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Good quality data is essential for making evidence-based decisions when managing health programs. Assessing and improving overall data quality on a health system goes beyond implementing data collection validations and other mechanisms to ensure that the collected data is within expected ranges. A health system needs to ensure that as the data flows between different forms and systems, the process is done with the necessary accuracy in a timely manner. When necessary, corrective measures in the form of action plans, capacity building and other activities should be recommended and implemented to ensure appropriate data quality.
| The RDQA DHIS2 configuration evaluates assessment outcomes in real time using PSI’s DHIS2 Android app as well as standard 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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PSI’s RDQA methodology was based on the DQA tool (Data Quality Tool) originally developed by various multilateral and bilateral organizations in 2007 as an evaluation tool for external agents to an organization. The RDQA tool is designed to be used by personnel who are part of the organization. The tool is available as a series of MS Excel templates as part of Measure’s data quality tools collection.
Improved data quality allows for better decision making at all levels of health systems. At service level, routine data quality assessments (RDQA) are a critical activity to
- Verify the quality of reported data against source documents,
- Identify causes of poor-quality data by assessing the ability of the data management system to collect, manage and report quality data,
- Support the development of data quality improvement plans to strengthen the data management and reporting system to improve data quality.
The DHIS2 implementation solution meets on the ground M&E teams’ demands for a mobile tool to collect RDQA data, as well as immediate rendering of the assessment outcomes, plus the development of corrective actions plans with those responsible for the data.
We modeled the RDQA workflows in DHIS2 tracker, and created a fork of the University of Oslo (UiO) Data Capture DHIS2 Android app to enable and richer UX experience on the form of a feedback module. You will need to use PSI’s DHIS2 android app release to use the feedback module, or the standard DHIS2 app if you just want the assessment module. (Version 2.4 or greater, otherwise some calculations will not work correctly).
The system needs to be configured with your own list of Health Areas and Indicators. Additionally you can also tailor the feedback content, action plan level of detail, as well as the district and country level dashboards.
- System assessment - enables the qualitative assessment of the strengths and weaknesses of the functional areas of the data management and reporting system.
- Data verification - identifies causes of poor-quality data by assessing the ability of the data management system to collect, manage and report quality data.
- Results: Indicators - gives an immediate calculation to the assessor on how the site did and supports giving immediate results and feedback to the site on how they performed.
- Feedback - highlight passed and failed scores and standardized guidance to highlight in a debrief at the end of the day to accompany the action plan. This is only available on PSI’s fork app.
- Action plan - for developing an improvement plan to strengthen the data management and reporting system to improve data quality.
The process ensures M&E teams can invest their time to solve data quality issues at the service delivery point with an evidence-based approach. PSI has already implemented RDQA in over 10 countries across several health areas including HIV, VMMC, TB, SRHR, IMCI, Safe Motherhood, Malaria, DREAMS, and WASH.
DHIS2 includes a built-in “Data Quality” app which allows users to identify out-of-range values or those that are violating explicit validation rules. The app reports the individual Organization Unit and period for which a particular value is either an outlier or violates a particular validation rule.
A validation rule is a ‘boolean’ expression, as when evaluated can only result in true or false. A ‘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 side’. If the evaluation is not met, a report indicating what value/ period/ org unit triggered the violation is generated for further enquiries.
Additionally, validation rules can be run during data entry, or as a periodic process that gets sent as DHIS2 messages.
DHIS2 allows you to run two types of outlier analysis post data entry: (i) Minimum maximum outlier analysis, for which you can define the Min/Max value manually or generate them automatically, which makes sense if you data is normally distributed across time and space, but should be avoided is the data is highly-skewed or zero inflated. It is also possible to do (ii) standard deviation outlier analysis to identify values that are numerically distant from the rest of the data, potentially indicating that they are outliers.
Read more at DHIS2 Documentation
DHIS2 Quality app helps to identify out of range values for the captured data based on outlier analytics or by using explicit defined validations rules. RDQA is a tool to conduct data quality assessments that looks holistically into all M&E processes, and the full life cycle of the data for a selected set of data points at a facility. DHIS2 Data Quality app doesn’t require a site visit (physical or remote). DHIS2 Data Quality app is limited to flag values that can be out of range and can be marked for follow up. RDQA is a holistic assessment that is conducted on site or remotely and looks not just at the data, but to 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. The combination of both techniques (DHIS2 Data Quality and RDQA assessments) should result in better data across your health systems in the medium and long term.