How do you control a disease that has a dynamic risk profile? That’s the big question around HIV today.
Over the years, the development of rapid testing, effective treatments and a range of prevention options has transformed the virus from a fatal disease to a chronic, manageable condition. We no longer need population-wide prevention strategies and the funding that went with them has been cut accordingly. But HIV still presents a serious health issue, especially to certain groups of society.
Today, HIV prevention is a three-fold challenge:
1. HIV risk is unevenly distributed and transient. New infections are higher in marginalized groups, such as female sex workers, transgender people and men having sex with men. But the risk is linked to certain activities, so it isn’t the same for everyone, every day.
2. There’s more choice when it comes to prevention options. Options range from condoms to pills and injectable treatments that can be taken daily, monthly or occasionally. It’s difficult for health workers to figure out who needs what.
3. Effective prevention happens at the point of risk. People only need to take precautions when they’re at risk, so they are unlikely to comply with a rigid prevention regime like taking a pill every day.
This is about helping vulnerable individuals to assess their risk, evaluate their options and use them effectively. It takes a user-driven approach, and Fractal has developed a framework to help.
Driving a change in perspective
At Fractal we have been advocating a data-driven, behavioral science perspective on HIV for over a decade. Now, our expertise in behavioral segmentation is helping to drive a shift towards a more user-centric approach to promoting prevention options.
Our framework for user-driven HIV prevention provides the tools to put that approach into practice. To develop it, we rigorously analyzed existing research. Then we conducted targeted interviews and workshopped our ideas with leading disease prevention bodies including the United States President's Emergency Plan for AIDS Relief, the Centers for Disease Control and Prevention and the Bill and Melinda Gates Foundation. We also sought input from all stakeholders, including funders, ministries, implementers, non-governmental organizations and advocacy groups, to help finalize the framework.
A multi-layered approach
Effective, user-driven self-care comes from collaborative decision-making and support. That’s why Fractal’s framework takes a multi-layered approach that recognizes the various elements at play, from health practitioners to cultural contexts.
It begins with three user decision contexts:
Risk assessment – accurately identifying risks and the need to mitigate them.
Opportunity evaluation – selecting suitable methods to address the risk that has been identified.
Effective use – building on the first two contexts to develop effective responses to those risks, vulnerabilities and prevention opportunities.
In each decision context, the framework translates overall objectives into individual goals for both service users and providers.
To enable a more nuanced understanding of HIV prevention behaviors, the framework also factors in three ecosystem levels:
Individual behaviors – such as ability to cope with stigma, or perceptions of a prevention method’s effectiveness.
Interactions – the factors that impact the way users engage with their ecosystem while accessing HIV prevention services.
Systemic influences – the policies, system design and cultures that significantly influence ecosystem dynamics.
By combining the impact of all these elements – decision contexts and ecosystem levels – we can identify how they shape the demand for HIV prevention. This means that as well as supporting program monitoring, the framework allows programmers and funders to better understand prevention decisions and identify gaps to inform future planning.
AI can open the dialogue
It will be interesting to see how HIV prevention organizations apply this framework – or parts of it – to reach more vulnerable people in the coming years. Technology isn’t an essential part of those efforts, but we do see potential for AI to support them.
Something as simple as a discussion with an AI chatbot, for instance, could make HIV prevention more accessible and personalized. Initially, users could take this route to find out whether certain behaviors or situations put them at risk and understand their options for prevention. Enabled by behavioral models, the chatbot could provide a more personalized approach by understanding which prevention products the user might choose, suggesting the most appropriate options and predicting why, and under which conditions, those options might change.
Self-testing is another emerging area where AI could help to alleviate concerns about the stigma of visiting a clinic. In this context, a chatbot or app could provide a channel for users to find out whether they should get tested and to order that test for delivery to their home. Users might feel more comfortable discussing their test results and options with a chatbot, rather than in a clinician’s office. Then they could decide on their next steps, such as ordering prevention products for delivery or booking an appointment at a suitable clinic.
Of course, it will take time for user-centered approaches like these to filter into HIV prevention programs. But our framework provides a tool to help organizations think about ways to apply this concept, and technology exists to support a multitude of approaches. With these tools, we hope that service providers will find effective ways to put control into users’ hands – and enable a more productive dialog on HIV prevention with the people who need it most.
Author speak
Ram is a pioneer in applied behavioral science. As CEO and co-founder of FinalMile, he has built a strong and diverse team of behavioral science and design experts. Today, he and his team are creating new value propositions by integrating AI, behavioral science and design to continue innovating in the field of behavior change.
