Speaker
Description
Background: Human immunodeficiency virus (HIV) remains one of the leading causes of death globally, with South Africa bearing a significant burden. As an effective way of reducing HIV transmission, HIV testing interventions are crucial and require the involvement of key stakeholders, including healthcare professionals and policymakers. New technologies like machine learning are remarkably reshaping the healthcare landscape, especially in HIV testing. However, the implementation of machine learning in HIV testing from the stakeholders’ point of view remains unclear. This study explored the perspectives of key stakeholders on the status of machine learning in HIV testing in Gauteng Province, South Africa.
Methods: The study included 15 stakeholders working in government and non-government institutions rendering HIV testing services through an exploratory qualitative approach. The study participants were healthcare professionals such as public health experts, lab scientists, medical doctors, nurses, HTS, and retention counsellors. Individual-based in-depth interviews were conducted using open-ended questions. Thematic content analysis was done using ATLAS.ti version 23.4.0, and THE results were presented in themes and sub-themes.
Results: Three main themes were determined, namely awareness level, existing applications, and perceived potential of machine learning in HIV testing interventions. A total of nine sub-themes were discussed in the study: limited knowledge among frontline workers, research vs. implementation gaps, need for education, self-testing support, data analysis tools, counselling aids, youth engagement, system efficiency, and data-driven decisions.
Conclusion: Stakeholders highlighted the potential of machine learning to improve HIV testing in South Africa. The integration of machine learning is believed to enhance HIV risk prediction, individualised testing through HIV self-testing, and youth engagement. The findings suggest that machine learning is a promising tool to reduce HIV transmission, address stigma, and optimise resource allocation. However, the study reveals an underutilization of machine learning initiatives in HIV testing services beyond statistical analysis due to gaps in implementing research findings and a lack of awareness among frontline workers and users. Hence, policymakers should design educational programs on machine learning and its intersection with HIV testing. Evidence from studies using machine learning approaches should also be translated into HIV testing services for improved HIV testing in South Africa.
