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Best practices for conducting a remote data quality assessment

David Boone, PhD
Michelle Li, MS
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It is good practice to conduct periodic data quality assessments to identify the strengths and weaknesses of health program performance data, enhance the understanding of data collection methods and determine whether the data are of sufficient quality to influence management decisions. The COVID-19 pandemic has necessitated program implementers and their funders to pivot to new ways of working for routine activities such as conducting DQAs, a shift that presents both challenges and opportunities.

With support from USAID, the Data for Implementation (Data.FI) project developed a remote DQA methodology to review partner-reported data. The approach focused on indicators for activities implemented by projects supported by USAID in response to COVID-19, but yielded lessons that may be applicable to the conduct of remote DQAs on indicators for well-established global health programs, such as HIV, tuberculosis, malaria, and maternal and child health.

We adapted existing DQA tools for the assessment, developing a hybrid tool based on the MEASURE Evaluation’s Routine Data Quality Assessment Tool and the USAID DQA Checklist to review partner systems and reported data to assess the validity (accuracy), reliability, timeliness and integrity of COVID-19 data reported by partners to USAID. The approach covered preparations for a remote DQA, including indicator selection, data collection tool development and key informant interviews. We conducted the DQA by communicating virtually with programs in Bangladesh, Ethiopia, Ghana, Kenya, the Philippines and Senegal, focusing on risk communication and community engagement, laboratory systems, and COVID-19 infection prevention and control.

What did we learn? We encountered numerous challenges associated with the assessment of data quality in a virtual environment but found advantages to conducting DQAs remotely, including an expansion of the geographical reach of the assessment and a reduction in the cost and time it can take to collect data. A principal disadvantage is that the approach is cumbersome for indicators requiring extensive review of source documents, such as “Current on ART.”

Using a remote DQA approach makes it possible to continue routine monitoring in emergency settings or when travel is not possible. Moreover, remote approaches that allow monitoring and evaluation practitioners to collect data virtually protect the health and safety of service providers and government staff, as well as DQA data collectors and supervisors. With advances in virtual collaboration technologies, remote DQAs may be relied on with increasing frequency in the future.

For more information

Our article summarizing best practices for remote DQAs, “Conducting a Remote Data Quality Assessment on COVID-19 Indicators Reported by USAID Projects,” is available online.





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