Ensuring routine health data is of good quality – accurate, timely, and complete – is critical to a thriving RHIS. Poor quality data doesn’t help decision makers make better choices about where budget and resources will be allocated. There are multiple tools available to assess systems for data quality, identify strengths, and weaknesses, and develop action plans for improvements.
Data Quality Tools
The DQA Tool focuses exclusively on (1) verifying the quality of reported data, and (2) assessing the underlying data management and reporting systems for standard program-level output indicators. The DQA Tool is not intended to assess the entire M&E system of a country’s response to a given disease area. Currently, the WHO and partners are collaborating to create a new Data Quality Review suite of tools that can be used in both electronic and paper-based systems.
Two versions of the DQA Tool have been developed: (1) the “Data Quality Audit Tool” which provides guidelines to be used by an external audit team to assess a program/project’s ability to report quality data; and (2) the “Routine Data Quality Assessment Tool” (RDQA) which is a simplified version of the DQA Tool for auditing that allows programs and projects to assess the quality of their data and strengthen their data management and reporting systems.
Both the DQA and RDQA tools include two key parts:
- a System Assessment to qualitatively assess the capacity of the health information to produce quality data, and identify opportunities for system strengthening interventions
- a Data Verification tool used to conduct data quality checks of accuracy, timeliness, and completeness of data reported within the RHIS—the tool is indicator agnostic and can be used to asses the quality of any data for which you have source records
Why the RDQA?
The RDQA tool can be adapted to the country or organizational context, and facilitates routine review of data quality by the data users and producers with the greatest opportunity to improve the quality of routine data.
In Botswana, the tool and related training materials were adapted for routine use by health information professionals within the health system and standard operating procedures were developed to promote the routine use of the tool. By conducting annual system assessments, developing data quality improvement plans with specific timelines, and conducting quarterly data verifications, staff are actively engaged in the process of monitoring and improving routine health data.
Key drivers of success in adapting the RDQA for local routine use included country ownership of the process, having a local champion, and decentralizing the process of introducing the new tool. You can read more about the adaptation in this MEASURE Evaluation project case study.
For more information and to download the most recent versions of the various data quality assessment tools, see the MEASURE Evaluation project website.