Abstract
The 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, their implementation from the
stakeholders’ point of view remains unclear. This study explored the
perspectives of key stakeholders in Gauteng Province on the status of machine
learning applications in HIV testing in South Africa.
Methods: The study used an exploratory qualitative approach to recruit 15
stakeholders working in government and non-government institutions rendering
HIV testing services. The study participants were healthcare professionals such
as public health experts, lab scientists, medical doctors, nurses, HIV testing
services, and retention counselors. Individual-based in-depth interviews were
conducted using open-ended questions. Thematic content analysis was used,
and 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 gap, need
for education, self-testing support, data analysis tools, counseling aids, youth
engagement, system efficiency, and data-driven decisions. The study shows
that integration of machine learning would enhance HIV risk prediction,
individualized testing through HIV self-testing, and youth engagement. This is
crucial for reducing HIV transmission, addressing stigma, and optimizing
resource allocation. Despite the potential, machine learning is underutilized in
HIV testing services beyond statistical analysis in South Africa. Key gaps
identified were a lack of implementation of research findings and a lack of
awareness among frontline workers and end-users.
Conclusion: Policymakers should design educational programs to improve
awareness of existing machine learning initiatives and encourage the
implementation of research findings into HIV testing services. A follow-up study
should assess the feasibility, structural challenges, and design implementation
strategies for the integration of machine learning in HIV testing in South Africa.