Description of Session
This session will explore how to effectively incorporate machine learning solutions to guide allocation of funding, staff, and supplies to optimize health outcomes in global health programs. It will share best practices and use cases for how to: - frame machine learning within the context of a holistic analytics strategy; - identify scenarios that present greatest possible return on investment from machine learning; - stand up the appropriate technology environment for machine learning solutions; - select and prepare datasets for use in machine learning solutions; - decide methods for identifying model of best fit; - interpret results and their accuracy; - integrate machine learning solutions into decision-making processes; - and provide access to machine learning solutions and their parameters and results via visualizations and dashboards. To illustrate how these strategies have been implemented in an actual intervention, and how the complexities of intervention size and geographical complexity can come into play, this session will present a use case for how machine learning is currently being used to optimize the diagnosis of HIV positive clients in Lesotho, Nigeria, and Ethiopia to increase the effectiveness and reach of testing campaigns. In this use case, data scientists collaborate with in-country implementers to collect several quarters of site-level testing results, then analyze the data using machine learning to identify a series of practical improvements that could increase overall HIV testing yields. The analysis is done using R programs running on an AWS-enabled data science platform. The results of these analyses are made available via dashboards and used by decision-makers ranging from in-country implementers to global leadership to identify how to most effectively target limited resources during iterative program planning cycles. This solution is being rolled out to several other countries where the HIV burden is high.
