Community health workers in Rwanda are on the front lines of testing women and their families for malaria. But there’s no reliable way to ensure health workers properly administer rapid diagnostic tests (RDTs) or read the results accurately. This can lead to a person receiving antimalarial medicine when they don’t actually need it.

Complicating the situation is the fact that World Health Organization’s quality control guidelines offer limited guidance on administering RDTs in communities and on quality assurance standards. Additionally, country standards of care don’t yet offer consistent protocols to ensure that people who are treated for malaria truly have malaria and that they are first confirmed by a positive test before being treated.

Jhpiego has partnered with Audere and the Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center to implement an evidence-based mobile app, HealthPulse AI, with community health workers in Rwanda. The goal of the app is to facilitate high-quality administration of malaria tests in the community and generate real-time surveillance data of testing results. The digital app is currently being evaluated and used by leveraging existing community-based testing efforts in programs led by the government of Rwanda, and in some areas funded by the President’s Malaria Initiative. This Jhpiego/Audere/MOPDD project will assess both the feasibility and usability of the technology by community health workers in their daily routine, the impact of the collected data, and insights from use of the app.

Here’s how it works: A community health worker uses the app (whether online or offline) on the mobile device to take a photo of each RDT. Artificial intelligence (AI) in the app interprets the RDT result, allowing for a comparison between the health worker’s recorded interpretation and the AI interpretation, therefore illuminating the true positive rate of malaria cases. Shedding light onto the true positive rate will help identify issues and where additional training or quality improvement interventions are needed.

Digitally enhanced rapid malaria testing provides transparency into the number of malaria cases identified and treated and insight on how to improve the process. With this information, teams can improve program components in a timely fashion with a focus on improving treatment outcomes.

This project is being supported through Jhpiego’s funding for new innovative and catalytic projects. While this project is focused on malaria, other disease-specific programs that use RDTs, including HIV, COVID-19, and sexually transmitted infections, could benefit from similar digital tools. Results will be shared after the project concludes in July 2023.

The global health community is acutely aware and focused on the value of quality surveillance data to support the aim of ensuring that community health workers perform malaria tests before treating patients. Over-prescription of antimalarials continues to be a global concern, especially in malaria endemic countries with issues of emerging partial antimalarial drug resistance, prohibitive costs of medicines, and interrupted stock supply.

Solutions that support health workers to accurately administer RDTs, using AI to check on results, could increase access to services for patients by reducing the strain on overburdened health care resources by reducing costs for medication.

Empowering health workers to conduct diagnostic testing within their communities has been shown to effectively support underserved populations. Our project aims to optimize their efforts through timely feedback, efficient supervision, and transparent data, providing valuable insights for stakeholders.

This story was originally published on Medium.com. Read it here.

Financial support for Audere’s work was provided by the Bill & Melinda Gates Foundation, grant numbers INV-007492 and INV-054895.