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

• URL: http://arxiv.org/abs/2408.07629v1
• Date: Wed, 14 Aug 2024 15:55:31 GMT
• Title: Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings
• Authors: África Periáñez, Kathrin Schmitz, Lazola Makhupula, Moiz Hassan, Moeti Moleko, Ana Fernández del Río, Ivan Nazarov, Aditya Rastogi, Dexian Tang,
• Abstract summary: The CHARM app is an AI-native mobile app for community health workers (CHWs) This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions.
• Score: 2.619524972111665
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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