 With this video, I'm going to demonstrate how to use the outliers page of the dashboard of the WHO data quality tool. We've set the dashboard with data set to core, period set to June, organization unit with a national boundary and disaggregation to district. So if we click on the outliers tab and wait a bit for the moving rectangle to disappear, we will see a result table so that we can see all of the result table. It's best to make the settings window disappear by clicking on the settings icon. The result table has a large number of rows as you see and in fact it even spills over into more than one page. And the result table consists of rows like this with 12 monthly values and one or more of the cells, one or more of the values is highlighted in red. These values highlighted in red are outliers. They are suspicious because they are different from the values reported for other months. We've set disaggregation to district. So each row presents results for one district and for one indicator or data element. Here you see the 12 monthly values leading up to June of 2020, the period that we have selected. If we want to see how different this value is from the normal values, the values reported in other months, we can use the menu at the end of the row and select visualize. We then see this chart showing that in January of 2020, the ANC1 visits of district B2 were quite different from the values reported in other months. This of course is that outlier that we found when looking at the consistency time and consistency data pages of the dashboard of the WHO data quality tool. So the same outlier keeps appearing in different ways on the different dashboards. Here we see how very different it is from the ANC1 visits reported in other months by district B2. We can make this chart disappear by clicking on close. Notice this options button. The options button allows you to reveal additional information in your results table and it allows you to filter the results table. One of the boxes in the options field options window is this one to include the Z score. The Z score is a measure of how suspicious the red value is, the higher the Z score, the more suspicious it is. And a standard Z score of three or more is extremely suspicious. So we see that this top row of the results table has quite a high value of the standard Z score and the modified Z score, which is simply another way of measuring how suspicious the value is. In fact, the tool automatically sorts these suspicious values so that the most suspicious of all is placed right at the top. So the tool automatically finds the outliers that deserve the most attention. We can also filter this table. Now we've got so many rows and as I say, it continues on multiple pages, three pages. We can, in order to find only the most suspicious values, we can filter by clicking as follows. First we say we only want to see the outliers. We only want to see those that have a modified Z score that's extreme. What this does is it only shows you the rows which have a modified Z score of greater than five. And notice that there is a much smaller results table when we limit it to rows with a modified Z score of greater than five. There's one other thing that you can do with the outlier page of the dashboard and that is you can drill down. What do we mean by drill down? Well at present, each row shows you the values for the district and we may want to know which specific health facility or health facilities is responsible for this extreme outlier. And to do that, we click on the menu at the end of the row and select drill down. Notice what happens. We get a table which has rows all for district B2. But each row is for one health facility in district B2. We could filter this if we only wanted to see the rows that had very high modified Z scores. And right at the top we see that facility 147 is the one that reported 35,888 first ANC visits. And compared to the values reported in other months, we see that this is almost certainly an error. Perhaps some people added some extra eights when these data were entered. This is why this extreme outlier has appeared in the data set. In fact, if we visualized it, we'd see this really very suspicious chart. There's another thing we can do with this menu at the end of the row. And that is, we can actually send a message through the DHIS2 internal messaging service and we can contact the person who's responsible for the data in that district. So we could select the data manager for district B2 and type a message in here. We want to specify the health facility, the dates, the value, and ask them to please report back. And then we could send this message through the DHIS2 internal messaging service. Notice that some of the cells of the result table are colored pale yellow and have missing values. Missing values are highlighted in this way and the outlier page can also be used to identify health facilities which have large amounts of incomplete or missing data. To do this, you turn off the outlier filter under options and click on missing data. And the result table now shows at the top those health facilities and those indicators for which the monthly values have not been reported. One final feature of the outlier page is the download button. By clicking on this and then clicking download a second time, we generate what is called a CSV file which is like an Excel file. This Excel file is on the computer. It's not online so it can be viewed even when not connected to the internet. If we open it, we see a typical spreadsheet and like any Excel spreadsheet, you can use the data sort functions to arrange these by such things as the modified Z score. Now we see once again the health facility B2 which has this very large A and C first visit value or we could sort the spreadsheet according to missing values. But it's called the gap weight which is the total amount of missing data. And now we've identified this health facility again that has a series of months without data. So returning to the outlier page, let's summarize. The outlier page is probably the most powerful of the tools of the dashboard. It allows you to quickly and automatically identify the most suspicious values, not only at the level of the district but you can easily drill down and identify the specific health facility which has reported the suspicious value. And you can identify health facilities with large amounts of missing data. This is the final page of the dashboard of the WHO data quality tool. We've seen how it is possible to quickly make use of the data quality review functions and it doesn't require any configuration on the part of the user other than to set the data group, the period, and the organization unit. This makes the tool quite useful for those who would want to quickly use the WHO data quality tool routinely every month to find the most suspicious values. And we will show you in a subsequent session how to quickly train district staff to use the outlier dashboard in particular. However, one of the limitations of the dashboard is that it permits you only to look at those indicators which have been configured for the dashboard. In the next session, we're going to demonstrate how to use the analysis function to look at the consistency and look for outliers in any data element for which there are data in DHIS.