Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings
<|reference_start|>Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings: 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.<|reference_end|>
@article{periáñez2024optimizing,
    title={Optimizing HIV Patient Engagement with Reinforcement Learning in
  Resource-Limited Settings},
    author={'Africa Peri'a~nez, Kathrin Schmitz, Lazola Makhupula, Moiz Hassan,
  Moeti Moleko, Ana Fern'andez del R'io, Ivan Nazarov, Aditya Rastogi and
  Dexian Tang},
    journal={arXiv preprint arXiv:2408.07629},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07629},
    primaryClass={cs.LG cs.AI cs.CY}
}