RDQA DHIS2 Metadata Package Description (FR)

Introduction

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 will enable data assessors to capture inputs using a DHIS2 Android app and 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.

Implementing the RDQA metadata package within the DHIS2 Android app meets the on-the-ground M&E team's demands for a mobile tool to collect RDQA data while providing assessment outcomes and corrective action plans to those responsible for supporting data quality.

The metadata package comprises multiple components, which can be customized for a project as necessary. This includes:

• Data elements coded by assessment area

• An RDQA Assessment dataset that allows technical advisors to establish data quality targets and baselines

• Indicators

• Programs and stages

• Program Indicators

• Dashboards

• Feedback Module - when used in conjunction with the PSI Android App

The system needs to be configured with your Health Areas and Indicators list. You can also tailor the feedback content, action plan level of detail, and district and country-level dashboards.

DHIS2 Data Quality App - What's that?

DHIS2 includes a built-in "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 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' expression, as it can only result in true or false when evaluated. 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 inquiries.

Additionally, validation rules can be run during data entry or as a periodic process sent as DHIS2 messages.

DHIS2 outlier analysis

DHIS2 allows you to run two types of outlier analysis post-data entry:

• Minimum, maximum outlier analysis, for which you can manually define the Min/Max value or generate them automatically. It makes sense if your data is usually distributed across time and space, but it should be avoided if 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.

Read more at DHIS2 Documentation

RDQA and the DHIS2 Data Quality App: What's the difference?

Think of RDQA and the DHIS2 Data Quality app as peanut butter and jelly. They are two completely different tools, but when used together, they 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 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 at the data 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.

Using both tools together should result in better data across your health systems in the medium and long term.