Data Quality

+ Data Quality + The data quality module provides means to improve the quality of the data in the system. This can be done through validation rules and various statistical checks. +

+ Learning Objectives + After reading this module you will be able to understand: + + + What is data quality and its importance for HMIS. + + + How to do data quality check at point of data entry. + + + How to create data validation rules. + + + How to carry out data triangulation. + + + How to analyze data status. + + +

+ Overview of data quality check + Ensuring data quality is a key concern in building an effective HMIS. Data quality has different dimensions including: + + + Correctness: Data should be within the normal range for data collected at that facility. There should be no gross discrepancies when compared with data from related data elements. + + + Completeness: Data for all data elements for all health facilities/blocks/Taluka/districts should have been submitted. + + + Consistency: Data should be consistent with data entered during earlier months and years while allowing for changes with reorganization, increased work load, etc. and consistent with other similar facilities. + + + Timeliness: All data from all health facilities/blocks/Taluka/districts should be submitted at the appointed time. + + +

+ Data quality checks + Data quality checking can be done through various means, including: + + + At point of data entry, the software can check the data entered to see if it falls within the min-max ranges of that data element over the last six months or as defined by the user. + + + Defining various validation rules, which can be run once the user has finished data entry. The user can also check the entered data for a particular period and Organization Unit(s) against the validation rules, and display the violations for these validation rules. + + + Analysis of data sets, ie. examining gaps in data. + + + Data triangulation which is comparing the same data or indicator from different sources. + + +

+ Data quality check at the point of data entry + Data quality can be checked at the point of data entry through setting the minimum and maximum value range for each element manually or generating the min-max values using the DHIS 2 if there is historical data available for that data element. + +

+ Setting the minimum and maximum value range manually + If you are using the default entry screen click on the element for which you want to set the min-max value. A pop-up window will appear in which you can enter the vaules. On subsequent data entry if the value entered does not fall within the set min-max range the text box will change colour to red. The user will also get a pop-up as shown below. This change in colour is a prompt to check the data entered and make necessary correction. On the data entry screen the users also have the option to add a comment on how the discrepant figure might be explained (if required). This you can do by using the drop down menu of the ‘comment’ box. In case you are using the custom data entry screen which is displayed when you deselect the ‘default data entry form’ option on the top right corner of the screen. In this case the minimum and maximum values can be added by double-clicking on the data entry box instead of the data element. +

+ Generated min-max values + It is possible to generate the min-max value, element-wise, using the DHIS2. In such case you merely need to click on the ‘Generate min-max’ button near the upper right corner. In case of default data entry screen the min and max values, when generated, will appear on the left and right side of the data entry box. In case you deselect the default data entry form the generated values will appear on the top right end of the screen. +

+ Validation Rule + This module provides management of validation rules. A validation rule is based on an expression which defines a relationship between a number of data elements. The expression has a left side and a right side and an operator which defines whether the former must be less than, equal to or greater than the latter. The expression forms a condition which should assert that certain logical criterias are met. For instance, a validation rule could assert that the total number of vaccines given to infants is less than or equal to the total number of infants. + To add a validation rule, click the add new button. First, provide a descriptive name for the validation rule. The name must be unique among the validation rules. Second, provide a description for the validation rule. Third, select an operator. The operator options are equal, not equal, greater than, greater than or equal, less than, less than or equal to. Then define the left side and right side of the validation rule expression. First, provde a description for the expression. Second, build the expression with the expression builder. The expression is mathematical and contain data elements as well as integers and mathematical operators. Data elements can be included by double-clicking one in the available data elements list to the righ. Alternatively one can select a data element and click the insert button. Mathematical operators can be included by clicking the corresponding button under the expression builder area. Save the expression by clicking save, then save the validation rule by clicking save. + To edit a validation rule, click the editicon next to the relevant validation rule in the list. Then follow the same producedures as above. + To delete a validation rule, click the deleteicon next to the relevant validation rule in the list. + To view validation rule details, click the view detailsicon next to the relevant validation rule in the list. +

+ Validation Rule Group + A validation rule group provides a mechanism for classifying related data elements. Another advantate of using validation rule grops is that it can later be run separately, contrary to running all validation rules. +

+ Validation Rule Analysis + The validation rule analysis function will test validation rules against the data in the system. Validation violations will be reported in cases where the condition defined through the validation rule expression is not met, ie. the condition is false. + First, enter a start date and an end date for which data should be included in the analysis. The date picker widget may be used to select dates. Second, choose between including all validation rules or a single group. Third, choose between including the selected organisation unit only or the selected organisation unit with all children in the analysis. Fourth, select the organisation unit. Finally, click validate. + The analysis process while run for a while dependending on the amount of data that is being anaylysed. If there were no violations of the validation rules a message saying validation passed successfully is displayed. If there were validation violations they will be presented in a list. The organisation unit, period, left side description and value, operator, and right side value and description for each validation violation are displayed. The show details icon can be clicked in order to get more information about a validation violation. This will open a popup screen that provides information about the data elements included in the validation rules and their corresponding data values. This information can be used in order to correct incorrect data. + The validation violations can be exported to a PDF document by clicking on the export to PDF button and to a Microsoft Excel workbook by clicking on the export to workbook button. +

+ Std Dev Outlier Analysis + The standard deviation based outlier analysis provides a mechanism for revealing values that are numerically distant from the rest of the data. Outliers can occur by chance but they often indicate either measurement error or a heavy-tailed distribution. In the former case one wishes to discard them while in the latter case one should be cautios in using tools or interpretations that assume a normal distribution. The analysis is based on the standard normal distribution. + First, select the from and to date for the data to include in the analysis. Second, select the data set from which to pick data elements from. Third, select all or some of the data elements in the data set by double-clicking or marking them and clicking the add/remove buttons. Fourth, select the parent organisation unit to use. All children of the organisation unit will be included. Fifth, select the number of standard deviations. This refers to the number of standard devations the data is allowed to deviate from the mean before it is classified as an outlier. + The possible outlier values discovered will be presented in a list after the analysis process is finished. The data element, organisation unit, period, minimum value, actual value, maximum value will be displayed for each outlier. The minimum and maximum value refers to the border values derived from the number of standard deviations selected for the analysis. Each outlier value can be modified directly in the analysis result page. The value can be modified by clicking inside the corresponding field in the value column, entering a value and then navigate away from that field either by clicking tab or anywhere outside the field. The system will provide an alert if the value is still outside the defined minimum and maximum values, but the value will saved in any case. The field will have a red background color if the value is outside the range, and a green if inside. Each outlier value can be marked for further follow-up by clicking the star icon. +

+ Min-Max Outlier Analysis + The min-max value based outlier analysis provides a mechanism for revealing values that are outside the defined minimum and maximum values. Minimum and maximum values can be custom defined or automatically defined by the system in the data entry module. See section about Std dev outlier analysis for further details on usage. +

+ Gap Analysis + The gap analysis provides a mechanism for revealing gaps in the data. A gap exists in the context of a data element and organisation unit. A gap is defined as a period with preceding and succeeding periods which have registered data values, but without registered data values itself. Such a gap might indicate a data capture error or omission and could be further investigated. See section about Std dev outlier analysis for further details on usage. +

+ Follow-Up Analysis + The follow-up analysis function will list all data values which are marked for follup-up. A data value can be marked for follow-up in the data entry module and in the other validation analysis variants in this module. See section about Std dev outlier analysis for further details on usage. +

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