Five ways that remote data quality assurance approaches can improve insights into the patient experience during the COVID-19 pandemic

Speeches Shim

Authors: The authors work in the Office of HIV/AIDS in the Strategic Information, Evaluation, and Informatics Division. Angela Chen, Data.FI Core Management; Ana Djapovic Scholl, Branch Chief for Evaluation; Webert Jose, Data Quality Advisor; Josephine Mungurere-Baker, Data Quality Advisor; Emily Harris, Data.FI Agreement Officer Representative.

Data clerk entering pharmacy data into iDart in Sofala, Mozambique
Data clerk entering pharmacy data into iDart in Sofala, Mozambique

The COVID-19 pandemic has fundamentally disrupted health care services, making the data that are used to understand the patient care experience more critical than ever for improving services, care delivery, and controlling epidemics -- but also more challenging than ever to assess.

Remote data quality assurance approaches could be the answer to this COVID-19 challenge, as USAID supports remote implementation in exceptionally difficult, non-permissive environments. COVID-19 upended the application of most established data quality assurance methods globally, such as data quality assessment (DQA), a capacity building assessment tool used to facilitate routine data quality checks for monitoring, evaluation, and performance improvement.

Prior to the COVID-19 pandemic, a traditional DQA was conducted in-person and included site visits for review of records maintained by implementing partners and the collection of data from primary and secondary sources including interviews, registers, and databases. A data validation process is then undertaken to review and validate the data obtained. From drafting a DQA protocol, going to the site, compiling and analyzing data, and developing a final DQA report, the entire process can be resource-intensive in cost and time, taking months to complete.

In response to COVID-19 and the need for innovative methods, USAID shifted its practice from mostly in-person, traditional data quality assurance approaches to innovative remote applications. The USAID managed and Palladium-led Data.FI project harnessed machine learning to develop an anomaly detection solution that allowed USAID teams to rapidly conduct remote reviews of PEPFAR reported aggregate facility-level data quality. Data.FI also developed a remote DQA to assess COVID-19 data, leveraging virtual communication tools and technology to review pandemic reported data. These innovations for data quality assurance can inform USAID’s ongoing and future emergency responses.

1. Ensuring data quality in contingencies

Data quality assurance approaches like anomaly detection and remote DQAs allow for continued monitoring of data quality in emergency situations. As we’ve adapted to using virtual technologies to stay informed and connected, we have also begun to look at ways to leverage technology advancement and make digital options a standard part of broader data improvement efforts.

During the COVID-19 pandemic, the USAID COVID-19 remote DQA team adapted existing DQA tools, focusing on making necessary adjustments to an already established, standard approach. Interviews required for DQAs were carried out using videoconferencing technologies (Zoom, Google Meets, Microsoft Teams, etc.) and source data and reports were obtained in an electronic format. All of this information was reviewed and compared to reported values for the selected indicators, which are relevant standard metrics used to measure performance and communicate results.

During serious disruptions and emergencies, remote approaches to assessing data quality are important tools to identify potential quality issues from afar. These measures protect staff from COVID-19 and avoid further disruption to already strained health service delivery.

2. Generating more efficient, sustainable, and resilient solutions

Remote data quality monitoring and assessment tools and approaches provide a practical and sustainable means to capitalize on available digital technologies to ensure the global health community can maintain, enhance, and better target site monitoring data quality assurance and improvement efforts.

Anomaly detection, for example, helped our USAID teams on the ground pinpoint and prioritize sites for further inquiry for data monitoring and assessment. Remote data quality assurance approaches such as these are sustainable solutions for continued data quality assurance focused on ensuring access to key, high-quality data to accelerate and maintain HIV and COVID-19 epidemic control. These methods are key to building resilient and capacitated health systems, leading to sustainable epidemic control.

3. Providing a faster and less expensive solution

Remote data quality assurance approaches have the potential to optimize value for money and impact. Remote DQAs can allow for savings in cost and time for data gathering because costs like in-person travel are eliminated. While accessing data at the point of service delivery during data validation represents the DQA gold standard and may require in-person data collection, novel remote approaches allow for secure access and review of data closest to the point of service delivery for remote data validation. Novel remote approaches also allow for quick and efficient identification of sites that may require in-person attention. Remote data quality assurance represents an innovation that adds to the global repertoire of advanced data quality approaches.

4. Expanding reach geographically

Traditional DQAs are often limited in geographic scope due to the nature of in-person data collection. Remote data quality approaches, including remote DQAs, by contrast, can expand geographic reach and facilitate a more targeted, precision-based approach to in-person assessments. They rely on data that has been collected electronically (e.g. spreadsheets or databases), can be shared electronically (e.g. through scanning forms and log sheets), or accessed remotely within a specific framework that will prevent exposing any personally identifiable information (PII). Additionally, the use of virtual platforms for key informant interviews and data quality review meetings can allow for greater and more flexible coordination among staff at different levels.

With the COVID-19 remote DQA, many more individuals were involved thanks to virtual technologies. Emphasis was placed on ensuring that appropriate staff were included in online meetings and that objectives and expectations were clear well in advance of meetings. A more frequent cadence of shorter meetings was found to be more productive and manageable, especially for accounting for the time differences as the remote DQA was being conducted with expanded geographic reach, encompassing countries in East and South Asia and East and West Africa.

In some cases, however, a remote DQA may not be the best choice. For example, for indicators whose data are not available in an electronic format—for example, data in paper registries—conducting an in-person DQA is more practical and efficient. Having different approaches and tools available in our toolbelt allows us to assess which approach is the most advantageous for a specific situation.

Tackling other health challenges

The PEPFAR Strategy: Vision 2025 places an emphasis on building enduring capacities and health systems that can “tackle other health challenges…and adapt to adversity.” Remote data quality assurance approaches and tools developed by USAID and our partners have demonstrated effectiveness during the pandemic for evaluating indicators related to COVID-19 and HIV. Virtual analytic approaches can also be utilized for tackling other health challenges (such as evaluating indicators of established health programs including maternal and child health, tuberculosis, immunizations, and malaria), and to inform broad program quality improvement. While remote data quality assurance methods, like all approaches, have limitations, they have demonstrated their adaptability, advantages in times of adversity, and potential for long-term implementation.

For more information about USAID’s remote data quality assurance approaches and the USAID Guide for Remote Data Quality that leverages Operating Unit/country data systems for safe assessment of data quality reach out to

For more information about Data.FI’s process and takeaways on best practices for conducting a remote DQA on COVID-19 indicators, please see the Data.FI technical brief here.

Last updated: March 22, 2022

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