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RDQA DHIS2 metadata package provides an out-of-the-box DHIS2 configuration that allows DHIS2 system administrators efficiently establish a routine quality assessment module within DHIS2. The RDQA metadata package creates data quality assessment tools that allow will enable data assessors to capture inputs using a DHIS2 Android app and provides provide outcomes and analysis in real-time using standardized DHIS2 dashboards, allowing for improved data quality and decision-making at facility, district, and country levels. As gaps are identified, management can allocate resources and technical assistance where needed.
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If using the mobile version, it is necessary to install installing the DHIS2 Android or the PSI Android app version 2.4 or greater is necessary. Otherwise, otherwise some calculations will not work correctly. This also may be different depending on the server’s server's version. |
The system needs to be configured with your list of Health Areas and Indicators list. Additionally, you You can also tailor the feedback content, action plan level of detail, as well as and district and country-level dashboards.
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DHIS2 Data Quality App -
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What's that?
DHIS2 includes a built-in “Data Quality” "Data Quality" app that allows users to analyze data quality in various ways. The app reports the individual Organization Unit and period for which a particular value is an outlier or violates a particular specific validation rule.
At the point of data entry, DHIS 2 can check the data entered to see if it falls within the minimum and maximum value ranges of that data element (based on all previous data registered).
By defining 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.
By analyzing data sets, that is, examine gaps in the data.
By data triangulation, that is, comparing the same data or indicator from different sources.
DHIS 2 Validation Rules
A validation rule is a ‘boolean’ 'boolean' expression, as when evaluated, it can only result in true or false when evaluated. A ‘left side’ 'left side' could contain any number of data elements or a number, followed by a logical operator (greater than, less than, equal to…), which is compared to a ‘right 'right side.’ ' If the evaluation is not met, a report indicating what value/ period/ org unit triggered the violation is generated for further inquiries.
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Minimum, maximum outlier analysis, for which you can manually define the Min/Max value manually or generate them automatically. It makes sense if your data is normally usually distributed across time and space, but it should be avoided if the data it is highly - skewed or zero-inflated. It is also possible to do.
Standard deviation outlier analysis to identify values that are numerically distant from the rest of the data, potentially indicating that they are outliers.
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Think of RDQA and the DHIS2 Data Quality app like as peanut butter and jelly. They are two completely different tools, but when used together, they beomce become a powerful combination to improve data quality at your organization.
The DHIS2 Data Quality app helps to identify out-of-range values for the captured data based on outlier analytics or by using explicitly defined validation rules across all data capture apps.
The RDQA is a tool to conduct data quality assessments that look holistically into all M&E processes and the full entire life cycle of the data for a selected set of data points at a facility. It is part of routine supervision and reporting health facility site visits to monitor data quality continuously.
DHIS2 Data Quality app allows system administrators to define data value criteria to flag values out of range and marked for follow-up. It can be used with the RDQA metadata package to customize the assessment tool further.
RDQA is a holistic assessment conducted on-site or remotely and looks not just at the data but at and the people and processes that manipulate that data to ensure that best practices are being followed. As gaps are identified, it suggests corrective actions and encourages the creation of improvement plans to address gaps across related processes and people.
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