Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings

TL;DR

This paper presents CHARM, an AI-powered mobile app for community health workers, integrating reinforcement learning to improve engagement, efficiency, and patient outcomes in resource-limited settings.

Contribution

It introduces a novel reinforcement learning-based adaptive intervention system within the CHARM app for community health workers.

Findings

Enhanced health worker engagement and efficiency.

Potential for improved patient outcomes.

Successful integration of AI in resource-limited healthcare settings.

Abstract

By providing evidence-based clinical decision support, digital tools and electronic health records can revolutionize patient management, especially in resource-poor settings where fewer health workers are available and often need more training. When these tools are integrated with AI, they can offer personalized support and adaptive interventions, effectively connecting community health workers (CHWs) and healthcare facilities. The CHARM (Community Health Access & Resource Management) app is an AI-native mobile app for CHWs. Developed through a joint partnership of Causal Foundry (CF) and mothers2mothers (m2m), CHARM empowers CHWs, mainly local women, by streamlining case management, enhancing learning, and improving communication. This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions, all aimed at enhancing health worker engagement, efficiency, and patient outcomes, thereby enhancing CHWs' capabilities and community health.