for the second question, okay, I think if I understood you clearly, your question is about the local database on an Android device, right? So if that's the case, I mean if I'm understanding correctly, so if that's the case then of course yes, I mean temporary with this Android application we have a lot of local database which we are using but then a feasible approach in case if you want to kind of take the data out, I suggest would be to synchronize and try to get it from the, you know like the central DHS to instance itself because then you will have so many different methods of pulling the data out using the web API and so many other technologies but like trying to get that data directly from Android device may not be the best of the ideas, that's my feeling. If I understood your question correctly, is that what you wanted to know like to take data directly out from the local database of Android device, is that so? Yes, that was my question, thank you. Yes, but I think a better approach where you have, we will have more flexibility is to kind of synchronize data and take it out when it is in the DHS to central instance. Sorry, thank you. Alright, okay, from Arsalan there's a question on use case scenario, can we manage a health care practitioner assigned to a patient? Right, so if I understand it clearly, I mean okay, I think I am familiar with this use case because I kind of closely work with your team. So it's like this Arsalan, so the approach should be like, it all depends on how we customize and model the tracker. So for example, like if I mean, if I just don't focus on your specific use case and think of a very generic scenario where we have to collect data and we have the patients as well as health care practitioners, we can think of customizing DHS to for that particular generic example. But I think in your case, you have some other requirements where like you, you are having public and private facilities. So there we need to really model the DHS to in a way where we can, you know, like make some relations, I mean, some some analysis, even based on the general practitioners. So I don't want to dive deep into that particular use case because it's a kind of a very special use case, which is not too straightforward. But what I want to highlight here is because the tracker data model is so customizable. Even though you have DHS to the next step that you really have to do is to understand the requirements and customize DHS to and adopt the DHS to data model to best suit your use case. So this your use case of course means correction as well as analysis of data. So this is this is kind of like my take home message for this particular question. And maybe like when we are actually going into the content in the next few days when we are doing the academy, we can take one or two examples like this and and I mean dive a bit deep. But Arsalan for your specific use case, I think I can I mean discuss with you separately. Are there any more questions? Right, if not, Saurabh, I think we should be able to conclude the session for today. Yeah, we can move towards the closing and then look forward to the participation for tomorrow's webinar as well. Right, so thank you very much all of you for attending to this webinar and tomorrow also we have a webinar. We will be discussing mostly about what we are going to do specifically in this in this academy and we will introduce the facilitators and all of you who are attending this webinar and of course and the DHS to academy. And of course we will also introduce you to the the training environment and how to access the resources as well as the practice I mean the DHS to demo instance to do all the practical sessions. So all this will be discussed tomorrow. So it's going to be more hands on. So looking forward to see you all tomorrow. So thank you so much. Have a good day.


This is really important because Elliott got a bunch of seats and I think that they're now in command. If that's the case you're gonna see Larry Lawson come in most likely as CEO and Larry Lawson someone who, who Klaus Kleinfeld said would be doing some short-term things that would boost the stock not long term. Well you know as shareholders for my charitable trust we're concerned about overall we've been in for a while. We are happy if they do something that is going to bring the stock higher. That's why we own it. Lawson worked at Spirit and he did a great job. Elliott I guess had the votes. I mean I think that's what was happened. We were urging people to vote with Elliott against management. We think that they demonstrated a field to the, and to a strategy that was without Klaus the management did not have.